Inferential Procedures for Log Logistic Distribution with Doubly Interval Censored Data

Abstract

The log logistic model with doubly interval censored data is examined. Three methods of constructing confidence interval estimates for the parameter of the model were compared and discussed. The results of the coverage probability study indicated that the Wald outperformed the likelihood ratio and jackknife inferential procedures.

A Strategy for Using Bias and RMSE as Outcomes in Monte Carlo Studies in Statistics

“…The information provided in equation 1suggests SampVar should be reported when possible to ensure RMSE is not misinterpreted. Equation 1seems to be widely known (e.g., Aydin & Şenoğlu, 2015;Bray, Lanza, & Tan, 2015) although studies describing RMSE solely as a measure of accuracy still appear (e.g., Loh, Arasan, Midi, & Abu Bakar, 2017;Tofighi, MacKinnon, & Yoon, 2012). Guidelines for treating RMSE as unacceptably large are informal.…”

Comparison of Several Imputation Techniques for Log Logistic Model with Covariate and Interval Censored Data

“…Previously, there are some researchers who had done research on the log logistic model with covariate, uncensored, right, and interval censored data. Loh et al (2017) studied the estimation procedure and Wald method for the parameters of the log logistic model with doubly interval, interval, right censored, and uncensored data. In a recent work conducted by Lai and Arasan (2020), the adequacy of the log logistic model with covariate, right, and interval censored data was investigated by applying different types of imputation techniques.…”