Bootstrapping $ m $-generalized order statistics with variable rank
Copyright policy )Bootstrapping $ m $-generalized order statistics with variable rank
<abstract><p>In this paper, several bootstrap properties of $ m $-generalized order statistics ($ m $-GOSs) with variable rank (central and intermediate) are revealed. We study the inconsistency, weak consistency and strong consistency of bootstrapping central and intermediate $ m $-GOSs when the normalizing constants are assumed to be known or estimated from the re-sampled data using a proper re-sample size. Furthermore, sufficient conditions for the weak and strong consistencies of the bootstrapping distributions of central and intermediate $ m $-GOSs based on the normalizing constant estimators are given. Finally, a simulation study is conducted to determine the optimal bootstrap re-sample size corresponding to the best fitting of the bootstrapping distribution.</p></abstract>
- Helwan University Egypt
- Mansoura University Egypt
- Zagazig University Egypt
Statistics and Probability, intermediate order statistics, Social Sciences, Sample size determination, Estimator, Decision Sciences, FOS: Economics and business, Skew Distributions and Applications in Statistics, QA1-939, FOS: Mathematics, Econometrics, consistency, generalized order statistics, Statistics, bootstrap method, Discrete mathematics, Bootstrapping (finance), Computer science, Programming language, Economics, Econometrics and Finance, Modeling and Forecasting Financial Volatility, Rank (graph theory), Combinatorics, Physical Sciences, Uncertainty Quantification and Sensitivity Analysis, Multivariate Normality, Statistics, Probability and Uncertainty, central order statistics, Mathematics, Finance, Consistency (knowledge bases), Constant (computer programming)
Statistics and Probability, intermediate order statistics, Social Sciences, Sample size determination, Estimator, Decision Sciences, FOS: Economics and business, Skew Distributions and Applications in Statistics, QA1-939, FOS: Mathematics, Econometrics, consistency, generalized order statistics, Statistics, bootstrap method, Discrete mathematics, Bootstrapping (finance), Computer science, Programming language, Economics, Econometrics and Finance, Modeling and Forecasting Financial Volatility, Rank (graph theory), Combinatorics, Physical Sciences, Uncertainty Quantification and Sensitivity Analysis, Multivariate Normality, Statistics, Probability and Uncertainty, central order statistics, Mathematics, Finance, Consistency (knowledge bases), Constant (computer programming)
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