Author: Philip Hans Franses Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Unit root tests and cointegration tests are sensitive to atypical events as outliers and structural breaks. This paper uses outlier robust estimation techniques to reduce the impact of these events on cointegration analysis. As a byproduct of computing the robust estimator, we obtain weights for all observations in the sample. These weights can be used to identify approximate dates of those atypical events. We evaluate our method via some illustrative simulated data. Furthermore, since our robust approach involves a few additional decisions on the values of key parameters, we investigate the sensitivity of our method through extensive Monte- Carlo simulations. Finally, we present an empirical example based on real-life data to show that OLS based cointegration can yield spurious cointegration.
Author: Tiziano Bellini Publisher: ISBN: Category : Languages : en Pages : 28
Book Description
Cointegration analysis is particularly sensitive to outlying observations. Traditional robust approaches rely on parameter estimates based on weighting schemes to penalize aberrant units. This, in particular, is the idea underlying pseudo maximum likelihood (PML) robust estimators. Atypical observations, however, can reveal useful information about the investigated phenomenon. Aiming to detect these observations, we extend the forward search (FS) procedure to the cointegrated VAR model. The analysis is carried out building up subsets of increasing dimension and monitoring suitable statistics at each subset size. Simulation experiments and real data analysis highlight that our FS is more effective than the PML in detecting atypical units and data structures.
Author: Philip Hans Franses Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Standard unit root tests and cointegration tests are sensitive to atypical events such as outliers and structural breaks. This paper uses outlier robust estimation techniques to reduce the impact of these events on cointegration analysis. As a byproduct of computing the robust estimator, we obtain weights for all observations in the sample. These weights can be used to identify the approximate dates of the atypical events. We evaluate our method using some illustrative simulated data. Furthermore, since our robust approach involves a few additional decisions on the values of key parameters, we investigate the sensitivity of our method through extensive Monte-Carlo simulations. Finally, we present an empirical example based on real-life data to show that OLS-based cointegration tests can spuriously indicate stationarity.
Author: Manish Gupta Publisher: Springer Nature ISBN: 3031019059 Category : Computers Languages : en Pages : 110
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. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies
Author: Charu C. Aggarwal Publisher: Springer Science & Business Media ISBN: 1461463963 Category : Computers Languages : en Pages : 457
Book Description
With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions– the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.
Author: Muhammad Aslam Publisher: Infinite Study ISBN: Category : Mathematics Languages : en Pages : 4
Book Description
The existing Dixon’s test (DT) under classical statistics has been widely applied in a variety of fields. The main target of DT is to recognize the outlier or suspicious observation in the sample. The DT available in the literature is workable when all the observations in the sample or the population are precise, determined and certain. In practice, under the complex system, it may not possible that all observations in the data are determined.
Author: Philip Hans Franses
Author: Tiziano Bellini
Author: Philip Hans Franses
Author: Heino Bohn Nielsen
Author: Philip Hans Franses
Author: Manish Gupta
Author: Charu C. Aggarwal
Author: Muhammad Aslam
Author: G. S. Maddala
Author: Gabriel Rodríguez