Naziye Ak, Zeynep Tuz, Esra Aydin, Ferhat Ferhatoğlu, Murat Sari, Nail Paksoy, İzzet Doğan, Anıl Yildiz, Rian Dişçi, Pınar Mualla Saip
The effect of parity, breastfeeding history, and duration on clinical and pathological characteristics of breast cancer patients
Turkish journal of medical sciences
conventional supervised learning usually operates premise data collected underlying population however challenges may arise integrating new data different populations resulting phenomenon known dataset shift paper focuses prior probability shift distribution outcome varies across datasets conditional distribution features given outcome remains tackle challenges posed shift propose estimation algorithm efficiently combine information multiple sources unlike existing methods restricted discrete outcomes proposed approach accommodates discrete continuous outcomes also handles highdimensional covariate vectors variable selection using adaptive least absolute shrinkage selection operator penalty producing efficient estimates possess oracle property moreover novel semiparametric likelihood ratio test proposed check validity prior probability shift assumptions embedding null conditional density function neymans smooth alternatives neyman 1937 testing studyspecific parameters demonstrate effectiveness proposed method extensive simulations real data example proposed methods serve useful addition repertoire tools dealing dataset shifts