| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1155211 | Statistics & Probability Letters | 2008 | 7 Pages |
Abstract
The problem of approximation of the moment-determinate cumulative distribution function (cdf) from its moments is studied. This method of recovering an unknown distribution is natural in certain incomplete models like multiplicative-censoring or biased sampling when the moments of unobserved distributions are related in a simple way to the moments of an observed distribution. In this article some properties of the proposed construction are derived. The uniform and L1L1-rates of convergence of the approximated cdf to the target distribution are obtained.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Robert M. Mnatsakanov,