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- W168656276 abstract "This chapter presents the conventional linear-statistical models, estimators, and hypothesis testing framework. Sampling theory and Bayes estimators that permit sample information and various types of nonsample information are specified and evaluated. In the chapter, testing frameworks are specified for evaluating the compatibility of the sample information. The various types of nonsample information and the corresponding pretest estimators are derived, compared, and evaluated. The chapter discusses inadmissibility of the least-squares estimator and a range of Stein-rule estimators for alternative loss functions and design matrices. Alternatives to least squares are considered for the stochastic regressor case. The chapter also discusses the problem of collinear data and the ridge-type and general minimax estimators. The statistical implications of these biased alternatives for econometric theory and practice are discussed in the chapter." @default.
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- W168656276 date "1983-01-01" @default.
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- W168656276 title "Chapter 10 Biased estimation" @default.
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- W168656276 doi "https://doi.org/10.1016/s1573-4412(83)01014-4" @default.
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