| Registro completo |
| Provedor de dados: |
AgEcon
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| País: |
United States
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| Título: |
A Semi-Parametric Basis for Combining Estimation Problems Under Quadratic Loss
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| Autores: |
Judge, George G.
Mittelhammer, Ronald C.
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| Data: |
2006-02-06
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| Ano: |
2003
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| 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
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| 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.
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| Tipo: |
Working or Discussion Paper
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| Idioma: |
Inglês
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| Identificador: |
19795
http://purl.umn.edu/25103
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| Editor: |
AgEcon Search
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| Relação: |
University of California, Berkeley>Department of Agricultural and Resource Economics>CUDARE Working Papers
CUDARE Working Paper 948
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| Formato: |
30
application/pdf
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