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.

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.

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.

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.

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries, including business and healthcare. It is necessary to develop specific software programs that can analyze and interpret large amounts of data quickly in order to ensure adequate usage and predictive results. Cognitive Analytics: Concepts, Methodologies, Tools, and Applications provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques. It also examines the incorporation of pattern management as well as decision-making and prediction processes through the use of data management and analysis. Highlighting a range of topics such as natural language processing, big data, and pattern recognition, this multi-volume book is ideally designed for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, software engineers, IT specialists, and academicians.

Chemometrics Explore chemometrics from basic statistics to the latest artificial intelligence and neural network developments in this new edition Chemometrics is an area of study combining chemistry and mathematics. It governs the interpretation of data generated by chemical analysis, and its growth as a subfield promises to streamline and revolutionize analytical chemistry. Chemometrics has long been the leading introductory textbook in this subject. Beginning with an introduction to the statistical-mathematical evaluation of chemical measurements, it leads readers through modern chemometric approaches in a pedagogically sound and highly readable style. Now fully updated to reflect the latest research and applications of this exciting discipline, it provides essential tools for a new generation of analytical chemists. Readers of the fourth edition of Chemometrics will also find: New or expanded treatment of subjects such as deep learning, ANNOVA simultaneous component analysis, instrumental data output, and more Detailed discussion of approaches to signal processing, design and optimization of experiments, pattern recognition and classification, and many other areas Balance of theoretical and practical knowledge to enable rapid application of key techniques Chemometrics is ideal for advanced students in chemistry, analytical chemistry, pharmaceutical chemistry, biochemistry, or related subjects, and as a useful reference for practicing researchers and laboratory professionals.

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.