Economists who use survey or administrative data for inferences regarding a population
may want to combine information obtained from two or more samples drawn from the …

This package includes both Mathematical Statistics: Basic Ideas and Selected Topics,
Volume I, Second Edition, as well as Mathematical Statistics: Basic Ideas and Selected …

Bayesian inference involves two main computational challenges. First, in estimating the
parameters of some model for the data, the posterior distribution may well be highly multi …

We introduce a method for measuring the slopes of mass profiles within dwarf spheroidal
(dSph) galaxies directly from stellar spectroscopic data and without adopting a dark matter …

We present a new estimator for computing free energy differences and thermodynamic
expectations as well as their uncertainties from samples obtained from multiple equilibrium …

Empirical likelihood provides inferences whose validity does not depend on specifying a
parametric model for the data. Because it uses a likelihood, the method has certain inherent …

Nonparametric function estimation with stochastic data, otherwise known as smoothing, has
been studied by several generations of statisticians. Assisted by the recent availability of …

Page 1 NSF-CBMS Regional Conference Series in Probability and Statistics Volume 2
EMPIRICAL PROCESSES: THEORY AND APPLICATIONS David Pollard Yale University …

We show that semiparametric profile likelihoods, where the nuisance parameter has been
profiled out, behave like ordinary likelihoods in that they have a quadratic expansion. In this …

In this article, the construction of confidence regions by approximating the sampling
distribution of some statistic is studied. The true sampling distribution is estimated by an …