On a discrete Hill's statistical process based on sum-product statistics and its finite-dimensional asymptotic theory
Gane Samb Lo

TL;DR
This paper introduces a new class of sum-product statistics generalizing Hill's estimator, analyzes their asymptotic behavior, and describes the covariance structure of their limiting Gaussian process, extending known results in extreme value theory.
Contribution
It develops a finite-dimensional asymptotic theory for a new family of sum-product statistics based on order statistics, generalizing classical tail index estimators.
Findings
Limiting laws of the process are derived.
Covariance function of the Gaussian limit is explicitly described.
Asymptotic normality and laws of the iterated logarithm are extended to these estimators.
Abstract
The following class of sum-product statistics T_n(p)=\frac{1}{k}\sum_{h=1}^p \sum_{(s_1...s_h)\in P(p,h)} \sum_{i_1=l+1}^{i_0} ... \sum_{i_h=l+1}^{i_{h-1}} i_h \prod_{i=i_1}^{i_h} \frac{(Y_{n-i+1,n}-Y_{n-i,n})^{s_i}}{s_i!} (where and n are positive integers, is the set of all ordered parititions of into positive integers and are the order statistics based on a sequence of independent random variables with underlying distribution ), is introduced. For each p, is an estimator of the index of a distribution whose upper tail varies regularly at infinity. \ This family generalizes the so called Hill statistic and the Dekkers-Einmahl-De Haan one. We study the limiting laws of the process and completely describe…
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Taxonomy
TopicsBayesian Methods and Mixture Models · Stochastic processes and statistical mechanics · advanced mathematical theories
