Robust Inference for the Skew Normal Regression Model Under Type II Censoring
Keywords:
Reliability, modified maximum likelihood estimators, Monte Carlo simulation, skewness, censored samplesAbstract
In this paper, we concentrate on statistical inference for the regression model with skew normal (SN) distributed error terms under type-II censoring. Iteratively reweighting algorithm (IRA) is used for computing maximum likelihood (ML) estimates of the model parameters, see Arslan (2009). We also use the non-iterative modified maximum likelihood (MML) methodology to obtain the explicit
estimators of the model parameters, see Tiku (1967). Additionally, confidence intervals for the model parameters are constructed based on the proposed estimators. Monte Carlo simulation study is used to compare the efficiencies of the ML and MML estimators, and also the performances of the corresponding confidence intervals.
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