Statistical Inference for the Weibull Distribution Based on δ-Record Data
| Autor: | Gerardo Sanz, Lina Maldonado, Raúl Gouet, F. Javier López |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
maximum likelihood prediction
Physics and Astronomy (miscellaneous) General Mathematics Computation Monte Carlo method maximum likelihood estimation 02 engineering and technology 01 natural sciences 010104 statistics & probability Consistency (statistics) Statistics 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Statistical inference Statistics::Methodology 0101 mathematics Mathematics Weibull distribution Bayes estimator Series (mathematics) Bayes prediction Estimator near-records Statistics::Computation Chemistry (miscellaneous) Bayes estimation 020201 artificial intelligence & image processing δ-records |
| Zdroj: | Symmetry Volume 12 Issue 1 |
| ISSN: | 2073-8994 |
| Popis: | We consider the maximum likelihood and Bayesian estimation of parameters and prediction of future records of the Weibull distribution from &delta record data, which consists of records and near-records. We discuss existence, consistency and numerical computation of estimators and predictors. The performance of the proposed methodology is assessed by Montecarlo simulations and the analysis of monthly rainfall series. Our conclusion is that inferences for the Weibull model, based on &delta record data, clearly improve inferences based solely on records. This methodology can be recommended, more so as near-records can be collected along with records, keeping essentially the same experimental design. |
| Databáze: | OpenAIRE |
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