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Characterizations using record moments in a random record model and applications

Part of: Stochastic processes Distribution theory Nonparametric inference

Published online by Cambridge University Press:  14 July 2016

H. N. Nagaraja  and
Gadi Barlevy
H. N. Nagaraja*
Affiliation:
Ohio State University
Gadi Barlevy*
Affiliation:
Northwestern University
*
∗Postal address: Department of Statistics, Ohio State University, 1958 Neil Avenue, Columbus, OH 43210-1247, USA. Email address: [email protected]
∗∗Current address: Economic Research Department, Federal Reserve Bank of Chicago, 230 South LaSalle Street, Chicago, IL 60604-1413, USA
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Abstract

We consider a random record model from a continuous parent X with cumulative distribution function F, where the number of available observations is geometrically distributed. We show that, if E(|X|) is finite, then so is E(|Rn|) whenever Rn, the nth upper record value, exists. We prove that appropriately chosen subsequences of E(Rn) characterize F and subsequences of E(Rn − Rn−1) identify F up to a location shift. We discuss some applications to the identification of wage-offer distributions in job search models.

Keywords

Moment sequencerecord spacingorder statisticsgeometric distributionjob search model

MSC classification

Primary: 62E10: Characterization and structure theory
Secondary: 60G70: Extreme value theory; extremal processes 62G30: Order statistics; empirical distribution functions
Type
Short Communications
Information
Journal of Applied Probability , Volume 40 , Issue 3 , September 2003 , pp. 826 - 833
Copyright
Copyright © Applied Probability Trust 2003 

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