Predicting Panel Data Binary Choice with the Gibbs Posterior

DOI10.1162/NECO_A_00172zbMath1231.62119OpenAlexW2041316416MaRDI QIDQ3116948

Martin A. Tanner, Lili Yao, Wenxin Jiang

Publication date: 14 February 2012

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00172

zbMATH Keywords

Bayesian binary classification

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (3)

GENERAL INEQUALITIES FOR GIBBS POSTERIOR WITH NONADDITIVE EMPIRICAL RISK ⋮ On extensions of Hoeffding's inequality for panel data ⋮ Joint production in stochastic non-parametric envelopment of data with firm-specific directions

Cites Work

• From \(\varepsilon\)-entropy to KL-entropy: analysis of minimum information complexity density estima\-tion
• Gibbs posterior for variable selection in high-dimensional classification and data mining
• An MCMC approach to classical estimation.
• Nonparametric Discrete Choice Models With Unobserved Heterogeneity
• Information-theoretic upper and lower bounds for statistical estimation
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• A Smoothed Maximum Score Estimator for the Binary Response Model
• Stochastic Limit Theory
• RISK MINIMIZATION FOR TIME SERIES BINARY CHOICE WITH VARIABLE SELECTION

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