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The Foundations of Statistics: A Simulation-based Approach

The Foundations of Statistics: A Simulation-based Approach
Author: Shravan Vasishth
Publisher: Springer Science & Business Media
Total Pages: 187
Release: 2010-11-11
Genre: Mathematics
ISBN: 3642163130

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Statistics and hypothesis testing are routinely used in areas (such as linguistics) that are traditionally not mathematically intensive. In such fields, when faced with experimental data, many students and researchers tend to rely on commercial packages to carry out statistical data analysis, often without understanding the logic of the statistical tests they rely on. As a consequence, results are often misinterpreted, and users have difficulty in flexibly applying techniques relevant to their own research — they use whatever they happen to have learned. A simple solution is to teach the fundamental ideas of statistical hypothesis testing without using too much mathematics. This book provides a non-mathematical, simulation-based introduction to basic statistical concepts and encourages readers to try out the simulations themselves using the source code and data provided (the freely available programming language R is used throughout). Since the code presented in the text almost always requires the use of previously introduced programming constructs, diligent students also acquire basic programming abilities in R. The book is intended for advanced undergraduate and graduate students in any discipline, although the focus is on linguistics, psychology, and cognitive science. It is designed for self-instruction, but it can also be used as a textbook for a first course on statistics. Earlier versions of the book have been used in undergraduate and graduate courses in Europe and the US. ”Vasishth and Broe have written an attractive introduction to the foundations of statistics. It is concise, surprisingly comprehensive, self-contained and yet quite accessible. Highly recommended.” Harald Baayen, Professor of Linguistics, University of Alberta, Canada ”By using the text students not only learn to do the specific things outlined in the book, they also gain a skill set that empowers them to explore new areas that lie beyond the book’s coverage.” Colin Phillips, Professor of Linguistics, University of Maryland, USA


Monte-Carlo Simulation-Based Statistical Modeling

Monte-Carlo Simulation-Based Statistical Modeling
Author: Ding-Geng (Din) Chen
Publisher: Springer
Total Pages: 430
Release: 2017-02-01
Genre: Medical
ISBN: 9811033072

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This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.


Introductory Statistics with Randomization and Simulation

Introductory Statistics with Randomization and Simulation
Author: David M. Diez
Publisher:
Total Pages: 354
Release: 2014-07-18
Genre: Statistics
ISBN: 9781500576691

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This textbook may be downloaded as a free PDF on the project's website, and the paperback is sold royalty-free. OpenIntro develops free textbooks and course resources for introductory statistics that exceeds the quality standards of traditional textbooks and resources, and that maximizes accessibility options for the typical student. The approach taken in this textbooks differs from OpenIntro Statistics in its introduction to inference. The foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches, where the normal and t distributions are used for hypothesis testing and the construction of confidence intervals.


OpenIntro Statistics

OpenIntro Statistics
Author: David Diez
Publisher:
Total Pages:
Release: 2015-07-02
Genre:
ISBN: 9781943450046

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The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources.


Statistical Inference as Severe Testing

Statistical Inference as Severe Testing
Author: Deborah G. Mayo
Publisher: Cambridge University Press
Total Pages: 503
Release: 2018-09-20
Genre: Mathematics
ISBN: 1108563309

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Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.


Handbook of Simulation

Handbook of Simulation
Author: Jerry Banks
Publisher: John Wiley & Sons
Total Pages: 868
Release: 1998-09-14
Genre: Technology & Engineering
ISBN: 9780471134039

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Dieses Buch ist eine unschätzbare Informationsquelle für alle Ingenieure, Designer, Manager und Techniker bei Entwicklung, Studium und Anwendung einer großen Vielzahl von Simulationstechniken. Es vereint die Arbeit internationaler Simulationsexperten aus Industrie und Forschung. Alle Aspekte der Simulation werden in diesem umfangreichen Nachschlagewerk abgedeckt. Der Leser wird vertraut gemacht mit den verschiedenen Techniken von Industriesimulationen sowie mit Einsatz, Anwendungen und Entwicklungen. Neueste Fortschritte wie z.B. objektorientierte Programmierung werden ebenso behandelt wie Richtlinien für den erfolgreichen Umgang mit simulationsgestützten Prozessen. Auch gibt es eine Liste mit den wichtigsten Vertriebs- und Zulieferadressen. (10/98)


Probability, Statistics, and Data

Probability, Statistics, and Data
Author: Darrin Speegle
Publisher: CRC Press
Total Pages: 513
Release: 2021-11-25
Genre: Business & Economics
ISBN: 1000504166

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This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included, using calculus when appropriate, but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized, quantified using simulations, and demonstrated with real data. The book compares parametric and non-parametric methods through simulation, allowing for a thorough investigation of testing error and power. The text builds R skills from the outset, allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers, so that you are working with current, real data, which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr, ggplot2, tidyr, stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University, and the majority of the more than 400 exercises have been classroom tested. The exercises in the book have been added to to the free and open online homework system myopenmath (https://www.myopenmath.com/) which may be useful to instructors.


All of Statistics

All of Statistics
Author: Larry Wasserman
Publisher: Springer Science & Business Media
Total Pages: 446
Release: 2013-12-11
Genre: Mathematics
ISBN: 0387217363

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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.


Improving and extending quantitative reasoning in second language research

Improving and extending quantitative reasoning in second language research
Author: John M. Norris
Publisher: John Wiley & Sons
Total Pages: 274
Release: 2015-06-08
Genre: Education
ISBN: 1119148200

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Currents in Language Learning is a biennial book series published by Wiley and the Language Learning Research Club at the University of Michigan. It provides programmatic state-of-the-art overviews of current issues in the language sciences and their applications in first, second, and bi/multilingual language acquisition in naturalistic and tutored contexts. It brings together disciplinary perspectives from linguistics, psychology, education, anthropology, sociology, cognitive science, and neuroscience.


Statistical Foundations of Data Science

Statistical Foundations of Data Science
Author: Jianqing Fan
Publisher: CRC Press
Total Pages: 752
Release: 2020-09-21
Genre: Mathematics
ISBN: 1466510854

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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.