Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships. / Gorskikh, Olga; Malo, Pekka; Ilmonen, Pauliina.

Proceedings of the International Conference on Compute and Data Analysis (ICCDA). ACM, 2017. p. 143-149.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Gorskikh, O, Malo, P & Ilmonen, P 2017, Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships. in Proceedings of the International Conference on Compute and Data Analysis (ICCDA). ACM, pp. 143-149, International Conference on Compute and Data Analysis , Lakeland, United States, 19/05/2017. https://doi.org/10.1145/3093241.3093282

APA

Gorskikh, O., Malo, P., & Ilmonen, P. (2017). Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships. In Proceedings of the International Conference on Compute and Data Analysis (ICCDA) (pp. 143-149). ACM. https://doi.org/10.1145/3093241.3093282

Vancouver

Gorskikh O, Malo P, Ilmonen P. Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships. In Proceedings of the International Conference on Compute and Data Analysis (ICCDA). ACM. 2017. p. 143-149 https://doi.org/10.1145/3093241.3093282

Author

Gorskikh, Olga ; Malo, Pekka ; Ilmonen, Pauliina. / Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships. Proceedings of the International Conference on Compute and Data Analysis (ICCDA). ACM, 2017. pp. 143-149

Bibtex - Download

@inproceedings{5528c7208a8b47dab981da9d107ef633,
title = "Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships",
abstract = "The problem of detecting structural changes in a regression study has become crucially important in a wide variety of fields, since data generating processes in a real world are usually unstable. Taking into account the fact that relationships within observed data are often in a continuous flux, it can be challenging to make any distributional assumptions. In the current paper, we propose a new nonparametric technique which allows estimation of an unknown number of structural change points in multivariate data having univariate response. The Nonparametric Splitting algorithm is a heuristic smart search for relationship changes based on a consequential division of the data into smaller parts. The approach utilizes a nonparametric change point test to find narrow regions of change locations. Our preliminary experiments are promising and suggest potential for the high efficiency and prediction accuracy of the introduced method.",
keywords = "change point, structural changes, predictive relationships",
author = "Olga Gorskikh and Pekka Malo and Pauliina Ilmonen",
year = "2017",
month = "5",
doi = "10.1145/3093241.3093282",
language = "English",
isbn = "978-1-4503-5241-3",
pages = "143--149",
booktitle = "Proceedings of the International Conference on Compute and Data Analysis (ICCDA)",
publisher = "ACM",

}

RIS - Download

TY - GEN

T1 - Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships

AU - Gorskikh, Olga

AU - Malo, Pekka

AU - Ilmonen, Pauliina

PY - 2017/5

Y1 - 2017/5

N2 - The problem of detecting structural changes in a regression study has become crucially important in a wide variety of fields, since data generating processes in a real world are usually unstable. Taking into account the fact that relationships within observed data are often in a continuous flux, it can be challenging to make any distributional assumptions. In the current paper, we propose a new nonparametric technique which allows estimation of an unknown number of structural change points in multivariate data having univariate response. The Nonparametric Splitting algorithm is a heuristic smart search for relationship changes based on a consequential division of the data into smaller parts. The approach utilizes a nonparametric change point test to find narrow regions of change locations. Our preliminary experiments are promising and suggest potential for the high efficiency and prediction accuracy of the introduced method.

AB - The problem of detecting structural changes in a regression study has become crucially important in a wide variety of fields, since data generating processes in a real world are usually unstable. Taking into account the fact that relationships within observed data are often in a continuous flux, it can be challenging to make any distributional assumptions. In the current paper, we propose a new nonparametric technique which allows estimation of an unknown number of structural change points in multivariate data having univariate response. The Nonparametric Splitting algorithm is a heuristic smart search for relationship changes based on a consequential division of the data into smaller parts. The approach utilizes a nonparametric change point test to find narrow regions of change locations. Our preliminary experiments are promising and suggest potential for the high efficiency and prediction accuracy of the introduced method.

KW - change point

KW - structural changes

KW - predictive relationships

U2 - 10.1145/3093241.3093282

DO - 10.1145/3093241.3093282

M3 - Conference contribution

SN - 978-1-4503-5241-3

SP - 143

EP - 149

BT - Proceedings of the International Conference on Compute and Data Analysis (ICCDA)

PB - ACM

ER -

ID: 15931728