METODE SCREENING KOLMOGOROV-SMIRNOV UNTUK DATA SURVIVAL BERDIMENSI TINGGI
Abstract
Ada banyak metode screening variabel yang bisa menangani data berdimensi tinggi. Beberapa dari metode tersebut bisa mengurangi dimensi data secara efektif dan menjamin semua variabel aktif tetap muncul dengan probabilitas tinggi. Namun, kebanyakan prosedur screening yang ada saat ini dikembangkan hanya untuk data lengkap berdimensi tinggi dan tidak layak diterapkan pada data survival dengan informasi tersensor. Metode Screening Kolmogorov-Smirnov dapat dimodifikasi untuk mengatasi masalah ini dengan mengganti fungsi distribusi kumulatif dengan fungsi survival yang diestimasi dengan estimator Kaplan-Meier. Metode ini dapat bekerja dengan berbagai tipe kovariat baik itu kontinu, diskrit, maupun kategorikal. Performa dari metode ini diukur berdasarkan studi simulasi. Suatu contoh data riil mengenai ekspresi gen juga digunakan sebagai aplikasi dari metode ini.
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DOI: https://doi.org/10.15548/map.v3i1.2779
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