Nonparametric estimation of functionals of density from finite number of samples is an important tool in domains such as statistics, signal processing and machine learning. While several estimators have been proposed in literature, the performance of these estimators is not known. We propose a kNN class of plug-in estimators for estimating non-linear functionals of density, such as entropy, mut...

In this paper, we consider the estimation of the unknown parameter of the scaled logistic distribution on the basis of record values. The maximum likelihood method does not provide an explicit estimator for the scale parameter. In this article, we present a simple method of deriving an explicit estimator by approximating the likelihood function. Bayes estimator is obtained using importance samp...

BACKGROUND Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into acc...

Although machine learning algorithms have been successfully used in many problems, and are emerging as valuable data analysis tools, their serious practical use is affected by the fact that often they cannot produce reliable and unbiased assessments of their predictions’ quality. There exist several approaches for estimating reliability or confidence for individual classifications, and many of ...