An approach to the mean shift outlier model by Tikhonov regularization and conic programming
[ X ]
Tarih
2014
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ios Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In statistical research, regression models based on data play a central role; one of these models is the linear regression model. However, this model may give misleading results when data contain outliers. The outliers in linear regression can be resolved in two stages: by using the Mean Shift Outlier Model (MSOM) and by providing a new solution for this model. First, we construct a Tikhonov regularization problem for the MSOM. Then, we treat this problem using convex optimization techniques, specifically conic quadratic programming, permitting the use of interior point methods. We present numerical examples, which reveal very good results, and we conclude with an outlook to future studies.
Açıklama
Anahtar Kelimeler
Linear Models, Outlier Observation, Mean Shift Outliers Models, Continuous Optimization, Conic Quadratic Programming, Convexity, Statistics
Kaynak
Intelligent Data Analysis
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
18
Sayı
1
