TY  - THES
N1  - Dr. Epha Diana Supandi, S.Si., M.Sc. dan Lilih Deva Martias, M.Sc.
ID  - digilib78690
UR  - https://digilib.uin-suka.ac.id/id/eprint/78690/
A1  - Juliana Nur Azizah, NIM.: 22106010063
Y1  - 2026/08/06/
N2  - Outliers in regression analysis can distort parameter estimation and reduce model accuracy, potentially leading to invalid conclusions about influential factors. Outliers caused by measurement or data entry errors should be corrected, while outliers reflecting genuine regional conditions should be retained to preserve important information. Robust regression is used to address outliers without discarding such observations. This study compares the performance of MM-estimator, S-estimator, and Least Trimmed Squares (LTS) in modeling the Human Development Index (HDI) across 38 provinces in Indonesia in 2024, using Gross Regional Domestic Product (GRDP), Adjusted Per Capita Expenditure, Provincial Minimum Wage (UMP), Open Unemployment Rate (TPT), and Poverty Ratio as independent variables. Outlier detection using Leverage and DFFITS identified five provinces as outliers, reflecting genuine socio-economic disparities and thus retained in the analysis. Based on the highest Adjusted
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - Regresi Robust
KW  -  MM-Estimator
KW  -  S-Estimator
KW  -  LTS
KW  -  Indeks Pembangunan Manusia
KW  -  Outlier
KW  -  Adjusted
M1  - skripsi
TI  - PERBANDINGAN KINERJA METODE REGRESI ROBUST (MM, S, DAN LTS) DALAM ANALISIS INDEKS PEMBANGUNAN MANUSIA PADA DATA PROVINSI DI INDONESIA
AV  - restricted
EP  - 142
ER  -