-
1
-
-
0000501656
-
Information theory and an extension of the maximum likelihood principle
-
In: Petrov B. N., Csaki F., editors Budapest, Hungary, Budapest,: Akademiai Kiado
-
Akaike, H. 1973. " Information theory and an extension of the maximum likelihood principle ". In Second international symposium on information theory, Edited by: Petrov, B. N. and Csaki, F. 267 - 281. Budapest, Hungary: Akademiai Kiado.
-
(1973)
Second international symposium on information theory
, pp. 267-281
-
-
Akaike, H.1
-
2
-
-
21144438166
-
Likelihood of a model and information criteria
-
Akaike, H. 1981. Likelihood of a model and information criteria. Journal of Econometrics, 16: 3 - 14.
-
(1981)
Journal of Econometrics
, vol.16
, pp. 3-14
-
-
Akaike, H.1
-
3
-
-
33845722419
-
Factor analysis and AIC
-
Akaike, H. 1987. Factor analysis and AIC. Psychometrika, 52: 317 - 332.
-
(1987)
Psychometrika
, vol.52
, pp. 317-332
-
-
Akaike, H.1
-
5
-
-
0142041441
-
Distributional assumptions of growth mixture models: Implications for over-extraction of latent classes
-
Bauer, D. J. and Curran, P. J. 2003. Distributional assumptions of growth mixture models: Implications for over-extraction of latent classes. Psychological Methods, 8: 338 - 363.
-
(2003)
Psychological Methods
, vol.8
, pp. 338-363
-
-
Bauer, D.J.1
Curran, P.J.2
-
6
-
-
1942507464
-
The integration of continuous and discrete latent variable models: Potential problems and promising opportunities
-
Bauer, D. J. and Curran, P. J. 2004. The integration of continuous and discrete latent variable models: Potential problems and promising opportunities. Psychological Methods, 9: 3 - 29.
-
(2004)
Psychological Methods
, vol.9
, pp. 3-29
-
-
Bauer, D.J.1
Curran, P.J.2
-
7
-
-
0034228914
-
Assessing a mixture model for clustering with the integrated completed likelihood
-
Biernacki, C., Celeux, G. and Govaert, G. 2000. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22: 267 - 272.
-
(2000)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.22
, pp. 267-272
-
-
Biernacki, C.1
Celeux, G.2
Govaert, G.3
-
8
-
-
0000308948
-
Using the classification likelihood to choose the number of clusters
-
Biernacki, C. and Govaert, G. 1997. Using the classification likelihood to choose the number of clusters. Computing Science and Statistics, 29: 451 - 457.
-
(1997)
Computing Science and Statistics
, vol.29
, pp. 451-457
-
-
Biernacki, C.1
Govaert, G.2
-
9
-
-
34250108028
-
Model selection and Akaike's information criterion (AIC): The general theory and its analytic extensions
-
Bozdogan, H. 1987. Model selection and Akaike's information criterion (AIC): The general theory and its analytic extensions. Psychometrika, 52: 317 - 332.
-
(1987)
Psychometrika
, vol.52
, pp. 317-332
-
-
Bozdogan, H.1
-
10
-
-
0030351528
-
An entropy criterion for assessing the number of classes in a mixture model
-
Celeux, G. and Soromenho, G. 1996. An entropy criterion for assessing the number of classes 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
-
11
-
-
0035755636
-
A comparison of inclusive and restrictive strategies in modern missing data procedures
-
Collins, L. M., Schafer, J. L. and Kam, C.-M. 2001. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6: 330 - 351.
-
(2001)
Psychological Methods
, vol.6
, pp. 330-351
-
-
Collins, L.M.1
Schafer, J.L.2
Kam, C.-M.3
-
14
-
-
33745601740
-
Local solutions in the estimation of growth mixture models
-
Hipp, J. R. and Bauer, D. J. 2006. Local solutions in the estimation of growth mixture models. Psychological Methods, 11: 36 - 53.
-
(2006)
Psychological Methods
, vol.11
, pp. 36-53
-
-
Hipp, J.R.1
Bauer, D.J.2
-
15
-
-
70349119250
-
Regression and time series model selection in small samples
-
Hurvich, C. M. and Tsai, C. 1989. Regression and time series model selection in small samples. Biometrika, 76: 297 - 307.
-
(1989)
Biometrika
, vol.76
, pp. 297-307
-
-
Hurvich, C.M.1
Tsai, C.2
-
16
-
-
0003985164
-
-
Boston, MA, Boston, MA,: Houghton Mifflin
-
Lazarsfeld, P. F. and Henry, N. W. 1968. Latent structure analysis, Boston, MA: Houghton Mifflin.
-
(1968)
Latent structure analysis
-
-
Lazarsfeld, P.F.1
Henry, N.W.2
-
17
-
-
0038183179
-
Testing the number of components in a normal mixture
-
Lo, Y., Mendell, N. and Rubin, D. B. 2001. Testing the number of components in a normal mixture. Biometrika, 88: 767 - 778.
-
(2001)
Biometrika
, vol.88
, pp. 767-778
-
-
Lo, Y.1
Mendell, N.2
Rubin, D.B.3
-
18
-
-
33846951724
-
Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters
-
Lubke, G. and Muthén, B. O. 2007. Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling, 14: 26 - 47.
-
(2007)
Structural Equation Modeling
, vol.14
, pp. 26-47
-
-
Lubke, G.1
Muthén, B.O.2
-
19
-
-
33846999872
-
Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood?
-
Lubke, G. and Neale, M. C. 2006. Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood?. Multivariate Behavioral Research, 41: 499 - 532.
-
(2006)
Multivariate Behavioral Research
, vol.41
, pp. 499-532
-
-
Lubke, G.1
Neale, M.C.2
-
20
-
-
17444413035
-
Investigating population heterogeneity with factor mixture models
-
Lubke, G. H. and Muthén, B. 2005. Investigating population heterogeneity with factor mixture models. Psychological Methods, 10: 21 - 39.
-
(2005)
Psychological Methods
, vol.10
, pp. 21-39
-
-
Lubke, G.H.1
Muthén, B.2
-
21
-
-
0023570352
-
On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture
-
McLachlan, G. J. 1987. On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Applied Statistics, 36: 318 - 324.
-
(1987)
Applied Statistics
, vol.36
, pp. 318-324
-
-
McLachlan, G.J.1
-
22
-
-
79952599395
-
Commentary on Steinley and Brusco (2011): Recommendations and cautions
-
McLachlan, G. J. 2011. Commentary on Steinley and Brusco (2011): Recommendations and cautions. Psychological Methods, 16: 80 - 81.
-
(2011)
Psychological Methods
, vol.16
, pp. 80-81
-
-
McLachlan, G.J.1
-
23
-
-
0004066260
-
-
New York, NY, New York, NY,: Wiley
-
McLachlan, G. J. and Peel, D. 2000. Finite mixture models, New York, NY: Wiley.
-
(2000)
Finite mixture models
-
-
McLachlan, G.J.1
Peel, D.2
-
24
-
-
58149368765
-
The unicorn, the normal curve, and other improbable creatures
-
Micceri, T. 1989. The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105: 156 - 166.
-
(1989)
Psychological Bulletin
, vol.105
, pp. 156-166
-
-
Micceri, T.1
-
25
-
-
0037926209
-
On the arbitrary nature of latent variables
-
In: Clogg C., von Eye A., editors Thousand Oaks, CA, Thousand Oaks, CA,: Sage
-
Molenaar, P. and von Eye, A. 1994. " On the arbitrary nature of latent variables ". In Latent variables analysis: Applications for developmental research, Edited by: Clogg, C. and von Eye, A. 226 - 242. Thousand Oaks, CA: Sage.
-
(1994)
Latent variables analysis: Applications for developmental research
, pp. 226-242
-
-
Molenaar, P.1
von Eye, A.2
-
26
-
-
0142136682
-
Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003)
-
Muthén, B. (2003. Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Method, 8: 369 - 377.
-
(2003)
Psychological Method
, vol.8
, pp. 369-377
-
-
Muthén, B.1
-
27
-
-
33746906608
-
Should substance use disorders be considered as categorical or dimensional?
-
Muthén, B. 2006. Should substance use disorders be considered as categorical or dimensional?. Addiction, 101: 6 - 16.
-
(2006)
Addiction
, vol.101
, pp. 6-16
-
-
Muthén, B.1
-
28
-
-
33646828382
-
Item response mixture modeling: Application to tobacco dependence criteria
-
Muthén, B. and Asparouhov, T. 2006. Item response mixture modeling: Application to tobacco dependence criteria. Addictive Behaviors, 31: 1050 - 1066.
-
(2006)
Addictive Behaviors
, vol.31
, pp. 1050-1066
-
-
Muthén, B.1
Asparouhov, T.2
-
29
-
-
33745758361
-
Advances in behavioral genetics modeling using Mplus: Applications of factor mixture modeling to twin data
-
Muthén, B., Asparouhov, T. and Rebollo, I. 2006. Advances in behavioral genetics modeling using Mplus: Applications of factor mixture modeling to twin data. Twin Research and Human Genetics, 9: 313 - 324.
-
(2006)
Twin Research and Human Genetics
, vol.9
, pp. 313-324
-
-
Muthén, B.1
Asparouhov, T.2
Rebollo, I.3
-
30
-
-
8544268508
-
Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data
-
In: Kaplan D., editors Newbury Park, CA, Newbury Park, CA,: Sage
-
Muthén, B. O. 2004. " Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data ". In Handbook of quantitative methodology for the social sciences, Edited by: Kaplan, D. 345 - 368. Newbury Park, CA: Sage.
-
(2004)
Handbook of quantitative methodology for the social sciences
, pp. 345-368
-
-
Muthén, B.O.1
-
31
-
-
36849091981
-
Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study
-
Nylund, K. L., Asparouhov, T. and Muthén, B. O. 2007. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14: 535 - 569.
-
(2007)
Structural Equation Modeling
, vol.14
, pp. 535-569
-
-
Nylund, K.L.1
Asparouhov, T.2
Muthén, B.O.3
-
32
-
-
0038525564
-
Monte Carlo experiments: Design and implementation
-
Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J. and Chen, F. 2001. Monte Carlo experiments: Design and implementation. Structural Equation Modeling, 8: 287 - 312.
-
(2001)
Structural Equation Modeling
, vol.8
, pp. 287-312
-
-
Paxton, P.1
Curran, P.J.2
Bollen, K.A.3
Kirby, J.4
Chen, F.5
-
34
-
-
84861591786
-
How well does growth mixture modeling identify heterogeneous growth trajectories? A simulation study examining GMM's performance characteristics
-
Peugh, J. L. and Fan, X. 2012. How well does growth mixture modeling identify heterogeneous growth trajectories? A simulation study examining GMM's performance characteristics. Structural Equation Modeling, 19: 204 - 226.
-
(2012)
Structural Equation Modeling
, vol.19
, pp. 204-226
-
-
Peugh, J.L.1
Fan, X.2
-
35
-
-
0000120766
-
Estimating the dimensions of a model
-
Schwartz, G. 1978. Estimating the dimensions of a model. The Annals of Statistics, 6: 461 - 464.
-
(1978)
The Annals of Statistics
, vol.6
, pp. 461-464
-
-
Schwartz, G.1
-
36
-
-
0000386489
-
Application of model selection criteria to some problems in multivariate analysis
-
Sclove, L. S. 1987. Application of model selection criteria to some problems in multivariate analysis. Psychometrika, 52: 333 - 343.
-
(1987)
Psychometrika
, vol.52
, pp. 333-343
-
-
Sclove, L.S.1
-
37
-
-
79952595368
-
Evaluating mixture modeling for clustering: Recommendations and cautions
-
Steinley, D. and Brusco, M. J. 2011a. Evaluating mixture modeling for clustering: Recommendations and cautions. Psychological Methods, 16: 63 - 79.
-
(2011)
Psychological Methods
, vol.16
, pp. 63-79
-
-
Steinley, D.1
Brusco, M.J.2
-
38
-
-
79952593483
-
K-means clustering and mixture model clustering: Reply to McLachlan (2011) and Vermunt (2011)
-
Steinley, D. and Brusco, M. J. 2011b. K-means clustering and mixture model clustering: Reply to McLachlan (2011) and Vermunt (2011). Psychological Methods, 16: 89 - 92.
-
(2011)
Psychological Methods
, vol.16
, pp. 89-92
-
-
Steinley, D.1
Brusco, M.J.2
-
39
-
-
41149175664
-
Identifying the correct number of classes in growth mixture models
-
In: Hancock G. R., Samuelsen K. M., editors Charlotte, NC, Charlotte, NC,: Information Age
-
Tofighi, D. and Enders, C. K. 2008. " Identifying the correct number of classes in growth mixture models ". In Advances in latent variable mixture models, Edited by: Hancock, G. R. and Samuelsen, K. M. 317 - 341. Charlotte, NC: Information Age.
-
(2008)
Advances in latent variable mixture models
, pp. 317-341
-
-
Tofighi, D.1
Enders, C.K.2
-
40
-
-
79952581001
-
K-means may perform as well as mixture model clustering but may also be much worse: Comment on Steinley and Brusco (2011)
-
Vermunt, J. K. 2011. K-means may perform as well as mixture model clustering but may also be much worse: Comment on Steinley and Brusco (2011). Psychological Methods, 16: 82 - 88.
-
(2011)
Psychological Methods
, vol.16
, pp. 82-88
-
-
Vermunt, J.K.1
-
41
-
-
0012253128
-
Latent class cluster analysis
-
In: Hagenaars J. A., McCutcheon A. L., editors Cambridge, UK, Cambridge,: Cambridge University Press
-
Vermunt, J. K. and Magidson, J. 2002. " Latent class cluster analysis ". In Applied latent class analysis, Edited by: Hagenaars, J. A. and McCutcheon, A. L. 89 - 106. Cambridge, UK: Cambridge University Press.
-
(2002)
Applied latent class analysis
, pp. 89-106
-
-
Vermunt, J.K.1
Magidson, J.2
-
43
-
-
26444483914
-
Evaluating latent class analysis models in qualitative phenotype identification
-
Yang, C.-C. 2006. Evaluating latent class analysis models in qualitative phenotype identification. Computational Statistics & Data Analysis, 50: 1090 - 1104.
-
(2006)
Computational Statistics & Data Analysis
, vol.50
, pp. 1090-1104
-
-
Yang, C.-C.1
|