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Error Estimation And Model Selection
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Book Synopsis Model Selection and Error Estimation in a Nutshell by : Luca Oneto
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 135 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.
Book Synopsis Festschrift for Lucien Le Cam by : David Pollard
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.
Book Synopsis Forecasting: principles and practice by : Rob J Hyndman
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.
Book Synopsis High Dimensional Probability II by : Evarist Giné
Download or read book High Dimensional Probability II written by Evarist Giné and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: High dimensional probability, in the sense that encompasses the topics rep resented in this volume, began about thirty years ago with research in two related areas: limit theorems for sums of independent Banach space valued random vectors and general Gaussian processes. An important feature in these past research studies has been the fact that they highlighted the es sential probabilistic nature of the problems considered. In part, this was because, by working on a general Banach space, one had to discard the extra, and often extraneous, structure imposed by random variables taking values in a Euclidean space, or by processes being indexed by sets in R or Rd. Doing this led to striking advances, particularly in Gaussian process theory. It also led to the creation or introduction of powerful new tools, such as randomization, decoupling, moment and exponential inequalities, chaining, isoperimetry and concentration of measure, which apply to areas well beyond those for which they were created. The general theory of em pirical processes, with its vast applications in statistics, the study of local times of Markov processes, certain problems in harmonic analysis, and the general theory of stochastic processes are just several of the broad areas in which Gaussian process techniques and techniques from probability in Banach spaces have made a substantial impact. Parallel to this work on probability in Banach spaces, classical proba bility and empirical process theory were enriched by the development of powerful results in strong approximations.
Book Synopsis Evaluating Learning Algorithms by : Nathalie Japkowicz
Download or read book Evaluating Learning Algorithms written by Nathalie Japkowicz and published by Cambridge University Press. This book was released on 2011-01-17 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
Book Synopsis Nonparametric Functional Estimation and Related Topics by : George Roussas
Download or read book Nonparametric Functional Estimation and Related Topics written by George Roussas and published by Springer Science & Business Media. This book was released on 1991-04-30 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engineering and medicine. While there has been interest in nonparametric functional estimation for many years, this has grown of late, owing to increasing availability of large data sets and the ability to process them by means of improved computing facilities, along with the ability to display the results by means of sophisticated graphical procedures.
Book Synopsis Regression and Time Series Model Selection by : Allan D. R. McQuarrie
Download or read book Regression and Time Series Model Selection written by Allan D. R. McQuarrie and published by World Scientific. This book was released on 1998 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.
Book Synopsis Discrete Choice Methods with Simulation by : Kenneth Train
Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Book Synopsis Fundamentals of Pattern Recognition and Machine Learning by : Ulisses Braga-Neto
Download or read book Fundamentals of Pattern Recognition and Machine Learning written by Ulisses Braga-Neto and published by Springer Nature. This book was released on 2020-09-10 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.
Download or read book LEDA written by Kurt Mehlhorn and published by Cambridge University Press. This book was released on 1999-11-11 with total page 1050 pages. Available in PDF, EPUB and Kindle. Book excerpt: LEDA is a library of efficient data types and algorithms and a platform for combinatorial and geometric computing on which application programs can be built. In each of the core computer science areas of data structures, graph and network algorithms, and computational geometry, LEDA covers all (and more) that is found in the standard textbooks. LEDA is the first such library; it is written in C++ and is available on many types of machine. Whilst the software is freely available worldwide and is installed at hundreds of sites, this is the first book devoted to the library. Written by the main authors of LEDA, it is the definitive account, describing how the system is constructed and operates and how it can be used. The authors supply ample examples from a range of areas to show how the library can be used in practice, making the book essential for all workers in algorithms, data structures and computational geometry.
Book Synopsis Bootstrap Methods and Their Application by : A. C. Davison
Download or read book Bootstrap Methods and Their Application written by A. C. Davison and published by Cambridge University Press. This book was released on 1997-10-28 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disk contains the library functions and documentation for use with Splus for Windows.
Book Synopsis The SAGE Handbook of Multilevel Modeling by : Marc A. Scott
Download or read book The SAGE Handbook of Multilevel Modeling written by Marc A. Scott and published by SAGE. This book was released on 2013-08-31 with total page 954 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.
Book Synopsis Automotive Electronics Reliability by : Ronald K Jurgen
Download or read book Automotive Electronics Reliability written by Ronald K Jurgen and published by SAE International. This book was released on 2010-08-10 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicle reliability problems continue to be the news because of major vehicle recalls from several manufacturers. This book includes 40 SAE technical papers, published from 2007 through 2010, that describe the latest research on automotive electronics reliability technology. This book will help engineers and researchers focus on the design strategies being used to minimize electronics reliability problems, and how to test and verify those strategies. After an overview of durability, risk assessment, and failure mechanisms, this book focuses on state-of-the-art techniques for reliability-based design, and reliability testing and verification. Topics include: powertrain control monitoring distributed automotive embedded systems model-based design x-by-wire systems battery durability design verification fault tree analysis The book also includes editor Ronald K. Jurgen’s introduction ,“Striving for Maximum Reliability in a Highly Complex Electronic Environment”, and a concluding section on the future of electronics reliability, including networking technology, domain control units, the use of AUTOSAR, and embedded software.
Book Synopsis Artificial Intelligence and Machine Learning in Health Care and Medical Sciences by : Gyorgy J. Simon
Download or read book Artificial Intelligence and Machine Learning in Health Care and Medical Sciences written by Gyorgy J. Simon and published by Springer Nature. This book was released on with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis New Developments in Statistical Information Theory Based on Entropy and Divergence Measures by : Leandro Pardo
Download or read book New Developments in Statistical Information Theory Based on Entropy and Divergence Measures written by Leandro Pardo and published by MDPI. This book was released on 2019-05-20 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics, based on maximum likelihood estimators, as well as Wald’s statistics, likelihood ratio statistics and Rao’s score statistics, share several optimum asymptotic properties, but are highly non-robust in cases of model misspecification under the presence of outlying observations. It is well-known that a small deviation from the underlying assumptions on the model can have drastic effect on the performance of these classical tests. Specifically, this book presents a robust version of the classical Wald statistical test, for testing simple and composite null hypotheses for general parametric models, based on minimum divergence estimators.
Book Synopsis Data Mining for Bioinformatics by : Sumeet Dua
Download or read book Data Mining for Bioinformatics written by Sumeet Dua and published by CRC Press. This book was released on 2012-11-06 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to he
Book Synopsis Error Estimation for Pattern Recognition by : Ulisses M. Braga Neto
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 2015-07-07 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).
