Testing for long memory in the presence of a general trend

The paper studies the impact of a broadly understood trend, which includes a change point in mean and monotonic trends studied by Bhattacharyaet al.(1983), on the asymptotic behaviour of a class of tests designed to detect long memory in a stationary sequence. Our results pertain to a family of tests which are similar to Lo's (1991) modifiedR/Stest. We show that both long memory and nonstationarity (presence of trend or change points) can lead to rejection of the null hypothesis of short memory, so that further testing is needed to discriminate between long memory and some forms of nonstationarity. We provide quantitative description of trends which do or do not fool theR/S-type long memory tests. We show, in particular, that a shift in mean of a magnitude larger thanN-½, whereNis the sample size, affects the asymptotic size of the tests, whereas smaller shifts do not do so.

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A change-point problem and inference for segment signals

ESAIM Probability and Statistics ◽

10.1051/ps/2018014 ◽

2018 ◽

Vol 22 ◽

pp. 210-235

Author(s):

Victor-Emmanuel Brunel

Keyword(s):

Change Point ◽

Rates Of Convergence ◽

Extensive Study ◽

Change Points ◽

Point Problem ◽

Minimal Size ◽

Detection And Estimation ◽

Change Point Problem ◽

Minimax Rates ◽

The Impact

We address the problem of detection and estimation of one or two change-points in the mean of a series of random variables. We use the formalism of set estimation in regression: to each point of a design is attached a binary label that indicates whether that point belongs to an unknown segment and this label is contaminated with noise. The endpoints of the unknown segment are the change-points. We study the minimal size of the segment which allows statistical detection in different scenarios, including when the endpoints are separated from the boundary of the domain of the design, or when they are separated from one another. We compare this minimal size with the minimax rates of convergence for estimation of the segment under the same scenarios. The aim of this extensive study of a simple yet fundamental version of the change-point problem is two-fold: understanding the impact of the location and the separation of the change points on detection and estimation and bringing insights about the estimation and detection of convex bodies in higher dimensions.

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A first order autoregressive process with a change point: A bayesian approach based on model selection

Afrika Statistika ◽

10.16929/as/2020.2395.165 ◽

2020 ◽

Vol 15 (3) ◽

pp. 2395-2412

Author(s):

Ahmed Hamimes ◽

Chellai Fatih ◽

Rachid Benamirouche

Keyword(s):

Model Selection ◽

Change Point ◽

Applied Research ◽

Bayes Factor ◽

Autoregressive Process ◽

Selection Method ◽

Change Points ◽

First Order ◽

Model Selection Method ◽

The Impact

The change points have considerable effects in different areas of applied research. We will use in this work the pseudo-bayes factor in three autoregressive models of order (1); this method permits to analyse the impact of choice between models and allows the use of a simpler technique with model selection in time series. For application, the monthly fluctuations of the DOW-JONES series between January 1999 and September 2009 have been used; we try to detect the financial crisis between 2007 and 2008 to evaluate the model selection method.

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Long-Term Dependency Structure and Structural Breaks: Evidence from the U.S. Sector Returns and Volatility

Review of Pacific Basin Financial Markets and Policies ◽

10.1142/s021909151850008x ◽

2018 ◽

Vol 21 (02) ◽

pp. 1850008 ◽

Cited By ~ 1

Author(s):

Geoffrey Ngene ◽

Ann Nduati Mungai ◽

Allen K. Lynch

Keyword(s):

Long Memory ◽

Structural Breaks ◽

Parameter Estimates ◽

Dependency Structure ◽

Fractional Differencing ◽

Short Memory ◽

The Impact ◽

Sector Returns ◽

Daily Returns

The study investigates the impact of structural breaks on the long memory of daily returns and variance of 11 sectors. Using multiple sequential structural breaks tests, we uncover numerous and roughly shared structural breaks. Results from two non-parametric, three semi-parametric, and three parametric fractional differencing models using break-adjusted and break-unadjusted returns reveal incidence of short memory and anti-persistence in sector returns. Regarding variance, we find that the removal of breaks from the sector series dampens the fractional differencing parameter estimates. Therefore, the observed long memory in variance may be attributable to the occurrence of structural breaks in the sector series.

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The Impact of Anglicizing Former German-Language Psychology Journals on Authorship and Citation Frequencies

European Psychologist ◽

10.1027/1016-9040/a000074 ◽

2012 ◽

Vol 17 (3) ◽

pp. 190-198 ◽

Cited By ~ 2

Author(s):

Günter Krampen ◽

Thomas Huckert ◽

Gabriel Schui

Keyword(s):

Side Effects ◽

English Language ◽

General Trend ◽

Native Language ◽

Impact Factors ◽

German Language ◽

Journal Articles ◽

Number Of Citations ◽

The Impact ◽

Number Of Authors

Exemplary for other than English-language psychology journals, the impact of recent Anglicization of five former German-language psychology journals on (1) authorship (nationality, i.e., native language, and number of authors, i.e., single or multiple authorships), (2) formal characteristics of the journal (number of articles per volume and length of articles), and (3) number of citations of the articles in other journal articles, the language of the citing publications, and the impact factors (IF) is analyzed. Scientometric data on these variables are gathered for all articles published in the four years before anglicizing and in the four years after anglicizing the same journal. Results reveal rather quick changes: Citations per year since original articles’ publication increase significantly, and the IF of the journals go up markedly. Frequencies of citing in German-language journals decrease, citing in English-language journals increase significantly after the Anglicization of former German-language psychology journals, and there is a general trend of increasing citations in other languages as well. Side effects of anglicizing former German-language psychology journals include the publication of shorter papers, their availability to a more international authorship, and a slight, but significant increase in multiple authorships.

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Reconstituting the International, 1918–1923

10.1093/oso/9780199641048.003.0003 ◽

2017 ◽

Author(s):

Talbot C. Imlay

Keyword(s):

Seismic Events ◽

Great War ◽

Bolshevik Revolution ◽

Meaning And Purpose ◽

Post War ◽

The Impact ◽

First Time ◽

The Great War ◽

Do So ◽

Lengthy Process

This chapter examines the post-war efforts of European socialists to reconstitute the Socialist International. Initial efforts to cooperate culminated in an international socialist conference in Berne in February 1919 at which socialists from the two wartime camps met for the first time. In the end, however, it would take four years to reconstitute the International with the creation of the Labour and Socialist International (LSI) in 1923. That it took so long to do so is a testimony to the impact of the Great War and to the Bolshevik revolution. Together, these two seismic events compelled socialists to reconsider the meaning and purpose of socialism. The search for answers sparked prolonged debates between and within the major parties, profoundly reconfiguring the pre-war world of European socialism. One prominent stake in this lengthy process, moreover, was the nature of socialist internationalism—both its content and its functioning.

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Corruption, Campaigning, and Novelty: The 2020 Parliamentary Elections and the Evolving Patterns of Party Politics in Slovakia

East European Politics and Societies and Cultures ◽

10.1177/08883254211012765 ◽

2021 ◽

pp. 088832542110127

Author(s):

Tim Haughton ◽

Marek Rybář ◽

Kevin Deegan-Krause

Keyword(s):

Central And Eastern Europe ◽

Good Governance ◽

Political Competition ◽

General Trend ◽

Party Politics ◽

Ordinary People ◽

Electoral Rules ◽

Forward Movement ◽

Parliamentary Elections ◽

The Impact

Party politics across Central and Eastern Europe has become less structured. Many of the divides that anchored political competition have waned in recent years, weakening the attachment of voters to the existing palette of parties and making them more likely to be attracted to new and non-traditional electoral vehicles. But for such parties to succeed at the ballot box, they need to be able to frame elections and campaign effectively. Drawing on data from a specially commissioned survey, we find that the success of Ordinary People and Independent Personalities (OĽaNO) led by Igor Matovič in the 2020 parliamentary elections in Slovakia owed much to the crafting of an anti-corruption appeal combined with an effective campaign. Both mobilization and conversion of voters, particularly through television and the leaders’ debates, in the months leading up to election day ensured OĽaNO won a quarter of the vote. OĽaNO stands in stark contrast to other parties whose leaders failed to craft as effective a message, miscalculated the impact of electoral rules and in some cases were unable to distance themselves enough from their past actions. The success of OĽaNO underlines that themes related to anti-corruption and good governance have become central to party politics and political contestation. More broadly, the election and its aftermath continued a general trend of forward movement of voters from old parties to new to newer still, indicating the churn of party politics in Slovakia is likely to continue.

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Hate Crimes and Race-Based Trauma on Latinx Populations: A Critical Review of the Current Research

Trauma Violence & Abuse ◽

10.1177/1524838020979688 ◽

2020 ◽

pp. 152483802097968

Author(s):

Sarah Lockwood ◽

Carlos A. Cuevas

Keyword(s):

Interpersonal Violence ◽

Hate Crime ◽

The United States ◽

Hate Crimes ◽

Crime Victimization ◽

Physical Health Outcomes ◽

Trauma Responses ◽

The Impact ◽

Mental And Physical Health ◽

Do So

Traditionally, the literature has sought to understand the impact of racial minority status and trauma as it relates to interpersonal violence, domestic violence, and sexual assault. What has not been as extensively reviewed and summarized is how racially or ethnically motivated hate crimes impact the mental health of minorities—particularly Latinx/Hispanic groups. This review aims to summarize the current body of literature on the intersection of race-motivated hate crime and trauma responses within Latinx community. To do so, the theoretical foundation for this inquiry will build from a race-based trauma perspective. Specifically, this review connects existing frameworks for race and trauma and integrates literature that examines Latinx or Hispanic populations that have experienced discrimination, bias, or hate crime as a result of their identity or perceived identity. The importance of situating bias or hate events within the trauma literature stems from a lack of overall formal evaluation of these events, and how these occurrences are historically overlooked as a traumatic stressor. The findings of this review suggest that (1) experiencing racially motivated victimization can cause adverse mental and physical health outcomes in Latinxs and (2) currently, there is only one study that has examined the impact of hate crime on Latinxs in the United States. This leaves the field with unanswered questions about the impact of hate crime victimization among Latinxs, which is an ever-growing area in need of attention.

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Substance use and misuse in psychiatric wards

Psychiatric Bulletin ◽

10.1192/pb.24.2.43 ◽

2000 ◽

Vol 24 (2) ◽

pp. 43-46 ◽

Cited By ~ 12

Author(s):

Richard Williams ◽

Jeff Cohen

Keyword(s):

Substance Use ◽

General Trend ◽

Drug Misuse ◽

Socially Disadvantaged ◽

The World ◽

The Impact

Substance use has reached endemic proportions. Inevitably, the world of psychiatric wards must reflect issues arising in our society. Given that the populations of psychiatric wards are disproportionately younger, male and socially disadvantaged (quite apart from the impact of patients' problems that prompt admission), one might expect that drug misuse in them would match, if not outstrip, the general trend. Recognition of its impact is a key issue for patients and staff alike.

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Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽

10.3390/w13121633 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1633

Author(s):

Elena-Simona Apostol ◽

Ciprian-Octavian Truică ◽

Florin Pop ◽

Christian Esposito

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Change Point ◽

Time Series Data ◽

Multivariate Time Series ◽

Change Point Detection ◽

Change Points ◽

Series Data ◽

Prediction And Forecasting ◽

Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

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