Improving predictive accuracy of logistic regression model using ranked set samples

ABSTRACT

Logistic regression is often confronted with separation of likelihood problem, especially with unbalanced success–failure distribution. We propose to address this issue by drawing a ranked set sample (RSS). Simulation studies illustrated the advantages of logistic regression models fitted with RSS samples with small sample size regardless of the distribution of the binary response. As sample size increases, RSS eventually becomes comparable to SRS, but still has the advantage over SRS in mitigating the problem of separation of likelihood. Even in the presence of ranking errors, models from RSS samples yield higher predictive ability than its SRS counterpart.

Keywords:

• Logistic regression model
• Ranked set sampling
• Rare characteristics
• Separation of likelihood

Mathematics Subject Classification:

• 62J12
• 62D05

Acknowledgment

The authors would like to thank Joseph Ryan Lansangan for generously helping in writting the codes.

Funding

The authors would like to thank the Philippine Statistical Research and Training Institute (PSRTI) for the Thesis Fellowship Grant for this study.