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The Normal Distribution: Characterizations with Applications
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Catalogue Information
Field name
Details
Dewey Class
519.2
Title
The Normal Distribution ([EBook] :) : Characterizations with Applications / / by Wlodzimierz Bryc.
Author
Bryc, Wlodzimierz
Other name(s)
SpringerLink (Online service)
Publication
New York, NY : : Springer New York, , 1995.
Physical Details
VIII, 139 p. : online resource.
Series
Lecture Notes in Statistics
0930-0325 ; ; 100
ISBN
9781461225607
Summary Note
This book is a concise presentation of the normal distribution on the real line and its counterparts on more abstract spaces, which we shall call the Gaussian distributions. The material is selected towards presenting characteristic properties, or characterizations, of the normal distribution. There are many such properties and there are numerous rel evant works in the literature. In this book special attention is given to characterizations generated by the so called Maxwell's Theorem of statistical mechanics, which is stated in the introduction as Theorem 0.0.1. These characterizations are of interest both intrin sically, and as techniques that are worth being aware of. The book may also serve as a good introduction to diverse analytic methods of probability theory. We use characteristic functions, tail estimates, and occasionally dive into complex analysis. In the book we also show how the characteristic properties can be used to prove important results about the Gaussian processes and the abstract Gaussian vectors. For instance, in Section 5.4 we present Fernique's beautiful proofs of the zero-one law and of the integrability of abstract Gaussian vectors. The central limit theorem is obtained via characterizations in Section 7.3.:
Contents note
1 Probability tools -- 1.1 Moments -- 1.2 Lp-spaces -- 1.3 Tail estimates -- 1.4 Conditional expectations -- 1.5 Characteristic functions -- 1.6 Symmetrization -- 1.7 Uniform integrability -- 1.8 The Mellin transform -- 1.9 Problems -- 2 Normal distributions -- 2.1 Univariate normal distributions -- 2.2 Multivariate normal distributions -- 2.3 Analytic characteristic functions -- 2.4 Hermite expansions -- 2.5 Cramer and Marcinkiewicz theorems -- 2.6 Large deviations -- 2.7 Problems -- 3 Equidistributed linear forms -- 3.1 Two-stability -- 3.2 Measures on linear spaces -- 3.3 Linear forms -- 3.4 Exponential analogy -- 3.5 Exponential distributions on lattices -- 3.6 Problems -- 4 Rotation invariant distributions -- 4.1 Spherically symmetric vectors -- 4.2 Rotation invariant absolute moments -- 4.3 Infinite spherically symmetric sequences -- 4.4 Problems -- 5 Independent linear forms -- 5.1 Bernstein’s theorem -- 5.2 Gaussian distributions on groups -- 5.3 Independence of linear forms -- 5.4 Strongly Gaussian vectors -- 5.5 Joint distributions -- 5.6 Problems -- 6 Stability and weak stability -- 6.1 Coefficients of dependence -- 6.2 Weak stability -- 6.3 Stability -- 6.4 Problems -- 7 Conditional moments -- 7.1 Finite sequences -- 7.2 Extension of Theorem 7.1.2 -- 7.3 Central Limit Theorem -- 7.4 Empirical mean and variance -- 7.5 Infinite sequences and conditional moments -- 7.6 Problems -- 8 Gaussian processes -- 8.1 Construction of the Wiener process -- 8.2 Levy’s characterization theorem -- 8.3 Arbitrary trajectories -- 8.4 Second order conditional structure -- A Solutions of selected problems -- A.1 Solutions for Chapter 1 -- A.2 Solutions for Chapter 2 -- A.3 Solutions for Chapter 3 -- A.4 Solutions for Chapter 4 -- A.5 Solutions for Chapter 5 -- A.6 Solutions for Chapter 6 -- A.7 Solutions for Chapter 7.
System details note
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site
http://dx.doi.org/10.1007/978-1-4612-2560-7
Links to Related Works
Subject References:
Mathematics
.
Probabilities
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Probability theory and stochastic processes
.
Authors:
Bryc, Wlodzimierz
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Corporate Authors:
SpringerLink (Online service)
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Series:
Lecture Notes in Statistics
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Classification:
519.2
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