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Author : Tobias Scheffer
Publisher :
ISBN 13 : 9783896012258
Total Pages : 126 pages
Book Rating : 4.0/5 (122 download)
Download or read book Error Estimation and Model Selection written by Tobias Scheffer and published by . This book was released on 1999 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Luca Oneto
Publisher : Springer
ISBN 13 : 3030243591
Total Pages : 132 pages
Book Rating : 4.0/5 (32 download)
Download or read book Model Selection and Error Estimation in a Nutshell written by Luca Oneto and published by Springer. This book was released on 2019-07-17 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can we select the best performing data-driven model? How can we rigorously estimate its generalization error? Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80’s and includes the most recent results. It discusses open problems and outlines future directions for research.
Author : Peter Bartlett
Publisher :
ISBN 13 :
Total Pages : 48 pages
Book Rating : 4.:/5 (248 download)
Download or read book Model Selection and Error Estimation written by Peter Bartlett and published by . This book was released on 2000 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : David Pollard
Publisher : Springer Science & Business Media
ISBN 13 : 1461218802
Total Pages : 456 pages
Book Rating : 4.4/5 (612 download)
Download or read book Festschrift for Lucien Le Cam written by David Pollard and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contributed in honour of Lucien Le Cam on the occasion of his 70th birthday, the papers reflect the immense influence that his work has had on modern statistics. They include discussions of his seminal ideas, historical perspectives, and contributions to current research - spanning two centuries with a new translation of a paper of Daniel Bernoulli. The volume begins with a paper by Aalen, which describes Le Cams role in the founding of the martingale analysis of point processes, and ends with one by Yu, exploring the position of just one of Le Cams ideas in modern semiparametric theory. The other 27 papers touch on areas such as local asymptotic normality, contiguity, efficiency, admissibility, minimaxity, empirical process theory, and biological medical, and meteorological applications - where Le Cams insights have laid the foundations for new theories.
Author : Timo Teräsvirta
Publisher :
ISBN 13 :
Total Pages : 21 pages
Book Rating : 4.:/5 (577 download)
Download or read book Model Selection, Smoothing and Parameter Estimation in Linear Models Under Squared Error Loss written by Timo Teräsvirta and published by . This book was released on 1986 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Rob J Hyndman
Publisher : OTexts
ISBN 13 : 0987507117
Total Pages : 380 pages
Book Rating : 4.9/5 (875 download)
Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author : Kenneth P. Burnham
Publisher : Springer Science & Business Media
ISBN 13 : 0387224564
Total Pages : 512 pages
Book Rating : 4.3/5 (872 download)
Download or read book Model Selection and Multimodel Inference written by Kenneth P. Burnham and published by Springer Science & Business Media. This book was released on 2007-05-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.
Author : Ulisses M. Braga Neto
Publisher : John Wiley & Sons
ISBN 13 : 1119079373
Total Pages : 336 pages
Book Rating : 4.1/5 (19 download)
Download or read book Error Estimation for Pattern Recognition written by Ulisses M. Braga Neto and published by John Wiley & Sons. This book was released on 2015-06-22 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification. Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators. This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas. The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation, and training-set error estimation. The remaining chapters in this book cover results on the performance and representation of training-set error estimators for various pattern classifiers. Additional features of the book include: • The latest results on the accuracy of error estimation • Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches • Highly interactive computer-based exercises and end-of-chapter problems This is the first book exclusively about error estimation for pattern recognition. Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. Dr. Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member. Edward R. Dougherty is a Distinguished Professor, Robert F. Kennedy ’26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award. Dr. Dougherty has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).
Author : Tobias Scheffer
Publisher :
ISBN 13 :
Total Pages : 26 pages
Book Rating : 4.:/5 (411 download)
Download or read book Estimating the Expected Error of Empirical Minimizers for Model Selection written by Tobias Scheffer and published by . This book was released on 1998 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Model selection is considered the problem of choosing a 'good' hypothesis language from a given ensemble of models. Here, a 'good' model is one for which the true (or generalization) error of the hypothesis returned by a learner which takes the model as hypothesis language is low. The crucial part of model selection is to somehow assess the true error of the apparently best hypothesis (the empirical minimizer) of a model. In this paper, we discuss a new, very efficient approach to model selection. Our approach is inherently Bayesian, but instead of using priors on target functions or hypotheses, we talk about priors on error values -- which leads us to a new insightful characterization of the expected true error. Consequently, our solution is based on the prior of error values for the given problem which is, of course, unknown. But we show next that this prior can be estimated efficiently for a given learning problem by recording the empirical errors of a constant number of randomly drawn hypotheses. Using this estimated prior, our framework yields an estimate of the true error of the empirical minimizer of a model. We report on several controlled experiments (based on artificial problems and boolean concepts) which provide strong empirical evidence for the usefulness of the approach: In terms of accuracy, our algorithm becomes slightly superior to 10-fold cross-validation as the size of the models grows. In terms of time complexity and scalability, our algorithm is quite superior to cross-validation: Whie cross validation requires n invocations of the learner per model, a fast version of our algorithm is constant in the size of the models."
Author : J. S. Urban. Hjorth
Publisher : CRC Press
ISBN 13 : 1351458752
Total Pages : 272 pages
Book Rating : 4.3/5 (514 download)
Download or read book Computer Intensive Statistical Methods written by J. S. Urban. Hjorth and published by CRC Press. This book was released on 2017-10-19 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on computer intensive statistical methods, such as validation, model selection, and bootstrap, that help overcome obstacles that could not be previously solved by methods such as regression and time series modelling in the areas of economics, meteorology, and transportation.
Author : Yingwen Dong
Publisher :
ISBN 13 :
Total Pages : 200 pages
Book Rating : 4.:/5 (319 download)
Download or read book Inference After Model Selection written by Yingwen Dong and published by . This book was released on 2007 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Asad Zaman
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Variance Estimates and Model Selection written by Asad Zaman and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The large majority of the criteria for model selection are functions of the usual variance estimate for a regression model. The validity of the usual variance estimate depends on some assumptions, most critically the validity of the model being estimated. This is often violated in model selection contexts, where model search takes place over invalid models. A cross validated variance estimate is more robust to specification errors (see, for example, Efron, 1983). We consider the effects of replacing the usual variance estimate by a cross validated variance estimate, namely, the Prediction Sum of Squares (PRESS) in the functions of several model selection criteria. Such replacements improve the probability of finding the true model, at least in large samples.
Author : Joel Ratsaby
Publisher :
ISBN 13 :
Total Pages : 15 pages
Book Rating : 4.:/5 (595 download)
Download or read book Towards Robust Model Selection Using Estimation and Approximation Error Bounds written by Joel Ratsaby and published by . This book was released on 1996 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Scott Fortmann-Roe
Publisher :
ISBN 13 :
Total Pages : 198 pages
Book Rating : 4.:/5 (957 download)
Download or read book Methods for Comparative Model Selection and Parameter Estimation in Diverse Modeling Applications written by Scott Fortmann-Roe and published by . This book was released on 2014 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive accuracy of a model is of key importance in research and to a lay audience. Diverse modeling methods and parameter estimation methods exist, such that a wide range of techniques are available from which to select when approaching a modeling task. Given this, two questions naturally arise in relation to a modeling task: model selection and model parameter estimation. This dissertation is intended to advance the theory and practice of model selection and parameter estimation for the topics discussed here. * In Chapter 2, I develop A3, a novel method for assessing predictive accuracy and enabling direct comparisons between competing models in an accessible framework. This method uses resampling techniques to "wrap" predictive modeling methods and estimate a standard set of error metrics for both the model as a whole and additionally for each explanatory variable utilized by the model. Two case studies in the chapter illustrate the applied utility of the method and how improved models may not only result in increased predictive accuracy, but also potentially alter inferences and conclusions about the effects of parameters in the model. An R package implementing the method is made available on CRAN. * In Chapter 3, I develop ICE, a novel method of home range estimation. ICE uses a competitive method for estimating home ranges. Effectively, an estimator of estimators, ICE pits existing home range estimators against each other, each of which may be best suited for a given type of data. By selecting between different approaches, ICE can theoretically improve on the performance of any individual estimator across heterogeneous data sets. * In Chapter 4, I develop Contingent Kernel Density Estimation, an extension to Kernel Density Estimation designed to account for the case when observations are measured with a specific form of error. Chapter 4 develops the method and derives contingent kernels for commonly-used kernels and sampling regimes. An application of the method is presented to data collected from the social networking site, Twitter, to estimate the national distribution of a sample of Twitter users. * The study in Chapter 5 analyzes a large data set collected from Twitter. This study is based on data from over four million Twitter users and estimates parameters of this population with a primary focus on color preference choices made by these users. Novel results are found in this "big data" analysis approach that may not have been able to be identified with earlier, traditional approaches of sampling and surveying the behavior of individuals.
Author : Davide Verotta
Publisher :
ISBN 13 :
Total Pages : 276 pages
Book Rating : 4.:/5 (33 download)
Download or read book Nonparametric Estimation and Model Selection Using Constrained Splines in Linear Inversion Problems written by Davide Verotta and published by . This book was released on 1992 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Wayne A. Fuller
Publisher : John Wiley & Sons
ISBN 13 : 0470317337
Total Pages : 474 pages
Book Rating : 4.4/5 (73 download)
Download or read book Measurement Error Models written by Wayne A. Fuller and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The effort of Professor Fuller is commendable . . . [the book] provides a complete treatment of an important and frequently ignored topic. Those who work with measurement error models will find it valuable. It is the fundamental book on the subject, and statisticians will benefit from adding this book to their collection or to university or departmental libraries." -Biometrics "Given the large and diverse literature on measurement error/errors-in-variables problems, Fuller's book is most welcome. Anyone with an interest in the subject should certainly have this book." -Journal of the American Statistical Association "The author is to be commended for providing a complete presentation of a very important topic. Statisticians working with measurement error problems will benefit from adding this book to their collection." -Technometrics " . . . this book is a remarkable achievement and the product of impressive top-grade scholarly work." -Journal of Applied Econometrics Measurement Error Models offers coverage of estimation for situations where the model variables are observed subject to measurement error. Regression models are included with errors in the variables, latent variable models, and factor models. Results from several areas of application are discussed, including recent results for nonlinear models and for models with unequal variances. The estimation of true values for the fixed model, prediction of true values under the random model, model checks, and the analysis of residuals are addressed, and in addition, procedures are illustrated with data drawn from nearly twenty real data sets.
Author : Maurizio Tiso
Publisher :
ISBN 13 :
Total Pages : 372 pages
Book Rating : 4.:/5 (319 download)
Download or read book Nonparametric Model Selection written by Maurizio Tiso and published by . This book was released on 1999 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:
