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Stochastic processes in physics and chemistry
The third edition of Van Kampen's standard work has been revised and updated.The main difference with the second edition is that the contrived application of the quantum master equation in section 6 of chapter XVII has been replaced with a satisfactory treatment of quantum fluctuations.
eBook
Applying the Rasch Model
Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background.
Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business and other social and health sciences. Professionals in these areas will also appreciate the book's accessible introduction.
Highlights of the new edition include:
More learning tools to strengthen readers' understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings.
Greater emphasis on the use of R packages; readers can download the R code from the Routledge website.
Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4).
A new four-option data set from the IASQ (Instrumental Attitude toward Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6).
Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10).
eBook
Understanding The New Statistics
This is the first book to introduce the new statistics - effect sizes, confidence intervals, and meta-analysis - in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics - which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines.
Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book's exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice.
The book's pedagogical program, built on cognitive science principles, reinforces learning:
Boxes provide \"evidence-based\" advice on the most effective statistical techniques.
Numerous examples reinforce learning, and show that many disciplines are using the new statistics.
Graphs are tied in with ESCI to make important concepts vividly clear and memorable.
Opening overviews and end of chapter take-home messages summarize key points.
Exercises encourage exploration, deep understanding, and practical app
eBook
Modeling, measuring and managing risk
This book is the first in the market to treat single- and multi-period risk measures (risk functionals) in a thorough, comprehensive manner. It combines the treatment of properties of the risk measures with the related aspects of decision making under risk.
eBook
Data analysis and statistics for geography, environmental science, and engineering
This practical, classroom-tested textbook helps readers learn quantitative methodology, including how to implement advanced analysis methods using an open-source software platform. Based on the author's many years of teaching undergraduate and graduate students in several countries, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applied to a variety of geographical and environmental models. Theory is accompanied by practical hands-on computer exercises, progressing from easy to difficult. The text also presents a review of mathematical methods, making the book self-contained.
eBook
Handbook of statistical analysis and data mining applications
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation.
eBook
Statistical learning for big dependent data
Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets.
eBook
How to do linguistics with R : data exploration and statistical analysis
This book provides a linguist with a statistical toolkit for exploration and analysis of linguistic data. It employs R, a free software environment for statistical computing, which is increasingly popular among linguists. How to do Linguistics with R: Data exploration and statistical analysis is unique in its scope, as it covers a wide range of classical and cutting-edge statistical methods, including different flavours of regression analysis and ANOVA, random forests and conditional inference trees, as well as specific linguistic approaches, among which are Behavioural Profiles, Vector Space Models and various measures of association between words and constructions. The statistical topics are presented comprehensively, but without too much technical detail, and illustrated with linguistic case studies that answer non-trivial research questions. The book also demonstrates how to visualize linguistic data with the help of attractive informative graphs, including the popular ggplot2 system and Google visualization tools.This book has a companion website: http://doi.org/10.1075/z.195.website
eBook
Distribution-Free Predictive Inference for Regression
We develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a prediction band for the response variable using any estimator of the regression function. The resulting prediction band preserves the consistency properties of the original estimator under standard assumptions, while guaranteeing finite-sample marginal coverage even when these assumptions do not hold. We analyze and compare, both empirically and theoretically, the two major variants of our conformal framework: full conformal inference and split conformal inference, along with a related jackknife method. These methods offer different tradeoffs between statistical accuracy (length of resulting prediction intervals) and computational efficiency. As extensions, we develop a method for constructing valid in-sample prediction intervals called rank-one-out conformal inference, which has essentially the same computational efficiency as split conformal inference. We also describe an extension of our procedures for producing prediction bands with locally varying length, to adapt to heteroscedasticity in the data. Finally, we propose a model-free notion of variable importance, called leave-one-covariate-out or LOCO inference. Accompanying this article is an R package
conformalInference
that implements all of the proposals we have introduced. In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.
Journal Article