@phdthesis{digilib74861,
           month = {December},
           title = {PERBANDINGAN METODE REGRESI ROBUST ESTIMASI M DENGAN PEMBOBOT TUKEY BISQUARE DAN ESTIMASI LTS DALAM MENGATASI OUTLIER (STUDI KASUS: FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH PADA TAHUN 2024)},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 21106010068 Edia Hestiningtias},
            year = {2025},
            note = {Sri Utami Zuliana, S.Si., M.Sc., Ph.D.},
        keywords = {Regresi Robust, Estimasi M, Estimasi LTS, Pencilan, IPM},
             url = {https://digilib.uin-suka.ac.id/id/eprint/74861/},
        abstract = {This study aims to analyze the effectiveness of robust regression methods in
handling outliers in modeling the Human Development Index (HDI) in Central Java
Province in 2024. The dependent variable used is the HDI (Y ), while the independent
variables include Expected Years of Schooling (X1), Mean Years of Schooling
(X2), Life Expectancy at Birth (X3), and Real Per Capita Expenditure (X4), based
on regency and city data in Central Java Province in 2024. The analysis was conducted
using the Ordinary Least Squares (OLS) method, M-estimation with Tukey
Bisquare weighting, and the Least Trimmed Squares (LTS) estimation, following
classical assumption testing and outlier detection using leverage, DFFITS, Cook?s
Distance, and R-Student. The results indicate that the OLS method produces the
largest Residual Standard Error (RSE) of 0,1443, while M-estimation with Tukey
Bisquare weighting yields a smaller RSE of 0,1171. The LTS method demonstrates
the best performance, with the smallest RSE of 0,05997 and the highest coefficient
of determination of 0,9996. Therefore, the LTS method is the most optimal approach
for addressing outliers in HDI data and produces a more stable and accurate
regression model.}
}