Joint variable selection for fixed and random effects in linear mixed-effects models.
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Statistical Learning Methods for Longitudinal High-dimensional DataThe E-MS Algorithm: Model Selection with Incomplete Data.Variable Selection and Inference Procedures for Marginal Analysis of Longitudinal Data with Missing Observations and Covariate Measurement ErrorUltrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study.Covariate Selection for Multilevel Models with Missing Data.Fixed and Random Effects Selection by REML and Pathwise Coordinate Optimization.Flexible estimation of covariance function by penalized spline with application to longitudinal family data.Fixed and random effects selection in mixed effects models.Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data.Simultaneous variable selection for joint models of longitudinal and survival outcomesMOMENT-BASED METHOD FOR RANDOM EFFECTS SELECTION IN LINEAR MIXED MODELSBayesian model selection methods in modeling small area colon cancer incidence.Statistical Approaches for the Study of Cognitive and Brain Aging.Detection of gene-environment interactions in a family-based population using SCAD.Penalized nonlinear mixed effects model to identify biomarkers that predict disease progression.Model selection in multivariate semiparametric regression.Spatio-temporal Bayesian model selection for disease mapping.VARIABLE SELECTION IN LINEAR MIXED EFFECTS MODELSUltrahigh dimensional time course feature selection.Spatially-dependent Bayesian model selection for disease mapping.Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity.Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.Feature selection for high-dimensional temporal data.Oracle estimation of parametric models under boundary constraints.A penalty approach to differential item functioning in Rasch models.Bayesian nonparametric centered random effects models with variable selection.Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies.The Fence Methods
P2860
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P2860
Joint variable selection for fixed and random effects in linear mixed-effects models.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
2010年论文
@zh
2010年论文
@zh-cn
name
Joint variable selection for fixed and random effects in linear mixed-effects models.
@en
type
label
Joint variable selection for fixed and random effects in linear mixed-effects models.
@en
prefLabel
Joint variable selection for fixed and random effects in linear mixed-effects models.
@en
P2093
P2860
P1433
P1476
Joint variable selection for fixed and random effects in linear mixed-effects models.
@en
P2093
Arun Krishna
Howard D Bondell
Sujit K Ghosh
P2860
P304
P356
10.1111/J.1541-0420.2010.01391.X
P407
P577
2010-12-01T00:00:00Z