238
Views
3
CrossRef citations to date
0
Altmetric
Research Article

A semiparametric Bayesian approach to binomial distribution logistic mixed-effects models for longitudinal data

, , &
Pages 1438-1456 | Received 25 Mar 2021, Accepted 22 Oct 2021, Published online: 08 Nov 2021

References

  • Wang X, Roy V. Analysis of the Pólya-Gamma block Gibbs sampler for Bayesian logistic linear mixed models. Stat Probab Lett. 2018;137:251–256.
  • Drum M, McCullagh P. REML estimation with exact covariance in the logistic mixed model. Biometrics. 1993;49(3):677–689.
  • Polson NG, Scott JG, Windle J. Bayesian inference for logistic models using Pólya-Gamma latent variables. J Am Stat Assoc. 2013;108(504):1339–1349.
  • Zhang D, Davidian M. Linear mixed models with flexible distribution of random effects for longitudinal data. Biometrics. 2001;57(3):795–802.
  • Lai TL, Shih MC. Nonparametric estimation in nonlinear mixed effects models. Biometrika. 2003;90(1):1–13.
  • Ferguson TS. A Bayesian analysis of some nonparametric problems. Ann Stat. 1973;1(2):209–230.
  • Ishwaran H, Zarepour M. Markov chain Monte Carlo in approximate Dirichlet and beta two-parameter process hierarchical models. Biometrika. 2000;87:371–390.
  • Kleinman KP, Ibrahim JG. A semi-parametric Bayesian approach to generalized linear mixed models. Stat Med. 1998;17:2579–2596.
  • Kleinman KP, Ibrahim JG. A semiparametric Bayesian approach to the random effects model. Biometrics. 1998;54:921–938.
  • Chow SM, Tang N, Yuan Y, et al. Bayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior. British J Math Stat Psychol. 2011;64:69–106.
  • Tang NS, Zhao YY. Semi-parametric Bayesian analysis of nonlinear reproductive dispersion mixed models for longitudinal data. J Multivar Anal. 2013;115:68–83.
  • Quintana FA, Johnson WO, Waetjen LE, et al. Bayesian nonparametric longitudinal data analysis. J Am Stat Assoc. 2016;111(515):1168–1181.
  • Guglielmi A, Leva F, Paganoni AM, et al. Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival. J R Stat Soc Ser C. 2014;63:25–46.
  • Tang NS, Duan XD. A semiparametric Bayesian approach to generalized partial linear mixed models for longitudinal data. Comput Stat Data Anal. 2012;56(12):4348–4365.
  • Jara A, Hanson T, Quintana FA, et al. DPpackage: Bayesian semi- and nonparametric modeling in R. J Stat Softw. 2011;40(5):1–30.
  • Sethuraman J. A constructive definition of Dirichlet measures. Stat Sin. 1994;4(2):639–650.
  • Muliere P, Tardella L. Approximating distributions of random functionals of Ferguson–Dirichlet priors. Canadian J Stat. 1998;26(2):283–297.
  • Ishawaran H, James LF. Gibbs sampling methods for stick breaking priors. J Am Stat Assoc. 2001;96(453):161–173.
  • Geman S, Geman D. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell. 1984;6(6):721–741.
  • Metropolis N, Rosenbluth AW, Rosenbluth MN, et al. Equations of state calculations by fast computing machine. J Chem Phys. 1953;21:1087–1091.
  • Hastings WK. Monte Carlo sampling methods using Markov chains and their application. Biometrika. 1970;57:97–109.
  • Gelman A. Inference and monitoring convergence. In: Gilks WR, Richardson S, Spiegelhalter DJ, editors. Markov chain Monte Carlo in Practice. London: Chapman and Hall; 1996.p. 131–144.
  • Geyer CJ. Practical Markov chain Monte Carlo. Stat Sci. 1992;7(4):473–483.
  • Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90(430):773–795.
  • Geisser S, Eddy WF. A predictive approach to model selection. J Amer Stat Assoc. 1979;74(365):153–160.
  • Spiegelhalter DJ, Best NG, Linde CAVD. Bayesian measures of model complexity and fit (with discussion). J Royal Stat Soc Ser B. 2002;64(4):583–639.
  • Gelman A, Carlin JB, Stern HS. Bayesian data analysis. Vol. 2, Boca Raton, FL: CRC press; 2014.
  • Alderdice DF, Forrester CR. Some effects of salinity and temperature on early development and survival of the English sole (Parophrys vetulus). J Fisheries Res Board of Canada. 1968;25(3):495–521.
  • Zhang X, Paul S, Li D. Modelling heterogeneity in longitudinal binomial responses by generalized estimating equations. J Stat Comput Simul. 2016;86(10):1912–1920.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.