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Statistical Properties of the Generalized Inverse Gaussian Distribution

of the hyperbolic distributions as mixtures of normal distributions. The statistical properties of the generalized inverse Gaussian distribution;

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The Inverse Gaussian Distribution: Theory

This monograph is a compilation of research on the inverse Gaussian distribution. It emphasizes the presentation of the statistical;

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The Inverse Gaussian Distribution: Theory

This monograph is a compilation of research on the inverse Gaussian distribution. It emphasizes the presentation of the statistical;

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CRC Handbook of Tables for Order Statistics from Inverse Gaussian Distributions with Applications

First derived within the context of life-testing, inverse Gaussian distribution has become one of the most important and widely employed;

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CRC Handbook of Tables for Order Statistics from Inverse Gaussian Distributions with Applications

First derived within the context of life-testing, inverse Gaussian distribution has become one of the most important and widely employed;

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Handbook of Percentage Points of the Inverse Gaussian Distributions

The purpose of this handbook is to provide comprehensive tables of percentage points of the inverse Gaussian distribution. There is no;

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Statistical Inference in Financial and Insurance Mathematics with R

, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments;

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Handbook on Univariate Statistical Distributions

Univariate statistical distributions, with their basic properties, are an important part of advance statistics. The Handbook on Univariate;

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Stochastic Analysis for Gaussian Random Processes and Fields

random fields indexed by measures and generalized Gaussian random fields indexed by Schwartz space. The Markov property for generalized random;

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Stochastic Analysis for Gaussian Random Processes and Fields

properties connecting probabilistic notions. In particular, it studies Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs). The;

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Introduction to General and Generalized Linear Models

Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models;

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Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the;

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Theory and Statistical Applications of Stochastic Processes

This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It;

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Gaussian Process Regression Analysis for Functional Data

discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new;

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Seismic Reservoir Modeling - Theory, Examples, and Algorithms

distribution of rock and fluid properties given the measured geophysical data and allows quantifying the uncertainty of the predicted results. The;

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Exponential Distribution

The exponential distribution is one of the most significant and widely used distribution in statistical practice. It possesses several;

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The Exponential Distribution

The exponential distribution is one of the most significant and widely used distribution in statistical practice. It possesses several;

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Generalized Trigonometric and Hyperbolic Functions

generalized sine functions, followed by the construction of the associated cosine functions, and from this, various properties of the generalized;

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Fitting Statistical Distributions

Throughout the physical and social sciences, researchers face the challenge of fitting statistical distributions to their data. Although;

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Fitting Statistical Distributions

Throughout the physical and social sciences, researchers face the challenge of fitting statistical distributions to their data. Although;

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Generalized Linear Models and Extensions

various GLMs are special cases of the exponential family, showing general properties of this family of distributions, and showing the derivation;

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Extreme Value Theory-based Methods for Visual Recognition

several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability;

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Generalized, Linear, and Mixed Models

An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models;

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Statistical Mechanics for Chemists

With an emphasis on finding solutions to common problems in chemistry, topics covered include: The Maxwell--Boltzmann velocity distribution;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Statistical Portfolio Estimation

framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many;

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