Infinitely Divisible Distributions in Statistical Inference: Heavy-Tailed Distributions and Convolution Models,
Abstract
The family of infinitely divisible distributions is shown to provide alternative formulations in several inferential situations. In particular, the family provides heavy-tailed distributions and distributions for use in models involving convolutions, such as signal-plus-noise models. Characterizations of sub-families of the infinitely divisible family are used to obtain statistical tests of membership in those sub-families. Special attention is given to the normal and normal-plus-Poisson sub-families. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 1976
- Accession Number
- ADA033385
Entities
People
- Stanley L. Sclove
Organizations
- University of Illinois at Chicago