Classification into Kullback–Leibler balls in exponential families

Exponential families

Interval Estimation in Exponential Families

In this paper we consider interval estimation of the mean in the natural Exponential family with a quadratic variance function; the family comprises the binomial, Poisson, negative binomial, normal, gamma, and a sixth distribution. For the three discrete cases, the Wald confidence interval and three alternative intervals are examined by means of two term Edgeworth expansions of the coverage pro...

Nonparametric Regression in Exponential Families

Most results in nonparametric regression theory are developed only for the case of additive noise. In such a setting many smoothing techniques including wavelet thresholding methods have been developed and shown to be highly adaptive. In this paper we consider nonparametric regression in exponential families which include, for example, Poisson regression, binomial regression, and gamma regressi...

Optimally approximating exponential families

This article studies exponential families E on finite sets such that the information divergence D(P E) of an arbitrary probability distribution from E is bounded by some constant D > 0. A particular class of low-dimensional exponential families that have low values of D can be obtained from partitions of the state space. The main results concern optimality properties of these partition exponent...

Asymptotics of Exponential Families

if we use the convention that additive terms that do not contain the parameter may be dropped from log likelihoods (because they do not affect any likelihood-based statistical inferences). Thus we have another definition of exponential families: a statistical model is an exponential family of distributions if there exists a statistic Y and a parameter θ such that the log likelihood has the form...