# On the Estimation of Population Size from a Dependent Triple Record System

## TL;DR

This paper introduces a new model for estimating population size using triple record systems that accounts for dependencies between capture attempts, improving accuracy over existing methods in real-world applications.

## Contribution

A novel dependency-incorporating model for population size estimation with maximum likelihood estimation methodology is proposed, enhancing accuracy and interpretability.

## Key findings

- The model outperforms existing methods in real data analysis.
- Simulation results show improved estimation accuracy.
- Application to public health data demonstrates practical utility.

## Abstract

Population size estimation based on capture-recapture experiment under triple record system is an interesting problem in various fields including epidemiology, population studies, etc. In many real life scenarios, there exists inherent dependency between capture and recapture attempts. We propose a novel model that successfully incorporates the possible dependency and the associated parameters possess nice interpretations. We provide estimation methodology for the population size and the associated model parameters based on maximum likelihood method. The proposed model is applied to analyze real data sets from public health and census coverage evaluation study. The performance of the proposed estimate is evaluated through extensive simulation study and the results are compared with the existing competitors. The results exhibit superiority of the proposed model over the existing competitors both in real data analysis and simulation study.