Zobrazeno 1 - 10
of 370
pro vyhledávání: '"Hui, Francis"'
Structural equation models (SEMs) are commonly used to study the structural relationship between observed variables and latent constructs. Recently, Bayesian fitting procedures for SEMs have received more attention thanks to their potential to facili
Externí odkaz:
http://arxiv.org/abs/2407.08140
Linear mixed models (LMMs), which typically assume normality for both the random effects and error terms, are a popular class of methods for analyzing longitudinal and clustered data. However, such models can be sensitive to outliers, and this can le
Externí odkaz:
http://arxiv.org/abs/2407.01883
Generalized Linear Mixed Models (GLMMs) are widely used for analysing clustered data. One well-established method of overcoming the integral in the marginal likelihood function for GLMMs is penalized quasi-likelihood (PQL) estimation, although to dat
Externí odkaz:
http://arxiv.org/abs/2405.01026
1. Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species- and the community-level. The family of generalized li
Externí odkaz:
http://arxiv.org/abs/2403.11562
We propose a new joint mean and correlation regression model for correlated multivariate discrete responses, that simultaneously regresses the mean of each response against a set of covariates, and the correlations between responses against a set of
Externí odkaz:
http://arxiv.org/abs/2402.12803
Restricted maximum likelihood (REML) estimation is a widely accepted and frequently used method for fitting linear mixed models, with its principal advantage being that it produces less biased estimates of the variance components. However, the concep
Externí odkaz:
http://arxiv.org/abs/2402.12719
Understanding the dependence structure between response variables is an important component in the analysis of correlated multivariate data. This article focuses on modeling dependence structures in multivariate binary data, motivated by a study aimi
Externí odkaz:
http://arxiv.org/abs/2401.13379
We consider the problem of surrogate sufficient dimension reduction, that is, estimating the central subspace of a regression model, when the covariates are contaminated by measurement error. When no measurement error is present, a likelihood-based d
Externí odkaz:
http://arxiv.org/abs/2310.13858
Generalized linear latent variable models (GLLVMs) are a class of methods for analyzing multi-response data which has garnered considerable popularity in recent years, for example, in the analysis of multivariate abundance data in ecology. One of the
Externí odkaz:
http://arxiv.org/abs/2107.02627
We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization and its adaptive version for achieving sparsity in estimating the dimensions of the central subspace. The new estimators use the Cholesky decomposition
Externí odkaz:
http://arxiv.org/abs/2104.09838