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Statistical Inference Based on Divergence Measures

statistic in discrete models, many statisticians remain unaware of this powerful approach. Statistical Inference Based on Divergence Measures;

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Statistical Inference Based on Divergence Measures

statistic in discrete models, many statisticians remain unaware of this powerful approach. Statistical Inference Based on Divergence Measures;

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Aspects of Statistical Inference

Relevant, concrete, and thorough--the essential data-based text on statistical inference The ability to formulate abstract concepts and;

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Measuring Statistical Evidence Using Relative Belief

of recent work on developing a theory of statistical inference based on measuring statistical evidence. It shows that being explicit about how to;

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Measuring Statistical Evidence Using Relative Belief

of recent work on developing a theory of statistical inference based on measuring statistical evidence. It shows that being explicit about how to;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications;

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Introduction to the Theory of Statistical Inference

Based on the authors lecture notes, Introduction to the Theory of Statistical Inference presents concise yet complete coverage;

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Introduction to the Theory of Statistical Inference

Based on the authors' lecture notes, Introduction to the Theory of Statistical Inference presents concise yet complete coverage;

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Model Based Inference in the Life Sciences: A Primer on Evidence

science philosophy based on multiple working hypotheses and statistical models to represent them. The text is written for people new to the;

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Geometrical Foundations of Asymptotic Inference

Differential geometry provides an aesthetically appealing and often revealing view of statistical inference. Beginning with an elementary;

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Introduction to Statistical Decision Theory

of statistical science based on decision theory. Features Covers approaches for making decisions under certainty, risk, and;

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Introduction to Statistical Decision Theory

of statistical science based on decision theory. Features Covers approaches for making decisions under certainty, risk, and uncertainty;

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From Finite Sample To Asymptotic Methods In Statistics

justified by asymptotic arguments but are still based on the concepts and principles that underlie exact statistical inference. With this;

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Pareto Distributions

computer-based inference methods. Pareto Distributions, Second Edition provides broad, up-to-date coverage of the Pareto model and its extensions;

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Pareto Distributions

computer-based inference methods. Pareto Distributions, Second Edition provides broad, up-to-date coverage of the Pareto model and its extensions;

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Fundamentals of Statistical Inference

make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population. By;

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Nondeductive Inference

which exhibits the difficulties of choosing probability measures over infinite sets, and a comparison of rival views on the foundations;

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Nondeductive Inference

which exhibits the difficulties of choosing probability measures over infinite sets, and a comparison of rival views on the foundations;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Confidence Intervals in Generalized Regression Models

generalized regression model (GRM)-of various types of regression models. It also uses a likelihood-based approach for performing statistical;

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Confidence Intervals in Generalized Regression Models

generalized regression model (GRM)-of various types of regression models. It also uses a likelihood-based approach for performing statistical;

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Statistical Inference

Distance Approach gives a thorough account of density-based minimum distance methods and their use in statistical inference. It covers statistical;

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Statistical Inference

thorough account of density-based minimum distance methods and their use in statistical inference. It covers statistical distances, density-based;

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Statistical Inference Based on the likelihood

introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used;

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Statistical Inference Based On The Likelihood

introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used;

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Constrained Statistical Inference

An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from;

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