Statistical Modeling Using Local Gaussian Approximation

Produk Detail:
  • Author : Dag Tjostheim
  • Publisher : Academic Press
  • Pages : 458 pages
  • ISBN : 0128154454
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Statistical Modeling Using Local Gaussian Approximation

Download or Read online Statistical Modeling Using Local Gaussian Approximation full in PDF, ePub and kindle. this book written by Dag Tjostheim and published by Academic Press which was released on 05 October 2021 with total page 458 pages. We cannot guarantee that Statistical Modeling Using Local Gaussian Approximation book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation. This extension enables the reader to follow new methods in assessing dependence and conditional dependence, in estimating probability and spectral density functions, and in discrimination. Chapters in this release cover Parametric, nonparametric, locally parametric, Dependence, Local Gaussian correlation and dependence, Local Gaussian correlation and the copula, Applications in finance, and more. Additional chapters explores Measuring dependence and testing for independence, Time series dependence and spectral analysis, Multivariate density estimation, Conditional density estimation, The local Gaussian partial correlation, Regression and conditional regression quantiles, and a A local Gaussian Fisher discriminant. Reviews local dependence modeling with applications to time series and finance markets Introduces new techniques for density estimation, conditional density estimation, and tests of conditional independence with applications in economics Evaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differences Integrates textual content with three useful R packages

Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling Using Local Gaussian Approximation
  • Author : Dag Tjostheim,Håkon Otneim,Bård Stove
  • Publisher : Academic Press
  • Release : 05 October 2021
GET THIS BOOK Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation. This extension enables the reader to follow new methods in assessing dependence and conditional dependence, in estimating probability and spectral density functions, and in discrimination. Chapters in this release cover Parametric, nonparametric, locally parametric, Dependence, Local Gaussian correlation and dependence, Local Gaussian correlation and the

Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling Using Local Gaussian Approximation
  • Author : Dag Tjostheim,Håkon Otneim,Bård Stove
  • Publisher : Elsevier
  • Release : 01 November 2021
GET THIS BOOK Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation. This extension enables the reader to follow new methods in assessing dependence and conditional dependence, in estimating probability and spectral density functions, and in discrimination. Chapters in this release cover Parametric, nonparametric, locally parametric, Dependence, Local Gaussian correlation and dependence, Local Gaussian correlation and the

Stochastic Models Statistics and Their Applications

Stochastic Models  Statistics and Their Applications
  • Author : Ansgar Steland,Ewaryst Rafajłowicz,Krzysztof Szajowski
  • Publisher : Springer
  • Release : 04 February 2015
GET THIS BOOK Stochastic Models Statistics and Their Applications

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of

Handbook of Research on Cloud Computing and Big Data Applications in IoT

Handbook of Research on Cloud Computing and Big Data Applications in IoT
  • Author : Gupta, B. B.,Agrawal, Dharma P.
  • Publisher : IGI Global
  • Release : 12 April 2019
GET THIS BOOK Handbook of Research on Cloud Computing and Big Data Applications in IoT

Today, cloud computing, big data, and the internet of things (IoT) are becoming indubitable parts of modern information and communication systems. They cover not only information and communication technology but also all types of systems in society including within the realms of business, finance, industry, manufacturing, and management. Therefore, it is critical to remain up-to-date on the latest advancements and applications, as well as current issues and challenges. The Handbook of Research on Cloud Computing and Big Data Applications in

Probabilistic Finite Element Model Updating Using Bayesian Statistics

Probabilistic Finite Element Model Updating Using Bayesian Statistics
  • Author : Tshilidzi Marwala,Ilyes Boulkaibet,Sondipon Adhikari
  • Publisher : John Wiley & Sons
  • Release : 23 September 2016
GET THIS BOOK Probabilistic Finite Element Model Updating Using Bayesian Statistics

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The

Biomedical Image Segmentation

Biomedical Image Segmentation
  • Author : Ayman El-Baz,Xiaoyi Jiang,Jasjit S. Suri
  • Publisher : CRC Press
  • Release : 17 November 2016
GET THIS BOOK Biomedical Image Segmentation

As one of the most important tasks in biomedical imaging, image segmentation provides the foundation for quantitative reasoning and diagnostic techniques. A large variety of different imaging techniques, each with its own physical principle and characteristics (e.g., noise modeling), often requires modality-specific algorithmic treatment. In recent years, substantial progress has been made to biomedical image segmentation. Biomedical image segmentation is characterized by several specific factors. This book presents an overview of the advanced segmentation algorithms and their applications.

Surrogates

Surrogates
  • Author : Robert B. Gramacy
  • Publisher : CRC Press
  • Release : 10 March 2020
GET THIS BOOK Surrogates

Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. •

Introduction to Bayesian Methods in Ecology and Natural Resources

Introduction to Bayesian Methods in Ecology and Natural Resources
  • Author : Edwin J. Green,Andrew O. Finley,William E. Strawderman
  • Publisher : Springer Nature
  • Release : 26 November 2020
GET THIS BOOK Introduction to Bayesian Methods in Ecology and Natural Resources

This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach has a number of desirable qualities, three chief ones being: i) the mathematical procedure is always the same,

Bayesian Statistics 7

Bayesian Statistics 7
  • Author : Dennis V. Lindley
  • Publisher : Oxford University Press
  • Release : 03 July 2003
GET THIS BOOK Bayesian Statistics 7

This volume contains the proceedings of the 7th Valencia International Meeting on Bayesian Statistics. This conference is held every four years and provides the main forum for researchers in the area of Bayesian statistics to come together to present and discuss frontier developments in the field.

Essays in Nonlinear Time Series Econometrics

Essays in Nonlinear Time Series Econometrics
  • Author : Niels Haldrup,Mika Meitz,Pentti Saikkonen
  • Publisher : Oxford University Press
  • Release : 01 May 2014
GET THIS BOOK Essays in Nonlinear Time Series Econometrics

This edited collection concerns nonlinear economic relations that involve time. It is divided into four broad themes that all reflect the work and methodology of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The themes are: Testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial

Handbook of Statistics

Handbook of Statistics
  • Author : Anonim
  • Publisher : Elsevier
  • Release : 18 May 2012
GET THIS BOOK Handbook of Statistics

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The

Handbook of Parallel Computing and Statistics

Handbook of Parallel Computing and Statistics
  • Author : Erricos John Kontoghiorghes
  • Publisher : CRC Press
  • Release : 21 December 2005
GET THIS BOOK Handbook of Parallel Computing and Statistics

Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts

Uncertainty Quantification in Multiscale Materials Modeling

Uncertainty Quantification in Multiscale Materials Modeling
  • Author : Yan Wang,David L. McDowell
  • Publisher : Woodhead Publishing Limited
  • Release : 12 March 2020
GET THIS BOOK Uncertainty Quantification in Multiscale Materials Modeling

Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the

Computational Photography

Computational Photography
  • Author : Rastislav Lukac
  • Publisher : CRC Press
  • Release : 19 December 2017
GET THIS BOOK Computational Photography

Computational photography refers broadly to imaging techniques that enhance or extend the capabilities of digital photography. This new and rapidly developing research field has evolved from computer vision, image processing, computer graphics and applied optics—and numerous commercial products capitalizing on its principles have already appeared in diverse market applications, due to the gradual migration of computational algorithms from computers to imaging devices and software. Computational Photography: Methods and Applications provides a strong, fundamental understanding of theory and methods, and