Multiple Outliers Detection Procedures in Linear Regression
作者： Robiah Adnan , Mohd Nor Mohamad , Halim Setan
DOI: 10.11113/MATEMATIKA.V19.N.502
关键词: Mathematics 、 Statistics 、 Outlier 、 Linear regression
摘要: Kertas kerja ini menghuraikan satu prosedur untuk mengenalpasti gandaan data terpenncil dalam regresi linear. Prosedur menggunakan kaedah penyesuan teguh iaitu kuasa dua trim terkecil dan berkelompok pautann tunggal mengecam terpencil yang mungkin. Kemudian, diagnostik kes berganda digunakan terpencil. Prestasi dibandingkan dengan Serbert. Simulasi Monte Carlo terbaik semua keadaan linear. Katakunci: berganda; linear; penyesuain teguh; terkecil; pautan tunggal. This paper describes a procedure for identifying multiple outliers in linear regression. This uses robust fit which is the least of trimmed squares (LTS) and single linkage clustering method to obtain potential outliers. Then multiple-case diagnostics are used from these The performance this also compared Serbert's method. simulations determining performed best all regression scenarios. Keywords: Multiple outliers, regression, fit, Least squares, linkage. Keywords: outliers; regression; fit; squares; linkage.
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