Search

If I were a committed I Stata i / I Eviews i / I Matlab etc. i person, for example, then this text probably wouldn't be for me, as there is a strong, and pervasive reliance on the freeware package I R i , and for a lot of the Bayesian components, I Stan i , which can be run in the I R i environment. I think that the text is a very brave attempt at bringing together (most of) the major topics in the analysis of I CLRVs i (we will return to this), from I both i Frequentist and Bayesian approaches, along with a very hands-on and empirical approach, and all bound together with the ubiquitous use of the I R i software. This text considers a I modern applied i regression analysis of categorical and limited dependent (response) variables - I'll use the author's acronym I CLRVs i - from both a Frequentist and Bayesian approach, with an explicit reliance on the open-source software package, I R i . [Extracted from the article]

The authors have put great effort into rewriting this final chapter for the third edition of I Statistical Analysis with Missing Data i and have made the examples and discussion very relevant for contemporary data analysts. The authors detail estimates for uncertainty for the point estimates introduced in Chapter 4. At the end of Chapter 11, the authors revisit the relation between measurement error and missing data that was briefly introduced in Chapter 1. [Extracted from the article]

"Applied Linear Regression for Longitudinal Data: With an Emphasis on Missing Observations" by Frans E. S. Tan and Shahab Jolani is a book that focuses on the analysis of quantitative longitudinal outcomes using linear regression models. It is designed for researchers and students in the fields of epidemiology, health, social, and behavioral sciences. The book emphasizes interpretation rather than mathematics and provides practical examples to help readers understand the concepts. It also addresses the treatment of missing data, which is common in longitudinal studies. The book is well-written and suitable for those interested in longitudinal data analysis, although it could benefit from providing more details on the mathematical aspects of statistical methods. [Extracted from the article]

I found this to be a well-written, practical, concise, and very clear handbook on the appropriate analysis of data from randomized experiments using the freely available R software. This chapter also provides good discussion of Latin Square designs, including code for creating and analyzing them in R. Chapter 6 dives into mixed-effects models. The author discusses the advantages of balanced incomplete block designs (BIBDs) and talks about building BIBDs in R, including code for how to assess whether you actually have a BIBD (using functions that I was previously not aware of!). [Extracted from the article]

Models by one and many input variables are considered, the coefficients are interpreted via the log odds linear link function, and goodness-of-fit and model parsimony are described. 5 Lipovetsky S. (2021), "Game Theory in Regression Modeling: A Brief Review on Shapley Value Regression", Model Assisted Statistics and Applications, 16, 165 - 168. 6 Lipovetsky S. (2021), "Modified Ridge and Other Regularization Criteria: A Brief Review on Meaningful Regression Models", Model Assisted Statistics and Applications, 16, 225 - 227. [Extracted from the article]

As Winter emphasises throughout the book, statistical modelling instead encourages us to think more deeply about what the data actually look like, which mathematical functions would be useful to fit the data and what meanings we can extract from the data. For example, in my own statistics training during my graduate study, I was told that we should include as many factors as possible in ANOVA to soak up variance, but Winter has informative discussion on why this is bad practice, and argues that model construction should be a subjective, theory-driven process (see especially §16.5). Winter repeatedly argues that it is important that the data and the code are published together with a paper, in order to promote open and reproducible research practice in linguistics. Finally, another aspect which makes this book special as a textbook is that Winter challenges some common practices in the field. [Extracted from the article]

The monograph belongs to the I The R i series, and it can serve as a convenient way for learning data science and statistics simultaneously with the I R i language. Type I and II errors are discussed, together with choice of I alpha i and I beta i probabilities, I z i - and I t i -statistics for one and two samples, and interpretation of the results on I p i -values and their visualizing in histograms. Multiple datasets examples are used, including the same data on the airports for comparing airtime of Hawaiian and Alaska airlines flights departing. [Extracted from the article]

The book is organized in two parts and eighteen chapters: the first part considers "Preparation for Analysis" (Chapters 1-6), and the second part is called "Regression Analysis" (Chapters 7-18) although it covers many other related methods as well. Added in this edition new Chapter 4 discusses various methods of data visualization with specific tools for different types of bivariate and multivariate variables, and gives multiple illustrations in graphs and figures. Chapter 7 presents simple linear regression, its residual errors, confidence intervals, parameter hypotheses testing, correlation, fixed predictor variable, bivariate regression as the conditional distribution. [Extracted from the article]

Chapters 6 and 7 discuss the horseshoe prior and other global-local shrinkage priors. The book has four parts: spike-and-slab priors (Chapters 1-5), shrinkage priors (Chapters 6-8), extensions to various models (Chapters 9-14), and other approaches to Bayesian variable selection (Chapters 15-19). Bayesian approaches to variable selection first specify suitable prior distributions such as spike-and-slab priors and shrinkage priors. [Extracted from the article]

Chapter 17 describes the partial-dependence (PD) plots, aka PD profiles, introduced in the context of gradient boosting machine (GBM) and implemented in many data-science oriented packages. Part III of Dataset-level exploration focuses in its seven chapters on techniques for exploration of a model by the entire dataset. The book is organized in 4 parts and 22 chapters, each containing sections of Intuition, Method, Example, Pros and cons, Code snippets in R, and Code snippets in Python, divided further to multiple subsections. [Extracted from the article]

Computational algorithm and software development have attempted to resolve the computational burden for Bayesian inference (Lunn I et al i ., [5]), including the development of the integrated nested Laplace approximation (INLA) method. The theory of INLA is presented in chapter 2, with an emphasis on latent Gaussian models, Gaussian Markov random fields, and the approximation method itself of INLA. 6 Martino, S. and Rue, H. (2009) Implementing Approximate Bayesian Inference Using Integrated Nested Laplace Approximation: A Manual for the INLA Program. [Extracted from the article]

The article presents a review of the book "Subset Selection in Regression," by Alan J. Miller.

The article reviews the book 'The Application of Regression Analysis,' by Dick R. Wittink.

Focusing on resampling techniques for the creation of confidence intervals and the conducting of hypothesis tests is a deviation from typical introductory books. The R package infer, by the authors, provides a Tidyverse workflow for creating confidence intervals and conducting hypothesis tests using resampling techniques. Chapter 8 discusses the construction of confidence intervals via bootstrap resampling. [Extracted from the article]

In summary, this book provides a good introduction to data analysis with RStudio, while imparting some statistical knowledge. This book is suitable for beginners, especially those with basic knowledge of statistical concepts, and those who want to perform data analysis using R software. The book provides guidance to select appropriate statistical methods, analyze data, and interpret the findings using RStudio. [Extracted from the article]

Chapter 9 then introduces longitudinal data as a special kind of multilevel data, while Chapter 10 addresses more complex multilevel models with more than two levels. Opening each chapter with a list of learning objectives is helpful, but in some ways, it felt as if front-loading the chapters with case studies could make it harder for learners - who are still unfamiliar with the mathematical groundwork - to follow how the chapter's statistical concepts were applied. Namely, Chapters 4-6 introduce generalized linear models, including Poisson regression and logistic regression, while Chapters 8-11 introduce multilevel statistical models with correlated data. [Extracted from the article]

This well-written book is a good source for the Bayesian concepts and methods to practice the spatial-temporal analysis using R and WinBugs codes. Noteworthy features in this book are I aims of the book, structures of the book, heterogeneity, data sparsity, data versus parameter uncertainty, list of data sets used in the book, why it matters to map data i among others. Knowledge on statistical theory and regression concepts are essential to read, comprehend, appreciate, and use the rich contents of this fascinating book. [Extracted from the article]

The article reviews the book "Spatial Regression Models," by M. D. Ward and K. S. Gleditsch.

The article reviews the book "Regression Modeling: Methods, Theory, and Computation with SAS," by Michael Panik.

The article reviews the book "Applied Linear Regression," Third Edition by Sanford Weisberg.

The article reviews the book "An R and S-PLUS Companion to Applied Regression," by John Fox.

Reviews the book "Robust Diagnostic Regression Analysis," by Anthony Atkinson and Marco Riani.

Reviews the book "A Primer on Regression Artifacts," by Donald T. Campbell and David A. Kenny.

Time series: a data analysis approach using R: by Robert H. Shumway and David S. Stoffer. Chapter 3 presents the ordinary linear regression approach for analyzing time series and some exploratory data analysis techniques such as detrending and differencing for coercing non-stationary data to stationarity. In summary, this book offers many data analysis examples with detailed R codes and output to illustrate time series analysis methods in different applications, which I believe is valuable to both researchers and practitioners. [Extracted from the article]

Agreement between two methods of clinical measurement can be quantified using the differences between observations made using the two methods on the same subjects. The 95% limits of agreement, estimated by mean difference +/- 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie. We describe how graphical methods can be used to investigate the assumptions of the method and we also give confidence intervals. We extend the basic approach to data where there is a relationship between difference and magnitude, both with a simple logarithmic transformation approach and a new, more general, regression approach. We discuss the importance of the repeatability of each method separately and compare an estimate of this to the limits of agreement. We extend the limits of agreement approach to data with repeated measurements, proposing new estimates for equal numbers of replicates by each method on each subject, for unequal numbers of replicates, and for replicated data collected in pairs, where the underlying value of the quantity being measured is changing. Finally, we describe a nonparametric approach to comparing methods. [ABSTRACT FROM AUTHOR]

In the Lung Health Study (LHS), compliance with the use of inhaled medication was assessed at each follow-up visit both by self-report and by weighing the used medication canisters. One or both of these assessments were missing if the participant failed to attend the visit or to return all canisters. Approximately 30% of canister-weight data and 5% to 15% of self-report data were missing at different visits. We use Gibbs sampling with data augmentation and a multivariate Hastings update step to implement a Bayesian hierarchical model for LHS inhaler compliance. Incorporating individual-level random effects to account for correlations among repeated measures on the same participant, our model is a longitudinal extension of the Tobit models used in econometrics to deal with partially unobservable data. It enables (a) assessment of the relationships among visit attendance, canister return, self-reported compliance level, and canister weight compliance, and (b) determination of demographic, physiological, and behavioral predictors of compliance. In addition to addressing the estimation and prediction questions of substantive interest, we use sampling-based methods for covariate screening and model selection and investigate a range of informative priors on missing data. [ABSTRACT FROM AUTHOR]

The article reviews the book "Nonlinear Regression," by G.A.F. Seber and C.J. Wild.

The article reviews the book "Nonlinear Regression Analysis & Its Application," by Douglas M. Bates and Donald G. Watts.

The article reviews the book "Multiple Regression in Practice," by William D. Berry and Stanly Feldman.

The article reviews the book "Regression Diagnostics: Identifying Influential Data and Sources of Collinearity," by David A. Belsley, Edwin Kuh and Roy E. Welsch.

The article reviews the book "Applied Linear Regression," by Sanford Weisberg.

This article presents a review of the book "Multiple Regression and Analysis of Variance," by George O. Wesolowsky.

The article presents a review of the book "Searching for Structure (Alias - Aid - III)," by John A. Sonquist, Elizabeth Lauh Baker, and James N. Morgan.

The article reviews the book "A Modern Approach to Regression With R," by S. J. Sheather.

The article reviews the book "Modern Regression Techniques Using R: A Practical Guide for Students and Researchers," by D. B. Wright and K. London.

The article reviews the book "Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables," by Royston P. Sauerbrei.

Elements of Copula Modeling With R: by Marius Hofert, Ivan Kojadinovic, Martin Mächler, and Jun Yan. Elements of Copula Modeling With R by Marius Hofert Ivan Kojadinovic Martin Mächler, and Jun Yan. The book closes with Chapter 6 that touches on such practical problems as ties in the data for copula modeling, non-stationarity and serial dependence, and regression modeling. [Extracted from the article]

Reviews the book "Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences," 3rd ed., by Jacob Cohen, Patricia Cohen, Stephen G. West and Leona S. Aiken.