(2017). Resampling Techniques for Estimating the Perameters of Grubbs model with asymmetric heavy-tailed Distributions. The Egyptian Statistical Journal, 61(2), 125-139. doi: 10.21608/esju.2017.270129
. "Resampling Techniques for Estimating the Perameters of Grubbs model with asymmetric heavy-tailed Distributions". The Egyptian Statistical Journal, 61, 2, 2017, 125-139. doi: 10.21608/esju.2017.270129
(2017). 'Resampling Techniques for Estimating the Perameters of Grubbs model with asymmetric heavy-tailed Distributions', The Egyptian Statistical Journal, 61(2), pp. 125-139. doi: 10.21608/esju.2017.270129
Resampling Techniques for Estimating the Perameters of Grubbs model with asymmetric heavy-tailed Distributions. The Egyptian Statistical Journal, 2017; 61(2): 125-139. doi: 10.21608/esju.2017.270129
Resampling Techniques for Estimating the Perameters of Grubbs model with asymmetric heavy-tailed Distributions
In this paper, three resampling techiques are considered, namely, bootstrap, jack-knife and jackknife after bootstrap. The main objective is to study the performance of these techniques for maximum likehood estimation for the parameters using expectation conditional maximization either (ECME)algorithm for Grubbs model when the latent response follows asymmetric heavy – tailed distributions such as scale mixture of skew normal distributions (such as skew-t (ST), skew Slash (SSL) , skew contaminated normal (SCN). Also, the performance of these techniques id discussed for detection of the influential observations using local influence method for assessing the robustness of these parameter estimates under different perturbation schemes for Grubbs model. The performance is illustrated through an application using real data set under different bootstrap replications. Our results provide resampling techniques with better fit, protect against outlying observations and more precise inferences than tranditional techniques
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