Search results for "Shrinkage estimator"

showing 3 items of 3 documents

Sampling properties of the Bayesian posterior mean with an application to WALS estimation

2022

Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these learning methods in repeated samples is assessed using the variance of the posterior distribution of the parameters of interest given the data. This may be permissible when the sample size is large because, under the conditions of the Bernstein--von Mises theorem, the posterior variance agrees asymptotically with the frequentist variance. In finite samples, however, things are less clear. In this pa…

Economics and EconometricsWALS.SDG 16 - PeaceSettore SECS-P/05Monte Carlo methodBayesian probabilityPosterior probabilitySettore SECS-P/05 - EconometriaDouble-shrinkage estimators01 natural sciencesLeast squares010104 statistics & probabilityFrequentist inference0502 economics and businessStatisticsPosterior moments and cumulantsStatistics::Methodology0101 mathematicsdouble-shrinkage estimator050205 econometrics MathematicsWALSLocation modelApplied Mathematics05 social sciencesSDG 16 - Peace Justice and Strong InstitutionsUnivariateSampling (statistics)EstimatorVariance (accounting)/dk/atira/pure/sustainabledevelopmentgoals/peace_justice_and_strong_institutionsJustice and Strong InstitutionsSample size determinationposterior moments and cumulantNormal location modelJournal of Econometrics
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Shrinkage efficiency bounds: An extension

2023

Hansen (2005) obtained the efficiency bound (the lowest achievable risk) in the p-dimensional normal location model when p≥3, generalizing an earlier result of Magnus (2002) for the one-dimensional case (p=1). The classes of estimators considered are, however, different in the two cases. We provide an alternative bound to Hansen's which is a more natural generalization of the one-dimensional case, and we compare the classes and the bounds.

RiskStatistics and ProbabilityLower boundSettore SECS-P/05 - EconometriaShrinkage estimatorNormal location modelCommunications in Statistics - Theory and Methods
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Weighted-Average Least Squares (WALS): Confidence and Prediction Intervals

2022

We extend the results of De Luca et al. (2021) to inference for linear regression models based on weighted-average least squares (WALS), a frequentist model averaging approach with a Bayesian flavor. We concentrate on inference about a single focus parameter, interpreted as the causal effect of a policy or intervention, in the presence of a potentially large number of auxiliary parameters representing the nuisance component of the model. In our Monte Carlo simulations we compare the performance of WALS with that of several competing estimators, including the unrestricted least-squares estimator (with all auxiliary regressors) and the restricted least-squares estimator (with no auxiliary reg…

Shrinkage estimatorStatistics::TheorySettore SECS-P/05Economics Econometrics and Finance (miscellaneous)Linear model WALS condence intervals prediction intervals Monte Carlo simulations.Prediction intervalEstimatorSettore SECS-P/05 - EconometriaComputer Science ApplicationsLasso (statistics)Frequentist inferenceBayesian information criterionStatisticsStatistics::MethodologyAkaike information criterionJackknife resamplingMathematics
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