Focused estimation for noisy and small data sets: a Bayesian minimum expected loss estimator approach

DOI10.1111/anzs.12271zbMath1440.62097arXiv1809.06996OpenAlexW2970570204WikidataQ127331546 ScholiaQ127331546MaRDI QIDQ5117663

Manuel Correa-Giraldo, Andrés Ramírez Hassan

Publication date: 26 August 2020

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1809.06996

zbMATH Keywords

hidden Markov modelsBayesian estimatorfunctions of parametersfrequentist variability

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Bayesian inference (62F15)

Related Items (1)

Optimal portfolio choice: a minimum expected loss approach

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Using invalid instruments on purpose: focused moment selection and averaging for GMM
• Estimation of functions of population means and regression coefficients including structural coefficients. A minimum expected loss (MELO) approach
• Why are estimates of agricultural supply response so variable?
• The finite sample properties of simultaneous equations' estimates and estimators. Bayesian and non-Bayesian approaches
• An MCMC approach to classical estimation.
• Bayesian inference and the parametric bootstrap
• Generalization of a theorem by v. Neumann concerning zero sum two person games
• Minimum Expected Loss (MELO) Estimators for Functions of Parameters and Structural Coefficients of Econometric Models
• Frequentist Accuracy of Bayesian Estimates
• CHALLENGES FOR ECONOMETRIC MODEL SELECTION
• Foundations of a General Theory of Sequential Decision Functions

This page was built for publication: Focused estimation for noisy and small data sets: a Bayesian minimum expected loss estimator approach