Show simple item record

AdvisorBakleezi, Ayman
AuthorAbou Ghaida, Wassim R.
Available date2020-07-20T08:25:25Z
Publication Date2020-06
URIhttp://hdl.handle.net/10576/15306
AbstractThe main purpose of this thesis is to study the prediction of future observations of a Log-Logistic distribution from Hybrid Censored Samples. We will study parameter point estimation, interval estimation, different point predictors will be formed such as Maximum Likelihood Predictor (MLP), Best Unbiased Predictor (BUP), and Conditional Median Predictor (CMP). Different Prediction intervals will be constructed such as Intervals based on Pivotal quantities, and High-Density Intervals (HDI). A simulation study will be run using the R software to investigate and compare the performance of all point predictors and prediction intervals. It is observed that the (BUP) is the best point predictor and the (HDI) is the best prediction interval.
Languageen
SubjectHybrid Censoring Scheme
Log-Logistic distribution
Maximum Likelihood Predictor (MLP)
Best Unbiased Predictor (BUP)
Conditional Median Predictor (CMP)
Prediction Intervals (PI)
High Density Intervals (HDI)
Maximum Likelihood Estimation (MLE)
TitleParameter Estimation and Prediction of Future Failures in the Log-Logistic Distributions Based on Hybrid-Censored Data
TypeMaster Thesis
DepartmentApplied Statistics
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record