1 - 6 of 6 Chapters
[Inference under uncertain prior information has been common for several decades in statistical and related literature. In the context of a regression model, we introduce the basic notion of full model, submodel, pretest, and shrinkage estimation strategies. We briefly discuss some penalty...
[In this chapter, we consider the estimation of the mean parameter from two commonly used statistical models in practice. In a classical approach, we estimate the parameter based on the available sample information at hand only. On the other hand, in Bayesian framework, we assume prior...
[Pooling data from various sources to improve the parameter estimation is an important problem from the practitioner’s perspective. If the pooling procedure is carried out judiciously, a much more efficient estimation strategy can be achieved for the targeted parameter. However, it is imperative...
[In this chapter we present various large sample estimation strategies in a classical multiple regression model for estimation of the regression coefficients. These strategies are motivated by Stein-rule and pretest estimation procedures. In the context of two competing regression models (the...
[This chapter showcases the estimation problem in a partially linear regression model, when it is a priori suspected that the regression coefficient may be restricted to a subspace. Various asymptotic estimation strategies are presented. These estimators are based on the James–Stein and pretests...
[We consider the application of shrinkage and penalty estimation for a Poisson regression model. We present a large sample theory for the full model, submodel, and shrinkage estimators in terms of their respective asymptotic bias and risk. Generally speaking, shrinkage estimators are more...
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