Smoothing Of Multivariate Data
DOWNLOAD
Download Smoothing Of Multivariate Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Smoothing Of Multivariate Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Smoothing Of Multivariate Data
DOWNLOAD
Author : Jussi Sakari Klemelä
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-04
Smoothing Of Multivariate Data written by Jussi Sakari Klemelä and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-04 with Mathematics categories.
An applied treatment of the key methods and state-of-the-art tools for visualizing and understanding statistical data Smoothing of Multivariate Data provides an illustrative and hands-on approach to the multivariate aspects of density estimation, emphasizing the use of visualization tools. Rather than outlining the theoretical concepts of classification and regression, this book focuses on the procedures for estimating a multivariate distribution via smoothing. The author first provides an introduction to various visualization tools that can be used to construct representations of multivariate functions, sets, data, and scales of multivariate density estimates. Next, readers are presented with an extensive review of the basic mathematical tools that are needed to asymptotically analyze the behavior of multivariate density estimators, with coverage of density classes, lower bounds, empirical processes, and manipulation of density estimates. The book concludes with an extensive toolbox of multivariate density estimators, including anisotropic kernel estimators, minimization estimators, multivariate adaptive histograms, and wavelet estimators. A completely interactive experience is encouraged, as all examples and figurescan be easily replicated using the R software package, and every chapter concludes with numerous exercises that allow readers to test their understanding of the presented techniques. The R software is freely available on the book's related Web site along with "Code" sections for each chapter that provide short instructions for working in the R environment. Combining mathematical analysis with practical implementations, Smoothing of Multivariate Data is an excellent book for courses in multivariate analysis, data analysis, and nonparametric statistics at the upper-undergraduate and graduatelevels. It also serves as a valuable reference for practitioners and researchers in the fields of statistics, computer science, economics, and engineering.
Smoothing Methods In Statistics
DOWNLOAD
Author : Jeffrey S. Simonoff
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Smoothing Methods In Statistics written by Jeffrey S. Simonoff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue.
Multivariate Data Analysis On Matrix Manifolds
DOWNLOAD
Author : Nickolay Trendafilov
language : en
Publisher: Springer Nature
Release Date : 2021-09-15
Multivariate Data Analysis On Matrix Manifolds written by Nickolay Trendafilov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-15 with Mathematics categories.
This graduate-level textbook aims to give a unified presentation and solution of several commonly used techniques for multivariate data analysis (MDA). Unlike similar texts, it treats the MDA problems as optimization problems on matrix manifolds defined by the MDA model parameters, allowing them to be solved using (free) optimization software Manopt. The book includes numerous in-text examples as well as Manopt codes and software guides, which can be applied directly or used as templates for solving similar and new problems. The first two chapters provide an overview and essential background for studying MDA, giving basic information and notations. Next, it considers several sets of matrices routinely used in MDA as parameter spaces, along with their basic topological properties. A brief introduction to matrix (Riemannian) manifolds and optimization methods on them with Manopt complete the MDA prerequisite. The remaining chapters study individual MDA techniques in depth. The number of exercises complement the main text with additional information and occasionally involve open and/or challenging research questions. Suitable fields include computational statistics, data analysis, data mining and data science, as well as theoretical computer science, machine learning and optimization. It is assumed that the readers have some familiarity with MDA and some experience with matrix analysis, computing, and optimization.
Multivariate Methods And Forecasting With Ibm Spss Statistics
DOWNLOAD
Author : Abdulkader Aljandali
language : en
Publisher: Springer
Release Date : 2017-07-06
Multivariate Methods And Forecasting With Ibm Spss Statistics written by Abdulkader Aljandali and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-06 with Business & Economics categories.
This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS).
Applied Smoothing Techniques For Data Analysis
DOWNLOAD
Author : Adrian W. Bowman
language : en
Publisher: OUP Oxford
Release Date : 1997-08-14
Applied Smoothing Techniques For Data Analysis written by Adrian W. Bowman and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-08-14 with Mathematics categories.
The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can be applied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawn from a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level, as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scripts are provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.
Smoothing Spline Anova Models
DOWNLOAD
Author : Chong Gu
language : en
Publisher: Springer
Release Date : 2015-06-25
Smoothing Spline Anova Models written by Chong Gu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-25 with Mathematics categories.
Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of methods have proved successful for univariate smoothing, ones practical in multivariate settings number far less. Smoothing spline ANOVA models are a versatile family of smoothing methods derived through roughness penalties, that are suitable for both univariate and multivariate problems. In this book, the author presents a treatise on penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with censored life time data and covariates. The unifying themes are the general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions. Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence. Most of the computational and data analytical tools discussed in the book are implemented in R, an open-source platform for statistical computing and graphics. Suites of functions are embodied in the R package gss, and are illustrated throughout the book using simulated and real data examples. This monograph will be useful as a reference work for researchers in theoretical and applied statistics as well as for those in other related disciplines. It can also be used as a text for graduate level courses on the subject. Most of the materials are accessible to a second year graduate student with a good training in calculus and linear algebra and working knowledge in basic statistical inferences such as linear models and maximum likelihood estimates.
Kernel Smoothing
DOWNLOAD
Author : M.P. Wand
language : en
Publisher: CRC Press
Release Date : 1994-12-01
Kernel Smoothing written by M.P. Wand and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-12-01 with Mathematics categories.
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function. This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilita
Concise Handbook Of Analytical Spectroscopy The Theory Applications And Reference Materials In 5 Volumes
DOWNLOAD
Author : Jerome (Jerry) James Workman, Jr
language : en
Publisher: World Scientific
Release Date : 2016-06-17
Concise Handbook Of Analytical Spectroscopy The Theory Applications And Reference Materials In 5 Volumes written by Jerome (Jerry) James Workman, Jr and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-17 with Science categories.
The concept of improving the use of electromagnetic energy to achieve a variety of qualitative and quantitative spectroscopic measurements on solid and liquid materials has been proliferating at a rapid rate. The use of such technologies to measure chemical composition, appearance, for classification, and to achieve detailed understanding of material interactions has prompted a dramatic expansion in the use and development of spectroscopic techniques over a variety of academic and commercial fields.The Concise Handbook of Analytical Spectroscopy is integrated into 5 volumes, each covering the theory, instrumentation, sampling methods, experimental design, and data analysis techniques, as well as essential reference tables, figures, and spectra for each spectroscopic region. The detailed practical aspects of applying spectroscopic tools for many of the most exciting and current applications are covered. Featured applications include: medical, biomedical, optical, physics, common commercial analysis methods, spectroscopic quantitative and qualitative techniques, and advanced methods.This multi-volume handbook is designed specifically as a reference tool for students, commercial development and quality scientists, and researchers or technologists in a variety of measurement endeavours.Number of Illustrations and Tables: 393 b/w illus., 304 colour illus, 413 tables.Related Link(s)
Vibrational Spectroscopy For Plant Varieties And Cultivars Characterization
DOWNLOAD
Author :
language : en
Publisher: Elsevier
Release Date : 2018-04-30
Vibrational Spectroscopy For Plant Varieties And Cultivars Characterization written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-30 with Science categories.
Vibrational Spectroscopy for Plant Varieties and Cultivars Characterization, Volume 80, provides an overview on the application of vibrational spectroscopy to characterize plant cultivars and varieties. It covers a variety of aspects, including the potential of this technique for taxonomic purposes (species and cultivars/varieties identification), how to discriminate plants according to their ages and geographic regions, how to depict soil properties through plant characteristics, etc. Currently, most of these studies are performed through somewhat laborious techniques. This book presents reliable alternatives to such techniques, while also systematizing information concerning the application of vibration spectroscopy in this context. - Guides academics through the application of vibrational spectroscopy - Presents a valuable source of information for plant producers
Statistical Inference In Stochastic Processes
DOWNLOAD
Author : N.U. Prabhu
language : en
Publisher: CRC Press
Release Date : 2020-08-13
Statistical Inference In Stochastic Processes written by N.U. Prabhu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-13 with Mathematics categories.
Covering both theory and applications, this collection of eleven contributed papers surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the jump mechanism of a di