Eugene Demidenko's Books

Mixed Models

Theory and Applications with R

By: Eugene Demidenko

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Mixed Models

Theory and Applications with R

By: Eugene Demidenko

Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.” —Journal of the American Statistical Association Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R. The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing. Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as: Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.

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768

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118091574

ISBN 13

9781118091579

Advanced Statistics with Applications in R

By: Eugene Demidenko

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Advanced Statistics with Applications in R

By: Eugene Demidenko

Advanced Statistics with Applications in R fills the gap between several excellent theoretical statistics textbooks and many applied statistics books where teaching reduces to using existing packages. This book looks at what is under the hood. Many statistics issues including the recent crisis with p-value are caused by misunderstanding of statistical concepts due to poor theoretical background of practitioners and applied statisticians. This book is the product of a forty-year experience in teaching of probability and statistics and their applications for solving real-life problems. There are more than 442 examples in the book: basically every probability or statistics concept is illustrated with an example accompanied with an R code. Many examples, such as Who said π? What team is better? The fall of the Roman empire, James Bond chase problem, Black Friday shopping, Free fall equation: Aristotle or Galilei, and many others are intriguing. These examples cover biostatistics, finance, physics and engineering, text and image analysis, epidemiology, spatial statistics, sociology, etc. Advanced Statistics with Applications in R teaches students to use theory for solving real-life problems through computations: there are about 500 R codes and 100 datasets. These data can be freely downloaded from the author's website dartmouth.edu/~eugened. This book is suitable as a text for senior undergraduate students with major in statistics or data science or graduate students. Many researchers who apply statistics on the regular basis find explanation of many fundamental concepts from the theoretical perspective illustrated by concrete real-world applications.

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880

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118387988

ISBN 13

9781118387986

M-statistics

Optimal Statistical Inference for a Small Sample

By: Eugene Demidenko

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M-statistics

Optimal Statistical Inference for a Small Sample

By: Eugene Demidenko

M-STATISTICS A comprehensive resource providing new statistical methodologies and demonstrating how new approaches work for applications M-statistics introduces a new approach to statistical inference, redesigning the fundamentals of statistics, and improving on the classical methods we already use. This book targets exact optimal statistical inference for a small sample under one methodological umbrella. Two competing approaches are offered: maximum concentration (MC) and mode (MO) statistics combined under one methodological umbrella, which is why the symbolic equation M=MC+MO. M-statistics defines an estimator as the limit point of the MC or MO exact optimal confidence interval when the confidence level approaches zero, the MC and MO estimator, respectively. Neither mean nor variance plays a role in M-statistics theory. Novel statistical methodologies in the form of double-sided unbiased and short confidence intervals and tests apply to major statistical parameters: Exact statistical inference for small sample sizes is illustrated with effect size and coefficient of variation, the rate parameter of the Pareto distribution, two-sample statistical inference for normal variance, and the rate of exponential distributions. M-statistics is illustrated with discrete, binomial, and Poisson distributions. Novel estimators eliminate paradoxes with the classic unbiased estimators when the outcome is zero. Exact optimal statistical inference applies to correlation analysis including Pearson correlation, squared correlation coefficient, and coefficient of determination. New MC and MO estimators along with optimal statistical tests, accompanied by respective power functions, are developed. M-statistics is extended to the multidimensional parameter and illustrated with the simultaneous statistical inference for the mean and standard deviation, shape parameters of the beta distribution, the two-sample binomial distribution, and finally, nonlinear regression. Our new developments are accompanied by respective algorithms and R codes, available at GitHub, and as such readily available for applications. M-statistics is suitable for professionals and students alike. It is highly useful for theoretical statisticians and teachers, researchers, and data science analysts as an alternative to classical and approximate statistical inference.

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Page Count

244

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119891795

ISBN 13

9781119891796

Mixed Models

Theory and Applications with R

By: Eugene Demidenko

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Mixed Models

Theory and Applications with R

By: Eugene Demidenko

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Page Count

768

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118592999

ISBN 13

9781118592991

M-statistics

Optimal Statistical Inference for a Small Sample

By: Eugene Demidenko

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M-statistics

Optimal Statistical Inference for a Small Sample

By: Eugene Demidenko

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Solve jigsaw puzzle

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Page Count

244

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119891795

ISBN 13

9781119891796

Advanced Statistics with Applications in R

By: Eugene Demidenko

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Advanced Statistics with Applications in R

By: Eugene Demidenko

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Solve jigsaw puzzle

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Page Count

880

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118387988

ISBN 13

9781118387986

Mixed Models

Theory and Applications

By: Eugene Demidenko

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Mixed Models

Theory and Applications

By: Eugene Demidenko

A rigorous, self-contained examination of mixed model theory and application Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as applications such as the analysis of tumor regrowth, shape, and image. Paying special attention to algorithms and their implementations, the book discusses: Modeling of complex clustered or longitudinal data Modeling data with multiple sources of variation Modeling biological variety and heterogeneity Mixed model as a compromise between the frequentist and Bayesian approaches Mixed model for the penalized log-likelihood Healthy Akaike Information Criterion (HAIC) How to cope with parameter multidimensionality How to solve ill-posed problems including image reconstruction problems Modeling of ensemble shapes and images Statistics of image processing Major results and points of discussion at the end of each chapter along with "Summary Points" sections make this reference not only comprehensive but also highly accessible for professionals and students alike in a broad range of fields such as cancer research, computer science, engineering, and industry.

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Page Count

732

Publisher

John Wiley & Sons

Published Date

ISBN 10

0471726133

ISBN 13

9780471726135

Book's by Eugene Demidenko

Mixed Models

Mixed Models

Advanced Statistics with Applications in R

Advanced Statistics with Applications in R

M-statistics

M-statistics

Mixed Models

Mixed Models

M-statistics

M-statistics

Advanced Statistics with Applications in R

Advanced Statistics with Applications in R

Mixed Models

Mixed Models

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