Information-Theoretic Risk Estimates

On the Estimation of Chernoff Information

This work proves the $L_2$-consistency of the derivative estimator under mild regularity conditions on the densities and their domain and reformulates this optimization via a derivative condition, whose zero locates the optimal mixture parameter, and estimates the derivative directly using a-nearest-neighbor method.

On estimation of residual entropy function with length-biased data

In this article, we investigate methods for estimating the residual entropy function of a length-biased sample. We show that the proposed estimators are strongly consistent and asymptotically normal under suitable regularity conditions. We also conduct simulations to evaluate the performance of the proposed estimation...

Optimal Simulated Annealing for Partition Function Estimation

In this note, we give a simple analysis of a non-adaptive simulated annealing algorithm for estimating the partition function of Gibbs distributions. This yields the most efficient reduction of this kind so far. We also establish lower bounds for both general and non-adaptive algorithms, showing that our algorithm is o...

Bootstrapping likelihood-ratio-type tests based on convex M-estimation

We consider likelihood-ratio-type statistics for testing restrictions on parameters built from a convex M-estimation criterion. In general, these statistics are not asymptotically pivotal. We propose a general simple nonparametric bootstrap procedure that yields asymptotically valid critical and p-values. The method...

A Shift-Dependent Generalized Doubly Truncated (Interval) Information Measure and its Properties

ON SOME PROPERTIES OF POSTERIOR PREDICTIVE DISTRIBUTIONS

This article considers the method of posterior predictive distributions as the one of the specific tools for checking the correspondence between data and the statistical model. The generated examples demonstrate that in the role of posterior predictive testing statistics (as a discrepancy measure), dispersion measures...