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This Set Contains: Continuous Multivariate Distributions, Volume 1, Models and Applications, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. JohnsonContinuous Univariate Distributions, Volume 1, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. JohnsonContinuous Univariate Distributions, Volume 2, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. JohnsonDiscrete Multivariate Distributions by Samuel Kotz, N. Balakrishnan and Normal L. JohnsonUnivariate Discrete Distributions, 3rd Edition by Samuel Kotz, N. Balakris

Distribution (Probability theory)

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Probability and statistics

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This detailed introduction to distribution theory uses no measure theory, making it suitable for students in statistics and econometrics as well as for researchers who use statistical methods. Good backgrounds in calculus and linear algebra are important and a course in elementary mathematical analysis is useful, but not required. An appendix gives a detailed summary of the mathematical definitions and results that are used in the book. Topics covered range from the basic distribution and density functions, expectation, conditioning, characteristic functions, cumulants, convergence in distribution and the central limit theorem to more advanced concepts such as exchangeability, models with a group structure, asymptotic approximations to integrals, orthogonal polynomials and saddlepoint approximations. The emphasis is on topics useful in understanding statistical methodology; thus, parametric statistical models and the distribution theory associated with the normal distribution are covered comprehensively.

Stochastic processes --- Distribution (Probability theory) --- 519.2 --- Probability. Mathematical statistics --- 519.2 Probability. Mathematical statistics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Mathematical Sciences --- Probability

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Dieses Lehrbuch wendet sich an alle, die - ausgestattet mit Grundkenntnissen der Differential- und Intergralrechnung und der linearen Algebra - in die Ideenwelt der Stochastik eindringen möchten. Stochastik ist die Mathematik des Zufalls. Sie ist von größter Bedeutung für die Berufspraxis der Mathematiker. An vielen Schulen hat sie ihren festen Platz gefunden. Die beiden Hauptgebiete der Stochastik sind Wahrscheinlichkeitstheorie und Statistik. In der Wahrscheinlichkeitstheorie untersucht man zufällige Prozesse mit festen als bekannt angenommenen steuernden Wahrscheinlichkeiten. Dies ist theoretisch und praktisch von eigenständigem Interesse. Darüber hinaus liefert die Wahrscheinlichkeitstheorie Grundlagen für die Statistik, in der aus beobachteten Daten Schlüsse über unbekannte Wahrscheinlichkeiten und über zweckmäßiges Verhalten gezogen werden sollen. Die 8. Auflage enthält erweiterte historische Anmerkungen.

Probabilities. --- Probability Theory and Stochastic Processes.

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Mathematical statistics --- Méthode statistique --- Statistical methods --- Distribution (Probability theory)