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Volumn , Issue , 2012, Pages 1-231

Latent Markov models for longitudinal data

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EID: 85055396690     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b13246     Document Type: Book
Times cited : (93)

References (193)
  • 1
    • 0003535936 scopus 로고    scopus 로고
    • 2nd Edition. JohnWiley & Sons, Hoboken, NJ
    • A. Agresti (2002). Categorical Data Analysis, 2nd Edition. JohnWiley & Sons, Hoboken, NJ.
    • (2002) Categorical Data Analysis
    • Agresti, A.1
  • 2
    • 0000501656 scopus 로고
    • Information theory and an extension of the maximum likelihood principle
    • B. N. Petrov and F. Csáki, eds., Akadémiai Kiado, Budapest
    • H. Akaike (1973). Information theory and an extension of the maximum likelihood principle. In: B. N. Petrov and F. Csáki, eds., Second International Symposium of Information Theory, 267-281. Akadémiai Kiado, Budapest.
    • (1973) Second International Symposium of Information Theory , pp. 267-281
    • Akaike, H.1
  • 3
    • 33947231297 scopus 로고    scopus 로고
    • Mixed hidden Markov models: An extension of the hidden Markov model to the longitudinal data setting
    • R. M. Altman (2007). Mixed hidden Markov models: an extension of the hidden Markov model to the longitudinal data setting. Journal of the American Statistical Association, 102, 201-210.
    • (2007) Journal of the American Statistical Association , vol.102 , pp. 201-210
    • Altman, R.M.1
  • 4
    • 23244447194 scopus 로고    scopus 로고
    • Application of hidden Markov models to multiple sclerosis lesion count data
    • R. M. Altman and A. J. Petkau (2005). Application of hidden Markov models to multiple sclerosis lesion count data. Statistics in Medicine, 24, 2335-2344.
    • (2005) Statistics in Medicine , vol.24 , pp. 2335-2344
    • Altman, R.M.1    Petkau, A.J.2
  • 5
    • 84906807125 scopus 로고
    • Probabilitymodels for analysing time changes in attitudes
    • P. F. Lazarsfelsd, ed., The RAND Research Memorandum No. 455
    • T. W. Anderson (1951). Probabilitymodels for analysing time changes in attitudes. In: P. F. Lazarsfelsd, ed., The use of mathematical models in the measurement of the attitudes. The RAND Research Memorandum No. 455.
    • (1951) The Use of Mathematical Models in the Measurement of the Attitudes
    • Anderson, T.W.1
  • 7
    • 0001995592 scopus 로고    scopus 로고
    • A three-step method for choosing the number of bootstrap repetitions
    • D. W. K. Andrews and M. Buchinsky (2000). A three-step method for choosing the number of bootstrap repetitions. Econometrica, 68, 23-52.
    • (2000) Econometrica , vol.68 , pp. 23-52
    • Andrews, D.W.K.1    Buchinsky, M.2
  • 8
    • 42949177287 scopus 로고    scopus 로고
    • Multilevel mixture models
    • G. R. Hancock and K. M. Samuelson, eds., Information Age Publishing, Charlotte, NC
    • T. Asparouhov and B. Muthén (2008). Multilevel mixture models. In: G. R. Hancock and K. M. Samuelson, eds., Advances in Latent Variable Mixture Models. Information Age Publishing, Charlotte, NC, pp. 27-51.
    • (2008) Advances in Latent Variable Mixture Models , pp. 27-51
    • Asparouhov, T.1    Muthén, B.2
  • 9
    • 0442325064 scopus 로고    scopus 로고
    • Transmission of pneumococcal carriage in families: A latent Markov process model for binary data
    • K. Auranen, E. Arjas, T. Leino, and A. K. Takala (2000). Transmission of pneumococcal carriage in families: a latent Markov process model for binary data. Journal of the American Statistical Association, 95, 1044-1053.
    • (2000) Journal of the American Statistical Association , vol.95 , pp. 1044-1053
    • Auranen, K.1    Arjas, E.2    Leino, T.3    Takala, A.K.4
  • 13
    • 33644753140 scopus 로고    scopus 로고
    • Likelihood inference for a class of latent Markov models under linear hypotheses on the transition probabilities
    • F. Bartolucci (2006). Likelihood inference for a class of latent Markov models under linear hypotheses on the transition probabilities. Journal of the Royal Statistical Society, series B, 68, 155-178.
    • (2006) Journal of the Royal Statistical Society, Series B , vol.68 , pp. 155-178
    • Bartolucci, F.1
  • 14
    • 34548529020 scopus 로고    scopus 로고
    • A class of multidimensional IRT models for testing unidimensionality and clustering items
    • F. Bartolucci (2007). A class of multidimensional IRT models for testing unidimensionality and clustering items. Psychometrika, 72, 141-157.
    • (2007) Psychometrika , vol.72 , pp. 141-157
    • Bartolucci, F.1
  • 15
    • 6944224314 scopus 로고    scopus 로고
    • A recursive algorithm for Markov random fields
    • F. Bartolucci and J. Besag (2002). A recursive algorithm for Markov random fields. Biometrika, 89, 724-730.
    • (2002) Biometrika , vol.89 , pp. 724-730
    • Bartolucci, F.1    Besag, J.2
  • 16
    • 66549107991 scopus 로고    scopus 로고
    • A multivariate extension of the dynamic logit model for longitudinal data based on a latent Markov heterogeneity structure
    • F. Bartolucci and A. Farcomeni (2009). A multivariate extension of the dynamic logit model for longitudinal data based on a latent Markov heterogeneity structure. Journal of the American Statistical Association, 104, 816-831.
    • (2009) Journal of the American Statistical Association , vol.104 , pp. 816-831
    • Bartolucci, F.1    Farcomeni, A.2
  • 17
    • 23244461368 scopus 로고    scopus 로고
    • Likelihood inference on the underlying structure of IRT models
    • F. Bartolucci and A. Forcina (2005). Likelihood inference on the underlying structure of IRT models. Psychometrika, 70, 31-43.
    • (2005) Psychometrika , vol.70 , pp. 31-43
    • Bartolucci, F.1    Forcina, A.2
  • 18
    • 33745657910 scopus 로고    scopus 로고
    • A class of latent marginal models for capture-recapture data with continuous covariates
    • F. Bartolucci and A. Forcina (2006). A class of latent marginal models for capture-recapture data with continuous covariates. Journal of the American Statistical Association, 101, 786-794.
    • (2006) Journal of the American Statistical Association , vol.101 , pp. 786-794
    • Bartolucci, F.1    Forcina, A.2
  • 19
    • 1542469533 scopus 로고    scopus 로고
    • Positive quadrant dependence and marginal modelling in two-way tables with ordered margins
    • F. Bartolucci, A. Forcina, and V. Dardanoni (2001). Positive quadrant dependence and marginal modelling in two-way tables with ordered margins. Journal of the American Statistical Association, 96, 1497-1505.
    • (2001) Journal of the American Statistical Association , vol.96 , pp. 1497-1505
    • Bartolucci, F.1    Forcina, A.2    Dardanoni, V.3
  • 20
    • 77958057320 scopus 로고    scopus 로고
    • Latent Markov model for binary longitudinal data: An application to the performance evaluation of nursing homes
    • F. Bartolucci, M. Lupparelli, and G. E. Montanari (2009). Latent Markov model for binary longitudinal data: an application to the performance evaluation of nursing homes. Annals of Applied Statistics, 3, 611-636.
    • (2009) Annals of Applied Statistics , vol.3 , pp. 611-636
    • Bartolucci, F.1    Lupparelli, M.2    Montanari, G.E.3
  • 21
    • 34547679632 scopus 로고    scopus 로고
    • A class of latent Markov models for capture-recapture data allowing for time, heterogeneity and behavior effects
    • F. Bartolucci and F. Pennoni (2007). A class of latent Markov models for capture-recapture data allowing for time, heterogeneity and behavior effects. Biometrics, 63, 568-578.
    • (2007) Biometrics , vol.63 , pp. 568-578
    • Bartolucci, F.1    Pennoni, F.2
  • 23
    • 84885532152 scopus 로고    scopus 로고
    • C. Huber, N. Limnios, M. Mesbah, and M. Nikulin, eds., Mathematical Methods for Survival Analysis, Reliability and Quality of Life, Wiley, London
    • F. Bartolucci, F. Pennoni, and M. Lupparelli (2008). Likelihood inference for the latent Markov Rasch model. In: C. Huber, N. Limnios, M. Mesbah, and M. Nikulin, eds., Mathematical Methods for Survival Analysis, Reliability and Quality of Life, 239-254. Wiley, London.
    • (2008) Likelihood Inference for the Latent Markov Rasch Model , pp. 239-254
    • Bartolucci, F.1    Pennoni, F.2    Lupparelli, M.3
  • 25
    • 33644967739 scopus 로고    scopus 로고
    • Efficient Bayes factor estimation from the reversible jump output
    • F. Bartolucci, L. Scaccia, and A. Mira (2006). Efficient Bayes factor estimation from the reversible jump output. Biometrika, 93, 41-52.
    • (2006) Biometrika , vol.93 , pp. 41-52
    • Bartolucci, F.1    Scaccia, L.2    Mira, A.3
  • 26
    • 78649494656 scopus 로고    scopus 로고
    • Multidimensional latent Markov models in a developmental study of inhibitory control and attentional flexibility in early childhood
    • F. Bartolucci and I. L. Solis-Trapala (2010). Multidimensional latent Markov models in a developmental study of inhibitory control and attentional flexibility in early childhood. Psychometrika, 75, 725-743.
    • (2010) Psychometrika , vol.75 , pp. 725-743
    • Bartolucci, F.1    Solis-Trapala, I.L.2
  • 27
    • 84965063004 scopus 로고
    • An inequality with applications to statistical estimation for probabilistic functions of a Markov process and to a model for ecology
    • L. E. Baum and J. A. Egon (1967). An inequality with applications to statistical estimation for probabilistic functions of a Markov process and to a model for ecology. Bullettin of the American Meteorological Society, 73, 360-363.
    • (1967) Bullettin of the American Meteorological Society , vol.73 , pp. 360-363
    • Baum, L.E.1    Egon, J.A.2
  • 28
    • 0000342467 scopus 로고
    • Statistical inference for probabilistic functions of finite state Markov chains
    • L. E. Baum and T. Petrie (1966). Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statistics, 37, 1554-1563.
    • (1966) Annals of Mathematical Statistics , vol.37 , pp. 1554-1563
    • Baum, L.E.1    Petrie, T.2
  • 29
    • 0000353178 scopus 로고
    • A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains
    • L. E. Baum, T. Petrie, G. Soules, and N. Weiss (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics, 41, 164-171.
    • (1970) Annals of Mathematical Statistics , vol.41 , pp. 164-171
    • Baum, L.E.1    Petrie, T.2    Soules, G.3    Weiss, N.4
  • 30
    • 3042646471 scopus 로고    scopus 로고
    • Optimization of mixture models: Comparison of different strategies
    • A. Berchtold (2004). Optimization of mixture models: comparison of different strategies. Computational Statistics, 19, 385-406.
    • (2004) Computational Statistics , vol.19 , pp. 385-406
    • Berchtold, A.1
  • 32
  • 35
    • 13844251687 scopus 로고    scopus 로고
    • An information-theoretic perspective on order estimation
    • O. Cappé, E. Moulines, and T. Rydén, ed., Springer, New York
    • S. Boucheron and E. Gassiat (2005). An information-theoretic perspective on order estimation. In: O. Cappé, E. Moulines, and T. Rydén, ed., Inference in Hidden Markov Models, 565-601. Springer, New York.
    • (2005) Inference in Hidden Markov Models , pp. 565-601
    • Boucheron, S.1    Gassiat, E.2
  • 36
    • 0142247594 scopus 로고
    • Value-added analysis: A dynamic approach to the estimation of treatment effects
    • A. S. Bryk and H. I. Weisberg (1976). Value-added analysis: a dynamic approach to the estimation of treatment effects. Journal of Educational Statistics, 1, 127-155.
    • (1976) Journal of Educational Statistics , vol.1 , pp. 127-155
    • Bryk, A.S.1    Weisberg, H.I.2
  • 38
    • 0011467982 scopus 로고
    • A latent Markov model approach to the estimation of response errors in multiwave panel data
    • B. V. Bye and E. S. Schechter (1986). A latent Markov model approach to the estimation of response errors in multiwave panel data. Journal of the American Statistical Association, 81, 375-380.
    • (1986) Journal of the American Statistical Association , vol.81 , pp. 375-380
    • Bye, B.V.1    Schechter, E.S.2
  • 39
    • 33947138238 scopus 로고    scopus 로고
    • Recursive computation of the score and observed information matrix in hidden Markov models
    • O. Cappé and E. Moulines (2005). Recursive computation of the score and observed information matrix in hidden Markov models. In: 13th IEEE Workshop on Statistical Signal Processing, pp. 703-707.
    • (2005) 13Th IEEE Workshop on Statistical Signal Processing , pp. 703-707
    • Cappé, O.1    Moulines, E.2
  • 41
    • 0035603218 scopus 로고    scopus 로고
    • Binary choice with binary endogenous regressors in panel data: Estimating the effect of fertility on female labor participation
    • R. Carrasco (2001). Binary choice with binary endogenous regressors in panel data: estimating the effect of fertility on female labor participation. Journal of Business and Economic Statistics, 19, 385-394.
    • (2001) Journal of Business and Economic Statistics , vol.19 , pp. 385-394
    • Carrasco, R.1
  • 43
    • 0003019496 scopus 로고    scopus 로고
    • Bayesian inference for mixtures: The label-switching problem
    • R. Payne and P.J. Green, eds., Physica, Heidelberg
    • G. Celeux (1998). Bayesian inference for mixtures: the label-switching problem. In: R. Payne and P.J. Green, eds., COMPSTAT 98-Proc. in Computational Statistics, 227-232. Physica, Heidelberg.
    • (1998) COMPSTAT 98-Proc. in Computational Statistics , pp. 227-232
    • Celeux, G.1
  • 44
    • 53549114249 scopus 로고    scopus 로고
    • Selecting hidden Markov chain states number with cross-validated likelihood
    • G. Celeux and J. B. Durand (2008). Selecting hidden Markov chain states number with cross-validated likelihood. Computational Statistics, 23, 541-564.
    • (2008) Computational Statistics , vol.23 , pp. 541-564
    • Celeux, G.1    Durand, J.B.2
  • 45
    • 2242491935 scopus 로고    scopus 로고
    • Computational and inferential difficulties with mixture posterior distributions
    • G. Celeux, M. Hurn, and C. P. Robert (2000). Computational and inferential difficulties with mixture posterior distributions. Journal of the American Statistical Association, 95, 957-970.
    • (2000) Journal of the American Statistical Association , vol.95 , pp. 957-970
    • Celeux, G.1    Hurn, M.2    Robert, C.P.3
  • 46
    • 0030351528 scopus 로고    scopus 로고
    • An entropy criterion for assessing the number of clusters in a mixture model
    • G. Celeux and G. Soromenho (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13, 195-212.
    • (1996) Journal of Classification , vol.13 , pp. 195-212
    • Celeux, G.1    Soromenho, G.2
  • 48
    • 0003107701 scopus 로고    scopus 로고
    • Calculating posterior distributions and modal estimates in Markov mixture models
    • S. Chib (1996). Calculating posterior distributions and modal estimates in Markov mixture models. Journal of Econometrics, 75, 79-97.
    • (1996) Journal of Econometrics , vol.75 , pp. 79-97
    • Chib, S.1
  • 52
    • 0001398981 scopus 로고
    • Latent class models for stage-sequential dynamic latent variables
    • L. M. Collins and S. E. Wugalter (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27, 131-157.
    • (1992) Multivariate Behavioral Research , vol.27 , pp. 131-157
    • Collins, L.M.1    Wugalter, S.E.2
  • 53
    • 0043114218 scopus 로고    scopus 로고
    • Marginal regression models for the analysis of positive association of ordinal response variables
    • R. Colombi and A. Forcina (2001). Marginal regression models for the analysis of positive association of ordinal response variables. Biometrika, 88, 1007-1019.
    • (2001) Biometrika , vol.88 , pp. 1007-1019
    • Colombi, R.1    Forcina, A.2
  • 54
    • 24944555208 scopus 로고    scopus 로고
    • Bayesian model choice based on Monte Carlo estimates of posterior model probabilities
    • P. Congdon (2006). Bayesian model choice based on Monte Carlo estimates of posterior model probabilities. Computational Statistics and Data Analysis, 50, 346-357.
    • (2006) Computational Statistics and Data Analysis , vol.50 , pp. 346-357
    • Congdon, P.1
  • 55
    • 0033635019 scopus 로고    scopus 로고
    • Estimation of operating characteristics for dependent diagnostic tests based on latent Markov models
    • R. J. Cook, E. T. M. Ng, and M. O. Meade (2000). Estimation of operating characteristics for dependent diagnostic tests based on latent Markov models. Biometrics, 56, 1109-1117.
    • (2000) Biometrics , vol.56 , pp. 1109-1117
    • Cook, R.J.1    Ng, E.T.M.2    Meade, M.O.3
  • 58
    • 2642583687 scopus 로고    scopus 로고
    • Use of categorical and continuous covariates in latent class analysis
    • J. A. Hagenaars and A. L. McCutcheon, eds., Cambridge University Press, Cambridge, MA
    • C. M. Dayton and G. B. Macready (2002). Use of categorical and continuous covariates in latent class analysis. In: J. A. Hagenaars and A. L. McCutcheon, eds., Advances in Latent Class Modeling, 213-233. Cambridge University Press, Cambridge, MA.
    • (2002) Advances in Latent Class Modeling , pp. 213-233
    • Dayton, C.M.1    Macready, G.B.2
  • 59
    • 0001423343 scopus 로고
    • Maximum likelihood estimation in generalized Rasch models
    • J. De Leeuw and N. Verhelst (1986). Maximum likelihood estimation in generalized Rasch models. Journal of Educational Statistics, 11, 183-196.
    • (1986) Journal of Educational Statistics , vol.11 , pp. 183-196
    • De Leeuw, J.1    Verhelst, N.2
  • 60
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm (With discussion). Journal of the Royal Statistical Society
    • A. P. Dempster, N. M. Laird, and D. B. Rubin (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39, 1-38.
    • (1977) Series B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 61
    • 79959865799 scopus 로고    scopus 로고
    • Model selection for the binary latent class model: A Monte Carlo simulation
    • A. Ferligoj, V. Batagelj, H.-H. Bock and A. Ziberna, eds., Springer, Berlin
    • J. G. Dias (2006). Model selection for the binary latent class model: a Monte Carlo simulation. In: A. Ferligoj, V. Batagelj, H.-H. Bock and A. Ziberna, eds., Data Science and Classification, 91-100. Springer, Berlin.
    • (2006) Data Science and Classification , pp. 91-100
    • Dias, J.G.1
  • 62
    • 34548533999 scopus 로고    scopus 로고
    • Latent class modeling of website users’ search patterns: Implications for online market segmentation
    • J. G. Dias and J. K. Vermunt (2007). Latent class modeling of website users’ search patterns: implications for online market segmentation. Journal of Retailing and Consumer Services, 14, 359-368.
    • (2007) Journal of Retailing and Consumer Services , vol.14 , pp. 359-368
    • Dias, J.G.1    Vermunt, J.K.2
  • 65
    • 0032399629 scopus 로고    scopus 로고
    • How many latent classes of delinquent/criminal careers? Results from mixed Poisson regression analyses
    • A. V. D’Unger, K. C. Lund, P. L. McCall, and D. S. Nagin (1998). How many latent classes of delinquent/criminal careers? Results from mixed Poisson regression analyses. American Journal of Sociology, 103, 1593-1630.
    • (1998) American Journal of Sociology , vol.103 , pp. 1593-1630
    • D’Unger, A.V.1    Lund, K.C.2    McCall, P.L.3    Nagin, D.S.4
  • 72
    • 81955167668 scopus 로고    scopus 로고
    • Quantile regression for longitudinal data based on latent Markov subject-specific parameters
    • A. Farcomeni (2012). Quantile regression for longitudinal data based on latent Markov subject-specific parameters. Statistics and Computing, 22, 141-152.
    • (2012) Statistics and Computing , vol.22 , pp. 141-152
    • Farcomeni, A.1
  • 75
    • 47749152949 scopus 로고    scopus 로고
    • Identifiability of extended latent class models with individual covariates
    • A. Forcina (2008). Identifiability of extended latent class models with individual covariates. Computational Statistics and Data Analysis, 52, 5263-5268.
    • (2008) Computational Statistics and Data Analysis , vol.52 , pp. 5263-5268
    • Forcina, A.1
  • 76
    • 0039545959 scopus 로고
    • Linear logistic latent class analysis and the Rasch model
    • G. H. Fischer and I. W. Molenaar, eds., Springer, New York
    • A. K. Formann (1995). Linear logistic latent class analysis and the Rasch model. In: G. H. Fischer and I. W. Molenaar, eds., Rasch Models: Foundations, Recent Developments, and Applications, 239-255. Springer, New York.
    • (1995) Rasch Models: Foundations, Recent Developments, and Applications , pp. 239-255
    • Formann, A.K.1
  • 77
    • 4043072776 scopus 로고    scopus 로고
    • Identifying patterns and pathways of offending behaviour: A new approach to typologies of crime
    • B. Francis, K. Soothill, and R. Fligelstone (2004). Identifying patterns and pathways of offending behaviour: a new approach to typologies of crime. European Journal of Criminology, 1, 47-87.
    • (2004) European Journal of Criminology , vol.1 , pp. 47-87
    • Francis, B.1    Soothill, K.2    Fligelstone, R.3
  • 79
    • 1842815959 scopus 로고    scopus 로고
    • Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models
    • S. Frühwirth-Schnatter (2001). Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models. Journal of the American Statistical Association, 96, 194-209.
    • (2001) Journal of the American Statistical Association , vol.96 , pp. 194-209
    • Frühwirth-Schnatter, S.1
  • 80
    • 33845446905 scopus 로고    scopus 로고
    • Quantile regression for longitudinal data using the asymmetric Laplace distribution
    • M. Geraci and M. Bottai (2007). Quantile regression for longitudinal data using the asymmetric Laplace distribution. Biostatistics, 8, 140-154.
    • (2007) Biostatistics , vol.8 , pp. 140-154
    • Geraci, M.1    Bottai, M.2
  • 81
    • 0031268341 scopus 로고    scopus 로고
    • Factorial hidden Markov models
    • Z. Ghahramani and M. I. Jordan (1997). Factorial hidden Markov models. Machine Learning, 29, 245-273.
    • (1997) Machine Learning , vol.29 , pp. 245-273
    • Ghahramani, Z.1    Jordan, M.I.2
  • 83
    • 0037687812 scopus 로고    scopus 로고
    • A class of regression models for multivariate categorical responses
    • G. F. V. Glonek (1996). A class of regression models for multivariate categorical responses. Biometrika, 83, 15-28.
    • (1996) Biometrika , vol.83 , pp. 15-28
    • Glonek, G.F.V.1
  • 85
    • 34948888256 scopus 로고    scopus 로고
    • Multilevel structural equation models for the analysis of comparative data on educational performance
    • H. Goldstein, G. Bonnet, and T. Rocher (2007). Multilevel structural equation models for the analysis of comparative data on educational performance. Journal of Educational and Behavioral Statistics, 32, 252-286.
    • (2007) Journal of Educational and Behavioral Statistics , vol.32 , pp. 252-286
    • Goldstein, H.1    Bonnet, G.2    Rocher, T.3
  • 87
    • 85041975304 scopus 로고
    • Exploratory latent structure analysis using both identifiable and unidentifiable models
    • L. A. Goodman (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215-231.
    • (1974) Biometrika , vol.61 , pp. 215-231
    • Goodman, L.A.1
  • 88
    • 3042712671 scopus 로고    scopus 로고
    • Latent class analysis
    • A. L. McCutCheon and J. A. Hagenaars, eds., Cambridge University Press, Cambridge, MA
    • L. A. Goodman (2002). Latent class analysis. In: A. L. McCutCheon and J. A. Hagenaars, eds., Applied Latent Class Analysis. Cambridge University Press, Cambridge, MA.
    • (2002) Applied Latent Class Analysis
    • Goodman, L.A.1
  • 89
    • 0039182759 scopus 로고
    • A general solution for the latent class model of latent structure analysis
    • B. F. Green (1951). A general solution for the latent class model of latent structure analysis. Psychometrika, 16, 151-166.
    • (1951) Psychometrika , vol.16 , pp. 151-166
    • Green, B.F.1
  • 90
    • 77956889087 scopus 로고
    • Reversible jump Markov chain Monte Carlo computation and Bayesianmodel determination
    • P. J. Green (1995). Reversible jump Markov chain Monte Carlo computation and Bayesianmodel determination. Biometrika, 82, 711-732.
    • (1995) Biometrika , vol.82 , pp. 711-732
    • Green, P.J.1
  • 92
    • 0008554241 scopus 로고
    • Log-linear models for frequency tables derived by indirected observation: Maximum-likelihood equations
    • S. J. Haberman (1974). Log-linear models for frequency tables derived by indirected observation: maximum-likelihood equations. Annals of Statistics, 2, 911-924.
    • (1974) Annals of Statistics , vol.2 , pp. 911-924
    • Haberman, S.J.1
  • 94
    • 0001342006 scopus 로고
    • A new approach to the economic-analysis of nonstationary time-series and the business cycle
    • J. D. Hamilton (1989). A new approach to the economic-analysis of nonstationary time-series and the business cycle. Econometrica, 57, 357-384.
    • (1989) Econometrica , vol.57 , pp. 357-384
    • Hamilton, J.D.1
  • 95
    • 77956890234 scopus 로고
    • Monte Carlo sampling methods using Markov chais and their applications
    • W. K. Hastings (1970). Monte Carlo sampling methods using Markov chais and their applications. Biometrika, 57, 97-109.
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.K.1
  • 96
    • 0002453841 scopus 로고
    • Heterogeneity and state dependence
    • D. L. McFadden and C. A. Manski, eds., MIT Press, Cambridge, MA
    • J. J. Heckman (1981). Heterogeneity and state dependence. In: D. L. McFadden and C. A. Manski, eds., Structural Analysis of Discrete Data. MIT Press, Cambridge, MA.
    • (1981) Structural Analysis of Discrete Data
    • Heckman, J.J.1
  • 97
    • 51449109806 scopus 로고    scopus 로고
    • Sequential numerical integration in nonlinear state space models for microeconometric panel data
    • F. Heiss (2008). Sequential numerical integration in nonlinear state space models for microeconometric panel data. Journal of Applied Econometrics, 23, 373-389.
    • (2008) Journal of Applied Econometrics , vol.23 , pp. 373-389
    • Heiss, F.1
  • 98
    • 85055369809 scopus 로고
    • Loglinear multidimensional IRT models for polytomously scored items
    • P. W. Holland and P. R. Rosenbaum (1986). Loglinear multidimensional IRT models for polytomously scored items. Psychometrika, 59, 147-177.
    • (1986) Psychometrika , vol.59 , pp. 147-177
    • Holland, P.W.1    Rosenbaum, P.R.2
  • 99
    • 0003559593 scopus 로고    scopus 로고
    • 2nd Edition. Cambridge University Press, Cambridge, MA
    • C. Hsiao (2003). Analysis of Panel Data, 2nd Edition. Cambridge University Press, Cambridge, MA.
    • (2003) Analysis of Panel Data
    • Hsiao, C.1
  • 100
    • 7244221851 scopus 로고    scopus 로고
    • Building an identifiable latent class model, with covariate effects on underlying and measured variables
    • G.-H. Huang and K. Bandeen-Roche (2004). Building an identifiable latent class model, with covariate effects on underlying and measured variables. Psychometrika, 69, 5-32.
    • (2004) Psychometrika , vol.69 , pp. 5-32
    • Huang, G.-H.1    Bandeen-Roche, K.2
  • 101
    • 0034402610 scopus 로고    scopus 로고
    • Latent transition analysis with covariates, nonresponse, summary statistics and diagnostics: Modelling children’s drawing development
    • K. Humphreys and H. Janson (2000). Latent transition analysis with covariates, nonresponse, summary statistics and diagnostics: modelling children’s drawing development. Multivariate Behavioral Research, 35, 89-118.
    • (2000) Multivariate Behavioral Research , vol.35 , pp. 89-118
    • Humphreys, K.1    Janson, H.2
  • 103
    • 0000281073 scopus 로고    scopus 로고
    • State dependence, serial correlation and heterogeneity in intertemporal labor force participation of married women
    • D. R. Hyslop (1999). State dependence, serial correlation and heterogeneity in intertemporal labor force participation of married women. Econometrica, 67, 1255-1294.
    • (1999) Econometrica , vol.67 , pp. 1255-1294
    • Hyslop, D.R.1
  • 104
    • 22544479764 scopus 로고    scopus 로고
    • Markov chain Monte Carlo and the label switching problem in Bayesian mixture models
    • A. Jasra, C. C. Holmes, and D. A. Stephens (2005). Markov chain Monte Carlo and the label switching problem in Bayesian mixture models. Statistical Science, 20, 50-67.
    • (2005) Statistical Science , vol.20 , pp. 50-67
    • Jasra, A.1    Holmes, C.C.2    Stephens, D.A.3
  • 105
    • 0026206264 scopus 로고
    • Hidden Markov models for speech recognition
    • B. H. Juang and L. R. Rabiner (1991). Hidden Markov models for speech recognition. Technometrics, 33, 251-272.
    • (1991) Technometrics , vol.33 , pp. 251-272
    • Juang, B.H.1    Rabiner, L.R.2
  • 107
    • 42049095667 scopus 로고    scopus 로고
    • An overview of Markov chain methods for the study of stage-sequential developmental processes
    • D. Kaplan (2008). An overview of Markov chain methods for the study of stage-sequential developmental processes. Developmental Psychology, 44, 457-467.
    • (2008) Developmental Psychology , vol.44 , pp. 457-467
    • Kaplan, D.1
  • 109
    • 3442878267 scopus 로고    scopus 로고
    • Quantile regression for longitudinal data
    • R. Koenker (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91, 74-89.
    • (2004) Journal of Multivariate Analysis , vol.91 , pp. 74-89
    • Koenker, R.1
  • 110
    • 84925105967 scopus 로고    scopus 로고
    • Cambridge University Press, New York
    • R. Koenker (2005). Quantile Regression. Cambridge University Press, New York.
    • (2005) Quantile Regression
    • Koenker, R.1
  • 111
    • 0000273843 scopus 로고
    • Regression quantiles
    • R. Koenker and G. Bassett Jr. (1978). Regression quantiles. Econometrica, 46, 33-50.
    • (1978) Econometrica , vol.46 , pp. 33-50
    • Koenker, R.1    Bassett, G.2
  • 113
    • 84902090618 scopus 로고    scopus 로고
    • History and theoretical basics of hidden Markov models
    • P. Dymarski, ed., InTech, Rijeka, Croatia
    • G. L. Kouemou (2011). History and theoretical basics of hidden Markov models. In: P. Dymarski, ed., Hidden Markov Models, Theory and Applications, 3-26. InTech, Rijeka, Croatia.
    • (2011) Hidden Markov Models, Theory and Applications , pp. 3-26
    • Kouemou, G.L.1
  • 114
    • 0442293926 scopus 로고    scopus 로고
    • Association-marginal modeling of multivariate categorical responses: A maximum likelihood approach
    • J. B. Lang, J. W. McDonald, and P. W. F. Smith (1999). Association-marginal modeling of multivariate categorical responses: a maximum likelihood approach. Journal of the American Statistical Association, 94, 1161-71.
    • (1999) Journal of the American Statistical Association , vol.94 , pp. 1161-1171
    • Lang, J.B.1    McDonald, J.W.2    Smith, P.W.F.3
  • 115
    • 0002100563 scopus 로고
    • New development in latent class theory
    • R. Langeheine and J. Rost, eds., Plenum Press, New York
    • R. Langeheine (1988). New development in latent class theory. In: R. Langeheine and J. Rost, eds., Latent Trait and Latent Class Models, 77-108. Plenum Press, New York.
    • (1988) Latent Trait and Latent Class Models , pp. 77-108
    • Langeheine, R.1
  • 116
    • 0039047702 scopus 로고
    • A. von Eye and C. C. Clogg, eds., Latent Variables Analysis: Applications for Developmental Research, Sage, Thousand Oaks, CA
    • R. Langeheine (1994). Latent variables Markov models. In: A. von Eye and C. C. Clogg, eds., Latent Variables Analysis: Applications for Developmental Research, 373-395. Sage, Thousand Oaks, CA.
    • (1994) Latent Variables Markov Models , pp. 373-395
    • Langeheine, R.1
  • 117
    • 0007263224 scopus 로고
    • Discrete-time mixed Markov latent class models
    • eds., Analyzing Social and Political Change: A Casebook of Methods, Sage Publications, London
    • R. Langeheine and F. Van de Pol (1994). Discrete-time mixed Markov latent class models. In: A. Dale and R. B. Davies, eds., Analyzing Social and Political Change: A Casebook of Methods, 171-197. Sage Publications, London.
    • (1994) A. Dale and R. B. Davies , pp. 171-197
    • Langeheine, R.1    Van De Pol, F.2
  • 118
    • 0001453756 scopus 로고
    • The logical and mathematical foundation of latent structure analysis
    • E. A. Suchman, S. A. Stouffer, and L. Guttman, eds., Princeton University Press, New York
    • P. F. Lazarsfeld (1950). The logical and mathematical foundation of latent structure analysis. In: E. A. Suchman, S. A. Stouffer, and L. Guttman, eds., Measurement and Prediction. Princeton University Press, New York.
    • (1950) Measurement and Prediction
    • Lazarsfeld, P.F.1
  • 121
    • 0020734214 scopus 로고
    • An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition
    • S. E. Levinson, L. R. Rabiner, and M. M. Sondhi (1983). An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition. Bell System Technical Journal, 62, 1035-1074.
    • (1983) Bell System Technical Journal , vol.62 , pp. 1035-1074
    • Levinson, S.E.1    Rabiner, L.R.2    Sondhi, M.M.3
  • 122
    • 0001918459 scopus 로고
    • Semiparametric estimation in the Rasch model and related exponential response models, including a simple latent class model for item analysis
    • B. Lindsay, C. Clogg, and J. Grego (1991). Semiparametric estimation in the Rasch model and related exponential response models, including a simple latent class model for item analysis. Journal of the American Statistical Association, 86, 96-107.
    • (1991) Journal of the American Statistical Association , vol.86 , pp. 96-107
    • Lindsay, B.1    Clogg, C.2    Grego, J.3
  • 124
    • 0041110742 scopus 로고    scopus 로고
    • Quantile regression methods for longitudinal data with dropouts: Application to CD4 cell counts of patients infected with the human immunodeficiency virus
    • S. R. Lipsitz, G. M. Fitzmaurice, G. Molenberghs, and L. P. Zhao (1997). Quantile regression methods for longitudinal data with dropouts: application to CD4 cell counts of patients infected with the human immunodeficiency virus. Journal of the Royal Statistical Society, Series C, 46, 463-476.
    • (1997) Journal of the Royal Statistical Society, Series C , vol.46 , pp. 463-476
    • Lipsitz, S.R.1    Fitzmaurice, G.M.2    Molenberghs, G.3    Zhao, L.P.4
  • 126
    • 71449094734 scopus 로고    scopus 로고
    • Mixed-effects models for conditional quantiles with longitudinal data
    • Y. Liu and M. Bottai (2009). Mixed-effects models for conditional quantiles with longitudinal data. The International Journal of Biostatistics, 5.
    • (2009) The International Journal of Biostatistics , pp. 5
    • Liu, Y.1    Bottai, M.2
  • 127
    • 0001044972 scopus 로고
    • Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society
    • T. A. Louis (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B, 44, 226-233.
    • (1982) Series B , vol.44 , pp. 226-233
    • Louis, T.A.1
  • 128
    • 0036750717 scopus 로고    scopus 로고
    • Exact computation of the observed information matrix for hidden Markov models
    • T. C. Lystig and J. P. Hughes (2002). Exact computation of the observed information matrix for hidden Markov models. Journal of Computational and Graphical Statistics, 11, 678-689.
    • (2002) Journal of Computational and Graphical Statistics , vol.11 , pp. 678-689
    • Lystig, T.C.1    Hughes, J.P.2
  • 130
    • 0035741575 scopus 로고    scopus 로고
    • Latent class factor and cluster models, biplots and related graphical displays
    • J. Magidson and J. K. Vermunt (2001). Latent class factor and cluster models, biplots and related graphical displays. Sociological Methodology, 31, 223-264.
    • (2001) Sociological Methodology , vol.31 , pp. 223-264
    • Magidson, J.1    Vermunt, J.K.2
  • 131
    • 33750274518 scopus 로고    scopus 로고
    • Bayesian modelling and inference on mixture of distributions
    • D. Dey and C. R. Rao, eds., Elsevier Science, Amsterdam
    • J. M. Marin, K. L. Mengersen, and C. P. Robert (2005). Bayesian modelling and inference on mixture of distributions. In: D. Dey and C. R. Rao, eds., Handbooks of Statistics, vol. 25, 459-507. Elsevier Science, Amsterdam.
    • (2005) Handbooks of Statistics , vol.25 , pp. 459-507
    • Marin, J.M.1    Mengersen, K.L.2    Robert, C.P.3
  • 132
    • 82055200209 scopus 로고    scopus 로고
    • Mixed hidden Markov models for longitudinal data: An overview
    • A. Maruotti (2011). Mixed hidden Markov models for longitudinal data: an overview. International Statistical Review, 79, 427-454.
    • (2011) International Statistical Review , vol.79 , pp. 427-454
    • Maruotti, A.1
  • 133
    • 0000261668 scopus 로고
    • A Rasch model for partial credit scoring
    • G. N. Masters (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
    • (1982) Psychometrika , vol.47 , pp. 149-174
    • Masters, G.N.1
  • 137
    • 0001101196 scopus 로고
    • Efficient estimation and local identification in latent class analysis
    • R. B. McHugh (1956). Efficient estimation and local identification in latent class analysis. Psychometrika, 21, 331-347.
    • (1956) Psychometrika , vol.21 , pp. 331-347
    • McHugh, R.B.1
  • 141
    • 0027673750 scopus 로고
    • Adolescent-limited and life-course-persistent antisocial behavior: A developmental taxonomy
    • T. Moffitt (1993). Adolescent-limited and life-course-persistent antisocial behavior: a developmental taxonomy. Psychological Review, 100, 674-701.
    • (1993) Psychological Review , vol.100 , pp. 674-701
    • Moffitt, T.1
  • 142
    • 8544268508 scopus 로고    scopus 로고
    • Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data
    • D. Kaplan, ed., Sage Publications, Newbury Park, CA
    • B. Muthén (2004). Latent variable analysis: growth mixture modeling and related techniques for longitudinal data. In: D. Kaplan, ed., Handbook of Quantitative Methodology for the Social sciences, 345-368. Sage Publications, Newbury Park, CA.
    • (2004) Handbook of Quantitative Methodology for the Social Sciences , pp. 345-368
    • Muthén, B.1
  • 143
    • 0032969449 scopus 로고    scopus 로고
    • Finite mixture modeling with mixture outcomes using the EM algorithm
    • B. Muthén and K. Shedden (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
    • (1999) Biometrics , vol.55 , pp. 463-469
    • Muthén, B.1    Shedden, K.2
  • 144
    • 0033441152 scopus 로고    scopus 로고
    • Analyzing developmental trajectories: A semiparametric, group-based approach
    • D. Nagin (1999). Analyzing developmental trajectories: A semiparametric, group-based approach. Psychological Methods, 4, 139-157.
    • (1999) Psychological Methods , vol.4 , pp. 139-157
    • Nagin, D.1
  • 145
    • 21144468543 scopus 로고
    • Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed-Poisson model
    • D. S. Nagin and K. C. Land (1993). Age, criminal careers, and population heterogeneity: specification and estimation of a nonparametric, mixed-Poisson model. Criminology, 31, 327-362.
    • (1993) Criminology , vol.31 , pp. 327-362
    • Nagin, D.S.1    Land, K.C.2
  • 146
    • 0342412512 scopus 로고
    • Bayesian log linear estimates for three-way contingency tables
    • W. A. Nazaret (1987). Bayesian log linear estimates for three-way contingency tables. Biometrika, 74, 401-410.
    • (1987) Biometrika , vol.74 , pp. 401-410
    • Nazaret, W.A.1
  • 147
    • 0033475401 scopus 로고    scopus 로고
    • Direct calculation of the information matrix via the EM algorithm
    • D. Oakes (1999). Direct calculation of the information matrix via the EM algorithm. Journal of the Royal Statistical Society, Series B, 61, 479-482.
    • (1999) Journal of the Royal Statistical Society, Series B , vol.61 , pp. 479-482
    • Oakes, D.1
  • 148
    • 34848886931 scopus 로고    scopus 로고
    • Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products
    • L. J. Paas, J. K. Vermunt, and T. H. A. Bilmolt (2007). Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products. Journal of the Royal Statistical Society, Series A, 170, 955-974.
    • (2007) Journal of the Royal Statistical Society, Series A , vol.170 , pp. 955-974
    • Paas, L.J.1    Vermunt, J.K.2    Bilmolt, T.H.A.3
  • 149
    • 38249019422 scopus 로고
    • Mixed Markov and latent Markov modeling applied to brand choice data
    • C. S. Poulsen (1990). Mixed Markov and latent Markov modeling applied to brand choice data. International Journal of Research in Marketing, 7, 5-19.
    • (1990) International Journal of Research in Marketing , vol.7 , pp. 5-19
    • Poulsen, C.S.1
  • 153
    • 33845762069 scopus 로고    scopus 로고
    • Home Office, London
    • Research Development and Statistics Directorate (1998). The Offenders Index: Codebook. Home Office, London.
    • (1998) The Offenders Index: Codebook
  • 155
    • 18244378520 scopus 로고    scopus 로고
    • On Bayesian analysis of mixtures with an unknown number of components (With discussion)
    • S. Richardson and P. J. Green (1997). On Bayesian analysis of mixtures with an unknown number of components (with discussion). Journal of the Royal Statistical Society, Series B, 59, 731-792.
    • (1997) Journal of the Royal Statistical Society, Series B , vol.59 , pp. 731-792
    • Richardson, S.1    Green, P.J.2
  • 156
    • 43949124407 scopus 로고    scopus 로고
    • Latent class models for diary methods data: Parameter estimation by local computations
    • F. Rijmen, K. Vansteelandt, and P. De Boeck (2008). Latent class models for diary methods data: parameter estimation by local computations. Psychometrika, 73, 167-182.
    • (2008) Psychometrika , vol.73 , pp. 167-182
    • Rijmen, F.1    Vansteelandt, K.2    De Boeck, P.3
  • 158
    • 0034366269 scopus 로고    scopus 로고
    • Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method
    • C. P. Robert, T. Rydén, and D. M. Titterington (2000). Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method. Journal of the Royal Statistical Society, Series B, 62, 57-75.
    • (2000) Journal of the Royal Statistical Society, Series B , vol.62 , pp. 57-75
    • Robert, C.P.1    Rydén, T.2    Titterington, D.M.3
  • 159
    • 0001727688 scopus 로고
    • Identification in parametric models
    • T. J. Rothenberg (1971). Identification in parametric models. Econometrica, 39, 577-591.
    • (1971) Econometrica , vol.39 , pp. 577-591
    • Rothenberg, T.J.1
  • 161
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • G. Schwarz (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
    • (1978) Annals of Statistics , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 162
    • 0036489069 scopus 로고    scopus 로고
    • Bayesian methods for hidden Markov models: Recursive computing in the 21st century
    • S. L. Scott (2002). Bayesian methods for hidden Markov models: Recursive computing in the 21st century. Journal of the American Statistical Association, 97, 337-351.
    • (2002) Journal of the American Statistical Association , vol.97 , pp. 337-351
    • Scott, S.L.1
  • 163
    • 84907319426 scopus 로고
    • Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions
    • S. G. Self and K.-Y. Liang (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. Journal of the American Statistical Association, 82, 605-610.
    • (1987) Journal of the American Statistical Association , vol.82 , pp. 605-610
    • Self, S.G.1    Liang, K.-Y.2
  • 164
    • 0002710991 scopus 로고
    • Towards a unified theory of inequality constrained testing in multivariate analysis
    • A. Shapiro (1988). Towards a unified theory of inequality constrained testing in multivariate analysis. International Statistical Review, 56, 49-62.
    • (1988) International Statistical Review , vol.56 , pp. 49-62
    • Shapiro, A.1
  • 166
    • 6444241412 scopus 로고    scopus 로고
    • The restricted EM algorithm under inequality restrictions on the parameters
    • N.-Z. Shi, S.-R. Zheng, and J. Guo (2005). The restricted EM algorithm under inequality restrictions on the parameters. Journal of Multivariate Analysis, 92, 53-76.
    • (2005) Journal of Multivariate Analysis , vol.92 , pp. 53-76
    • Shi, N.-Z.1    Zheng, S.-R.2    Guo, J.3
  • 170
    • 62049085137 scopus 로고    scopus 로고
    • Reversible jump and the label switching problem in hidden Markov models
    • L. Spezia (2009). Reversible jump and the label switching problem in hidden Markov models. Journal of Statistical Planning and Inference, 139, 2305-2315.
    • (2009) Journal of Statistical Planning and Inference , vol.139 , pp. 2305-2315
    • Spezia, L.1
  • 171
    • 72949104907 scopus 로고    scopus 로고
    • Bayesian analysis of multivariate Gaussian hidden Markov models with an unknown number of regimes
    • L. Spezia (2010). Bayesian analysis of multivariate Gaussian hidden Markov models with an unknown number of regimes. Journal of Time Series Analysis, 31, 1-11.
    • (2010) Journal of Time Series Analysis , vol.31 , pp. 1-11
    • Spezia, L.1
  • 176
    • 42749085986 scopus 로고    scopus 로고
    • Direct maximization of the likelihood of a hidden Markovmodel
    • R. Turner (2008). Direct maximization of the likelihood of a hidden Markovmodel. Computational Statistics and Data Analysis, 52, 4147-4160.
    • (2008) Computational Statistics and Data Analysis , vol.52 , pp. 4147-4160
    • Turner, R.1
  • 177
    • 76649143337 scopus 로고    scopus 로고
    • Posterior predictive arguments in favor of the Bayes-Laplace prior as the consensus prior for binomial and multinomial parameters
    • F. Tuyl, R. Gerlach, and K. Mengersen (2009). Posterior predictive arguments in favor of the Bayes-Laplace prior as the consensus prior for binomial and multinomial parameters. Bayesian Analysis, 4, 151-158.
    • (2009) Bayesian Analysis , vol.4 , pp. 151-158
    • Tuyl, F.1    Gerlach, R.2    Mengersen, K.3
  • 179
    • 84861190153 scopus 로고    scopus 로고
    • Catching up faster by switching sooner: A predictive approach to adoptive estimation with an application to the AIC-BIC dilemma
    • T. van Erven, P. Grünwald, and S. de Roij (2012). Catching up faster by switching sooner: a predictive approach to adoptive estimation with an application to the AIC-BIC dilemma. Journal of the Royal Statistical Society, Series B, 74, 361-417.
    • (2012) Journal of the Royal Statistical Society, Series B , vol.74 , pp. 361-417
    • Van Erven, T.1    Grünwald, P.2    De Roij, S.3
  • 180
    • 81355125267 scopus 로고    scopus 로고
    • Longitudinal research with latent variables
    • K. van Montfort, J. H. L. Oud, and A. Satorra, eds., Springer, Heidelberg, Germany
    • J. K. Vermunt (2010). Longitudinal research with latent variables. In: K. van Montfort, J. H. L. Oud, and A. Satorra, eds., Hand-book of Advanced Multilevel Analysis, 119-152. Springer, Heidelberg, Germany.
    • (2010) Hand-Book of Advanced Multilevel Analysis , pp. 119-152
    • Vermunt, J.K.1
  • 181
    • 20444501102 scopus 로고    scopus 로고
    • Ordinal longitudinal data analysis
    • N. Cameron, R. C. Hauspie, and L. Molinari, eds., Cambridge University Press
    • J. K. Vermunt and J. A. Hagenaars (2004). Ordinal longitudinal data analysis. In: N. Cameron, R. C. Hauspie, and L. Molinari, eds., Methods in Human Growth Research, pp. 374-393. Cambridge University Press.
    • (2004) Methods in Human Growth Research , pp. 374-393
    • Vermunt, J.K.1    Hagenaars, J.A.2
  • 183
    • 84935113569 scopus 로고
    • Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
    • A. J. Viterbi (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13, 260-269.
    • (1967) IEEE Transactions on Information Theory , vol.13 , pp. 260-269
    • Viterbi, A.J.1
  • 184
    • 0000167944 scopus 로고
    • Note on the consistency of the maximum likelihood estimate
    • A. Wald (1949). Note on the consistency of the maximum likelihood estimate. The Annals of Mathematical Statistic, 20, 595-601.
    • (1949) The Annals of Mathematical Statistic , vol.20 , pp. 595-601
    • Wald, A.1
  • 186
  • 189
    • 27944460480 scopus 로고    scopus 로고
    • Can the strengths of AIC and BIC be shared? A conflict between model identification and regression estimation
    • Y. Yang (2005). Can the strengths of AIC and BIC be shared? A conflict between model identification and regression estimation. Biometrika, 92, 937-950.
    • (2005) Biometrika , vol.92 , pp. 937-950
    • Yang, Y.1
  • 192
    • 0026295989 scopus 로고
    • A hidden Markov model for space-time precipitation
    • W. Zucchini and P. Guttorp (1991). A hidden Markov model for space-time precipitation. Water Resources Research, 27, 1917-1923.
    • (1991) Water Resources Research , vol.27 , pp. 1917-1923
    • Zucchini, W.1    Guttorp, P.2


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