메뉴 건너뛰기




Volumn 56, Issue 6, 2012, Pages 1808-1828

Direct fitting of dynamic models using integrated nested Laplace approximations - INLA

Author keywords

Approximate Bayesian inference; Augmented model; Laplace approximation; Spatio temporal dynamic models; State space models

Indexed keywords

APPROXIMATE BAYESIAN INFERENCE; BAYESIAN ANALYSIS; BAYESIAN INFERENCE; COMPLEX MODEL; COMPUTATIONAL FRAMEWORK; COMPUTATIONAL TIME; DATA SETS; DYNAMIC LINEAR MODEL; DYNAMIC MODELING; ERROR STRUCTURES; HYPERPARAMETERS; LAPLACE APPROXIMATION; MARGINALS; RECURSIVE FORMS; SPATIO-TEMPORAL DYNAMIC MODELS; STATE VECTOR; STATE-SPACE MODELS; TEMPORAL STRUCTURES;

EID: 84857640888     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2011.10.024     Document Type: Article
Times cited : (61)

References (49)
  • 3
    • 10944251332 scopus 로고    scopus 로고
    • Particle methods for change detection, system identification, and control
    • C. Andrieu, A. Doucet, S.S. Singh, and V.B. Tadi Particle methods for change detection, system identification, and control Proceedings of the IEEE 92 2004 423 438
    • (2004) Proceedings of the IEEE , vol.92 , pp. 423-438
    • Andrieu, C.1    Doucet, A.2    Singh, S.S.3    Tadi, V.B.4
  • 5
    • 44649107771 scopus 로고    scopus 로고
    • An overview of existing methods and recent advances in sequential Monte Carlo
    • O. Capp, S.J. Godsill, and E. Moulines An overview of existing methods and recent advances in sequential Monte Carlo Proceedings of the IEEE 95 2007 899 924
    • (2007) Proceedings of the IEEE , vol.95 , pp. 899-924
    • Capp, O.1    Godsill, S.J.2    Moulines, E.3
  • 6
    • 0000193853 scopus 로고
    • On Gibbs sampling for state space models
    • C.K. Carter, and R. Kohn On Gibbs sampling for state space models Biometrika 81 1994 541 553
    • (1994) Biometrika , vol.81 , pp. 541-553
    • Carter, C.K.1    Kohn, R.2
  • 7
    • 0000761439 scopus 로고    scopus 로고
    • Markov chain Monte Carlo in conditionally Gaussian state space models
    • C.K. Carter, and R. Kohn Markov chain Monte Carlo in conditionally Gaussian state space models Biometrika 83 1996 589 601
    • (1996) Biometrika , vol.83 , pp. 589-601
    • Carter, C.K.1    Kohn, R.2
  • 9
    • 33646336648 scopus 로고    scopus 로고
    • Formulating state space models in R with focus on longitudinal regression models
    • C. Dethlefsen, and S. Lundbye-Christensen Formulating state space models in R with focus on longitudinal regression models Journal of Statistical Software 16 2006 1 15
    • (2006) Journal of Statistical Software , vol.16 , pp. 1-15
    • Dethlefsen, C.1    Lundbye-Christensen, S.2
  • 10
  • 11
    • 0034354798 scopus 로고    scopus 로고
    • Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives (with discussion)
    • J. Durbin, and S.J. Koopman Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives (with discussion) Journal of the Royal Statistical Society, Series B 62 2000 3 56
    • (2000) Journal of the Royal Statistical Society, Series B , vol.62 , pp. 3-56
    • Durbin, J.1    Koopman, S.J.2
  • 14
    • 84857652057 scopus 로고    scopus 로고
    • Approximate Bayesian inference for large spatial datasets using predictive process models
    • Norwegian University of Science and Technology, Trondheim, Norway
    • Eidsvik, J.; Finley, A.O.; Banerjee, S.; Rue, H.; 2010. Approximate Bayesian inference for large spatial datasets using predictive process models. Preprint Statistics No 9/2010, Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway. URL: http://www.math.ntnu.no/preprint/statistics/2010/S9-2010.pdf.
    • (2010) Preprint Statistics No 9/2010, Department of Mathematical Sciences
    • Eidsvik, J.1    Finley A., .O.2    Banerjee, S.3    Rue, H.4
  • 17
    • 0001053807 scopus 로고    scopus 로고
    • Markov chain Monte Carlo for dynamic generalised linear models
    • D. Gamerman Markov chain Monte Carlo for dynamic generalised linear models Biometrika 85 1998 215 227
    • (1998) Biometrika , vol.85 , pp. 215-227
    • Gamerman, D.1
  • 18
    • 0027580559 scopus 로고
    • Novel approach to nonlinear/non-Gaussian Bayesian state estimation
    • N.J. Gordon, D.J. Salmond, and A.F.M. Smith Novel approach to nonlinear/non-Gaussian Bayesian state estimation IEE ProceedingsF 140 1993 107 113
    • (1993) IEE ProceedingsF , vol.140 , pp. 107-113
    • Gordon, N.J.1    Salmond, D.J.2    Smith, A.F.M.3
  • 20
    • 0001308047 scopus 로고
    • The effects of seat belt legislation on British road casualties: A case study in structural time series modelling (with discussion)
    • A.C. Harvey, and J. Durbin The effects of seat belt legislation on British road casualties: a case study in structural time series modelling (with discussion) Journal of the Royal Statistical Society, Series A 149 1986 187 227
    • (1986) Journal of the Royal Statistical Society, Series A , vol.149 , pp. 187-227
    • Harvey, A.C.1    Durbin, J.2
  • 22
    • 79952929583 scopus 로고    scopus 로고
    • KFAS: Kalman filter and smoothers for exponential family state space models
    • Helske, J.; 2010. KFAS: Kalman filter and smoothers for exponential family state space models. R package version 0.6.0. http://CRAN.R-project.org/ package=KFAS.
    • (2010) R Package Version 0.6.0
    • Helske, J.1
  • 23
    • 0001729490 scopus 로고    scopus 로고
    • Statistical algorithms for models in state space using SsfPack 2.2
    • S.J. Koopman, N. Shephard, and J.A. Doornik Statistical algorithms for models in state space using SsfPack 2.2 Econometrics Journal 2 1999 113 166
    • (1999) Econometrics Journal , vol.2 , pp. 113-166
    • Koopman, S.J.1    Shephard, N.2    Doornik, J.A.3
  • 27
    • 78650583062 scopus 로고    scopus 로고
    • An R package for dynamic linear models
    • G. Petris An R package for dynamic linear models Journal of Statistical Software 36 2010 1 16
    • (2010) Journal of Statistical Software , vol.36 , pp. 1-16
    • Petris, G.1
  • 29
    • 41149087694 scopus 로고    scopus 로고
    • CODA: Convergence diagnosis and output analysis for MCMC
    • M. Plummer, N. Best, K. Cowles, and K. Vines CODA: convergence diagnosis and output analysis for MCMC R News 6 2006 7 11
    • (2006) R News , vol.6 , pp. 7-11
    • Plummer, M.1    Best, N.2    Cowles, K.3    Vines, K.4
  • 30
    • 84979376623 scopus 로고
    • Efficient Bayesian learning in non-linear dynamic models
    • A. Pole, and M. West Efficient Bayesian learning in non-linear dynamic models Journal of Forecasting 9 1990 119 136
    • (1990) Journal of Forecasting , vol.9 , pp. 119-136
    • Pole, A.1    West, M.2
  • 33
    • 79951480123 scopus 로고    scopus 로고
    • R Development Core Team R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-07-0
    • R Development Core Team, 2010. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-07-0. URL: http://www.R-project.org.
    • (2010) R: A Language and Environment for Statistical Computing
  • 35
    • 84857646492 scopus 로고    scopus 로고
    • Gender-specific differences and the impact of family integration on time trends in age-stratified swiss suicide rates
    • 10.1111/j.1467-985X.2011.01013.x
    • A. Riebler, L. Held, H. Rue, and M. Bopp Gender-specific differences and the impact of family integration on time trends in age-stratified swiss suicide rates Journal of the Royal Statistical Society, Series A 175 2012 10.1111/j.1467-985X.2011.01013.x
    • (2012) Journal of the Royal Statistical Society, Series A , vol.175
    • Riebler, A.1    Held, L.2    Rue, H.3    Bopp, M.4
  • 36
    • 33646344460 scopus 로고    scopus 로고
    • Time series in R 1.5.0
    • B.D. Ripley Time series in R 1.5.0 R News 2 2002 2 7
    • (2002) R News , vol.2 , pp. 2-7
    • Ripley, B.D.1
  • 37
    • 79958717589 scopus 로고    scopus 로고
    • Sensitivity analysis in Bayesian generalized linear mixed models for binary data
    • M. Roos, and L. Held Sensitivity analysis in Bayesian generalized linear mixed models for binary data Bayesian Analysis 6 2011 259 278
    • (2011) Bayesian Analysis , vol.6 , pp. 259-278
    • Roos, M.1    Held, L.2
  • 38
    • 34250219986 scopus 로고    scopus 로고
    • Approximate Bayesian inference for hierarchical Gaussian Markov random fields models
    • H. Rue, and S. Martino Approximate Bayesian inference for hierarchical Gaussian Markov random fields models Journal of Statistical Planning and Inference 137 2007 3177 3192
    • (2007) Journal of Statistical Planning and Inference , vol.137 , pp. 3177-3192
    • Rue, H.1    Martino, S.2
  • 39
    • 62849120031 scopus 로고    scopus 로고
    • Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion)
    • H. Rue, S. Martino, and N. Chopin Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion) Journal of the Royal Statistical Society, Series B 71 2009 319 392
    • (2009) Journal of the Royal Statistical Society, Series B , vol.71 , pp. 319-392
    • Rue, H.1    Martino, S.2    Chopin, N.3
  • 40
    • 84870292340 scopus 로고    scopus 로고
    • Preprint Statistics No 12/2010, Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
    • Ruiz-Cárdenas, R.; Krainski, E.T.; Rue, H.; 2010. Fitting dynamic models using integrated nested Laplace approximationsINLA. Preprint Statistics No 12/2010, Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway. URL: http://www.math.ntnu.no/inla/r-inla. org/papers/RuizKrainskiRueTR20111023.pdf.
    • (2010) Fitting Dynamic Models Using Integrated Nested Laplace ApproximationsINLA
    • Ruiz-Cárdenas, R.1    Krainski E., .T.2    Rue, H.3
  • 43
    • 0036475891 scopus 로고    scopus 로고
    • Particle filters for state-space models with the presence of unknown static parameters
    • G. Storvik Particle filters for state-space models with the presence of unknown static parameters IEEE Transactions on Signal Processing 50 2002 281 289
    • (2002) IEEE Transactions on Signal Processing , vol.50 , pp. 281-289
    • Storvik, G.1
  • 48
    • 80052895786 scopus 로고    scopus 로고
    • Efficient Bayesian inference for switching state-space models using particle Markov chain Monte Carlo methods
    • University of Bristol, Department of Mathematics
    • Whiteley, N.; Andrieu, C.; Doucet, A.; 2010. Efficient Bayesian inference for switching state-space models using particle Markov chain Monte Carlo methods. Statistics Group Report 10:04, University of Bristol, Department of Mathematics, p. 27.
    • (2010) Statistics Group Report 10:04 , pp. 27
    • Whiteley, N.1    Andrieu, C.2    Doucet, A.3
  • 49
    • 33745618417 scopus 로고    scopus 로고
    • Deterministic approximate inference techniques for conditionally Gaussian state space models
    • O. Zoeter, and T. Heskes Deterministic approximate inference techniques for conditionally Gaussian state space models Statistics and Computing 16 2006 279 292
    • (2006) Statistics and Computing , vol.16 , pp. 279-292
    • Zoeter, O.1    Heskes, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.