THE UNIT-LOGISTIC DISTRIBUTION: DIFFERENT METHODS OF ESTIMATION
| Autor: | Josmar Mazucheli, Sanku Dey, André Felipe Berdusco Menezes |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
parametric Bootstrap
021103 operations research Mean squared error Logistic distribution lcsh:Mathematics Monte Carlo method Interval estimation 0211 other engineering and technologies Coverage probability Estimator 02 engineering and technology Absolute difference Management Science and Operations Research lcsh:QA1-939 01 natural sciences Empirical distribution function Monte Carlo simulations estimation methods 010104 statistics & probability Unit-Logistic distribution Statistics 0101 mathematics Mathematics |
| Zdroj: | Pesquisa Operacional v.38 n.3 2018 Pesquisa operacional Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) instacron:SOBRAPO Pesquisa Operacional, Vol 38, Iss 3, Pp 555-578 |
| ISSN: | 1678-5142 0101-7438 |
| DOI: | 10.1590/0101-7438.2018.038.03.0555 |
| Popis: | This paper addresses the different methods of estimation of the unknown parameters of a two-parameter unit-logistic distribution from the frequentist point of view. We briefly describe different approaches, namely, maximum likelihood estimators, percentile based estimators, least squares estimators, maximum product of spacings estimators, methods of minimum distances: Cramér-von Mises, AndersonDarling and four variants of Anderson-Darling. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation for both small and large samples. The performances of the estimators have been compared in terms of their relative bias, root mean squared error, average absolute difference between the theoretical and empirical estimate of the distribution functions and the maximum absolute difference between the theoretical and empirical distribution functions using simulated samples. Also, for each method of estimation, we consider the interval estimation using the Bootstrap confidence interval and calculate the coverage probability and the average width of the Bootstrap confidence intervals. Finally, two real data sets have been analyzed for illustrative purposes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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