Moment conditions selection based on adaptive penalized empirical likelihood

DOI10.1155/2014/391719zbMath1472.62175OpenAlexW2073570260WikidataQ59037189 ScholiaQ59037189MaRDI QIDQ1724007

Yunquan Song

Publication date: 14 February 2019

Published in: Abstract and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2014/391719

zbMATH Keywords

data miningartificial intelligence (AI)

Mathematics Subject Classification ID

General topics in artificial intelligence (68T01) Statistical aspects of big data and data science (62R07)

Cites Work

• Unnamed Item
• Large Sample Properties of Generalized Method of Moments Estimators
• Extending the scope of empirical likelihood
• Empirical likelihood and general estimating equations
• Simulation and the Asymptotics of Optimization Estimators
• Quasi-Likelihood and Optimal Estimation, Correspondent Paper
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• GENERALIZED EMPIRICAL LIKELIHOOD–BASED MODEL SELECTION CRITERIA FOR MOMENT CONDITION MODELS
• ADAPTIVE GMM SHRINKAGE ESTIMATION WITH CONSISTENT MOMENT SELECTION
• Bayesian exponentially tilted empirical likelihood
• Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators

This page was built for publication: Moment conditions selection based on adaptive penalized empirical likelihood