Handbook for Applied Modeling: Non-Gaussian and Correlated Data PDF Download

Author: Jamie D. Riggs
Publisher: Cambridge University Press
ISBN: 1108210961
Category : Mathematics
Languages : en
Pages : 233

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Book Description
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.

Author: Zhiguang Xu
Publisher:
ISBN:
Category :
Languages : en
Pages : 122

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Book Description
We further extend our models to the non-Gaussian longitudinal analysis setting. We model an observed within-subject response series as a transformation from a latent Gaussian series. The latent series specifies the within-subject dependence structure and the transformation function specifies marginal distribution of response variable. Similar to CTAR models, a marginal distribution of the response variable has a nonparametric Bayesian prior distribution and is therefore flexible in shape. We conduct simulations and study a 100km-race real dataset where the response variable is noticeably non-Gaussian. The data analysis demonstrates the advantage of copula-transformed models' performance in model fitting and prediction compared with the Gaussian-based models when the data is truly non-Gaussian and when the mean function is correctly specified. We also study the situations where the mean function shifts in the out-of-sample data. We find that the model's predictive performance for individuals is impacted by the shifts. The copula-transformed models are more sensitive to the shift than the Gaussian-based models. We also study the predictive performance of the contrasts. The models' predictive performance remains fairly robust to the shifts, and the copula-transformed models outperform the Gaussian-based models in contrast predictions. The proposed method can be extended in many directions, including using other transformation functions (e.g., a transformation using Polya tree prior).

Author: Paul Roback
Publisher: CRC Press
ISBN: 1439885400
Category : Mathematics
Languages : en
Pages : 436

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Book Description
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)

Author: Pijush Samui
Publisher: Butterworth-Heinemann
ISBN: 0128165464
Category : Computers
Languages : en
Pages : 590

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Book Description
Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. Explains the application of advanced probabilistic models encompassing multidisciplinary research Applies probabilistic modeling to emerging areas in engineering Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Author: Luping Yang
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659181665
Category :
Languages : en
Pages : 136

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Book Description
Non-Gaussian data is encountered in a variety of fields. To make reliable judgement and reasonable simulation, it is important to establish an appropriate probability density function(PDF) model for non-Gaussian data. In this book, three PDF models are studied to represent the distribution of non-Gaussian data. They are Pade-Laplace Method, Maximum Entropy Method and Hermite Polynomial Method. Also, test of goodness of fit are conducted among the proposed PDF models and common PDF models to compare the flexibility and robustness of these PDF models.

Author: Lang Wu
Publisher: CRC Press
ISBN: 9781420074086
Category : Mathematics
Languages : en
Pages : 431

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Book Description
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.

Author: Michael H. Kutner
Publisher: McGraw-Hill/Irwin
ISBN: 9780072386882
Category : Mathematics
Languages : en
Pages : 1396

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Book Description
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.

Author: Zheng Yi Jiang
Publisher: Trans Tech Publications Ltd
ISBN: 3038135569
Category : Technology & Engineering
Languages : en
Pages : 1930

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Book Description
This collection of 356 peer-reviewed papers is devoted to the topics. of casting, forming and machining, processing and joining technologies, evolution of material properties in manufacturing processes, engineering or degradation of surfaces in manufacturing processes, design and behavior of equipment and tools; all seen from the perspective of the latest advances made and their practical application.

Author: Thomas D. Potter
Publisher: Wiley-Interscience
ISBN:
Category : Nature
Languages : en
Pages : 1028

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Book Description
A comprehensive survey of fundamental principles and the latest research on atmospheric, climatic, and hydrologic sciences The Handbook of Weather, Climate, and Water: Atmospheric Chemistry, Hydrology, and Societal Impacts is the first of two stand-alone volumes that will be landmarks in the meteorological literature for many years to come. Each volume encompasses both fundamental topics and critical issues that have recently surfaced in studies of the hydrosphere and atmosphere. Renowned experts have contributed to every part of this handbook. Each overview chapter is followed by topic-specific chapters written by specialists who present comprehensive discussions at a greater level of detail and complexity. The Handbook of Weather, Climate, and Water: Atmospheric Chemistry, Hydrology, and Societal Impacts covers topics that are essential for grasping the scientific bases of major issues such as global climate warming, the ozone hole, acid rain, floods, droughts, and other natural disasters. Cross-references between chapters allow readers to easily pursue a specific interest beyond a particular subtopic or individual chapter. Other topics include: Aerosols and smog Cloud chemistry Greenhouse gases Remote sensing techniques in hydrology Hydrologic forecasting and simulation Tropical deforestation effects on the climate system Societal impacts of the El Niño phenomenon The Handbook of Weather, Climate, and Water: Atmospheric Chemistry, Hydrology, and Societal Impacts will be an essential addition to the libraries of professionals and academics in the environmental sciences, and a valuable source book for university and technical libraries throughout the world.

Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 595

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Book Description
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.