One Line Or Two? Perspectives on Piecewise Regression PDF Download
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Author: R. P. Ewing
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Sometimes we are faced with data that could reasonably be represented either as a single line, or as two or more line segments. How do we identify the best breakpoint(s), and decide how many segments are ''really'' present? Most of us are taught to distrust piecewise regression, because it can be easily abused. The best method for identifying the breakpoint varies according to specifics of the data; for example, the minimum sum of squares method excels for ''well-behaved'' data. In some cases, hidden Markov methods are more likely to succeed than are more ''obvious'' methods. Likewise, the most appropriate method for deciding between one or two lines depends on your expectations and understanding of the data: an unexpected break requires more justification than an expected one, and some decision criteria (e.g., the Akaike Information Criterion) are less strict than others (e.g., the Bayesian Information Criterion). This presentation will review some options and make specific, practical recommendations.
Author: R. P. Ewing
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Sometimes we are faced with data that could reasonably be represented either as a single line, or as two or more line segments. How do we identify the best breakpoint(s), and decide how many segments are ''really'' present? Most of us are taught to distrust piecewise regression, because it can be easily abused. The best method for identifying the breakpoint varies according to specifics of the data; for example, the minimum sum of squares method excels for ''well-behaved'' data. In some cases, hidden Markov methods are more likely to succeed than are more ''obvious'' methods. Likewise, the most appropriate method for deciding between one or two lines depends on your expectations and understanding of the data: an unexpected break requires more justification than an expected one, and some decision criteria (e.g., the Akaike Information Criterion) are less strict than others (e.g., the Bayesian Information Criterion). This presentation will review some options and make specific, practical recommendations.
Author: Sandra E. Ryan
Publisher:
ISBN:
Category : River sediments
Languages : en
Pages : 48
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Book Description
"This tutorial demonstrates the application of piecewise regression to bedload data to define a shift in phase of transport so that the reader may perform similar analyses on available data. The use of piecewise regression analysis implicitly recognizes different functions fit to bedload data over varying ranges of flow. The transition from primarily low rates of sand transport (Phase I) to higher rates of sand and coarse gravel transport (Phase II) is termed "breakpoint" and is defined as the flow where the fitted functions intersect. The form of the model used here fits linear segments to different ranges of data, though other types of functions may be used. Identifying the transition in phases is one approach used for defining flow regimes that are essential for self-maintenance of alluvial gravel bed channels. First, the statistical theory behind piecewise regression analysis and its procedural approaches are presented. The reader is then guided through an example procedure and the code for generating an analysis in SAS is outlined. The results from piecewise regression analysis from a number of additional bedload datasets are presented to help the reader understand the range of estimated values and confidence limits on the breakpoint that the analysis provides. The identification and resolution of problems encountered in bedload datasets are also discussed. Finally, recommendations on a minimal number of samples required for the analysis are proposed."
Author: Richard A. Berk
Publisher: Springer Nature
ISBN: 3030401898
Category : Mathematics
Languages : en
Pages : 451
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Book Description
This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of “big data” on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.
Author: MICHAEL N. MITCHELL
Publisher: Stata Press
ISBN: 9781597183215
Category :
Languages : en
Pages : 610
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Book Description
Interpreting and Visualizing Regression Models Using Stata, Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures, the book illustrates linear models with continuous predictors (modeled linearly, using polynomials, and piecewise), interactions of continuous predictors, categorical predictors, interactions of categorical predictors, and interactions of continuous and categorical predictors. The book also illustrates how to interpret and visualize results from multilevel models, models where time is a continuous predictor, models with time as a categorical predictor, nonlinear models (such as logistic or ordinal logistic regression), and models involving complex survey data. The examples illustrate the use of the margins, marginsplot, contrast, and pwcompare commands. This new edition reflects new and enhanced features added to Stata, most importantly the ability to label statistical output using value labels associated with factor variables. As a result, output regarding marital status is labeled using intuitive labels like Married and Unmarried instead of using numeric values such as 1 and 2. All the statistical output in this new edition capitalizes on this new feature, emphasizing the interpretation of results based on variables labeled using intuitive value labels. Additionally, this second edition illustrates other new features, such as using transparency in graphics to more clearly visualize overlapping confidence intervals and using small sample-size estimation with mixed models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you.
Author: James Alleman
Publisher: Springer Science & Business Media
ISBN: 1461479932
Category : Business & Economics
Languages : en
Pages : 341
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Book Description
This volume grew out of a conference organized by James Alleman and Paul Rappoport, conducted on October 10, 2011 in Jackson Hole, Wyoming, in honor of the work of Lester D. Taylor, whose pioneering work in demand and market analysis has had profound implications on research across a wide spectrum of industries. In his Prologue, Eli M. Noam notes that demand analysis in the information sector must recognize the “public good” characteristics of media products and networks, while taking into account the effects of interdependent user behavior; the strong cross-elasticities in a market; as well as the phenomenon of supply creating its own demand. The second Prologue, by Timothy Tardiff and Daniel Levy, focuses more specifically on Taylor’s body of work, in particular its practical applications and usefulness in analyses of, and practices within, the Information and Communications Technology (ICT) sector (known in Europe and elsewhere as the Telecommunications, Media, and Technology (TMT) sector). The remainder of the book is organized into four parts: Advances in Theory; Empirical Applications; Evidence-Based Policy Applications; and a final Conclusion. The book closes with an Appendix by Sharon Levin and Stanford Levin detailing Taylor’s contributions using bibliometrics. Not only featuring chapters from distinguished scholars in economics, applied sciences, and technology, this volume includes two contributions directly from Lester Taylor, providing unique insight into economics from a lifetime in the field. “What a worthy book! Every applied researcher in communications encounters Lester Taylor’s work. Many empirical exercises in communications can trace their roots to Taylor’s pioneering research and his thoughtful leadership. This book assembles an impressive set of contributors and contributions to honor Taylor. No surprise, the collection extends far and wide into many of the core topics of communications and media markets. The emphasis is where it should be–on important and novel research questions informed by useful data. —Shane Greenstein, Professor of Management and Strategy, Kellogg School of Management, Northwestern University “For more than 40 years, Lester Taylor has been a leader in the application of consumer modeling, econometric techniques and microeconomic data to understand residential and business user behavior in telecommunications markets. During that time, he inspired a cadre of students and colleagues who applied this potent combination to address critical corporate and regulatory issues arising in the telecommunications sector. This volume collects the recent product of many of these same researchers and several other devotees who go beyond empirical analysis of fixed line service by extending Prof. Taylor’s approach to the next wave of services and technologies. These contributions, including two new papers by Prof. Taylor, offer an opportunity for the next generation to learn from his work as it grapples with the pressing issues of consumer demand in the rapidly evolving digital economy.” — Glenn Woroch, Adjunct Professor of Economics, University of California, Berkeley
Author: Qi Pan
Publisher: Frontiers Media SA
ISBN: 2832553753
Category : Medical
Languages : en
Pages : 392
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Book Description
Diabetes mellitus is a chronic metabolic disease characterized by elevated blood glucose levels, which severely affects the quality of life and survival of patients. The latest data from the International Diabetes Federation showed that there are approximately 537 million diabetic patients globally in 2021, and it is expected to reach 783 million by 2045. The treatment regime of diabetic patients mainly refers to the management of blood glucose and its complications. At present, typical hypoglycemic medications include insulin and non-insulin hypoglycemic drugs, which can be subdivided into a-glucosidase inhibitors, metformin, thiazolidinediones, sulfonylureas, glinides, DPP-4i, GLP-1RA, and SGLT-2i. Diabetic patients with poor islet function usually require insulin replacement, but the majority of patients fail to meet ideal therapeutic targets with multiple hypoglycemic drug combinations (including insulin). Furthermore, poor glycemic control is closely associated with an increased risk of diabetes complications, consequently an increased rate of mortality. More recently, promising new drugs and therapies emerged, which have many other important benefits beyond glucose-lowering. In addition, some traditional Chinese medicine have gradually revealed their therapeutic advantages. All of them provide more choices for the treatment of diabetes patients.
Author:
Publisher:
ISBN:
Category : Education, Higher
Languages : en
Pages : 590
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Book Description
Author:
Publisher:
ISBN:
Category : Electronic journals
Languages : en
Pages : 252
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Book Description
Author: Laurie S. Porth
Publisher: CreateSpace
ISBN: 9781511613958
Category :
Languages : en
Pages : 46
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Book Description
This tutorial demonstrates the application of piecewise regression to bedload data to define a shift in phase of transport so that the reader may perform similar analyses on available data. The use of piecewise regression analysis implicitly recognizes different functions fit to bedload data over varying ranges of flow. The transition from primarily low rates of sand transport (Phase I) to higher rates of sand and coarse gravel transport (Phase II) is termed "breakpoint" and is defined as the flow where the fitted functions intersect. The form of the model used here fits linear segments to different ranges of data, though other types of functions may be used. Identifying the transition in phases is one approach used for defining flow regimes that are essential for self-maintenance of alluvial gravel bed channels. First, the statistical theory behind piecewise regression analysis and its procedural approaches are presented. The reader is then guided through an example procedure and the code for generating an analysis in SAS is outlined. The results from piecewise regression analysis from a number of additional bedload datasets are presented to help the reader understand the range of estimated values and confidence limits on the breakpoint that the analysis provides. The identification and resolution of problems encountered in bedload datasets are also discussed. Finally, recommendations on a minimal number of samples required for the analysis are proposed.
Author: Klaus Pleschka
Publisher: John Libbey Eurotext
ISBN: 9782742000425
Category : Medical
Languages : en
Pages : 580
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Book Description
Proceedings of the International Symposium on Integrative and Cellular Aspects of Autonomic Functions Held in Bad Nauheim (Germany) on 29-30 July 1993.
