Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

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Organisaatiot

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the International Conference on Compute and Data Analysis (ICCDA)
TilaJulkaistu - toukokuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Compute and Data Analysis - Lakeland, Yhdysvallat
Kesto: 19 toukokuuta 201723 toukokuuta 2017

Conference

ConferenceInternational Conference on Compute and Data Analysis
LyhennettäICCDA
MaaYhdysvallat
KaupunkiLakeland
Ajanjakso19/05/201723/05/2017

ID: 15931728