Statistical Modeling Using Local Gaussian Approximation

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  • Author : Dag Tjostheim
  • Publisher : Elsevier
  • Pages : 458 pages
  • ISBN : 0128158611
  • 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 Elsevier which was released on 08 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. "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 : Elsevier
  • Release : 08 October 2021
GET THIS BOOK Statistical Modeling Using Local Gaussian Approximation

"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

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  • 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

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  • 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.

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  • 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

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  • Publisher : Springer Science & Business Media
  • Release : 29 June 2013
GET THIS BOOK Maximum Entropy and Bayesian Methods

Bayesian probability theory and maximum entropy methods are at the core of a new view of scientific inference. These `new' ideas, along with the revolution in computational methods afforded by modern computers, allow astronomers, electrical engineers, image processors of any type, NMR chemists and physicists, and anyone at all who has to deal with incomplete and noisy data, to take advantage of methods that, in the past, have been applied only in some areas of theoretical physics. This volume records

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  • 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

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  • Publisher : John Wiley & Sons
  • Release : 23 March 2022
GET THIS BOOK Computational Statistics in Data Science

Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste

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  • 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. •

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  • 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,

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  • 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

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  • Release : 03 July 2003
GET THIS BOOK Bayesian Statistics 7

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  • Publisher : Springer
  • Release : 17 January 2018
GET THIS BOOK New Advances in Statistics and Data Science

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research

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  • Release : 21 December 2005
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  • Publisher : Oxford University Press
  • Release : 01 May 2014
GET THIS BOOK Essays in Nonlinear Time Series Econometrics

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