arXiv
Open Access
2016
On approximations via convolution-defined mixture models
Hien D. Nguyen
Geoffrey J. McLachlan
Abstrak
An often-cited fact regarding mixing or mixture distributions is that their density functions are able to approximate the density function of any unknown distribution to arbitrary degrees of accuracy, provided that the mixing or mixture distribution is sufficiently complex. This fact is often not made concrete. We investigate and review theorems that provide approximation bounds for mixing distributions. Connections between the approximation bounds of mixing distributions and estimation bounds for the maximum likelihood estimator of finite mixtures of location- scale distributions are reviewed.
Penulis (2)
H
Hien D. Nguyen
G
Geoffrey J. McLachlan
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2016
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓