Nonparametric Splitting Algorithm for Detecting Structural Changes in Predictive Relationships

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the International Conference on Compute and Data Analysis (ICCDA)
KustantajaACM
Sivut143-149
Sivumäärä7
ISBN (elektroninen)978-1-4503-5241-3
ISBN (painettu)978-1-4503-5241-3
DOI - pysyväislinkit
TilaJulkaistu - toukok. 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Compute and Data Analysis - Lakeland, Yhdysvallat
Kesto: 19 toukok. 201723 toukok. 2017

Conference

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

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