Abstract
This paper presents an iterative algorithm that bounds the lower and upper partial moments of an arbitrary univariate random variable X by using the information contained in a sequence of finite moments of X. The obtained bounds on the partial moments imply bounds on the moments of the transformation f(X) for a certain function f:\mathbb {R}\rightarrow \mathbb {R} . Two examples illustrate the performance of the algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 89-122 |
| Journal | Computational Statistics |
| Volume | 34 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Mar 2019 |
Keywords
- Moment problem
- Bounds
- Censored distributions
- Iteration convergence
- PROBABILITY-DISTRIBUTIONS
- INFORMATION
- RECONSTRUCTION
- EXPECTATION
- TAIL