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

J. Giguelay and S. Huet

Computational Statistics & Data Analysis, 2018, vol. 127, issue C, 96-115

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.

Keywords: Discrete k-monotone distribution; Goodness-of-fit test; Model estimation; Estimation of the number of classes (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:127:y:2018:i:c:p:96-115

DOI: 10.1016/j.csda.2018.02.006

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