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Inferential Procedures For Log Logistic Distribution With Doubly Interval Censored Data, Yue Fang Loh, Jayanthi Arasan, Habshah Midi, M. R. Abu Bakar
Inferential Procedures For Log Logistic Distribution With Doubly Interval Censored Data, Yue Fang Loh, Jayanthi Arasan, Habshah Midi, M. R. Abu Bakar
Journal of Modern Applied Statistical Methods
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.
Jmasm 46: Algorithm For Comparison Of Robust Regression Methods In Multiple Linear Regression By Weighting Least Square Regression (Sas), Mohamad Shafiq, Wan Muhamad Amir, Nur Syabiha Zafakali
Jmasm 46: Algorithm For Comparison Of Robust Regression Methods In Multiple Linear Regression By Weighting Least Square Regression (Sas), Mohamad Shafiq, Wan Muhamad Amir, Nur Syabiha Zafakali
Journal of Modern Applied Statistical Methods
The aim of this study is to compare different robust regression methods in three main models of multiple linear regression and weighting multiple linear regression. An algorithm for weighting multiple linear regression by standard deviation and variance for combining different robust method is given in SAS along with an application.
A Comparison Of Different Methods Of Zero-Inflated Data Analysis And An Application In Health Surveys, Si Yang, Lisa L. Harlow, Gavino Puggioni, Colleen A. Redding
A Comparison Of Different Methods Of Zero-Inflated Data Analysis And An Application In Health Surveys, Si Yang, Lisa L. Harlow, Gavino Puggioni, Colleen A. Redding
Journal of Modern Applied Statistical Methods
The performance of several models under different conditions of zero-inflation and dispersion are evaluated. Results from simulated and real data showed that the zero-altered or zero-inflated negative binomial model were preferred over others (e.g., ordinary least-squares regression with log-transformed outcome, Poisson model) when data have excessive zeros and over-dispersion.