Extensions et applications de l'algorithme SAEM pour les modèles mixtes
Thèses d'Orsay, no. 714 (2006) , 152 p. 
@phdthesis{BJHTUP11_2006__0714__A1_0,
     author = {Meza, Cristian},
     title = {Extensions et applications de l'algorithme {SAEM} pour les mod\`eles mixtes},
     series = {Th\`eses d'Orsay},
     publisher = {Universite Paris-Sud Facult\'e des Sciences d'Orsay},
     number = {714},
     year = {2006},
     language = {fr},
     url = {http://www.numdam.org/item/BJHTUP11_2006__0714__A1_0/}
}
TY  - BOOK
AU  - Meza, Cristian
TI  - Extensions et applications de l'algorithme SAEM pour les modèles mixtes
T3  - Thèses d'Orsay
PY  - 2006
IS  - 714
PB  - Universite Paris-Sud Faculté des Sciences d'Orsay
UR  - http://www.numdam.org/item/BJHTUP11_2006__0714__A1_0/
LA  - fr
ID  - BJHTUP11_2006__0714__A1_0
ER  - 
%0 Book
%A Meza, Cristian
%T Extensions et applications de l'algorithme SAEM pour les modèles mixtes
%S Thèses d'Orsay
%D 2006
%N 714
%I Universite Paris-Sud Faculté des Sciences d'Orsay
%U http://www.numdam.org/item/BJHTUP11_2006__0714__A1_0/
%G fr
%F BJHTUP11_2006__0714__A1_0
Meza, Cristian. Extensions et applications de l'algorithme SAEM pour les modèles mixtes. Thèses d'Orsay, no. 714 (2006), 152 p. http://numdam.org/item/BJHTUP11_2006__0714__A1_0/

Agresti, A. (1999). Modeling ordered categorical data : recent advances and future challenges. Statistics in Medicine, 18:2191-2207. | DOI

Agresti, A. and Natarajan, R. (2001). Modeling clustered ordered categorical data : survey. International Statistical Review, 69:345-371. | Zbl | DOI

Ashford, J. and Sowden, R. (1970). Multi-variate probit analysis. Biometrics, 26:535-546. | DOI

Atchley, W. and Zhu, J. (1997). Developmental quantitative genetics, conditional epigenetic variability and growth in mice. Genetics, 147:765-776. | DOI

Bertalanffy, L. V. (1957). Quantitative laws in metabolism and growth. Q. Rev. Biol., 32:217-231. | DOI

Blasco, A., Piles, M., and Varona, L. (2003). A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits. Genet. Sel. Evol., 35:21-41. | DOI

Booth, J. and Hobert, J. (1999). Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. J. Roy. Statist. Soc. B., 61:265-285. | Zbl | DOI

Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. J. Amer. Statist. Assoc., 88:9-25. | Zbl

Breslow, N. E. and Lin, X. (1995). Bias correction in generalised linear mixed models with a single component of dispersion. Biometrika, 82:81-91. | MR | Zbl | DOI

Brody, S. (1945). In Bioenergetics and Growth with Special Reference to the Energetic Efficiency Complex in Domestic Animals. Reinhold Publ., New York.

Cappé, O., Guillin, A., Marin, J.-M., and Robert, C. (2004). Population monte carlo. J. Comput. Graph. Statist., 13:907-929. | MR | DOI

Celeux, G. and Diebolt, J. (1985). The SEM algorithm : a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Computational. Statistics Quaterly, 2:73-82.

Concordet, D. and Nunez, O. G. (2002). A simulated pseudo-maximum likelihood estimator for nonlinear mixed models. Comput. Statist. Data Anal., 39:187-201. | MR | Zbl | DOI

Davidian, M. and Giltinan, D. M. (2003). Nonlinear models for repeated measurements : An overview and update. J. Agric. Biol. Env. Statist., 8:387-419. | DOI

Delyon, B., Lavielle, M., and Moulines, E. (1999). Convergence of a stochastic approximation version of the EM algorithm. Ann. Statist., 27:94-128. | MR | Zbl | DOI

Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B, 39:1-38. With discussion. | MR | Zbl

Diggle, P. J., Liang, K. Y., and Zeger, S. L. (1994). Analysis of longitudinal data. Oxford University Press, Oxford. | Zbl

Falconer, D. (1989). An introduction to quantitative genetcis. 3rd edition, Longman Scientific and Technical, Harlow.

Fesslern, J. A. and Hero, A. O. (1994). Space-alternating generalized expectation-maximization algorithm. IEE Trans. Sig. Proc., 42:2664-2677. | DOI

Finney, D. J. (1971). Probit Analysis. Cambridge University Press, Cambridge University. | MR | Zbl

Fitzhugh, H. (1976). Analysis of growth curves and strategies for altering their shape. J. Anim. Sci., 42:1036-1051. | DOI

Foulley, J., Gianola, D., and Thompson, R. (1983). Prediction of genetic merit from data on categorical and quantitative variates with an application to calving difficulty, birth weight and pelvic opening. Genet. Sel. Evol., 15:401-424. | DOI

Foulley, J. and Manfredi, E. (1991). Approche statistique de l'évaluation de génétique des reproducteurs pour des caractères binaires à seuils. Genet. Sel. Evol., 23:309-338. | DOI

Foulley, J. and Quaas, R. (1995). Heterogeneous variances in gaussian linear mixed models. Genet. Sel. Evol., 27:211-228. | DOI

Foulley, J. and Van Dyk, D. (2000). The PX-EM algorithm for fast stable fitting of henderson's mixed model. Genetics Selection Evolution, 32:143-163. | DOI

Geyer, C. (1994). On the convergence of Monte Carlo maximum likelihood calculations. J. R. Statist. Soc. B., 56:261-274. | MR | Zbl

Gianola, D. and Sorensen, D. (2006). Inferring fixed effects in a mixed linear model from an integrated likelihood. Biometrika, Submitted.

Gianola, F. (1982). Theory and analysis of threshold characters. J. Anim. Sci., 54:1079-1096. | DOI

Gibbons, R. D. and Hedecker, D. (1994). Applcation of random-effects probit regression models. Journal of Computing and Clinical Psychology, 62:285-296. | DOI

Gilmour, A., Thompson, R., Cullis, B., and Welham, S. (2004). ASREML Manual. New South Wales Department of Agriculture, Orange, Australie.

Gueorguieva, R. V. and Agresti, A. (2001). A correlated probit model for joint modeling of clustered binary and continous responses. J. Amer. Statist. Assoc., 96:1102-1112. | MR | Zbl | DOI

Harville, D. (1974). Bayesian inference for variance components using only error contrats. Biometrika, 61:383-385. | MR | Zbl | DOI

Hausman, J. and Wise, D. (1978). A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogenous preferences. Econometrica, 46:403-426. | MR | Zbl | DOI

Hedeker, D. (1999). Mixno: a computer program for mixed-effects nominal logistic regression. J. Stat. Software, 4 (5): 1-92. | DOI

Hedeker, D. and Gibbons, R. D. (1996). Mixor: a computer program for mixed-effects ordinal regression analysis. Computer Methods and Programs in Biomedicine, 49): 157-176. | DOI

Huisman, A., Veerkamp, R., and Van Arendonk, J. (2002). Genetic parameters for various random regression models to describe weight data of pigs. J. Anim. Sci., 80:575-582. | DOI

Jaffrezic, F., Thompson, R., and Hill, W. (2003). Structured antedependence models for genetic analysis of multivariate repeated measures in quantitative traits. Genet. Res., 82:55-65. | DOI

Jaffrezic, F., Venot, E., Lalöe, D., Vinet, A., and Renand, G. (2004). Use of structured antedependence models for the genetic analysis of growth curves. J. Anim. Sci., 82:3465-3473. | DOI

Jank, W. S. (2004). Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM. CSDA, 48:685-701. | MR | Zbl

Kuhn, E. and Lavielle, M. (2004). Coupling a stochastic approximation version of EM with a MCMC procedure. ESAIM P&S. | MR | Zbl | Numdam | DOI

Kuhn, E. and Lavielle, M. (2005). Maximum likelihood estimation in nonlinear mixed effects models. CSDA, 49:1020-1038. | MR | Zbl

Kung, F. (1992). Fitting logistic growth curve with predetermined carrying capacity. Biometrics, 48:1-17.

Laird, A. (1966). Postnatal growth of birds and mammals. Growth, 30:349-363.

Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38:963-974. | Zbl | DOI

Lavielle, M. (2005). Monolix user guide manual. http://www.math.upsud.fr/~lavielle/monolix/logiciels.

Lavielle, M. and Meza, C. (2006). A parameter expansion version of the SAEM algorithm. Statistics and Computing, Submitted. | MR

Levine, R. A. and Casella, R. (2001). Implementations of the monte carlo EM algorithm. J. Comp. Graph. Statist., 10:422-439. | MR | DOI

Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73:13-22. | MR | Zbl | DOI

Liao, J. G. and Lipsitz, S. R. (2002). A type of restricted maximum likelihood estimator of variance components in generalised linear mixed models. Biometrika, 89:401-409. | MR | Zbl | DOI

Lin, X. and Breslow, N. E. (1996). Bias correction in GLMM with multiple components of dispersion. J. Amer. Statist. Assoc., 91:1007-1016. | MR | Zbl | DOI

Lindstrom, M. J. and Bates, D. M. (1990). Nonlinear mixed-effects models for repeated measures data. Biometrics, 46:673-787. | MR | DOI

Littell, R., Milliken, G., Stroup, W., and Wolfinger, R. (1996). SAS System for mixed models. SAS Institute Inc., Cary, NC, USA.

Little, R. J. A. and Rubin, D. B. (1987). Statistical analysis with missing data. Wiley, New York. | MR | Zbl

Liu, C., Rubin, D., and Wu, Y. (1998). Parameter expansion to accelerate EM: The PX-EM algorithm. Biometrika, 85:755-770. | MR | Zbl | DOI

Liu, C. and Rubin, D. B. (1994). The ECME algorithm: a simple extension of em and ecm with faster monotone convergence. Biometrika, 81:633-648. | MR | Zbl | DOI

Louis, T. (1982). Finding the observed information matrix when using the em algorithm. J. Roy. Statist. Soc. B., 44:226-233. | MR | Zbl

Ma, C.-X., Casella, G., and Wu, R. (2002). Functional mapping of quantitative trait loci underlying the character process: a theoretical framework. Genetics, 161:1751-1762. | DOI

Mccullagh, P. and Nelder, J. A. (1989). Generalized Linear Models. Chapman and Hall, New York. | MR | Zbl | DOI

Mcculloch, C. E. (1997). Maximum likelihood algorithms for generalized linear mixed models. J. Amer. Statist. Assoc., 92:162-170. | MR | Zbl | DOI

Mcfadden, D. (1989). A method of simulated moments for estimation of discrete repsonse models without numerical integration. Econometrica, 57:995-1026. | MR | Zbl | DOI

Mclachlan, G. J. and Krishnan, T. (1997). The EM algorithm and extensions. J. Wiley and Sons, New York. | MR | Zbl

Meng, X. and Van Dyk, D. (1998). Fast em implementations for mixed-effects models. J. Roy. Statist. Soc. B, 60:559-578. | MR | Zbl | DOI

Meng, X.-L. and Rubin, D. B. (1993a). Maximum likelihood estimation via the ECM algorithm : a general framework. Biometrika, 80 (2):267-278. | MR | Zbl | DOI

Meng, X.-L. and Rubin, D. B. (1993b). Maximum likelihood estimation via the ECM algorithm : a general framework. Biometrika, 80 (2):267-278. | MR | Zbl | DOI

Meza, C., Jaffrézic, F., and Foulley, J. L. (2006). REML estimation of variance parameters in nonlinear mixed effects models using the SAEM algorithm. Biometrical Journal, Submitted,. | MR | Zbl

Mialon, M. M., Renand, G., Krauss, D., and Ménissier, F. (2001). Variability of the postpartum recovery of sexual activity of Charolais cows. Livest. Prod. Sci., 69:217- 226. | DOI

Mignon-Grasteau, S., Piles, M., Varona, L., Poivey, J. P., et al. (2000). Genetic analysis of growth curve parameters for male and female chickens resulting from selection on shape of growth curve. J. Anim. Sci., 78:2532-2531. | DOI

Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. Springer, New York. | MR | Zbl

Nelder, J. A. and Lee, Y. (1992). Likelihood, quasi-likelihood and pseudolikelihood : Some comparisons. J. Roy. Statist. Soc. B, 54 (1):273-284. | MR

Neuhaus, J. and Segal, M. (1997). An assessment of approximate maximum likelihood estimators in generalized linear models. In Gregoire, T. G., editor, Modelling longitudinal and spatially correlated data : Methods, Applications and future directions. Lecture notes in Statistics, v. 122. Springer, New York. | Zbl | DOI

Nunez-Anton, V. and Zimmerman, D. (2000). Modeling non-stationary longitudinal data. Biometrics, 56:699-705. | Zbl | DOI

Patterson, H. and Thompson, R. (1971). Recovery of interblock information when block sizes are unequal. Biometrika, 58:545-554. | MR | Zbl | DOI

Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects models in S and S-Plus. Statistics and Computing, Springer, New York. | Zbl

Pletcher, S. D. and Jaffrézic, F. (2002). Generalized character process models: estimating the genetic basis of traits that cannot be observed and that change with age or environmental conditions. Biometrics, 58:157-162. | MR | Zbl | DOI

Raudenbush, S. W., Yang, M.-L., and Yosef, M. (2000). Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate laplace approximation. J. Comp. Graph. Stat., 9:141-157. | MR

Robert, C. P. (1995). Simulation of truncated normal variables. Statistics and Computing, 5:121-125. | DOI

Roberts, G. O., Gelman, A., and Gilks, W. (1997). Weak convergence and optimal scaling of random walk metropolis algorithm. Ann. Applied Prob., 7:110-120. | MR | Zbl

Roberts, G. O. and Rosenthal, J. S. (2001). Optimal scaling of various metropolis-hastings algorithms. Statistical Science, 16:351-367. | MR | Zbl | DOI

Rodríguez, G. and Goldman, N. (1995). An assessment of estimation procedures for multilevel models whitn binary responses. J. Roy. Statist. Soc. A, 158:73-89. | DOI

Schall, R. (1991). Estimation in generalized linear models with random effects. Biometrika, 78:719-727. | Zbl | DOI

Spiegelhalter, D., Thomas, A., and Best, N. (2004). Winbugs 1.4 user manual. Cambridge : Medical Research Council Biostatistics Unit. http://www.mrc-bsu.cam.ac.uk/bugs.

Thall, P. and Vail, S. (1990). Some covariance models for longitudinal count data with ovserdispersion. Biometrics, 46):657-671. | MR | Zbl | DOI

Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26:24-36. | MR | Zbl | DOI

Verbeke, G. and Molenberghs (2000). Linear mixed models for longitudinal data. Springer-Verlag, New York. | MR | Zbl

Vonesh, E. and Carter, R. (1992). Mixed-effects non linear regression for unbalanced repeated measures. Biometrics, 48:1-17. | MR | DOI

Wei, G. and Tanner, M. (1990a). A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. J. Amer. Statist. Assoc., 85:699-704. | DOI

Wei, G. and Tanner, M. (1990b). A monte carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. J. Amer. Statist. Assoc., 85:699-704. | DOI

Wolfinger, R. (1992). Laplace's approximation for nonlinear mixed models. Biometrika, 80:791-795. | MR | Zbl | DOI

Wu, C.-F. J. (1983). On the convergence properties of the EM algorithm. Ann. Statist., 11:95-103. | MR | Zbl

Wu, L. (2004). Exact and approximate inferences for nonlinear mixed-effects models with missing covariates. J. Amer. Statist. Assoc., 99:700-709. | MR | Zbl | DOI