Department of Statistics
Permanent URI for this collectionhttp://197.255.125.131:4000/handle/123456789/34524
Browse
2 results
Search Results
Item On the Estimation of Conditional Tail Index and Extreme Quantiles under Random Censoring(2016-09-29) Minkah, R.In the area of Statistics of Extremes, the main assumption on any set of univariate data is to regard them as a complete sample of independent and identically distributed observations from an unknown distribution function, F. However, in many real life applications such as survival analysis, observations are usually subject to random censoring and may be influenced by an underlying covariate information. In such case, the classical extreme value theory needs some adjustment to take into account the presence of censoring and covariates. In this presentation, we propose estimators of the conditional tail index and conditional extreme quantiles for heavy-tailed distributions in the presence of random censoring and covariate information. We compare the proposed estimators with the existing estimators in the literature in a large scale simulation study. The results show improvement in bias and median absolute deviation over the existing estimators of the conditional tail index and conditional extreme quantiles.Item On statistics of extremes under random censoring(2016-04-07) Minkah, R.; Asiedu, L.In the field of statistics of extremes, the most common assumption is to consider that samples are independent and identically distributed or weakly dependent and stationary from a distribution function F. However, in most real life applications such as survival analysis and reliability data, observations are usually censored. For such data sets, the classical estimators in statistics of extremes need some modification to take into account censoring. Compared to the classical statistics of extremes, the case of censoring is fairly new and in its early stages of development. In this presentation, we propose some estimators of the Extreme Value Index and extreme quantiles when data is randomly censored. We compare the proposed estimators with an exhaustive list of estimators in the literature in a large simulation study. The results show that our estimators are more robust to censoring. In addition, the estimators provide better confidence intervals when there is heavy censoring and more intermediate observations are included in the estimation.