Back to Search
Start Over
Kumaraswamy distribution: different methods of estimation
- Source :
- Computational and Applied Mathematics. 37:2094-2111
- Publication Year :
- 2017
- Publisher :
- Springer Science and Business Media LLC, 2017.
-
Abstract
- This paper addresses different methods of estimation of the unknown parameters of a two-parameter Kumaraswamy distribution from a frequentist point of view. We briefly describe ten different frequentist approaches, namely, maximum likelihood estimators, moments estimators, L-moments estimators, percentile based estimators, least squares estimators, weighted least squares estimators, maximum product of spacings estimators, Cramer–von-Mises estimators, Anderson–Darling estimators and right tailed Anderson–Darling estimators. Monte Carlo simulations and two real data applications are performed to compare the performances of the estimators for both small and large samples.
- Subjects :
- Percentile
021103 operations research
Applied Mathematics
0211 other engineering and technologies
Estimator
02 engineering and technology
M-estimator
01 natural sciences
Least squares
010104 statistics & probability
Computational Mathematics
Extremum estimator
Kumaraswamy distribution
Frequentist inference
Statistics
0101 mathematics
Bootstrapping (statistics)
Mathematics
Subjects
Details
- ISSN :
- 18070302 and 01018205
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- Computational and Applied Mathematics
- Accession number :
- edsair.doi...........ba7a3e3df653521b1c817a4690f354d7