Statistical distributions: a comprehensive approach/ Calanthia Wright
Publication details: Brooklyn: States Academic Press, 2022Description: vii, 236 pages: Charts, Diagrams; 27.5 cmISBN:- 9781639894956
- 23rd SA.031 W947
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
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Books | ISI Library, Kolkata | SA.031 W947 (Browse shelf(Opens below)) | Available | 138697 |
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Includes index
Alternative approaches for econometric modeling of panel data using mixture distribution -- The linearly decreasing stress Weibull (LDSWeibull): a new Weibull-like distribution -- Particle swarm based algorithms for finding locally and Bayesian D-optimal designs -- A new class of survival distribution for degradation processes subject to shocks -- The Power-Cauchy negative-binominal: properties and regression -- Multiclass analysis and prediction with network structured covariates -- The unified distribution -- A new generalization of generalized half-normal distribution: properties and regression models -- An R package for modeling and simulating generalized spherical and related distributions -- Bounds on the mean residual lifetime of progressive type II right censored order statistics -- Multivariate zero-truncated/adjusted Charlier series distributions with applications -- A new generalized Weibull family of distributions: mathematical properties and applications
A statistical distribution is a parameterized mathematical function that gives the probabilities of different outcomes for a random variable. There are discrete and continuous distributions depending on the random values they model. Some of the most prominent statistical distributions are Bernoulli distribution, binomial distribution, geometric distribution, uniform distribution, normal distribution, Poisson distribution, and exponential distribution. This group of statistical distributions has plentiful applications to studies in statistics and probability, and it also helps in solving real problems. Understanding statistical distributions plays a very important role for data scientists in order to know the data more rigorously, conduct better data analysis, and choose a more suitable model. This book provides significant information of this discipline to help develop a good understanding of statistical distributions and related fields. The topics included herein are of utmost significance and bound to provide incredible insights to readers. This book is a vital tool for all researching and studying in this field.
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