
On nonparametric estimation of a mixing density via the predictive recursion algorithm
Nonparametric estimation of a mixing density based on observations from ...
read it

Estimating βmixing coefficients
The literature on statistical learning for time series assumes the asymp...
read it

Nonparametric estimation of the conditional density function with rightcensored and dependent data
In this paper, we study the local constant and the local linear estimato...
read it

Bayesian estimation of a decreasing density
Suppose X_1,..., X_n is a random sample from a bounded and decreasing de...
read it

Estimating a mixing distribution on the sphere using predictive recursion
Mixture models are commonly used when data show signs of heterogeneity a...
read it

GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
We present GradientDICE for estimating the density ratio between the sta...
read it

Disentangling Mixture of Interventions on a Causal Bayesian Network Using Aggregate Observations
We study the problem of separating a mixture of distributions, all of wh...
read it
Stochastic Approximation Algorithm for Estimating Mixing Distribution for Dependent Observations
Estimating the mixing density of a mixture distribution remains an interesting problem in statistics literature. Using a stochastic approximation method, Newton and Zhang (1999) introduced a fast recursive algorithm for estimating the mixing density of a mixture. Under suitably chosen weights the stochastic approximation estimator converges to the true solution. In Tokdar et. al. (2009) the consistency of this recursive estimation method was established. However, the proof of consistency of the resulting estimator used independence among observations as an assumption. Here, we extend the investigation of performance of Newton's algorithm to several dependent scenarios. We first prove that the original algorithm under certain conditions remains consistent when the observations are arising form a weakly dependent process with fixed marginal with the target mixture as the marginal density. For some of the common dependent structures where the original algorithm is no longer consistent, we provide a modification of the algorithm that generates a consistent estimator.
READ FULL TEXT
Comments
There are no comments yet.