Predictive inference, sufficiency, entropy and an asymptotic likelihood principle
From MaRDI portal
Publication:3968278
DOI10.1093/biomet/70.1.175zbMath0502.62004OpenAlexW2078901290MaRDI QIDQ3968278
Publication date: 1983
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1093/biomet/70.1.175
entropydensity estimationpredictive inferencesmall samplerepeated samplingpredictive distributionAkaike criterionKullback- Leibler informationasymptotic likelihood principle
Foundations and philosophical topics in statistics (62A01) Statistical aspects of information-theoretic topics (62B10) Sufficient statistics and fields (62B05)
Related Items (6)
A maximum likelihood prediction function for the linear model with consistency results ⋮ ON THE UNBIASEDNESS PROPERTY OF AIC FOR EXACT OR APPROXIMATING LINEAR STOCHASTIC TIME SERIES MODELS ⋮ Predictive efficiency for simple non-linear models ⋮ Information theory as a unifying statistical approach for use in marketing research ⋮ A predictive density approach to predicting a future observable in multilevel models ⋮ Exact minimax estimation of the predictive density in sparse Gaussian models
This page was built for publication: Predictive inference, sufficiency, entropy and an asymptotic likelihood principle