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Cited by 913

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Bayesian Estimation and Prediction Using Asymmetric Loss Functions

Zellner, Arnold·1986·Journal of the American Statistical Association

Abstract

Estimators and predictors that are optimal relative to Varian's asymmetric LINEX loss function are derived for a number of well-known models. Their risk functions and Bayes risks are derived and compared with those of usual estimators and predictors. It is shown that some usual estimators, for example, a scalar sample mean or a scalar least squares regression coefficient estimator, are inadmissible relative to asymmetric LINEX loss by providing alternative estimators that dominate them uniformly in terms of risk.

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Metadata

Authors
Zellner, A.
Venue
Journal of the American Statistical Association
Published
1986-06
Volume
81
Issue
394
Pages
446-451
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
OpenAlex
W2061885628
Cited by
913
References
6
FWCI
8.43
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Bayesian Estimation and Prediction Using Asymmetric Loss Functions. https://app.cochat.ai/papers/10.1080/01621459.1986.10478289
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Zellner, A. (1986). Bayesian Estimation and Prediction Using Asymmetric Loss Functions. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1986.10478289

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.