# Symmetrical and Asymmetrical Distributions in Statistics and Data Science

- ISBN978-3-7258-2150-1 (Hardback)
- ISBN978-3-7258-2149-5 (PDF)

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This is a Reprint of the Special Issue Symmetrical and Asymmetrical Distributions in Statistics and Data Science that was published in

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.

- Hardback

*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; maximum likelihood estimator; Bayes estimator; symmetric and asymmetric loss functions; Monte Carlo Markov chain; 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; maximum likelihood estimation; Bayesian estimation; Monte Carlo simulation; reliability analysis; Kavya Manoharan Kumaraswamy distribution; progressive hybrid generalized type-II censoring; Bayesian and classical estimators; Metropolis–Hastings algorithm; MCMC techniques; optimal plan for progressive censoring; n/a