Symmetrical and Asymmetrical Distributions in Statistics and Data Science
Probability distributions are a fundamental topic of statistics and data science that is highly relevant in both theory and practical applications. There are numerous probability distributions that come in many shapes and with different properties. In order to identify an appropriate distribution fo...
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| Formato: | Online |
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| Idioma: | inglês |
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MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Acesso em linha: | ONIX_20250220_9783725821501_137 |
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| collection | Directory of Open Access Books |
| description | Probability distributions are a fundamental topic of statistics and data science that is highly relevant in both theory and practical applications. There are numerous probability distributions that come in many shapes and with different properties. In order to identify an appropriate distribution for modeling the statistical properties of a population of interest, one should consider the shape of the distribution as the crucial factor. In particular, the symmetry or asymmetry of the distribution plays a decisive role. This reprint is a collection of articles on a wide range of topics in the field of symmetrical and asymmetrical distributions that are relevant in statistics and data science. The proposed methods and concepts are discussed in detail and illustrated with several real-life data examples. |
| format | Online |
| id | doab-20.500.12854ir-152773 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1527732025-02-20T13:02:49Z Symmetrical and Asymmetrical Distributions in Statistics and Data Science Johannssen, Arne Chukhrova, Nataliya Zhu, Quanxin one-sided EWMA X charts variable sampling interval monte-carlo simulation run length zero-state steady-state bivariate distribution copula correlation FGM copula maximum likelihood estimator meta-analysis normal distribution half-logistic class odd Fréchet class entropy simulation estimation method SPC RZ EWMA chart TEWMA chart VSI-TEWMA chart discretization methods Bayesian estimation symmetric and asymmetric loss functions prior distribution simulation analysis Monte Carlo Markov chain goodness-of-fit measures spatial autoregressive model (SAR) weights matrix model selection Akaike information criterion (AIC) maximum likelihood estimation encouraged arrival quality control feedback balking maintaining retention alpha power inverse Weibull distribution hybrid Type-II censoring ball bearing Bayes estimator maximum product spacing Fréchet model symmetric Bayes inference MCMC techniques maximum likelihood reliability analysis generalized Type-II progressive hybrid censoring average run length control chart multicollinearity regression estimator supplementary variable cause-specific hazard regression model additive hazard modified Weibull distribution Bayes estimate MCMC Gumbel Type II distribution multi-component stress-strength model Monte Carlo simulation Kavya Manoharan Kumaraswamy distribution progressive hybrid generalized type-II censoring Bayesian and classical estimators Metropolis–Hastings algorithm optimal plan for progressive censoring n/a thema EDItEUR::N History and Archaeology::NH History thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues Probability distributions are a fundamental topic of statistics and data science that is highly relevant in both theory and practical applications. There are numerous probability distributions that come in many shapes and with different properties. In order to identify an appropriate distribution for modeling the statistical properties of a population of interest, one should consider the shape of the distribution as the crucial factor. In particular, the symmetry or asymmetry of the distribution plays a decisive role. This reprint is a collection of articles on a wide range of topics in the field of symmetrical and asymmetrical distributions that are relevant in statistics and data science. The proposed methods and concepts are discussed in detail and illustrated with several real-life data examples. 2025-02-20T13:02:46Z 2025-02-20T13:02:46Z 2024 book ONIX_20250220_9783725821501_137 9783725821501 9783725821495 https://directory.doabooks.org/handle/20.500.12854/152773 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/9936 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2149-5 10.3390/books978-3-7258-2149-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725821501 9783725821495 268 Basel open access |
| spellingShingle | one-sided EWMA X charts variable sampling interval monte-carlo simulation run length zero-state steady-state bivariate distribution copula correlation FGM copula maximum likelihood estimator meta-analysis normal distribution half-logistic class odd Fréchet class entropy simulation estimation method SPC RZ EWMA chart TEWMA chart VSI-TEWMA chart discretization methods Bayesian estimation symmetric and asymmetric loss functions prior distribution simulation analysis Monte Carlo Markov chain goodness-of-fit measures spatial autoregressive model (SAR) weights matrix model selection Akaike information criterion (AIC) maximum likelihood estimation encouraged arrival quality control feedback balking maintaining retention alpha power inverse Weibull distribution hybrid Type-II censoring ball bearing Bayes estimator maximum product spacing Fréchet model symmetric Bayes inference MCMC techniques maximum likelihood reliability analysis generalized Type-II progressive hybrid censoring average run length control chart multicollinearity regression estimator supplementary variable cause-specific hazard regression model additive hazard modified Weibull distribution Bayes estimate MCMC Gumbel Type II distribution multi-component stress-strength model Monte Carlo simulation Kavya Manoharan Kumaraswamy distribution progressive hybrid generalized type-II censoring Bayesian and classical estimators Metropolis–Hastings algorithm optimal plan for progressive censoring n/a thema EDItEUR::N History and Archaeology::NH History thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues Symmetrical and Asymmetrical Distributions in Statistics and Data Science |
| title | Symmetrical and Asymmetrical Distributions in Statistics and Data Science |
| title_full | Symmetrical and Asymmetrical Distributions in Statistics and Data Science |
| title_fullStr | Symmetrical and Asymmetrical Distributions in Statistics and Data Science |
| title_full_unstemmed | Symmetrical and Asymmetrical Distributions in Statistics and Data Science |
| title_short | Symmetrical and Asymmetrical Distributions in Statistics and Data Science |
| title_sort | symmetrical and asymmetrical distributions in statistics and data science |
| topic | one-sided EWMA X charts variable sampling interval monte-carlo simulation run length zero-state steady-state bivariate distribution copula correlation FGM copula maximum likelihood estimator meta-analysis normal distribution half-logistic class odd Fréchet class entropy simulation estimation method SPC RZ EWMA chart TEWMA chart VSI-TEWMA chart discretization methods Bayesian estimation symmetric and asymmetric loss functions prior distribution simulation analysis Monte Carlo Markov chain goodness-of-fit measures spatial autoregressive model (SAR) weights matrix model selection Akaike information criterion (AIC) maximum likelihood estimation encouraged arrival quality control feedback balking maintaining retention alpha power inverse Weibull distribution hybrid Type-II censoring ball bearing Bayes estimator maximum product spacing Fréchet model symmetric Bayes inference MCMC techniques maximum likelihood reliability analysis generalized Type-II progressive hybrid censoring average run length control chart multicollinearity regression estimator supplementary variable cause-specific hazard regression model additive hazard modified Weibull distribution Bayes estimate MCMC Gumbel Type II distribution multi-component stress-strength model Monte Carlo simulation Kavya Manoharan Kumaraswamy distribution progressive hybrid generalized type-II censoring Bayesian and classical estimators Metropolis–Hastings algorithm optimal plan for progressive censoring n/a thema EDItEUR::N History and Archaeology::NH History thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues |
| topic_facet | one-sided EWMA X charts variable sampling interval monte-carlo simulation run length zero-state steady-state bivariate distribution copula correlation FGM copula maximum likelihood estimator meta-analysis normal distribution half-logistic class odd Fréchet class entropy simulation estimation method SPC RZ EWMA chart TEWMA chart VSI-TEWMA chart discretization methods Bayesian estimation symmetric and asymmetric loss functions prior distribution simulation analysis Monte Carlo Markov chain goodness-of-fit measures spatial autoregressive model (SAR) weights matrix model selection Akaike information criterion (AIC) maximum likelihood estimation encouraged arrival quality control feedback balking maintaining retention alpha power inverse Weibull distribution hybrid Type-II censoring ball bearing Bayes estimator maximum product spacing Fréchet model symmetric Bayes inference MCMC techniques maximum likelihood reliability analysis generalized Type-II progressive hybrid censoring average run length control chart multicollinearity regression estimator supplementary variable cause-specific hazard regression model additive hazard modified Weibull distribution Bayes estimate MCMC Gumbel Type II distribution multi-component stress-strength model Monte Carlo simulation Kavya Manoharan Kumaraswamy distribution progressive hybrid generalized type-II censoring Bayesian and classical estimators Metropolis–Hastings algorithm optimal plan for progressive censoring n/a thema EDItEUR::N History and Archaeology::NH History thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues |
| url | ONIX_20250220_9783725821501_137 |