DeCon: A tool to detect emotional concordance in multivariate time series data of emotional responding

Bulteel, Kirsten; Ceulemans, Eva; Thompson, Renee J.; Waugh, Christian E.; Gotlib, Ian H.; Tuerlinckx, Francis; Kuppens, Peter

doi:10.1016/j.biopsycho.2013.10.011

Cited by 35 publications

(39 citation statements)

References 41 publications

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“…DeCon bases change point detection on outlier identification using robust statistics (Bulteel et al, 2014). The method slides a time window of size W across the time series by sequentially deleting the first time point in the window, and adding one new observation as the last time point.…”

Section: Deconmentioning

confidence: 99%

“…For analyzing our hypothetical data, the window size was set to W = 20. In general, this parameter should be chosen considering the minimum time period within which no change is expected to occur (for more considerations and detailed simulation results, see Bulteel et al, 2014). ROBPCAwas applied to the first window, X 1 : 20 , then to the second window, X 2 : 21 , and so on, until the last window, X 31 : 50 .…”

Section: Apply Robust Pca In Each Time Window and Determinementioning

confidence: 99%

“…1 Note that Bulteel et al (2014) implemented an automatic procedure to determine the number of components and classified a time point as outlying if either the orthogonal distance or the score distance exceeds the respective cut-off. However, for the simulations settings reported in this paper, retaining all components worked equally well.…”

Section: Apply Robust Pca In Each Time Window and Determinementioning

confidence: 99%

“…Next, we apply the methods to two sets of simulated data based on Bulteel et al (2014) and on Matteson and James (2014) and to an empirical data set on pilot reactions. In the final section, the results are discussed and future research directions are enumerated.…”

mentioning

confidence: 99%

“…As soon as this sum equals at least five, the first outlying time point in the corresponding set of time points is declared as the change point. Note that the 5 out of 10 rule is recommended to balance Type 1 and Type 2 errors (see Bulteel et al, 2014). Going back to our example, Fig.…”

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confidence: 99%

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Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

et al. 2016

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Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.

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Section: Deconmentioning

confidence: 99%

Section: Apply Robust Pca In Each Time Window and Determinementioning

confidence: 99%

Section: Apply Robust Pca In Each Time Window and Determinementioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

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Emotion and the Development of Psychopathology

Cole

2016

Developmental Psychopathology

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This chapter considers the role of emotion in the development of psychopathology through the lens of the functional perspective on emotional development. First, a brief historical perspective on conceptualizations of the nature of emotion is offered, followed by a discussion of the view of emotion from the functional perspective. After summarizing prevailing views on the relation between emotional functioning and psychopathology, the chapter draws from the developmental psychopathology perspective, focusing on the typical development of emotional competence as a counterpoint to the development of psychopathology. Emotional competence is defined from the functional perspective, including its three main components: emotion expression, emotion understanding, and emotion regulation. Then the typical development of each of these components is described as well as the risks for psychopathology that are associated with each component. Conclusions about the central role of emotion in the development of competence and psychopathology are summarized with particular attention to what a functional perspective offers for future directions for research on this topic. Finally, translational implications of this perspective for clinical assessment and intervention are discussed.

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Change detection in dynamic attributed networks

Hewapathirana

2018

WIREs Data Min & Knowl

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A network provides powerful means of representing complex relationships between entities by abstracting entities as vertices, and relationships as edges connecting vertices in a graph. Beyond the presence or absence of relationships, a network may contain additional information that can be attributed to the entities and their relationships. Attaching these additional attribute data to the corresponding vertices and edges yields an attributed graph. Moreover, in the majority of real‐world applications, such as online social networks, financial networks and transactional networks, relationships between entities evolve over time. Change detection in dynamic attributed networks is an important problem in many areas, such as fraud detection, cyber intrusion detection, and health care monitoring. It is a challenging problem because it involves a time sequence of attributed graphs, each of which is usually very large and can contain many attributes attached to the vertices and edges, resulting in a complex, high‐dimensional mathematical object. In this survey we provide an overview of some of the existing change detection methods that utilize attribute information. We categorize these methods based on the levels of structure in the graph that are exploited to detect changes. These levels are vertices, edges, subgraphs, communities, and the overall graph. We focus attention on the strengths and weaknesses of these methods, including their performance and scalability. Furthermore, we discuss some publicly available dynamic network datasets and give a brief overview of models to generate dynamic attributed networks. Finally, we discuss the limitations of existing approaches identifying key areas for future research. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Algorithmic Development > Spatial and Temporal Data Mining Technologies > Machine Learning Application Areas > Business and Industry

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DeCon: A tool to detect emotional concordance in multivariate time series data of emotional responding

Cited by 35 publications

References 41 publications

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

Emotion and the Development of Psychopathology

Change detection in dynamic attributed networks

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