Author: Linyuan LiPublisher:
ISBN:
Category : Least squares
Languages : en
Pages : 176
Book Description
Nonlinear Wavelet-based Nonparametric Curve Estimation with Censored Data and Inference on Long Memory Processes
Author: Linyuan LiPublisher:
ISBN:
Category : Least squares
Languages : en
Pages : 176
Book Description
Nonparametric Curve Estimation from Time Series
Author: Lazlo GyörfiPublisher: Springer
ISBN: 146123686X
Category : Mathematics
Languages : en
Pages : 157
Book Description
Because of the sheer size and scope of the plastics industry, the title Developments in Plastics Technology now covers an incredibly wide range of subjects or topics. No single volume can survey the whole field in any depth and what follows is, therefore, a series of chapters on selected topics. The topics were selected by us, the editors, because of their immediate relevance to the plastics industry. When one considers the advancements of the plastics processing machinery (in terms of its speed of operation and conciseness of control), it was felt that several chapters should be included which related to the types of control systems used and the correct usage of hydraulics. The importance of using cellular, rubber-modified and engineering-type plastics has had a major impact on the plastics industry and therefore a chapter on each of these subjects has been included. The two remaining chapters are on the characterisation and behaviour of polymer structures, both subjects again being of current academic or industrial interest. Each of the contributions was written by a specialist in that field and to them all, we, the editors, extend our heartfelt thanks, as writing a contribution for a book such as this, while doing a full-time job, is no easy task.
Wavelet-based Estimation for Seasonal Long-memory Processes
Author: Brandon WhitcherAmerican Doctoral Dissertations
Author: Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 872
Book Description
Statistical Modeling by Wavelets
Author: Brani VidakovicPublisher: Wiley-Interscience
ISBN: 9780470148754
Category : Mathematics
Languages : en
Pages : 544
Book Description
Statistical Modeling by Wavelets, Second Edition compiles, organizes, and explains research data previously made available only in disparate journal articles. The author carefully balances both statistical and mathematical techniques, supplementing the material with a wealth of examples, more than 100 illustrations, extensive references with data sets, and MatLab® and WaveLab® wavelet overviews made available for downloading over the Internet. Accessible to anyone with a background in advanced calculus and algebra, this book has become the standard reference for statisticians and engineers seeking a comprehensive introduction to an ever-changing field.
Mathematical Reviews
Author: Nonparametric Econometrics
Author: Qi LiPublisher: Princeton University Press
ISBN: 1400841062
Category : Business & Economics
Languages : en
Pages : 769
Book Description
A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
Statistical Foundations of Data Science
Author: Jianqing FanPublisher: CRC Press
ISBN: 0429527616
Category : Mathematics
Languages : en
Pages : 942
Book Description
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Modern Statistical Methods for Astronomy
Author: Eric D. FeigelsonPublisher: Cambridge University Press
ISBN: 052176727X
Category : Science
Languages : en
Pages : 495
Book Description
Modern Statistical Methods for Astronomy: With R Applications.
Fundamentals of Nonparametric Bayesian Inference
Author: Subhashis GhosalPublisher: Cambridge University Press
ISBN: 0521878268
Category : Business & Economics
Languages : en
Pages : 671
Book Description
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.