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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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Likelihood & Bayesian Inference

discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing;

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Likelihood and Bayesian Inference

discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing;

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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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Mathematical Statistics

uniquely based on the likelihood function This book addresses mathematical statistics for upper-undergraduates and first year graduate students;

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Introductory Statistical Inference with the Likelihood Function

This textbook covers the fundamentals of statistical inference and statistical theory including Bayesian and frequentist approaches and;

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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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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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Likelihood

theory of statistical inference in science. Using the likelihood approach, he explores estimation, tests of significance, randomization;

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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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Semimartingales and their Statistical Inference

Statistical inference carries great significance in model building from both the theoretical and the applications points of view. Its;

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Semimartingales and Their Statistical Inference

Statistical inference carries great significance in model building from both the theoretical and the applications points of view. Its;

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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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Statistical Inference

Estimation is literally huge. Filling a statistical resource gap, Statistical Inference: The Minimum Distance Approach comprehensively overviews;

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Statistical Inference

is literally huge. Filling a statistical resource gap, Statistical Inference: The Minimum Distance Approach comprehensively overviews developments;

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Modes Of Parametric Statistical Inference

problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference: Frequentist;

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Maximum Likelihood Estimation and Inference

This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It;

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In All Likelihood

Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method;

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Statistical Inference in Stochastic Processes

statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the;

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Maximum Likelihood for Social Science

develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical;

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Maximum Likelihood for Social Science

develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical;

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Antedependence Models for Longitudinal Data

, the book also covers some important statistical inference procedures associated with these models. After describing unstructured and;

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Probability and Statistical Inference

of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer;

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