Results 301 to 310 of about 1,409,985 (359)

Linear mixed function‐on‐function regression models

Biometrics, 2014
SummaryWe develop a linear mixed regression model where both the response and the predictor are functions. Model parameters are estimated by maximizing the log likelihood via the ECME algorithm. The estimated variance parameters or covariance matrices are shown to be positive or positive definite at each iteration.
Wei Wang
semanticscholar   +4 more sources

Bayesian cusp regression and linear mixed model [PDF]

, 2023
First of all, we introduce the Bayesian mixture way of solving the Cusp Catastrophe model, which is designed to deal with piece-wise continuous outcomes. Simulation and real data analysis show that the new method beats the old stochastic different equation solution of Cusp Catastrophe model.
Jiayi Hou
openaire   +3 more sources

Person-time generalized linear regression mixed models in survival analysis

Statistics
This study presents a methodological contribution within a discrete-time survival analysis framework by introducing a person-time generalized linear mixed model. An application of this methodology, using logistic models with random effects, is provided in the context of academic career progression in Italy, a field where gender disparities remain a ...
Maura Mezzetti, Ilia Negri
semanticscholar   +2 more sources

Linear Mixed Model Robust Regression [PDF]

, 2019
Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. An incorrectly specified parametric means model may be
Waterman, Megan J., Birch, Jeffrey B., Schabenberger, Oliver   +2 more
openaire   +1 more source

Extending the Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models

Journal of the American Statistical Association, 2007
cover point processes in the modern sense of random measures. This chapter also is more technical than the subsequent two on Markov processes. Chapter 4 (on discrete-time countable state Markov processes) is extremely short.
J. Ormerod
semanticscholar   +2 more sources

Approximate inference in generalized linear mixed models

Journal of the American Statistical Association, 1993
N. Breslow, D. Clayton
semanticscholar   +3 more sources

Lasso regression in sparse linear model with $$\varphi $$-mixing errors

Metrika, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peng, Ling, Zhu, Yan, Zhong, Wenxuan
openaire   +2 more sources

Mixing partially linear regression models

Sankhya A, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liu, Song, Yang, Yuhong
openaire   +1 more source

Double Penalized Quantile Regression for the Linear Mixed Effects Model

Journal of Systems Science and Complexity, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Hanfang, Liu, Yuan, Luo, Youxi
openaire   +2 more sources

Non‐linear mixed regression models

Environmetrics, 1995
AbstractIn this paper we present an estimating equation approach to statistical inference for non‐linear random effects regression models for correlated data. With this approach, the distribution of the observations and the random effects need not be specified; only their expectation and covariance structure are required. The variance of the data given
Richard T. Burnett, W. H. Ross, Daniel Krewski   +2 more
openaire   +1 more source