Handbook for Applied Modeling: Non-Gaussian and Correlated Data

Handbook for Applied Modeling: Non-Gaussian and Correlated Data
Author: Jamie D. Riggs
Publisher: Cambridge University Press
Total Pages: 233
Release: 2017-07-14
Genre: Mathematics
ISBN: 1108210961

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for their own data analysis needs. Data, R, and SAS scripts can be found online at http://www.spesi.org.

Applied Linear Statistical Models

Applied Linear Statistical Models
Author: Michael H. Kutner
Publisher: McGraw-Hill/Irwin
Total Pages: 1396
Release: 2005
Genre: Mathematics
ISBN: 9780072386882

Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.

An Introduction to Applied Multivariate Analysis with R

An Introduction to Applied Multivariate Analysis with R
Author: Brian Everitt
Publisher: Springer Science & Business Media
Total Pages: 284
Release: 2011-04-23
Genre: Mathematics
ISBN: 1441996508

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

Mixed Effects Models for Complex Data

Mixed Effects Models for Complex Data
Author: Lang Wu
Publisher: CRC Press
Total Pages: 431
Release: 2009-11-11
Genre: Mathematics
ISBN: 9781420074086

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Handbook of Applied Hydrology, Second Edition

Handbook of Applied Hydrology, Second Edition
Author: Vijay P. Singh
Publisher: McGraw Hill Professional
Total Pages: 1438
Release: 2016-03-07
Genre: Technology & Engineering
ISBN: 0071835105

Fully Updated Hydrology Principles, Methods, and Applications Thoroughly revised for the first time in 50 years, this industry-standard resource features chapter contributions from a “who’s who” of international hydrology experts. Compiled by a colleague of the late Dr. Chow, Chow’s Handbook of Applied Hydrology, Second Edition, covers scientific and engineering fundamentals and presents all-new methods, processes, and technologies. Complete details are provided for the full range of ecosystems and models. Advanced chapters look to the future of hydrology, including climate change impacts, extraterrestrial water, social hydrology, and water security. Chow’s Handbook of Applied Hydrology, Second Edition, covers: · The Fundamentals of Hydrology · Data Collection and Processing · Hydrology Methods · Hydrologic Processes and Modeling · Sediment and Pollutant Transport · Hydrometeorologic and Hydrologic Extremes · Systems Hydrology · Hydrology of Large River and Lake Basins · Applications and Design · The Future of Hydrology

Applied Predictive Modeling

Applied Predictive Modeling
Author: Max Kuhn
Publisher: Springer Science & Business Media
Total Pages: 595
Release: 2013-05-17
Genre: Medical
ISBN: 1461468493

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Handbook of Missing Data Methodology

Handbook of Missing Data Methodology
Author: Geert Molenberghs
Publisher: CRC Press
Total Pages: 600
Release: 2014-11-06
Genre: Mathematics
ISBN: 1439854610

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

Materials Processing Technologies

Materials Processing Technologies
Author: Zheng Yi Jiang
Publisher: Trans Tech Publications Ltd
Total Pages: 1904
Release: 2010-10-27
Genre: Technology & Engineering
ISBN: 3038135569

Selected, peer reviewed papers from the 2010 International Conference on Advances in Materials and Manufacturing Processes (ICAMMP 2010), 6-8 November, 2010, Shenzhen, China