Adaptive Regression For Modeling Nonlinear Relationships
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Adaptive Regression For Modeling Nonlinear Relationships
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Author : George J. Knafl
language : en
Publisher: Springer
Release Date : 2016-09-20
Adaptive Regression For Modeling Nonlinear Relationships written by George J. Knafl and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-20 with Medical categories.
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs.
Hands On Machine Learning With R
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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07
Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Feature Engineering And Selection
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Author : Max Kuhn
language : en
Publisher: CRC Press
Release Date : 2019-07-25
Feature Engineering And Selection written by Max Kuhn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Business & Economics categories.
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Handbook Of Statistical Analysis And Data Mining Applications
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Author : Ken Yale
language : en
Publisher: Elsevier
Release Date : 2017-11-09
Handbook Of Statistical Analysis And Data Mining Applications written by Ken Yale and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-09 with Mathematics categories.
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, 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. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
Nonlinear Regression Analysis And Its Applications
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Author : Douglas M. Bates
language : en
Publisher: Wiley-Interscience
Release Date : 2007-04-23
Nonlinear Regression Analysis And Its Applications written by Douglas M. Bates and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-23 with Mathematics categories.
Provides a presentation of the theoretical, practical, and computational aspects of nonlinear regression. There is background material on linear regression, including a geometrical development for linear and nonlinear least squares.
Statistical And Machine Learning For Credit Risk Parameter Modeling
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Author : Marvin Zöllner
language : en
Publisher: Cuvillier Verlag
Release Date : 2023-10-19
Statistical And Machine Learning For Credit Risk Parameter Modeling written by Marvin Zöllner and has been published by Cuvillier Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-19 with categories.
Die Dissertation befasst sich mit der Anwendung von statistischem und maschinellem Lernen zur Modellierung der Verlustquote bei Ausfall (LGD). Im Forschungsgebiet der LGD-Modellierung gibt es eine Reihe von Fragen und Problemen, die bisher in der Literatur nicht berücksichtigt wurden. Erstens ist unklar, welche Merkmale einer LGD-Verteilung für die Prognosefähigkeit von Schätzmethoden entscheidend sind und welche Schätzmethode für die LGD-Modellierung am besten geeignet ist. Zweitens besteht ein Zielkonflikt zwischen der Transparenz und der Prognosegenauigkeit bei LGD-Schätzmethoden. Komplexe maschinelle Lernalgorithmen weisen eine bessere Vorhersageleistung auf, allerdings auf Kosten einer geringeren Erklärbarkeit. Umgekehrt bietet die lineare Regression eine hohe Interpretierbarkeit, scheint aber eine geringere Prognosegenauigkeit aufzuweisen. Um diesen Zielkonflikt zu lösen, besteht ein geeigneter Ansatz darin, die Vorhersagegenauigkeit der interpretierbaren linearen Regression durch maschinelles Lernen zu verbessern. Drittens stellt die Selektion optimaler Clustervariablen in der gruppierten Modellierung eine zu lösende Herausforderung dar. Die offenen Forschungsfragen werden in der Dissertation anhand von Kreditausfalldaten der Global Credit Data empirisch beantwortet.
Dimension Estimation And Models
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Author : Howell A M Tong
language : en
Publisher: World Scientific
Release Date : 1993-12-22
Dimension Estimation And Models written by Howell A M Tong and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-12-22 with Mathematics categories.
This volume is the first in the new series Nonlinear Time Series and Chaos. The general aim of the series is to provide a bridge between the two communities by inviting prominent researchers in their respective fields to give a systematic account of their chosen topics, starting at the beginning and ending with the latest state. It is hoped that researchers in both communities will find the topics relevant and thought provoking. In this volume, the first chapter, written by Professor Colleen Cutler, is a comprehensive account of the theory and estimation of fractal dimension, a topic of central importance in dynamical systems, which has recently attracted the attention of the statisticians. As it is natural to study a stochastic dynamical system within the framework of Markov chains, it is therefore relevant to study their limiting behaviour. The second chapter, written by Professor Kung-Sik Chan, reviews some limit theorems of Markov chains and illustrates their relevance to chaos. The next three chapters are concerned with specific models. Briefly, Chapter Three by Professor Peter Lewis and Dr Bonnie Ray and Chapter Four by Professor Peter Brockwell generalise the class of self-exciting threshold autoregressive models in different directions. In Chapter Three, the new and powerful methodology of multivariate adaptive regression splines (MARS) is adapted to time series data. Its versatility is illustrated by reference to the very interesting and complex sea surface temperature data. Chapter Four exploits the greater tractability of continuous-time Markov approach to discrete-time data. The approach is particularly relevant to irregularly sampled data. The concluding chapter, by Professor Pham Dinh Tuan, is likely to be the most definitive account of bilinear models in discrete time to date.
Into A Deeper Understanding Of Evolutionary Computing Exploration Exploitation And Parameter Control
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Author : Abdul Hanif Abdul Halim
language : en
Publisher: Springer Nature
Release Date : 2025-01-17
Into A Deeper Understanding Of Evolutionary Computing Exploration Exploitation And Parameter Control written by Abdul Hanif Abdul Halim and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-17 with Computers categories.
This book delves into fundamental and advanced strategies for enhancing evolutionary and metaheuristic algorithms, focusing on the crucial balance between exploration and exploitation in search mechanisms. As technological advancements increase optimization complexity, effectively managing this balance becomes essential for achieving optimal solutions within reasonable computational resources. The book's distinctive structure organizes content according to optimization stages and processes, offering a comprehensive discussion of various approaches supported by extensive literature. The authors note a scarcity of literature addressing the trade-offs between exploration and exploitation with contemporary references, making this work particularly valuable. It aims to deepen the reader's understanding of evolutionary computing, emphasizing exploration, exploitation, and parameter control. It is relevant not only to algorithm developers and the evolutionary computation community but also to students and researchers across scientific disciplines. The book is designed to be accessible to those without extensive algorithm development backgrounds, providing theoretical and practical insights into optimization methods.
Feature Engineering And Selection
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Author : Max Kuhn
language : en
Publisher: CRC Press
Release Date : 2019-07-25
Feature Engineering And Selection written by Max Kuhn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Business & Economics categories.
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Data Analysis In Pavement Engineering
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Author : Qiao Dong
language : en
Publisher: Elsevier
Release Date : 2023-11-06
Data Analysis In Pavement Engineering written by Qiao Dong and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-06 with Technology & Engineering categories.
Data Analysis in Pavement Engineering: Theory and Methodology offers a complete introduction to the basis of the finite element method, covering fundamental theory and worked examples in the detail required for readers to apply the knowledge to their own engineering problems and understand more advanced applications. This edition sees the significant addition of content addressing coupling problems, including Finite element analysis formulations for coupled problems; Details of algorithms for solving coupled problems; and Examples showing how algorithms can be used to solve for piezoelectricity and poroelasticity problems. Focusing on the core knowledge, mathematical and analytical tools needed for successful application, this book represents the authoritative resource of choice for graduate-level students, researchers and professional engineers involved in finite element-based engineering analysis. - This book is the first comprehensive resource to cover all potential scenarios of data analysis in pavement and transportation infrastructure research, including areas such as materials testing, performance modeling, distress detection, and pavement evaluation. - It provides coverage of significance tests, design of experiments, data mining, data modeling, and supervised and unsupervised machine learning techniques. - It summarizes the latest research in data analysis within pavement engineering, encompassing over 300 research papers. - It delves into the fundamental concepts, elements, and parameters of data analysis, empowering pavement engineers to undertake tasks typically reserved for statisticians and data scientists. - The book presents 21 step-by-step case studies, showcasing the application of the data analysis method to address various problems in pavement engineering and draw meaningful conclusions.