Nonlinear Contingency Analysis

Nonlinear Contingency Analysis is a guide to treating clinically complex behavior problems such as delusions and hallucinations. It’s also a framework for treating behavior problems, one that explores solutions based on the creation of new or alternative consequential contingencies rather than the elimination or deceleration of old or problematic thoughts, feelings, or behaviors. Chapters present strategies, analytical tools, and interventions that clinicians can use in session to think about clients’ problems using decision theory, experimental analysis of behavior, and clinical research and practice. By treating thoughts and emotions not as causes of behavior but as indicators of the environmental conditions that are responsible for them, patients can use that knowledge to make changes that not only result in changes in behavior, but in the thoughts and feelings themselves.

Nonlinear Contingency Analysis is a guide to treating clinically complex behavior problems such as delusions and hallucinations. It's also a framework for treating behavior problems, one that explores solutions based on the creation of new or alternative consequential contingencies rather than the elimination or deceleration of old or problematic thoughts, feelings, or behavior. Chapters present strategies, analytical tools, and interventions that clinicians can use in session to think about clients' problems using decision theory, experimental analysis of behavior, and clinical research and practice. By treating thoughts and emotions not as causes of behavior but as indicators of the environmental conditions that are responsible for them, patients can use that knowledge to make changes that not only result in changes in behavior, but in the thoughts and feelings themselves.

Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sam

Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.

Clinical behavior analysis uses verbally based interventions to treat a range of psychological problems in an outpatient context. This volume offers a collection of current research in this rapidly expanding field, with a special focus on acceptance issues in therapy and the importance of the therapeutic relationship.

Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization. First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding. If the model does not fit the data, our understanding of the mechanism was wrong or incomplete. Patterns in the residuals can guide model improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications. This book details methods of nonlinear regression, computational algorithms,model validation, interpretation of residuals, and useful experimental design. The focus is on practical applications, with relevant methods supported by fundamental analysis. This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model. The author has used the material in the undergraduate unit operations lab course and in advanced control applications.

A Programing Contingency Analysis of Mental Health presents Dr. Israel Goldiamond’s reflections on various ways we formulate behavioral and emotional problems, most often in traditional terms of mental health disorders, mental diseases or illnesses, psychopathological disorders, and so on – what he calls a pathological orientation. Here, Goldiamond argues for a groundbreaking alternative view from the vantage point of radical behaviorism. The book begins by discussing contingency relations between behavior and its past and present consequences, along with other environmental events. It reminds us that this approach sits comfortably alongside other consequential systems in the social and biological sciences, particularly decision theory and evolution. This behaviorist system regards most important human behaviors as being emitted rather than stimulus-elicited. Described are some of the diverse origins of behavior, including the effects of environmental consequences and the programing procedures of social and cultural inheritance. The exposition includes decision matrices which rationalize some of the programed patterns and the accompanying thoughts and emotions commonly found in mental illness. As a result of this nonlinear contingency analysis, such patterns may be considered adaptive rather than maladaptive. The book describes programs based on those matrices and outlines how they might be applied to mitigate any problems or costs associated with those patterns. The book concludes by moving from individual analysis to social analysis, with particular reference to some societal contingencies that may maintain the pathological orientation and others that might shift our gaze in the direction proposed here. Alongside Dr. Goldiamond’s original work, this volume features a new introduction from Dr. Paul Thomas Andronis and Dr. T. V. Joe Layng, as well as an article tracing the history of the non-linear thinking of Dr. Goldiamond, first published in The Behavior Analyst. It will be a must-read for anyone working in the analysis of and clinical intervention in problems associated with mental health, or those more generally interested in the work of Israel Goldiamond.

Over the last 15 years, singular spectrum analysis (SSA) has proven very successful. It has already become a standard tool in climatic and meteorological time series analysis and well known in nonlinear physics and signal processing. However, despite the promise it holds for time series applications in other disciplines, SSA is not widely known among statisticians and econometrists, and although the basic SSA algorithm looks simple, understanding what it does and where its pitfalls lay is by no means simple. Analysis of Time Series Structure: SSA and Related Techniques provides a careful, lucid description of its general theory and methodology. Part I introduces the basic concepts, and sets forth the main findings and results, then presents a detailed treatment of the methodology. After introducing the basic SSA algorithm, the authors explore forecasting and apply SSA ideas to change-point detection algorithms. Part II is devoted to the theory of SSA. Here the authors formulate and prove the statements of Part I. They address the singular value decomposition (SVD) of real matrices, time series of finite rank, and SVD of trajectory matrices. Based on the authors' original work and filled with applications illustrated with real data sets, this book offers an outstanding opportunity to obtain a working knowledge of why, when, and how SSA works. It builds a strong foundation for successfully using the technique in applications ranging from mathematics and nonlinear physics to economics, biology, oceanology, social science, engineering, financial econometrics, and market research.