Mengatasi Masalah Multikolinearitas dan Outlier Dengan Pendekatan ROBPCA (Studi Kasus Analisis Regresi Angka Kematian Bayi di Jawa Timur)  

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Jenis: Article from Journal - ilmiah nasional
Dalam koleksi: Jurnal Matematika, Sains, dan Teknologi vol. 12 no. 1 (Mar. 2011), page 1-10.
Topik: Infant Mortality Rate; Multicollinearity; Outlier; Regression Analysis; ROBPCA
Fulltext: Sony Sunaryo.pdf (3.71KB)

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Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ138.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Multirolfinearity and outliers existence in data can be detected by various techniques. Principal Component Analysis (PCA) is one of the statistical techniques that can be used to handle data reduction and multicollinearity problem. However, PeA is vety sensitive to outliers as it based on the mean and the covariance matrix. Hubert et.al. (2005) developed ROBPCA, a robust PCA to the outliers existence. The ROBPCA combine PP technique and Minimum Co-variant Determinant (MCD) method fix solving outliers problem, In the present study, ROBPCA is applied to the study case of the regression analysis of infant mortality rate in East Java Province in 2009. The result shows that ROBPCA is more robust compare to PCA when data contains outlier. ROBPCA can explain 85.6 percent of variation by principal components, whereas, PCA needs 3 principal components to explain 86.6 percent of variation. Moreover, ROBPCA produces higher coefficient determination which means the regression model using ROBPCA is better in explaining response variable. The study findings also revealed·that the average of duration of exclusive breastfeeding has the largest contribution in lowering infant mortality rate followed by percentage of delivery assisted by medical provider and percentage of households that have access to safe drinking water

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