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 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

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  • Author : Dag Tjostheim
  • Publisher : Academic Press
  • Pages : 458 pages
  • ISBN : 0128154454
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKStatistical Modeling Using Local Gaussian Approximation

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 BOOKStatistical 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 BOOKStochastic 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 : John Wiley & Sons
  • Release : 23 September 2016
GET THIS BOOKProbabilistic 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 : Oxford University Press
  • Release : 01 May 2014
GET THIS BOOKEssays 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

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  • Publisher : IGI Global
  • Release : 12 April 2019
GET THIS BOOKHandbook 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 : CRC Press
  • Release : 17 November 2016
GET THIS BOOKBiomedical 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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  • Author : Robert B. Gramacy
  • Publisher : CRC Press
  • Release : 10 March 2020
GET THIS BOOKSurrogates

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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  • Author : Edwin J. Green,Andrew O. Finley,William E. Strawderman
  • Publisher : Springer Nature
  • Release : 26 November 2020
GET THIS BOOKIntroduction 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 : Tze Leung Lai,Haipeng Xing
  • Publisher : Springer Science & Business Media
  • Release : 25 July 2008
GET THIS BOOKStatistical Models and Methods for Financial Markets

The idea of writing this bookarosein 2000when the ?rst author wasassigned to teach the required course STATS 240 (Statistical Methods in Finance) in the new M. S. program in ?nancial mathematics at Stanford, which is an interdisciplinary program that aims to provide a master’s-level education in applied mathematics, statistics, computing, ?nance, and economics. Students in the programhad di?erent backgroundsin statistics. Some had only taken a basic course in statistical inference, while others had taken a broad spectrum of M.

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  • Author : Dennis V. Lindley
  • Publisher : Oxford University Press
  • Release : 03 July 2003
GET THIS BOOKBayesian 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.

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  • Author : Ludwig Fahrmeir,Gerhard Tutz
  • Publisher : Springer Science & Business Media
  • Release : 14 March 2013
GET THIS BOOKMultivariate Statistical Modelling Based on Generalized Linear Models

The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.

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  • Publisher : Woodhead Publishing Limited
  • Release : 12 March 2020
GET THIS BOOKUncertainty Quantification in Multiscale Materials Modeling

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  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKTime Series Analysis and Applications to Geophysical Systems

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  • Publisher : Springer
  • Release : 17 January 2018
GET THIS BOOKNew 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