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On characterizations of distributions by regression of adjacent generalized order statistics
Authors:Mariusz Bieniek
Affiliation:(1) Institute of Mathematics, Maria Curie Skłodowska University, Pl. M. Curie Skłodowskiej 1, 20-031 Lublin, Poland
Abstract:Let $${{X_{*}^{(r)}}}$$ , r ≥ 1, denote generalized order statistics, with arbitrary parameters $${gamma_{1},dots,gamma_{r}}$$ , based on distribution function F. In this paper we characterize continuous distributions F by the regression of adjacent generalized order statistics, i.e. $${Ebig( psibig(X_{*}^{(r)}big) | X_{*}^{(r+1)} big)=gbig(X_{*}^{(r+1)}big)}$$ where $${psi,g:mathbb{R}mapstomathbb{R}}$$ are continuous and increasing functions and ψ is strictly increasing. Further we investigate in detail the case when ψ(x) = x and g is a linear function of the form g(x) = cx + d for some $${c,,dinmathbb{R}}$$.
Keywords:Generalized order statistics  Meijer’  s G-function  Characterizations of distributions  Regression
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