PROBABILISTIC APPROACH TO RISKS ASSOCIATED WITH TRUNCATED DATA
Abstract
This article presents a contribution to probabilistic modeling risks linked to truncated data. We first model that the sample density of the last random sample is given by the truncation of the distribution function. Then we show that our estimator is unbiased, and also the inverse of the variance measures whatever the sample size, the precision of the estimator and if the distribution function is log-concave, then the precision is shown to be much lower than the value to be estimated.
Fields of Study & Subjects
Risk and Portfolio Optimization
Financial Risk and Volatility Modeling
Probability and Risk Models
Publication details
- Journal / source
- Far East Journal of Theoretical Statistics
- Volume
- 70
- Issue
- 1
- Pages
- 17–37
- Publication date
- 3 January 2026