Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures
From MaRDI portal
Publication:6086487
DOI10.1111/insr.12161OpenAlexW1775404148MaRDI QIDQ6086487
Gerhard Tutz, Margret-Ruth Oelker
Publication date: 10 November 2023
Published in: International Statistical Review (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/insr.12161
Related Items
Random effects clustering in multilevel modeling: choosing a proper partition, Linear fixed-effects estimation with nonrepeated outcomes, Shrinkage for categorical regressors, Tree-structured modelling of categorical predictors in generalized additive regression, Tree-Structured Clustering in Fixed Effects Models
Cites Work
- The Adaptive Lasso and Its Oracle Properties
- Misspecifying the shape of a random effects distribution: why getting it wrong may not matter
- Sparse modeling of categorial explanatory variables
- \(\ell_{1}\)-penalization for mixture regression models
- Identifiability of finite mixtures of multinomial logit models with varying and fixed effects
- Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies
- A note on adaptive group Lasso
- Consistent estimation of a mixing distribution
- Extended ordered paired comparison models with application to football data from German Bundesliga
- A uniform framework for the combination of penalties in generalized structured models
- Finite mixture and Markov switching models.
- Models for discrete longitudinal data.
- Misspecified maximum likelihood estimates and generalised linear mixed models
- Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods
- Simultaneous Factor Selection and Collapsing Levels in ANOVA
- Variable Selection in Finite Mixture of Regression Models
- On the Pooling of Time Series and Cross Section Data
- Semiparametric Regression
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models
- Sparsity and Smoothness Via the Fused Lasso
- Regularization and model selection with categorical predictors and effect modifiers in generalized linear models
- Model Selection and Estimation in Regression with Grouped Variables
- Type I and Type II Error Under Random‐Effects Misspecification in Generalized Linear Mixed Models
- Linear mixed models for longitudinal data
- Multivariate statistical modelling based on generalized linear models.
- Unnamed Item
