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Testing k-monotonicity of a discrete distribution. Application to the estimation of the number of classes in a population

Authors :
Jade Giguelay
Sylvie Huet
Source :
Computational Statistics & Data Analysis. 127:96-115
Publication Year :
2018
Publisher :
Elsevier BV, 2018.

Abstract

The development of nonparametric procedures for testing shape constraint (monotonicity, convexity, unimodality, etc.) has received increasing interest. Nevertheless, testing the k -monotonicity of a discrete density for k larger than 2 has received little attention. To deal with this issue, several testing procedures based on the empirical distribution of the observations have been developed. They are non-parametric, easy to implement and proven to be asymptotically of the desired level and consistent. An estimator of the degree of k -monotonicity of the distribution is presented. An application to the estimation of the total number of classes in a population is proposed. A large simulation study makes it possible to assess the performances of the various procedures.

Details

ISSN :
01679473
Volume :
127
Database :
OpenAIRE
Journal :
Computational Statistics & Data Analysis
Accession number :
edsair.doi...........858fe543871b7508c80dda61ce1116d3