Univariate Discrete Distributions, 3rd Edition by Norman L. Johnson, Adrienne W. Kemp, Samuel Kotz

By Norman L. Johnson, Adrienne W. Kemp, Samuel Kotz

This Set Contains:Continuous Multivariate Distributions, quantity 1, versions and purposes, second version through Samuel Kotz, N. Balakrishnan and basic L. Johnson; non-stop Univariate Distributions, quantity 1, second variation via Samuel Kotz, N. Balakrishnan and general L. Johnson; non-stop Univariate Distributions, quantity 2, 2d variation via Samuel Kotz, N. Balakrishnan and basic L. Johnson; Discrete Multivariate Distributions via Samuel Kotz, N. Balakrishnan and common L. Johnson; Univariate Discrete Distributions, third version via Samuel Kotz, N. Balakrishnan and basic L. Johnson. Discover the most recent advances in discrete distributions theoryThe 3rd variation of the significantly acclaimed Univariate Discrete Distributions presents a self-contained, systematic remedy of the idea, derivation, and alertness of chance distributions for count number facts. Generalized zeta-function and q-series distributions were additional and are coated intimately. New households of distributions, together with Lagrangian-type distributions, are built-in into this completely revised and up-to-date textual content. extra functions of univariate discrete distributions are explored to illustrate the flexibleness of this robust method.A thorough survey of contemporary statistical literature attracts awareness to many new distributions and effects for the classical distributions. nearly 450 new references besides a number of new sections are brought to mirror the present literature and data of discrete distributions.Beginning with mathematical, likelihood, and statistical basics, the authors supply transparent assurance of the major subject matters within the box, including:* households of discrete distributions* Binomial distribution* Poisson distribution* adverse binomial distribution* Hypergeometric distributions* Logarithmic and Lagrangian distributions* combination distributions* Stopped-sum distributions* Matching, occupancy, runs, and q-series distributions* Parametric regression types and miscellaneaEmphasis remains to be put on the expanding relevance of Bayesian inference to discrete distribution, specifically in regards to the binomial and Poisson distributions. New derivations of discrete distributions through stochastic strategies and random walks are brought with no unnecessarily advanced discussions of stochastic approaches. through the 3rd variation, broad info has been further to mirror the recent function of computer-based applications.With its thorough assurance and balanced presentation of idea and alertness, this can be an outstanding and crucial reference for statisticians and mathematicians.

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Clearly FX (x) is a nondecreasing function of x and 0 ≤ FX (x) ≤ 1. If limx→−∞ FX (x) = 0 and limx→+∞ FX (x) = 1, then the distribution is proper. We shall be concerned only with proper distributions. The study of distributions is essentially a study of cdf’s. In all cases in these volumes the cdf belongs to one of two classes, discrete or continuous, or it can be constructed by mixing elements from the two classes. For discrete distributions FX (x) is a step function with only an enumerable number of steps.

The suffixes refer to the numbers of numerator and denominator parameters—there are two numerator parameters and one denominator parameter. Clearly 2 F1 [b, a; c; x] = 2 F1 [a, b; c; x]. We will only be interested in the case where a, b, c, and x are real. If a is a nonpositive integer, then (a)j is zero for j > −a, and the series terminates. When the series is infinite, it is absolutely convergent for |x| < 1 and divergent for |x| > 1. For |x| = 1, it is 1. absolutely convergent if c − a − b > 0; 2.

AA ; b1 , . . , bB ; q, z) ∞ = j =0 j (a1 ; q)j . . (aA ; q)j zj (−1)j q (2) (b1 ; q)j . . (bB ; q)j (q; q)j B−A+1 . 173) The only difference between the two definitions is the additional factor [(−1)j q j (j −1)/2 ]B−A+1 ; there is no difference when A = B + 1. The very considerable advantage conferred by the use of the additional factor when A = B + 1 is that limiting forms of G/R q-series as parameters tend to zero are themselves q-series. As q → 1, (q a ; q)j = (1 − q)j 1 − qa 1−q 1 − q a+1 1−q ··· 1 − q a+j −1 1−q = (1 + q + · · · + q a−1 )(1 + q + · · · + q a ) · · · (1 + q + · · · + q a+j −2 ) → a(a + 1) · · · (a + j − 1) = (a)j , where (a)j is Pochhammer’s symbol.

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