In recent years, log-concave density estimation via maximum likelihood estimation has
emerged as a fascinating alternative to traditional nonparametric smoothing techniques …

We prove identifiability of a broad class of deep latent variable models that (a) have
universal approximation capabilities and (b) are the decoders of variational autoencoders …

The mixtools package for R provides a set of functions for analyzing a variety of finite mixture
models. These functions include both traditional methods, such as EM algorithms for …

Compared to what? Variation in the impacts of early childhood education by alternative care
type Page 1 The Annals of Applied Statistics 2016, Vol. 10, No. 3, 1245–1285 DOI: 10.1214/16-AOAS910 …

Summary Let X 1,…, X n be independent and identically distributed random vectors with a
(Lebesgue) density f. We first prove that, with probability 1, there is a unique log-concave …

Finite mixture models have offered a very important tool for exploring complex data
structures in many scientific areas, such as economics, epidemiology and finance …

Job and wage mobility can arise from firms and workers learning about workers' ability and
from workers acquiring human capital with experience. To date, the relative importance of …

Despite the existence of a large number of clustering algorithms, clustering remains a
challenging problem. As large datasets become increasingly common in a number of …

We propose an algorithm for nonparametric estimation for finite mixtures of multivariate
random vectors that strongly resembles a true EM algorithm. The vectors are assumed to …

Suppose that univariate data are drawn from a mixture of two distributions that are equal up
to a shift parameter. Such a model is known to be nonidentifiable from a nonparametric …