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Linear Statistical Inference And Its Applications

. Efrom Translated into six major languages of the world, C. R. Rao's Linear Statistical Inference and Its Applications is one of the foremost;

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Introduction to the Theory of Statistical Inference

problems. Highlights Basic notations and ideas of statistical inference are explained in a mathematically rigorous, but understandable;

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Introduction to the Theory of Statistical Inference

problems. Highlights Basic notations and ideas of statistical inference are explained in a mathematically rigorous, but;

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Statistical Inference For Models With Multivariate T-Distrib

the development of linear statistical models with applications to engineering, the physical sciences, and mathematics * Contains an up-to-date;

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Proceedings of the International Conference on Linear Statistical Inference Linstat '93

This volume contains a selection of invited and contributed papers presented at the International Conference on Linear Statistical;

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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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Statistical Modeling and Computation

authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models;

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Inference and Prediction in Large Dimensions

This book offers a predominantly theoretical coverage of statistical prediction, with some potential applications discussed, when data and;

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Advanced Linear Models

This work details the statistical inference of linear models including parameter estimation, hypothesis testing, confidence intervals, and;

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

examines: binomial statistical inference; statistical inference by inverse probability; the central limit theorem and linear minimum variance;

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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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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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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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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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Confidence Intervals in Generalized Regression Models

inference from statistical evidence consisting of data and its statistical model. Provides a Large Collection of Models The book encompasses;

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Confidence Intervals in Generalized Regression Models

inference from statistical evidence consisting of data and its statistical model. Provides a Large Collection of Models The book encompasses;

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Statistical Inference a Concise Course

This brief course in statistical inference was extensively class tested by the author at the University of Illinois, and it requires only a;

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Inferential Models

interesting and challenging problems of conditional and marginal inference. This book delves into statistical inference at a foundational;

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Inferential Models

interesting and challenging problems of conditional and marginal inference. This book delves into statistical inference at a foundational level;

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Inference for Heavy-Tailed Data

Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied;

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Models for Probability and Statistical Inference

topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping. Ideal as a textbook;

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Statistical Inference for Engineers and Data Scientists

knowledge, this textbook is the 'go-to' guide for learning about the core principles of statistical inference and its application in engineering and;

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Statistical Inference

generalised linear models are also included. In this edition, the material on recent developments has been updated, and additional exercises are;

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Nonparametric Statistical Methods And Related Topics

-art research in statistical theory, with an emphasis on nonparametric inference, linear model theory, time series analysis and asymptotic;

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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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