Improving predictive accuracy of logistic regression model using ranked set samples
KCP Santos, EB Barrios - Communications in Statistics-Simulation …, 2017 - Taylor & Francis
Communications in Statistics-Simulation and Computation, 2017•Taylor & Francis
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 …
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 …
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.
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