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Examples in Parametric Inference with R

This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest;

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Predictive Inference

his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety;

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Predictive Inference

his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety;

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Multivariate Survival Analysis and Competing Risks

covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data;

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Multivariate Survival Analysis and Competing Risks

covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data;

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Introduction to Probability Theory and Statistical Inference

-Distribution, and the analysis of variance and non-parametric procedures. Contains numerous practical examples and exercises.;

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Modes Of Parametric Statistical Inference

Likelihood Fiducial Bayesian The author provides readers with specific examples and counterexamples of situations and datasets where the modes;

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Constrained Statistical Inference

, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical;

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Analysis of Longitudinal Data with Examples

including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case;

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A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713-1935

This book offers a detailed history of parametric statistical inference. Covering the period between James Bernoulli and R.A. Fisher, it;

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Statistical Inference in Financial and Insurance Mathematics with R

different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software;

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Inference and Asymptotics

This book provides a systematic account of some developments in asymptotic parametric inference from a likelihood-based perspective. It;

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Statistical Inference for Fractional Diffusion Processes

point of view. This book deals with Fractional Diffusion Processes and statistical inference for such stochastic processes. The main focus;

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Multiple Comparisons Using R

parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa. See Dr;

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Hierarchical Modeling and Inference in Ecology

, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the;

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Simultaneous Inference in Regression

applications of simultaneous confidence bands for various inferential purposes. It supplies examples and MATLAB (R) programs that make it easy to apply;

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Simultaneous Inference in Regression

applications of simultaneous confidence bands for various inferential purposes. It supplies examples and MATLAB (R) programs that make it easy to apply;

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Statistical Inference

Adopting a broad view of statistical inference, this text concentrates on what various techniques do, with mathematical proofs kept to a;

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Parametric Inference: An Introduction

Parametric Inference: An Introduction discusses the basic concept of sufficient statistic and the classical approach based on minimum;

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Statistical Paradigms

This volume consists of a collection of research articles on classical and emerging Statistical Paradigms parametric, non-parametric and;

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Non-Standard Parametric Statistical Inference

demonstrated by detailed examples drawn from real applications. Simple and practical model-building is an underlying theme. Parametric bootstrap;

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Bayesian inference with INLA

a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio;

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Bayesian inference with INLA

a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio;

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Mathematical Statistics

examples of exact confidence intervals along with the standard large sample confidence intervals based on the MLE's and bootstrap confidence;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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An Introduction to Statistical Inference and Its Applications with R

Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition;

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