A Modern Course on Statistical Distributions in Scientific Work

Table of Contents

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   Book Overview
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   Chapter 1 Inaugural Address — Concept and Conduct of Calgary Course and Conference: Some Thoughts
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   Chapter 2 Some Recent Advances with Power Series Distributions
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   Chapter 3 Multiparameter Stirling and C-Type Distributions
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   Chapter 4 Models for Gaussian Hypergeometric Distributions
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   Chapter 5 On the Probabilistic Structure and Properties of Discrete Lagrangian Distributions
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   Chapter 6 Estimation of Parameters on Some Extensions of the Katz Family of Discrete Distributions Involving Hypergeometric Functions
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   Chapter 7 A Characteristic Property of Certain Generalized Power Series Distributions
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   Chapter 8 Stable Distributions: Probability, Inference, and Applications in Finance—A Survey, and a Review of Recent Results
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    Chapter 9 Structural Properties and Statistics of Finite Mixtures
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    Chapter 10 Distribution Theory for the Von Mises-Fisher Distribution and Its Application
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    Chapter 11 Certain Statistical Distributions Involving Special Functions and their Applications
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    Chapter 12 Tailweight, Statistical Inference and Families of Distributions — A Brief Survey
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    Chapter 13 The Families with a “Universal” Location Estimator
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    Chapter 14 Approximation Theory, Moment Problems and Distribution Functions
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    Chapter 15 Kurtosis and Departure from Normality
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    Chapter 16 Convergence of Sequences of Transformations of Distribution Functions and Some Moment Problems
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    Chapter 17 Weak Convergence for Exponential and Monotone Likelihood Ratio Families and the Convergence of Confidence Limits
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    Chapter 18 On Efficiency and Exponential Families in Stochastic Process Estimation
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    Chapter 19 A Lagrangian Gamma Distribution
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    Chapter 20 Multivariate Distributions at a Cross Road
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    Chapter 21 Dependence Concepts and Probability Inequalities
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    Chapter 22 New Families of Multivariate Distributions
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    Chapter 23 Asymptotic Expansions for the Nonnull Distributions of the Multivariate Test Statistics
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    Chapter 24 A Multivariate Gamma Type Distribution Whose Marginal Laws are Gamma, and which has a Property Similar to a Characteristic Property of the Normal Case
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    Chapter 25 The Bivariate Burr Distribution
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    Chapter 26 Multivariate Beta Distribution
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    Chapter 27 Distribution of a Quadratic Form in Normal Vectors
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    Chapter 28 Bivariate and Multivariate Extreme Distributions
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    Chapter 29 On the Distribution of the Minimum and of the Maximum of a Random Number of I.I.D. Random Variables
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    Chapter 30 Transformation of the Pearson System with Special Reference to Type IV
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    Chapter 31 Distributions of Characteristic Roots of Random Matrices
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    Chapter 32 On the Arithmetic Means and Variances of Products and Ratios of Random Variables
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    Chapter 33 Exact and Approximate Sampling Distribution of The F-Statistic Under the Randomization Procedure