## The Annals of Statistics

### Exponential Models with Affine Dual Foliations

#### Abstract

Suppose an exponential model $\mathscr{M}$ is partitioned into submodels, all of the same parametric dimension. If each of these models corresponds to a linear hypothesis about the canonical parameter and also to a linear hypothesis about the mean parameter, we speak of the partitioning of $\mathscr{M}$ as an affine dual foliation. We study those cases where the parameter sets defining the hypotheses are parallel either in the canonical space or in the mean space, and obtain various characterisations and properties of these cases. It is shown, inter alia, that canonical parallelism and mean parallelism are related to likelihood independence of $\theta_1$ and $\tau_2$ (and hence to $S$-ancillarity and $S$-sufficiency), respectively to stochastic independence of $\hat{\theta}_1$ and $\hat{\tau}_2$. Here $(\theta_1, \tau_2)$ denotes a mixed parametrisation of $\mathscr{M}$ and $\hat{\theta}_1$ and $\hat{\tau}_2$ are the maximum likelihood estimators of $\theta_1$ and $\tau_2$. Also, the two types of parallelism are characterised in terms of observed and expected information. Mean parallelism is closely related to the concept of reproductivity of exponential models that forms the subject of a separate paper. A number of requisite general results for exponential families are established, and these are also of some independent interest.

#### Article information

Source
Ann. Statist., Volume 11, Number 3 (1983), 753-769.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346243

Digital Object Identifier
doi:10.1214/aos/1176346243

Mathematical Reviews number (MathSciNet)
MR707927

Zentralblatt MATH identifier
0529.62015

JSTOR