Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling Using Local Gaussian Approximation
Author: Dag Tjostheim,Håkon Otneim,Bård Stove
Publsiher: Academic Press
Total Pages: 458
Release: 2021-10-05
ISBN 10: 0128154454
ISBN 13: 9780128154458
Language: EN, FR, DE, ES & NL

Statistical Modeling Using Local Gaussian Approximation Book Review:

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

Stochastic Models Statistics and Their Applications

Stochastic Models  Statistics and Their Applications
Author: Ansgar Steland,Ewaryst Rafajłowicz,Krzysztof Szajowski
Publsiher: Springer
Total Pages: 492
Release: 2015-02-04
ISBN 10: 3319138812
ISBN 13: 9783319138817
Language: EN, FR, DE, ES & NL

Stochastic Models Statistics and Their Applications Book Review:

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.,Agrawal, Dharma P.
Publsiher: IGI Global
Total Pages: 609
Release: 2019-04-12
ISBN 10: 1522584080
ISBN 13: 9781522584087
Language: EN, FR, DE, ES & NL

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

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,Ilyes Boulkaibet,Sondipon Adhikari
Publsiher: John Wiley & Sons
Total Pages: 248
Release: 2016-09-23
ISBN 10: 111915300X
ISBN 13: 9781119153009
Language: EN, FR, DE, ES & NL

Probabilistic Finite Element Model Updating Using Bayesian Statistics Book Review:

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,Xiaoyi Jiang,Jasjit S. Suri
Publsiher: CRC Press
Total Pages: 526
Release: 2016-11-17
ISBN 10: 1482258560
ISBN 13: 9781482258561
Language: EN, FR, DE, ES & NL

Biomedical Image Segmentation Book Review:

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.

Performance Modeling Stochastic Networks and Statistical Multiplexing Second Edition

Performance Modeling  Stochastic Networks  and Statistical Multiplexing  Second Edition
Author: Ravi Mazumdar
Publsiher: Springer Nature
Total Pages: 197
Release: 2022-05-31
ISBN 10: 3031792602
ISBN 13: 9783031792601
Language: EN, FR, DE, ES & NL

Performance Modeling Stochastic Networks and Statistical Multiplexing Second Edition Book Review:

This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in computing performance measures. The monograph also covers stochastic network theory including Markovian networks. Recent results on network utility optimization and connections to stochastic insensitivity are discussed. Also presented are ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. In particular, the important concept of effective bandwidths as mappings from queueing level phenomena to loss network models is clearly presented along with a detailed discussion of accurate approximations for large networks.

Surrogates

Surrogates
Author: Robert B. Gramacy
Publsiher: CRC Press
Total Pages: 560
Release: 2020-03-10
ISBN 10: 1000766527
ISBN 13: 9781000766523
Language: EN, FR, DE, ES & NL

Surrogates Book Review:

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. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.

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
Publsiher: Springer Nature
Total Pages: 183
Release: 2020-11-26
ISBN 10: 303060750X
ISBN 13: 9783030607500
Language: EN, FR, DE, ES & NL

Introduction to Bayesian Methods in Ecology and Natural Resources Book Review:

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, allowing the analyst to concentrate on the scientific aspects of the problem; ii) historical information is readily used, when appropriate; and iii) hierarchical models are readily accommodated. This monograph contains numerous worked examples and the requisite computer programs. The latter are easily modified to meet new situations. A primer on probability distributions is also included because these form the basis of Bayesian inference. Researchers and graduate students in Ecology and Natural Resource Management will find this book a valuable reference.

Analytic Statistical Models

Analytic Statistical Models
Author: Ib M. Skovgaard
Publsiher: IMS
Total Pages: 167
Release: 1990
ISBN 10: 9780940600201
ISBN 13: 094060020X
Language: EN, FR, DE, ES & NL

Analytic Statistical Models Book Review:

Bayesian Statistics 7

Bayesian Statistics 7
Author: Dennis V. Lindley
Publsiher: Oxford University Press
Total Pages: 750
Release: 2003-07-03
ISBN 10: 9780198526155
ISBN 13: 0198526156
Language: EN, FR, DE, ES & NL

Bayesian Statistics 7 Book Review:

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.

Handbook of Parallel Computing and Statistics

Handbook of Parallel Computing and Statistics
Author: Erricos John Kontoghiorghes
Publsiher: CRC Press
Total Pages: 552
Release: 2005-12-21
ISBN 10: 9781420028683
ISBN 13: 1420028685
Language: EN, FR, DE, ES & NL

Handbook of Parallel Computing and Statistics Book Review:

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

Essays in Nonlinear Time Series Econometrics

Essays in Nonlinear Time Series Econometrics
Author: Niels Haldrup,Mika Meitz,Pentti Saikkonen
Publsiher: Oxford University Press
Total Pages: 352
Release: 2014-05
ISBN 10: 0199679959
ISBN 13: 9780199679959
Language: EN, FR, DE, ES & NL

Essays in Nonlinear Time Series Econometrics Book Review:

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 econometrics. All these research fields include contributions that represent state of the art in econometrics such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had and will continue to have, on the profession.

Uncertainty Quantification in Multiscale Materials Modeling

Uncertainty Quantification in Multiscale Materials Modeling
Author: Yan Wang,David L. McDowell
Publsiher: Woodhead Publishing Limited
Total Pages: 900
Release: 2020-03-12
ISBN 10: 0081029411
ISBN 13: 9780081029411
Language: EN, FR, DE, ES & NL

Uncertainty Quantification in Multiscale Materials Modeling Book Review:

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 unique characteristics of models framed at each scale, as well as common issues in modeling across scales.

Asymptotic Statistics

Asymptotic Statistics
Author: A. W. van der Vaart
Publsiher: Cambridge University Press
Total Pages: 470
Release: 2000-06-19
ISBN 10: 9780521784504
ISBN 13: 0521784506
Language: EN, FR, DE, ES & NL

Asymptotic Statistics Book Review:

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master s level statistics text, this book will also give researchers an overview of the latest research in asymptotic statistics.

New Advances in Statistics and Data Science

New Advances in Statistics and Data Science
Author: Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao
Publsiher: Springer
Total Pages: 348
Release: 2018-01-17
ISBN 10: 3319694162
ISBN 13: 9783319694160
Language: EN, FR, DE, ES & NL

New Advances in Statistics and Data Science Book Review:

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 ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

Computational Science ICCS 2021

Computational Science     ICCS 2021
Author: Maciej Paszynski,Dieter Kranzlmüller,Valeria V. Krzhizhanovskaya,Jack J. Dongarra,Peter M.A. Sloot
Publsiher: Springer Nature
Total Pages: 592
Release: 2021-06-09
ISBN 10: 3030779777
ISBN 13: 9783030779771
Language: EN, FR, DE, ES & NL

Computational Science ICCS 2021 Book Review:

The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually.

Phase Transitions and Renormalization Group

Phase Transitions and Renormalization Group
Author: Jean Zinn-Justin
Publsiher: Oxford University Press on Demand
Total Pages: 452
Release: 2007-07-05
ISBN 10: 0199227195
ISBN 13: 9780199227198
Language: EN, FR, DE, ES & NL

Phase Transitions and Renormalization Group Book Review:

No further information has been provided for this title.

Knowledge Incorporation in Evolutionary Computation

Knowledge Incorporation in Evolutionary Computation
Author: Yaochu Jin
Publsiher: Springer Science & Business Media
Total Pages: 548
Release: 2004-10-20
ISBN 10: 9783540229025
ISBN 13: 3540229027
Language: EN, FR, DE, ES & NL

Knowledge Incorporation in Evolutionary Computation Book Review:

Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu tionary search, into evolutionary algorithms has received increasing interest in the recent years. It has been shown from various motivations that knowl edge incorporation into evolutionary search is able to significantly improve search efficiency. However, results on knowledge incorporation in evolution ary computation have been scattered in a wide range of research areas and a systematic handling of this important topic in evolutionary computation still lacks. This edited book is a first attempt to put together the state-of-art and re cent advances on knowledge incorporation in evolutionary computation within a unified framework. Existing methods for knowledge incorporation are di vided into the following five categories according to the functionality of the incorporated knowledge in the evolutionary algorithms. 1. Knowledge incorporation in representation, population initialization, - combination and mutation. 2. Knowledge incorporation in selection and reproduction. 3. Knowledge incorporation in fitness evaluations. 4. Knowledge incorporation through life-time learning and human-computer interactions. 5. Incorporation of human preferences in multi-objective evolutionary com putation. The intended readers of this book are graduate students, researchers and practitioners in all fields of science and engineering who are interested in evolutionary computation. The book is divided into six parts. Part I contains one introductory chapter titled "A selected introduction to evolutionary computation" by Yao, which presents a concise but insightful introduction to evolutionary computation.

Statistical Models and Methods for Financial Markets

Statistical Models and Methods for Financial Markets
Author: Tze Leung Lai,Haipeng Xing
Publsiher: Springer Science & Business Media
Total Pages: 356
Release: 2008-09-08
ISBN 10: 0387778276
ISBN 13: 9780387778273
Language: EN, FR, DE, ES & NL

Statistical Models and Methods for Financial Markets Book Review:

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. S. - and Ph. D. -level statistics courses. On the other hand, all of them had already taken required core courses in investment theory and derivative pricing, and STATS 240 was supposed to link the theory and pricing formulas to real-world data and pricing or investment strategies. Besides students in theprogram,thecoursealso attractedmanystudentsfromother departments in the university, further increasing the heterogeneity of students, as many of them had a strong background in mathematical and statistical modeling from the mathematical, physical, and engineering sciences but no previous experience in ?nance. To address the diversity in background but common strong interest in the subject and in a potential career as a “quant” in the ?nancialindustry,thecoursematerialwascarefullychosennotonlytopresent basic statistical methods of importance to quantitative ?nance but also to summarize domain knowledge in ?nance and show how it can be combined with statistical modeling in ?nancial analysis and decision making. The course material evolved over the years, especially after the second author helped as the head TA during the years 2004 and 2005.

Medical Image Computing and Computer Assisted Intervention MICCAI 2000

Medical Image Computing and Computer Assisted Intervention   MICCAI 2000
Author: Scott L. Delp,Anthony M. DiGoia,Branislav Jaramaz
Publsiher: Springer Science & Business Media
Total Pages: 1254
Release: 2000-09-27
ISBN 10: 3540411895
ISBN 13: 9783540411895
Language: EN, FR, DE, ES & NL

Medical Image Computing and Computer Assisted Intervention MICCAI 2000 Book Review:

In previous work [6], we presented a novel information theoretic approach for calculating fMRI activation maps. The information-theoretic approach is - pealing in that it is a principled methodology requiring few assumptions about the structure of the fMRI signal. In that approach, activation was quanti'ed by measuring the mutual information (MI) between the protocol signal and the fMRI time-series at a givenvoxel.This measureis capable of detecting unknown nonlinear and higher-order statistical dependencies. Furthermore, it is relatively straightforward to implement. In practice,activation decisions at eachvoxelareindependent of neighboring voxels. Spurious responses are then removed by ad hoc techniques (e.g. morp- logicaloperators).Inthispaper,wedescribeanautomaticmaximumaposteriori (MAP) detection method where the well-known Ising model is used as a spatial prior.The Isingspatialpriordoes not assumethat the time-seriesofneighboring voxelsareindependentofeachother.Furthermore,removalofspuriousresponses is an implicit component of the detection formulation. In order to formulate the calculation of the activation map using this technique we ?rst demonstrate that the information-theoretic approach has a natural interpretation in the hypo- esis testing framework and that, speci'cally, our estimate of MI approximates the log-likelihood ratio of that hypothesis test. Consequently, the MAP det- tion problem using the Ising model can be formulated and solved exactly in polynomial time using the Ford and Fulkerson method [4]. We compare the results of our approach with and without spatial priors to an approachbased on the general linear model (GLM) popularized by Fristonet al [3]. We present results from three fMRI data sets. The data sets test motor, auditory, and visual cortex activation, respectively.