The Annals of Statistics
- Ann. Statist.
- Volume 24, Number 6 (1996), 2431-2461.
Using specially designed exponential families for density estimation
We wish to estimate the probability density $g(y)$ that produced an observed random sample of vectors $y_1, y_2, \dots, y_n$. Estimates of $g(y)$ are traditionally constructed in two quite different ways: by maximum likelihood fitting within some parametric family such as the normal or by nonparametric methods such as kernel density estimation. These two methods can be combined by putting an exponential family "through" a kernel estimator. These are the specially designed exponential families mentioned in the title. Poisson regression methods play a major role in calculations concerning such families.
Ann. Statist. Volume 24, Number 6 (1996), 2431-2461.
First available in Project Euclid: 16 September 2002
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Efron, Bradley; Tibshirani, Robert. Using specially designed exponential families for density estimation. Ann. Statist. 24 (1996), no. 6, 2431--2461. doi:10.1214/aos/1032181161. http://projecteuclid.org/euclid.aos/1032181161.