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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제28권 제2호
- 발행연도
- 2021.3
- 수록면
- 99 - 118 (20page)
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초록· 키워드
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In this paper, we introduce an extended form of the inverse power Lomax model via Marshall-Olkin approach. We call it the Marshall-Olkin inverse power Lomax (MOIPL) distribution. The four- parameter MOIPL distribution is very flexible which contains some former and new models. Vital properties of the MOIPL distribution are affirmed. Maximum likelihood estimators and approximate confidence intervals are considered under Type I censored samples. Maximum likelihood estimates are evaluated according to simulation study. Bayesian estimators as well as Bayesian credible intervals under symmetric loss function are obtained via Markov chain Monte Carlo (MCMC) approach. Finally, the flexibility of the new model is analyzed by means of two real data sets. It is found that the MOIPL model provides closer fits than some other models based on the selected criteria.
목차
Abstract
1. Introduction
2. Marshel–Olkin inverse power Lomax distribution
3. Structural properties
4. ML estimation based on TIC samples
5. Bayesian estimation via TIC
6. Monte Carlo simulation
7. Real data modelling
8. Concluding remarks
References
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