@article{ajams2021915,
author={{Senaweera, Oshada and Haddela, Prasanna S. and Dharmarathne, Gayan},
title={Effects of Random Sampling Methods on Maximum Likelihood Estimates of a Simple Logistic Regression Model},
journal={American Journal of Applied Mathematics and Statistics},
volume={9},
number={1},
pages={28--37},
year={2021},
url={http://pubs.sciepub.com/ajams/9/1/5},
issn={2328-7292},
abstract={The paper investigates the comparative effects of several random sampling methods on the maximum likelihood estimates of a simple logistic regression model. The study uses simulated data (logistic populations with pre-defined parameter values) that used Monte Carlo methods to simulate. Sampling techniques include Simple Random Sampling (SRS) and six variations of Stratified Sampling where two are single-stage Stratified Sampling and four are choice-based (two-phase) Stratified Sampling. Parameter estimates arising under each sampling technique were compared using performance measures Bias, Standard Error & Percentage of models that are feasibly estimated. The simulation-based analysis found that choice-based sampling with proportional allocation in both phases is the best-suited sampling technique for parameter estimation of a simple logistic regression model.},
doi={10.12691/ajams-9-1-5}
publisher={Science and Education Publishing}
}