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Empirical Bayes and Likelihood Inference

of Bayesian, likelihood and empirical Bayes methods, presented at a workshop held in Montreal, Canada. The goal of the workshop was to explore the;

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Likelihood and Bayesian Inference

. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes;

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Likelihood & Bayesian Inference

. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes;

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Statistical Inference

treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new;

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Statistical Inference

treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new;

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Empirical Bayes Methods

decision rules. In this second edition, details are provided of the derivation and the performance of empirical Bayes rules for a variety;

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Empirical Bayes Methods

decision rules. In this second edition, details are provided of the derivation and the performance of empirical Bayes rules for a variety;

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Applying Quantitative Bias Analysis to Epidemiologic Data

improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new;

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Statistical Inference

balanced emphasis on both perspectives than the first edition. New to the Second Edition New material on empirical Bayes and penalized likelihoods;

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Empirical Likelihood Method in Survival Analysis

empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out;

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Empirical Likelihood Method in Survival Analysis

empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out;

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Mathematical Foundations of Infinite-Dimensional Statistical Models

'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The;

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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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Likelihood

This new and expanded edition of A. W. F. Edwards' classic volume on scientific inference presents his most important published articles on;

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Econometric Modeling

Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical;

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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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Algebraic Geometry and Statistical Learning Theory

' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from;

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Computer Age Statistical Inference, Student Edition

, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and;

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Computer Age Statistical Inference

, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the;

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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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Examples in Parametric Inference with R

developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed;

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Large-Scale Inference

than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how;

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Inference and Asymptotics

focuses on first-order asymptotic theory, and discusses the need for higher-order theory.;

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In All Likelihood

unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are;

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Likelihood

This is a newly expanded edition of Edwards' classic volume on scientific inference, in which he arg;...

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Identification and Inference for Econometric Models

, generalized empirical likelihood estimation, and the bootstrap.;

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