N-Distances and their Applications

Kniha je věnována pravděpodobnostním metrikám vhodným pro charakterizaci náhodných měr v Hilbertově nebo Banachově prostoru. Je v ní podrobně rozebrána řada stochastických postupů, jako testování neparametrických statistických hypotéz, charakterizace pravděpodobnostních rozdělení a jejich stability nebo konstrukce vícerozměrných testů pro dva výběry. Publikace vychází v anglickém jazyce.

Preface
Chapter 1. Positive and Negative Definite Kernels and Their Properties
1. Definitions of positive and negative definite kernels
2. Examples of positive definite kernels
3. Positive definite functions
4. Negative definite kernels
5. Coarse embeddings of metric spaces into Hilbert space
6. Strictly and strongly positive and negative definite kernels
Chapter 2. N-Metrics in the Set of Probability Measures
1. A class of positive definite kernels in the set of probabilities and N-distances
Chapter 3. m-negative Definite Kernels and Metrics
Chapter 4. N-metrics and the Problem of Recovering Measures From Potential
1. Recovering Measures From Potential
2. Stability in the Problem of Recovering a Measure from Potential
Chapter 5. N-metrics in the Study of Certain Problems of Characterization of Distributions
1. Some characterization of Gaussian and related distributions
2. Characterization of distributions symmetric to a group of transformations
Chapter 6. Commutative Semigroups with Positive Definite Kernel
1. General considerations
2. Distances in X
3. Special representations
4. Properties of x(t)
5. Infinitely divisible elements
6. Accompanying infinitely divisible elements
7. Examples
Chapter 7. Statistical Estimates obtained by the Minimal Distances Method
1. Estimating a location parameter, I
2. Estimating a location parameter, II
3. Estimating a general parameter
4. Estimating a location parameter, III
5. Semiparametric estimation
Chapter 8. Some Statistical Tests Based on N-Distances
1. Multivariate two-sample test
2. Test for two distributions to belong to the same additive type
3. Some Tests for Observations to be Gaussian
4. A Test for Closeness of Probability Distributions
Chapter 9. A Permutation Test Motivated by Microarray Data Analysis 1. Introduction
2. Theoretical framework: the test statistic
3. Computational framework: accuracy of permutation quantiles and p-values
4. Power of the test: a simulation study
5. Examples of data analysis
6. Conclusions of the Chapter
Bibliography
Author Index
Index