Applied research conditions often make it impossible to point-identify causal estimands
without untenable assumptions. Partial identification—bounds on the range of possible …

Consider a finite sample from an unknown distribution over a countable alphabet.
Unobserved events are alphabet symbols which do not appear in the sample. Estimating the …

Consider a finite sample from an unknown multinomial distribution. Inferring the underlying
multinomial parameters is a basic problem in statistics and related fields. Currently known …

We study the relative entropy of the empirical probability vector with respect to the true
probability vector in multinomial sampling of k categories, which, when multiplied by sample …

Simultaneous confidence intervals (SCI) for multinomial proportions are a corner stone in
count data analysis and a key component in many applications. A variety of schemes were …

We study the relative entropy between the empirical estimate of a discrete distribution and
the true underlying distribution. If the minimum value of the probability mass function …

We present novel bounds for estimating discrete probability distributions under the
$\ell_\infty $ norm. These are nearly optimal in various precise senses, including a kind of …

This work proposes a new method for computing acceptance regions of exact multinomial
tests. From this an algorithm is derived, which finds exact p-values for tests of simple …

The Frequentist, Assisted by Bayes (FAB) framework aims to construct confidence regions
that leverage information about parameter values in the form of a prior distribution. FAB …

When the target of inference is a real-valued function of probability parameters in the k-
sample multinomial problem, variance estimation may be challenging. In small samples …