INFERENCE ON PARAMETERS OF THE LAPLACE DISTRIBUTION BASED ON TYPE-II CENSORED SAMPLES USING EDGEWORTH APPROXIMATION

2001 ◽  
Author(s):  
N. Balakrishnan ◽  
A. Childs ◽  
Z. Govindarajulu ◽  
M.P. Chandramouleeswaran
Keyword(s):  
Laplace Distribution ◽  
Type Ii ◽  
Censored Samples ◽  
Edgeworth Approximation

A New Method for Goodness-of-Fit Testing Based on Type-II Right Censored Samples

2008 ◽  
Vol 57 (4) ◽  
pp. 633-642 ◽  
Author(s):  
Chien-Tai Lin ◽  
Yen-Lung Huang ◽  
N. Balakrishnan
Keyword(s):  
Goodness Of Fit ◽  
New Method ◽  
Type Ii ◽  
Censored Samples ◽  
Goodness Of Fit Testing ◽  
Fit Testing

scholarly journals Model Selection Approaches for Predicting Future Order Statistics from Type II Censored Data

2018 ◽  
Vol 2018 ◽  
pp. 1-29
Author(s):  
Jyun-You Chiang ◽  
Shuai Wang ◽  
Tzong-Ru Tsai ◽  
Ting Li
Keyword(s):  
Model Selection ◽  
Order Statistics ◽  
Censored Data ◽  
Model Misspecification ◽  
Extreme Value Distribution ◽  
Type Ii ◽  
Underlying Distribution ◽  
Censored Samples ◽  
Scale Family ◽  
Type Ii Censored Data

This paper studies a discriminant problem of location-scale family in case of prediction from type II censored samples. Three model selection approaches and two types of predictors are, respectively, proposed to predict the future order statistics from censored data when the best underlying distribution is not clear with several candidates. Two members in the location-scale family, the normal distribution and smallest extreme value distribution, are used as candidates to illustrate the best model competition for the underlying distribution via using the proposed prediction methods. The performance of correct and incorrect selections under correct specification and misspecification is evaluated via using Monte Carlo simulations. Simulation results show that model misspecification has impact on the prediction precision and the proposed three model selection approaches perform well when more than one candidate distributions are competing for the best underlying distribution. Finally, the proposed approaches are applied to three data sets.

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