Open Access
May, 1981 A Density-Quantile Function Approach to Optimal Spacing Selection
R. L. Eubank
Ann. Statist. 9(3): 494-500 (May, 1981). DOI: 10.1214/aos/1176345454

Abstract

In this paper design techniques for continuous parameter time series regression analysis are employed to develop a general approach to optimal spacing selection for the linear estimation of location and scale parameters by sample quantiles from uncensored or censored samples. The spacings derived from this approach are asymptotically optimal in the sense that they result in near optimal asymptotic relative efficiencies for large values of $k$, the number of spacing elements. A comparison with the optimum efficiencies for several distribution types indicates that the asymptotically optimum spacings perform well for $k \geq 7$. The regression framework is also utilized to develop sufficient conditions for optimal spacing unicity and to obtain asymptotically optimal spacings for quantile estimation.

Citation

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R. L. Eubank. "A Density-Quantile Function Approach to Optimal Spacing Selection." Ann. Statist. 9 (3) 494 - 500, May, 1981. https://doi.org/10.1214/aos/1176345454

Information

Published: May, 1981
First available in Project Euclid: 12 April 2007

zbMATH: 0477.62074
MathSciNet: MR615426
Digital Object Identifier: 10.1214/aos/1176345454

Subjects:
Primary: 62F99
Secondary: 62F10 , 62F12 , 62M99

Keywords: censored samples , estimation , location parameter , order statistics , Quantile estimation , ‎reproducing kernel Hilbert ‎space , scale parameter

Rights: Copyright © 1981 Institute of Mathematical Statistics

Vol.9 • No. 3 • May, 1981
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