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Author: Maria Eduarda Silva Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.
Author: Maria Eduarda Silva Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.
Author: Roberto Baragona Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Identification and estimation of outliers in time series is proposed by using empirical likelihood methods. Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types. Some other useful outlier types are considered as well. A simulation experiment is used for studying the behaviour of the empirical likelihood-based method in finite samples and indicates that the proposed methods are preferable when dealing with the non-Gaussian data. Our simulations suggest that the usual sequential procedure for multiple outlier detection is suitable also for the methods based on empirical likelihood.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
(Uncorrected OCR) Abstract of the thesis entitled BAYES AND EMPIRICAL BAYES ESTIMATION FOR THE PANEL THRESHOLD AUTOREGRESSIVE MODEL AND NON-GAUSSIAN TIME SERIES Submitted by Liu Ka Yee for the degree of Master of Philosophy at The University of Hong Kong in January 2005 A panel of time series is a collection of time series that have similar characteristics. Panel time series analysis refers to the pooling of information in a number of similar time series in order to improve the efficiency of statistical inference about the panel or the individual series. Panel time series contains more information about the data than a single series, thus can give more accurate estimations and predictions. While panel data analysis is widely used in the statistical literature, relatively little attention is paid on panel time series analysis. With the growing availability of data and computer power, panel time series will be an important tool in data analysis. Some authors studied the problem of estimation of panel autoregressive time series. Pooling of the series is one of the solutions but the uniqueness in each of the series cannot retained. Bayesian approach is another solution. However, it usually imposes too many assumptions on the prior distribution which are usually not appropriate. Li and Hui considered an empirical Bayes procedure to estimate parameters of panel autoregressive time series model. On the other hand, Nandram and Petruccelli suggested an hierarchical Bayes approach. In the literature, little has been done on the analysis of panel non-linear or non-Gaussian time series. This thesis study attempts to fill this gap in the literature by considering estimation procedures for panel non-linear and non-Gaussian time series. The estimation procedures were unified under a Bayes-Empirical Bayes framework. Simulation results reveal that the three proposed Bayesian methods can improve the least squares estimates of the panel threshold autoregressive time series and the qua.
Author: Manish Gupta Publisher: Morgan & Claypool Publishers ISBN: 162705376X Category : Computers Languages : en Pages : 131
Book Description
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers.
Author: Wilfredo Palma Publisher: John Wiley & Sons ISBN: 1118634233 Category : Mathematics Languages : en Pages : 620
Book Description
A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.
Author: J. M. Bernardo Publisher: Oxford University Press ISBN: 9780198504856 Category : Mathematics Languages : en Pages : 886
Book Description
Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.
Author: Jiangyong Yin Publisher: ISBN: Category : Languages : en Pages :
Book Description
Gaussian stochastic process is the most commonly used approach for modeling time series and geo-statistical data. The Gaussianity assumption, however, is known to be insufficient or inappropriate in many problems. In this dissertation, I develop specific non-Gaussian models to capture the asymmetry and heavy tails of many real-world data indexed in the time, space or space-time domain.
Author: Peter Congdon Publisher: John Wiley & Sons ISBN: 0470035935 Category : Mathematics Languages : en Pages : 596
Book Description
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology
Author: Peter D. Congdon Publisher: CRC Press ISBN: 0429532903 Category : Mathematics Languages : en Pages : 506
Book Description
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website
Author: Maria Eduarda Silva
Author: Maria Eduarda Silva
Author: Roberto Baragona
Author: 
Author: Manish Gupta
Author: Wilfredo Palma
Author: J. M. Bernardo
Author: Jiangyong Yin
Author: Peter Congdon
Author: Peter D. Congdon
Author: