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Testing k-monotonicity of a discrete distribution. Application to the estimation of the number of classes in a population
- 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.
- Subjects :
- Statistics and Probability
education.field_of_study
Applied Mathematics
010102 general mathematics
Population
Nonparametric statistics
Estimator
01 natural sciences
Empirical distribution function
Unimodality
Convexity
010104 statistics & probability
Computational Mathematics
Computational Theory and Mathematics
Goodness of fit
Applied mathematics
Probability distribution
0101 mathematics
education
Mathematics
Subjects
Details
- ISSN :
- 01679473
- Volume :
- 127
- Database :
- OpenAIRE
- Journal :
- Computational Statistics & Data Analysis
- Accession number :
- edsair.doi...........858fe543871b7508c80dda61ce1116d3