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Permutation, Parametric, and Bootstrap Tests of Hypotheses

, business, clinical trials, economics, geology, law, medicine, social science and engineering along with twice the number of exercises.;

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Nonparametric Statistical Tests

different statistical methods, such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods;

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Nonparametric Statistical Tests

different statistical methods, such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods;

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A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling

Distribution-free resampling methods-permutation tests, decision trees, and the bootstrap-are used today in virtually every research area;

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Practitioner'S Guide To Resampling For Data Analysis, Data M

Distribution-free resampling methods-permutation tests, decision trees, and the bootstrap-are used today in virtually every research area;

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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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Semiparametic Regression for the Applied Econometrician

hypotheses such as parametric and semiparametric specifications, significance, monotonicity and additive separability. Other topics include;

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Themes in Modern Econometrics

hypotheses such as parametric and semiparametric specifications, significance, monotonicity and additive separability. Other topics include;

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Nonparametric Tests for Censored Data

This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and;

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Nonparametric Tests for Complete Data

This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness;

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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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Analyzing the Large Number of Variables in Biomedical and Satellite Imagery

introduction to the resampling methods the bootstrap, decision trees, and permutation tests, before the specialized methods applicable to large arrays;

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Biostatistics & Research Methodology

This book contains 13 chapters. They include Basic concepts, Probability and Probability distributions, Tests of Hypotheses, Chi-square;

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Bootstrap Methods and their Application

intervals, and significance tests. The methods apply for any level of modelling, and so can be used for fully parametric, semiparametric, and;

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Common Errors In Statistics (And How To Avoid Them)

interpretations and confidence intervals; counts and correlated data; model building and testing; Bayes' Theorem; bootstrap and permutation tests; and;

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A Primer of Permutation Statistical Methods

. Permutation methods are optimal for small data sets and non-random samples, and are free of distributional assumptions. The book follows the;

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A Primer of Permutation Statistical Methods

. Permutation methods are optimal for small data sets and non-random samples, and are free of distributional assumptions. The book follows the;

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Bootstrap Tests For Regression Models

An accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample;

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Bootstrap Tests for Regression Models

An accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample;

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Asymptotics, Nonparametrics, and Time Series

Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian;

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Asymptotics, Nonparametrics, and Time Series

"Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian;

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Permutation Tests in Shape Analysis

. This is a proposal for a new Brief on statistical shape analysis and the various new parametric and non-parametric methods utilized to;

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Chi-squared Goodness-of-fit Tests for Censored Data

-squared tests for various distributions widely used in practice, and also consider chi-squared tests for the parametric proportional hazards model;

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Permutation Statistical Methods with R

population model espoused by J. Neyman and E.S. Pearson and the permutation model first introduced by R.A. Fisher and E.J.G. Pitman. Numerous;

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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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Comparing Groups

applying newly derived data analytic techniques. Comparing Groups: Randomization and Bootstrap Methods Using R emphasizes the direct link between;

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An Introduction to Bootstrap Methods with Applications to R

statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with;

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