TL-Moments for Type-I Censored Data with an Application to the Weibull Distribution

Abstract

This paper aims to provide an adaptation of the trimmed L (TL)-moments method to censored data. The present study concentrates on Type-I censored data. The idea of using TL-moments with censored data may seem conflicting. However, our perspective is that we can use data censored from one side and trimmed from the other side. This study is applied to estimate the two unknown parameters of the Weibull distribution. The suggested point is compared with direct L-moments and maximum likelihood (ML) methods. A Monte Carlo simulation study is carried out to compare these methods in terms of estimate average, root of mean square error (RMSE), and relative absolute biases (RABs).

Statistical Analysis of Type-II Generalized Progressively Hybrid Alpha-PIE Censored Data and Applications in Electronic Tubes and Vinyl Chloride

“…This application provides an analysis of a data set, taken from the engineering area, representing twenty lifetime values (in hundred hours) of electronic tubes; see Table 3. This data set has been first reported by Dixit and Nooghabi [32] and later discussed by Ibrahim et al [33]. First, before proceeding, to highlight the utility of the offered model as competitors based on the complete data on electronic tubes, the alpha-PIE distribution is compared to five other inverted distributions (for x > 0, α > 0 is a shape parameter and µ > 0 is a scale parameter), namely: inverted exponential (IE(µ)) by Keller et al [2]; inverted Lindley (IL(µ)) by Sharma et al [34]; inverted Weibull (IW(α, µ)) by Keller et al [35]; inverted gamma (IG(α, µ)) by Glen [36]; and inverted Nadarajah-Haghighi (INH(α, µ)) by Tahir et al [37].…”