Analyzing Postpartum Amenorrhea Duration: Estimating Weibull Distribution Parameters with EM Algorithm Using Current Status Data

Abstract

Introduction: The duration of post-partum amenorrhea (PPA), a crucial aspect of reproductive health, re- mains a significant factor in family planning and maternal well-being. Understanding the distribution of this period provides valuable insights into fertility patterns and informs contraceptive strategies. However, this duration often involves current status data, presenting challenges in accurate estimation and analysis. This study aims to employ statistical modeling, specifically utilizing the Weibull distribution and the EM algorithm, to estimate parameters related to PPA duration. The primary objective is to develop a robust methodology for parameter estimation within current status data.

Methods: The research employs the Weibull distribution, known for its applicability in current status data analyses, as a framework for modeling PPA duration. Leveraging the EM algorithm, the study develops an approach to estimate the Weibull distribution parameters from the current status data. This methodology focuses on overcoming the challenges posed by interval-censored observations, providing a more accurate understanding of the duration.

Results: The application of the EM algorithm to estimate Weibull distribution parameters yields promising results. The methodology successfully addresses the complexities of current status data, offering estimates that enhance the understanding of PPA duration. The results highlight the efficacy of the proposed approach in handling such nuanced datasets.

Conclusion:This study underscores the significance of statistical modeling techniques, particularly the Weibull distribution coupled with the EM algorithm, in estimating parameters for PPA duration analysis. The successful application of this methodology emphasizes its potential for furthering the understanding of fertility patterns and aiding in informed decisionmaking concerning reproductive health strategies.