Statistical Foundations Reasoning And Inference

Author : Göran Kauermann,Helmut Küchenhoff,Christian Heumann
Publisher : Springer Nature
Page : 361 pages
File Size : 49,9 Mb
Release : 2021-09-30
Category : Mathematics
ISBN : 9783030698270

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Statistical Foundations, Reasoning and Inference by Göran Kauermann,Helmut Küchenhoff,Christian Heumann Pdf

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

Author : Göran Kauermann,Helmut Küchenhoff,Christian Heumann
Publisher : Unknown
Page : 0 pages
File Size : 42,9 Mb
Release : 2021
Category : Electronic
ISBN : 3030698289

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Statistical Foundations, Reasoning and Inference by Göran Kauermann,Helmut Küchenhoff,Christian Heumann Pdf

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

Author : Jianqing Fan,Runze Li,Cun-Hui Zhang,Hui Zou
Publisher : CRC Press
Page : 752 pages
File Size : 48,8 Mb
Release : 2020-09-21
Category : Mathematics
ISBN : 9781466510852

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Statistical Foundations of Data Science by Jianqing Fan,Runze Li,Cun-Hui Zhang,Hui Zou Pdf

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Author : Deborah G. Mayo
Publisher : Cambridge University Press
Page : 503 pages
File Size : 55,9 Mb
Release : 2018-09-20
Category : Mathematics
ISBN : 9781107054134

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Statistical Inference as Severe Testing by Deborah G. Mayo Pdf

Unlock today's statistical controversies and irreproducible results by viewing statistics as probing and controlling errors.

Author : Seymour Geisser,Wesley O. Johnson
Publisher : John Wiley & Sons
Page : 218 pages
File Size : 52,9 Mb
Release : 2006-01-27
Category : Mathematics
ISBN : 9780471743125

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Modes of Parametric Statistical Inference by Seymour Geisser,Wesley O. Johnson Pdf

A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode's strengths and weaknesses. The book begins with fascinating highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples and counterexamples of situations and datasets where the modes yield both similar and dissimilar results, including a violation of the likelihood principle in which Bayesian and likelihood methods differ from frequentist methods. Each example is followed by a detailed discussion of why the results may have varied from one mode to another, helping the reader to gain a greater understanding of each mode and how it works. Moreover, the author provides considerable mathematical detail on certain points to highlight key aspects of theoretical development. The author's writing style and use of examples make the text clear and engaging. This book is fundamental reading for graduate-level students in statistics as well as anyone with an interest in the foundations of statistics and the principles underlying statistical inference, including students in mathematics and the philosophy of science. Readers with a background in theoretical statistics will find the text both accessible and absorbing.

Author : Deborah G. Mayo,Aris Spanos
Publisher : Cambridge University Press
Page : 439 pages
File Size : 51,8 Mb
Release : 2011
Category : Business & Economics
ISBN : 9780521180252

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Error and Inference by Deborah G. Mayo,Aris Spanos Pdf

Explores the nature of error and inference, drawing on exchanges on experimental reasoning, reliability, and the objectivity of science.

Author : V. P. Godambe,D. A. Sprott,René Descartes Foundation
Publisher : Unknown
Page : 519 pages
File Size : 44,9 Mb
Release : 1971
Category : Electronic
ISBN : OCLC:14436685

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Foundations of Statistical Inference by V. P. Godambe,D. A. Sprott,René Descartes Foundation Pdf

Author : Larry Wasserman
Publisher : Springer Science & Business Media
Page : 446 pages
File Size : 50,6 Mb
Release : 2013-12-11
Category : Mathematics
ISBN : 9780387217369

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All of Statistics by Larry Wasserman Pdf

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Author : Henry Ely Kyburg
Publisher : Springer Science & Business Media
Page : 448 pages
File Size : 43,8 Mb
Release : 1974-07-31
Category : Mathematics
ISBN : 9027704309

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The Logical Foundations of Statistical Interference by Henry Ely Kyburg Pdf

When his uncle, Michael, dies of AIDS, Joel's dreams and thoughts of Michael keep his memory alive.

Author : University of Waterloo
Publisher : Unknown
Page : 519 pages
File Size : 51,9 Mb
Release : 1971
Category : Mathematical statistics
ISBN : 0039281035

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Foundations of Statistical Inference by University of Waterloo Pdf

Author : Ian Hacking
Publisher : Cambridge University Press
Page : 229 pages
File Size : 47,6 Mb
Release : 2016-08-26
Category : Mathematics
ISBN : 9781107144958

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Logic of Statistical Inference by Ian Hacking Pdf

This book showcases Ian Hacking's early ideas on the central issues surrounding statistical reasoning. Presented in a fresh twenty-first-century series livery, and with a specially commissioned new preface, this influential work is now available for a new generation of readers in statistics, philosophy of science and philosophy of maths.

Author : Anonim
Publisher : Unknown
Page : 120 pages
File Size : 40,5 Mb
Release : 1970
Category : Decision making
ISBN : UCAL:B2507951

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The Foundations of Statistical Inference by Anonim Pdf

Author : Randall Pruim
Publisher : American Mathematical Soc.
Page : 820 pages
File Size : 50,8 Mb
Release : 2018-04-04
Category : Mathematical statistics
ISBN : 9781470428488

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Foundations and Applications of Statistics by Randall Pruim Pdf

Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment R is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the R code has been updated throughout to take advantage of new R packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.

Author : Savage
Publisher : Unknown
Page : 112 pages
File Size : 42,6 Mb
Release : 1974-05-01
Category : Electronic
ISBN : 0470755180

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Foundations of Statistical Inference by Savage Pdf

Author : Theodore Coladarci,Casey D. Cobb
Publisher : John Wiley & Sons
Page : 448 pages
File Size : 50,7 Mb
Release : 2013-12-31
Category : Education
ISBN : 9781118425213

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Fundamentals of Statistical Reasoning in Education by Theodore Coladarci,Casey D. Cobb Pdf

Fundamentals of Statistical Reasoning in Education, 4th Edition is a text specifically geared towards the education community. This text gives educators the statistical knowledge and skills necessary in everyday classroom teaching, in running schools, and in professional development pursuits. It emphasises conceptual development with an engaging style and clear exposition.