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Statistical Modelling using Local Gaussian Approximation


Statistical Modelling using Local Gaussian Approximation
  • Author : Dag Bjarne Tjostheim
  • Publisher : Academic Press
  • Release : 2021-11-15
  • ISBN : 0128158611
  • Language : En, Es, Fr & De
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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 conditional distribution functions, conditional mean functions and conditional quantile functions. Three R packages are integrated with the text, based on local Gaussian correlation, density and conditional density estimation, and local spectral analysis. The book is of particular relevance and interest to researchers in econometrics and financial econometrics. Reviews local dependence modelling 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

Stochastic Models Statistics and Their Applications


Stochastic Models  Statistics and Their Applications
  • Author : Ansgar Steland
  • Publisher : Springer
  • Release : 2015-02-04
  • ISBN : 9783319138817
  • Language : En, Es, Fr & De
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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 application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

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.
  • Publisher : IGI Global
  • Release : 2019-04-12
  • ISBN : 9781522584087
  • Language : En, Es, Fr & De
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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 IoT is a pivotal reference source that provides relevant theoretical frameworks and the latest empirical research findings on principles, challenges, and applications of cloud computing, big data, and IoT. While highlighting topics such as fog computing, language interaction, and scheduling algorithms, this publication is ideally designed for software developers, computer engineers, scientists, professionals, academicians, researchers, and students.

Probabilistic Finite Element Model Updating Using Bayesian Statistics


Probabilistic Finite Element Model Updating Using Bayesian Statistics
  • Author : Tshilidzi Marwala
  • Publisher : John Wiley & Sons
  • Release : 2016-09-23
  • ISBN : 9781119153009
  • Language : En, Es, Fr & De
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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 Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students. Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

Biomedical Image Segmentation


Biomedical Image Segmentation
  • Author : Ayman El-Baz
  • Publisher : CRC Press
  • Release : 2016-11-17
  • ISBN : 9781482258561
  • Language : En, Es, Fr & De
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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.