While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.

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.

While ego psychological theory still holds a pre-eminent position in clinical social work practice, the field has changed in many ways. This revised edition addresses these major changes, bringing the reader up to date.

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

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.

I do not think of myself as primarily interested in method, but in the substance of psychology. Nevertheless, our discipline has such difficulties in coming to grips with its substance that I have found myself getting involved in fww to do it persistently and since the beginning of my career. That career has been divided between diagnosis and research, the balance between them swinging gradually from the former to the latter. To the astonishment of many of my students and colleagues, I have never become a psychotherapist nor a psychoanalyst, though I have looked closely over the shoulders of many friends at their work, have attended continuous case seminars, and have participated in research on psychotherapy and psychoanalysis enough to feel that I have a pretty good grasp of what that kind of endeavor is like. So I have been writing about method, diagnostic and investigative, for over 25 years, and was happy to accept the suggestion of Seymour Weingar ten, of Plenum Press, that I publish a collection of these papers. What has ended up as two volumes was originally conceived as one, for I feel that there is more similarity of method in assessment, prediction, and research than appears on the surface. The General Introduction and Chapter 1 of Volume 1 state the point of view of the entire work.

It has been said that "hypnosis is a collection of techniques in need of a unifying theory." (James A. Hall, Hypnosis: A Jungian Perspective). While the varied substrates of these techniques preclude the formation of any one theory of hypnosis, this volume presents a "state-of-the-science" view of existing theories of hypnosis. Written by eminent scholars and researchers, this uniquely authoritative resource also provides a wealth of information about the history of hypnosis, clinical and research perspectives on hypnosis, and the strengths and weaknesses of empirical methods used to address crucial theoretical questions. The streamlined organization of the volume facilitates the reader's ability to contrast and compare research findings and concepts across theories. In the introductory chapters, the editors describe hypnosis paradigms and schools of thought, including major points of convergence and divergence, as well as a broad vista of different perspectives on the history of hypnosis. The theoretical chapters that follow present definitive statements by an international array of eminent scholars who are at the forefront of conceptual advances in the realms of clinical and experimental hypnosis. Their contributions, written in lively first-person narratives, explore current thinking about hypnosis and represent important clinical and research traditions that extend beyond the territory of hypnosis to mainstream psychology. Providing a thorough discussion of hypnotic phenomena, the book tackles tough questions such as whether hypnosis evokes an altered state of consciousness; whether hypnotic behavior is involuntary; whether hypnotizability is stable, trait-like, and modifiable; and whether hypnotic and non-hypnotic behavior can be distinguished in meaningful ways. The diversity of viewpoints, including competitive ones, illuminates the debates which have expanded the frontiers of knowledge about hypnosis. In the concluding section, the editors compare and contrast these theories, discuss pertinent research issues, and lay out an agenda for future research. Given its stellar list of contributors and the unique niche it occupies as the first authoritative survey of its kind, THEORIES OF HYPNOSIS is of value to anyone interested in the topic. The editors' ten years of experience teaching hypnosis to psychology and medical students has resulted in a book with enormous appeal to students and instructors, as well as clinicians and researchers. A wide variety of professionals--academics, clinical psychologists, psychiatrists, social workers, dentists--will find it an authoritative introduction and invaluable reference to this still-growing, ever-fascinating field.

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.

First published in 1990. The field of personality assessment continues to grow and expand at a rapid rate. The present volume is a continuation of the author's effort to bring together significant original papers representing diverse theoretical perspectives, critical methodological issues, and a variety of assessment techniques. Diversity of assessment approaches are also considered in the present volume. These vary from traditional assessment approaches, such as the Rorschach and the MMPI, to newer instruments such as Temperament Inventory. This will be of interest to mental health professionals, as it provides new insights and information about important directions in which the field of personality assessment is going.