Focused vector information criterion model selection and model averaging regression with missing response

DOI10.1007/s00184-013-0446-8zbMath1297.62075OpenAlexW2064652501MaRDI QIDQ464389

Zhimeng Sun, Jingyi Ma, Zhi Su

Publication date: 17 October 2014

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00184-013-0446-8

zbMATH Keywords

model selectionmissing datamodel averagingfocused information criterion

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (4)

Model averaging procedure for varying-coefficient partially linear models with missing responses ⋮ Cross-validation-based model averaging in linear models with response missing at random ⋮ Model averaging for linear models with responses missing at random ⋮ Model selection and model averaging for semiparametric partially linear models with missing data

Cites Work

• Unnamed Item
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• Empirical likelihood for a partially linear model with covariate data missing at random
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Variable selection for semiparametric varying coefficient partially linear errors-in-variables models
• Estimation in partially linear models with missing responses at random
• Lasso-type recovery of sparse representations for high-dimensional data
• Empirical likelihood for linear models with missing responses
• Estimating the dimension of a model
• An Akaike information criterion for model selection in the presence of incomplete data.
• Weak convergence and empirical processes. With applications to statistics
• Focused information criterion and model averaging for generalized additive partial linear models
• Frequentist model averaging with missing observations
• High-dimensional graphs and variable selection with the Lasso
• Semiparametric Analysis of Isotonic Errors-in-Variables Regression Models with Missing Response
• Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression
• Focused Information Criteria and Model Averaging for the Cox Hazard Regression Model
• Partially linear models with missing response variables and error-prone covariates
• Information Theory and Mixing Least-Squares Regressions
• Variable Selection with Incomplete Covariate Data
• CAN ONE ESTIMATE THE UNCONDITIONAL DISTRIBUTION OF POST-MODEL-SELECTION ESTIMATORS?
• Adaptive Regression by Mixing
• THE FINITE-SAMPLE DISTRIBUTION OF POST-MODEL-SELECTION ESTIMATORS AND UNIFORM VERSUS NONUNIFORM APPROXIMATIONS
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Frequentist Model Average Estimators
• Varying-coefficient models and basis function approximations for the analysis of repeated measurements
• Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
• Variable Selection and Model Building via Likelihood Basis Pursuit
• New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis

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