Model selection in linear mixed-effect models
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Publication:2234730
DOI10.1007/s10182-019-00359-zzbMath1476.62156OpenAlexW2982375972MaRDI QIDQ2234730
Simona Buscemi, Antonella Plaia
Publication date: 19 October 2021
Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10182-019-00359-z
Ridge regression; shrinkage estimators (Lasso) (62J07) Statistical ranking and selection procedures (62F07) Statistical aspects of information-theoretic topics (62B10)
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- Fence methods for mixed model selection
- A simplified adaptive fence procedure
- The Adaptive Lasso and Its Oracle Properties
- Restricted fence method for covariate selection in longitudinal data analysis
- Model selection in linear mixed models
- Model selection in linear mixed effect models
- Inference for mixed models of ANOVA type with high-dimensional data
- Statistics for high-dimensional data. Methods, theory and applications.
- Conditional and unconditional methods for selecting variables in linear mixed models
- A new test for random effects in linear mixed models with longitudinal data
- Shrinkage estimation in linear mixed models for longitudinal data
- Identifiability of covariance parameters in linear mixed effects models
- Variable selection for linear mixed models with applications in small area estimation
- Conditional information criteria for selecting variables in linear mixed models
- Bootstrap variants of the Akaike information criterion for mixed model selection
- Stochastic complexity and modeling
- Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
- Estimating the dimension of a model
- Selection of fixed effects in high dimensional linear mixed models using a multicycle ECM algorithm
- Information methods for model selection in linear mixed effects models with application to HCV data
- Doubly regularized estimation and selection in linear mixed-effects models for high-dimensional longitudinal data
- Variable selection in linear mixed effects models
- Modified conditional AIC in linear mixed models
- Non-concave penalization in linear mixed-effect models and regularized selection of fixed effects
- Conditional Akaike information criterion in the Fay-Herriot model
- Small area estimation with spatio-temporal Fay-Herriot models
- New variable selection for linear mixed-effects models
- Pathwise coordinate optimization
- Selecting mixed-effects models based on a generalized information criterion
- Counting degrees of freedom in hierarchical and other richly-parameterised models
- Selection Strategy for Covariance Structure of Random Effects in Linear Mixed-effects Models
- Variable Selection in Linear Mixed Models Using an Extended Class of Penalties
- Predictive Cross-validation for the Choice of Linear Mixed-Effects Models with Application to Data from the Swiss HIV Cohort Study
- Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ1-Penalization
- Fixed and Random Effects Selection in Mixed Effects Models
- On the behaviour of marginal and conditional AIC in linear mixed models
- Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models
- A note on conditional AIC for linear mixed-effects models
- Random Effects Selection in Linear Mixed Models
- Model Selection for Linear Mixed Models Using Predictive Criteria
- Further analysis of the data by Akaike's information criterion and the finite corrections
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Adaptive LASSO for linear mixed model selection via profile log-likelihood
- A Statistical View of Some Chemometrics Regression Tools
- A simultaneous variable selection methodology for linear mixed models
- Conditional Akaike information under covariate shift with application to small area estimation
- A PARAMETER SUBSET SELECTION ALGORITHM FOR MIXED-EFFECTS MODELS
- A graphical model selection tool for mixed models
- Model selection via conditional conceptual predictive statistic under ridge regression in linear mixed models
- Conditional conceptual predictive statistic for mixed model selection
- An evaluation of ridge estimator in linear mixed models: an example from kidney failure data
- Selection of terms in random coefficient regression models
- Robust Variable Selection in Linear Mixed Models
- Minimum description length principle for linear mixed effects models
- General ridge predictors in a mixed linear model
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Regularization and Variable Selection Via the Elastic Net
- Modeling Longitudinal Data
- Conditional Akaike information for mixed-effects models
- Clustering high dimension, low sample size data using the maximal data piling distance
- Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures
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