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Non-Standard Parametric Statistical Inference

where the problem is non-standard when parameter estimates behave unusually, and (ii) the use of parametric bootstrap resampling methods;

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BAYESIAN INFERENCE

and outliers; hierarchical, multivariate and non-parametric models; asymptotics; computations and Monte Carlo methods; and Bayesian;

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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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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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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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Predictive Inference

of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive;

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Predictive Inference

of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive;

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Statistical Inference

, to decision theory and to non-parametric and robust inference. The increasingly important topics of computationally intensive methods and;

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Core Statistics

parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and;

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Core Statistics

parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and;

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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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Statistical Inference for Fractional Diffusion Processes

economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications;

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Statistical Inference

solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical;

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Statistical Inference

solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical;

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Mathematical Statistics

intervals. There's also a chapter on parametric statistical models featuring sections on non-iid observations, linear regression, logistic;

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Inference and Prediction in Large Dimensions

/ or parameters belong to a large or infinite dimensional space. It develops the theory of statistical prediction, non-parametric estimation by;

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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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Modes Of Parametric Statistical Inference

of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a;

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Advances In Statistical Modeling And Inference

narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods. These;

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Statistical Models of Characteristics of Metal Vapor Lasers

multiple linear and quasilinear regression and other parametric and non-parametric models for modelling laser efficiency and output power. The best;

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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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Permutation Testing for Isotonic Inference on Association Studies in Genetics

genetic study of diseases. There are some parametric and non-parametric methods available for this purpose. We deal with population-based;

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Understanding Social Science Research

definition of standard deviation with a guide for calculation; the concept of `power' in statistical inference; calculating correlations; and a;

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Understanding Social Science Research

definition of standard deviation with a guide for calculation; the concept of `power' in statistical inference; calculating correlations; and a;

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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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Theory of Statistical Inference

Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or;

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Simulation-based Econometric Methods

of classical parametric and semi-parametric non-linear estimation methods and a description of problems in which criterion functions contain integrals;

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