Data/hora: 13/06/2024 05:10:27

Provedor de dados: | 31
País: | United States
Título: | A Semi-Parametric Basis for Combining Estimation Problems Under Quadratic Loss
Autores: | Judge,   George G.; Mittelhammer,   Ronald C.
Data: | 2006-02-06
Ano: | 2003
Palavras-chave: | Stein-like shrinkage; Quadratic loss; Ill-conditioned design; Semiparametric estimation and inference; Data dependent shrinkage vector; Asymptotic and finite sample risk.; Research Methods/ Statistical Methods; Cl0; C24.
Resumo: | When  there  is  uncertainty  concerning  the  appropriate  statistical  model  to  use  in  representing the data sampling process and corresponding estimators, we consider a basis for optimally combining estimation  problems.    In the context of the multivariate linear  statistical model, we consider a semi-parametric  Stein-like (SPSL) estimator, ...that shrinks to a random data-dependent vector and, under quadratic loss, has superior performance relative to the conventional least squares estimator. The relationship of the SPSL estimator to the family of Stein estimators is noted and risk dominance extensions between correlated estimators are demonstrated.  As an application we consider the problem of a possibly ill-conditioned design matrix and devise a corresponding SPSL estimator.  Asymptotic and analytic finite sample risk properties of the estimator are demonstrated.  An extensive sampling experiment is used to investigate finite sample performance over a wide range of data sampling processes to illustrate the robustness of the estimator for an array of symmetric and skewed distributions.  Bootstrapping procedures are used to develop confidence sets and a basis for inference.
Tipo: | Working or Discussion Paper
Idioma: | Inglês

Identificador:

19795

http://purl.umn.edu/25103

Editor: | AgEcon Search

Relação:

University of California, Berkeley&gt;Department of Agricultural and Resource Economics&gt;CUDARE Working Papers

CUDARE Working Paper 948

Formato:

30

application/pdf