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Article
Publication date: 31 May 2017

Hwanseok Choi, Cheolwoo Lee and Jin Q Jeon

Conventional time series modeling may not satisfy the model validity for short-period time series data. In this study, we apply the Kernel Variant Multi-Way Principal Component…

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Abstract

Conventional time series modeling may not satisfy the model validity for short-period time series data. In this study, we apply the Kernel Variant Multi-Way Principal Component Analysis (KMPCA) to cluster multivariate time series data which havemultiple dimensions with auto- and cross-correlations. We then check whether this method works well in clustering those data by employing simulation for generalization. Two simulation studies with two different mean structures with nine combinations of auto- and cross-correlations were conducted. The results showed that KMPCA cluster two different mean structure groups over 90% success rates with an appropriate kernel function. We also found that when the mean structures are the same, auto-correlation, the number of temporal points, and the kernel function parameter have the statistically significant effects on clustering performance. The second and third order interaction effects with each of those factors also have effects on clustering success rates. Among the effects of the main factors, the kernel function parameter is the most critical factor to consider for obtaining better performance. A similar error structure may obstruct the clustering performance: strong cross-correlation, weak auto-correlation, and a larger number of temporal points. The paper also discussed some limitations of the KMPCA model and suggested directions for future research that could improve the model.

Details

Journal of Derivatives and Quantitative Studies, vol. 25 no. 2
Type: Research Article
ISSN: 2713-6647

Keywords

Article
Publication date: 1 August 2016

Joohee Lee, Tim Rehner, Hwanseok Choi, Alan Bougere and Tom Osowski

The purpose of the paper is to extend prior research on the psychological effects of the Deepwater Horizon oil spill disaster by developing and testing a conceptual model in which…

Abstract

Purpose

The purpose of the paper is to extend prior research on the psychological effects of the Deepwater Horizon oil spill disaster by developing and testing a conceptual model in which exposure to the oil spill through clean-up activity, physical symptoms, worry about the impact of the oil spill on health, and the disruption of the gulf/ocean-related lifestyle were hypothesized as predictors of depressive symptoms.

Design/methodology/approach

The analysis included a randomly selected sample of 354 subjects from the three most Southern Mississippi counties. The Center for Epidemiologic Studies Depression Scale was used to measure depressive symptoms.

Findings

Results indicated that physical symptoms since the oil spill were related to depressive symptoms directly and indirectly through worry about the impact of the oil spill on health and the disruption of the gulf/ocean-related lifestyle. Worry about the impact of the oil spill on health was related to depressive symptoms directly and indirectly through the disruption of the gulf/ocean-related lifestyle.

Originality/value

Study results highlight that uncertainty and worry about the impact of the disaster played a critical role in understanding the psychological effects of the oil spill disaster, especially among coastal residents whose lifestyles were bound up with the gulf/ocean.

Details

Disaster Prevention and Management, vol. 25 no. 4
Type: Research Article
ISSN: 0965-3562

Keywords

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