Further Results on Minimum Variance Unbiased Estimation and Additive Number Theory

Patil, G. P.; Joshi, S. W.

doi:10.1214/aoms/1177697096

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Cited by 9 publications

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“…It can be settled easily in the above situations of the support set A. When the support set A is not of the above types the situation becomes more interesting and the existence of minimum variance unbiased estimators has been studied by Patil [15] and Patil and Joshi [16] using additive number theory.…”

Section: Introductionmentioning

confidence: 99%

Basic Sequences and Unbiased Estimation in Quasi Power Series Distributions

Muhammad¹,

Khan²

2017

HJMS

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By using results from function space theory we give a characterization of when lacunary quasi power series sequences are basic in C[0, 1]. The paper discusses the links with unbiased estimable functions and the subspaces generated by the density of the lacunary quasi power series distributions. The paper also provides the rates of convergence of all the moments of the classic odds ratio estimator. This extends some known results in Bleimann, Butzer and Hahn's approximation operator.

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Section: Introductionmentioning

confidence: 99%

Basic Sequences and Unbiased Estimation in Quasi Power Series Distributions

Muhammad¹,

Khan²

2017

HJMS

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show abstract

“…These estimators are better than the classical minimum variance unbiased estimators (MVUE) as given by Patil (1963), and Patil and Joshi (1970) in the sense that these increase the range of the estimation and also have simpler forms. In particular, we derive the Bayesian estimator of ϕ θ θ , r ∈ ∞, ∞ .…”

Section: Introductionmentioning

confidence: 99%