1Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
2Department of Statistics, University of Colombo, Colombo, Sri Lanka
American Journal of Applied Mathematics and Statistics.
2021,
Vol. 9 No. 1, 28-37
DOI: 10.12691/ajams-9-1-5
Copyright © 2021 Science and Education PublishingCite this paper: Oshada Senaweera, Prasanna S. Haddela, Gayan Dharmarathne. Effects of Random Sampling Methods on Maximum Likelihood Estimates of a Simple Logistic Regression Model.
American Journal of Applied Mathematics and Statistics. 2021; 9(1):28-37. doi: 10.12691/ajams-9-1-5.
Correspondence to: Oshada Senaweera, Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka. Email:
oshada.senaweera@gmail.com; oshada.s@sliit.lkAbstract
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.
Keywords