European Journal of Statistics

Enhanced Possibilistic Fuzzy C-Means Clustering Stunting Prevalence in Indonesia

Alivia F. Zahro — 2026-02-24

Stunting in toddlers is a significant public health problem in Indonesia due to its potential to inhibit child development and cause long-term adverse effects. Clustering the prevalence of stunting provides valuable insights for designing effective prevention policies. This study employs the Possibilistic Fuzzy C-Means (PFCM) method, validated using the Modified Partition Coefficient (MPC) index, to cluster stunting prevalence in Indonesia. The PFCM method integrates Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM), balancing membership degrees with probabilistic measures. The primary advantages of this method are its capability to handle data with uncertain membership degrees, robustness against noise, and flexibility in defining probabilistic membership values. The results obtained show that clusters with high stunting prevalence are dominated by nine provinces, namely Aceh, Jambi, Bengkulu, Bangka Islands, Central Kalimantan, Central Sulawesi, Gorontalo, West Papua, and Papua. The MPC validity score of 0.704 confirms the effectiveness of the PFCM method in categorizing stunting prevalence well, making it a robust tool to support policymaking in stunting prevention efforts.

A Note on Local Maxima in Quadratic Transmuted Distributions Likelihoods

Edoh Katchekpele — 2026-01-09

Transmutation is a widely used technique for generalizing probability distributions to improve data fitting. Its implementation often relies on maximum likelihood estimation, which reduces to a box-constrained numerical optimization problem. Despite this, many studies overlook the crucial role of the initial values required to start the optimization algorithm. In this paper, we demonstrate through two case studies on real data that improper parameter initialization can lead to convergence toward local maxima, ultimately resulting in biased estimates and incorrect conclusions. We show that the choice of starting values can significantly affect both the convergence behavior and the reliability of the final results. This study highlights the need for greater methodological rigor and increased awareness regarding parameter initialization in iterative estimation procedures, particularly within the context of transmuted distributions in order to avoid erroneous conclusions.

The Role of MYB in Prostate Cancer: A Statistical Analysis

Allison Powell — 2026-01-09

Prostate cancer (PCa) continues to be a major health issue for men around the world. While treatment advances have improved outcomes for many, aggressive forms of PCa still lead to high mortality rates. Recent research highlights the MYB gene as an important contributor to tumor growth, therapy resistance, and recurrence. What is more, MYB appears to be expressed at significantly higher levels in tumors from Black men compared to White men, suggesting a possible explanation for observed racial disparities in outcomes. This paper examines MYB expression in relation to tumor progression, androgen receptor (AR) activity, and the likelihood of biochemical recurrence. Through a combination of tissue analysis, digital imaging, and public gene expression databases, we show that MYB may be a strong predictor of poor prognosis and could serve as a future target for more personalized treatment strategies.