Literature DB >> 9290222

Linear mixed models with heterogeneous within-cluster variances.

X Lin1, J Raz, S D Harlow.   

Abstract

This paper describes an extension of linear mixed models to allow for heterogeneous within-cluster variances in the analysis of clustered data. Unbiased estimating equations based on quasilikelihood/pseudolikelihood and method of moments are introduced and are shown to give consistent estimators of the regression coefficients, variance components, and heterogeneity parameter under regularity conditions. Cluster-specific random effects and variances are predicted by the posterior modes. The method is illustrated through an analysis of menstrual diary data and its properties are evaluated in a simulation study.

Mesh:

Year:  1997        PMID: 9290222

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  10 in total

1.  Modelling the random effects covariance matrix in longitudinal data.

Authors:  Michael J Daniels; Yan D Zhao
Journal:  Stat Med       Date:  2003-05-30       Impact factor: 2.373

2.  An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data.

Authors:  Donald Hedeker; Robin J Mermelstein; Hakan Demirtas
Journal:  Biometrics       Date:  2007-10-26       Impact factor: 2.571

Review 3.  Generalized linear mixed models: a review and some extensions.

Authors:  C B Dean; Jason D Nielsen
Journal:  Lifetime Data Anal       Date:  2007-11-14       Impact factor: 1.588

4.  A positive stable frailty model for clustered failure time data with covariate-dependent frailty.

Authors:  Dandan Liu; John D Kalbfleisch; Douglas E Schaubel
Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

5.  A Bayesian joint model of menstrual cycle length and fecundity.

Authors:  Kirsten J Lum; Rajeshwari Sundaram; Germaine M Buck Louis; Thomas A Louis
Journal:  Biometrics       Date:  2015-08-21       Impact factor: 2.571

6.  A joint modeling approach for multivariate survival data with random length.

Authors:  Shuling Liu; Amita K Manatunga; Limin Peng; Michele Marcus
Journal:  Biometrics       Date:  2016-10-04       Impact factor: 2.571

7.  A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects.

Authors:  Xin Huang; Gang Li; Robert M Elashoff; Jianxin Pan
Journal:  Lifetime Data Anal       Date:  2010-06-12       Impact factor: 1.588

8.  Zero-inflated count models for longitudinal measurements with heterogeneous random effects.

Authors:  Huirong Zhu; Sheng Luo; Stacia M DeSantis
Journal:  Stat Methods Med Res       Date:  2015-06-24       Impact factor: 3.021

9.  WiSER: Robust and scalable estimation and inference of within-subject variances from intensive longitudinal data.

Authors:  Christopher A German; Janet S Sinsheimer; Jin Zhou; Hua Zhou
Journal:  Biometrics       Date:  2021-06-18       Impact factor: 2.571

10.  The forecasting of menstruation based on a state-space modeling of basal body temperature time series.

Authors:  Keiichi Fukaya; Ai Kawamori; Yutaka Osada; Masumi Kitazawa; Makio Ishiguro
Journal:  Stat Med       Date:  2017-05-22       Impact factor: 2.373

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.