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Modeling with Robust Kernel Nonparametric Regression on Childhood Stunting in Kalimantan Samsul Arifin; Selvi Annisa; Siswanto
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.9914

Abstract

This study models stunting prevalence across 56 regencies/cities in Kalimantan using robust kernel nonparametric regression. This approach addresses the nonlinear relationship between stunting and four predictors: access to improved sanitation, low birth weight, population density, and poverty rate. An examination of influential observations using DFFITS identified five regencies as outliers; thus, the robust MM-estimator approach was applied to mitigate the influence of these extreme observations on the estimation results. Optimal bandwidth selection was performed using the Cross-Validation (CV) method across several kernel functions, namely Epanechnikov, Gaussian, and Uniform. The results demonstrated that the Uniform kernel function yielded the smallest CV value with a bandwidth combination of h1=0.6, h2=0.2, h3=0.6, and h4=0.2. The Robust Uniform Kernel model delivered the best performance, with an MSE of 2.0556, RMSE of 1.4337, MAE of 0.6581, and of 0.9510. This study indicates that robust MM-estimator kernel nonparametric regression can produce stunting prevalence estimates that are more accurate, flexible, and stable in the presence of outliers.
Identification of Prognostic Factors for Disease Recurrence-Free Survival (DRFS) in Breast Cancer Patients Using the Cox Proportional Hazards Model Putri, Dwi Cahyaning; Prabaningrum, Veny; Arifin, Samsul
Jurnal Biostatistik, Kependudukan, dan Informatika Kesehatan
Publisher : UI Scholars Hub

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Abstract

Breast cancer remains one of the most common malignancies among women worldwide, with recurrence remaining a major clinical challenge. Despite various treatment efforts, the mortality rate from breast cancer remains high, particularly among patients with worse clinical and molecular conditions. This study aims to identify factors influencing the survival of breast cancer patients using the Cox proportional hazards model. The study analyzes clinical and molecular data of breast cancer patients to identify key risk factors affecting Disease Recurrence-Free Survival (DRFS). Analysis was conducted using Kaplan-Meier, the Log Rank test, and the Cox proportional hazards model. The results showed that nodal status, tumor stage, estrogen receptor (ER) status, Genomic Grade Index (GGI), and pathological complete response (pCR) were significantly associated with Disease Recurrence-Free Survival (DRFS). Cox proportional hazards analysis demonstrated that patients with residual disease had a substantially higher risk of recurrence, whereas patients without lymph node involvement, with smaller tumors, ER-positive status, and low GGI exhibited lower recurrence risk. These findings suggest that treatment response, nodal status, tumor burden, and molecular characteristics play important roles in predicting recurrence among breast cancer patients.