11 citations · 42 across the 15 of their papers we have counts for
8 papers · 1 filter
On The Problem of Relevance in Statistical Inference
Subhadeep Mukhopadhyay, Kaijun Wang
This paper is dedicated to the "50 Years of the Relevance Problem" - a long-neglected topic that begs attention from practical statisticians who are concerned with the problem of d…
Nonparametric Universal Copula Modeling
Subhadeep Mukhopadhyay, Emanuel Parzen
To handle the ubiquitous problem of "dependence learning," copulas are quickly becoming a pervasive tool across a wide range of data-driven disciplines encompassing neuroscience, f…
A Nonparametric Approach to High-dimensional k-sample Comparison Problems
Subhadeep, Mukhopadhyay, Kaijun Wang
High-dimensional k-sample comparison is a common applied problem. We construct a class of easy-to-implement nonparametric distribution-free tests based on new tools and unexplored…
Decentralized Nonparametric Multiple Testing
Subhadeep Mukhopadhyay
Consider a big data multiple testing task, where, due to storage and computational bottlenecks, one is given a very large collection of p-values by splitting into manageable chunks…
Bayesian Modeling via Goodness-of-fit
Subhadeep, Mukhopadhyay, Douglas Fletcher
The two key issues of modern Bayesian statistics are: (i) establishing principled approach for distilling statistical prior that is consistent with the given data from an initial b…
Large-Scale Mode Identification and Data-Driven Sciences
Subhadeep Mukhopadhyay
Bump-hunting or mode identification is a fundamental problem that arises in almost every scientific field of data-driven discovery. Surprisingly, very few data modeling tools are a…
CDfdr: A Comparison Density Approach to Local False Discovery Rate Estimation
Subhadeep Mukhopadhyay
Efron et al. (2001) proposed empirical Bayes formulation of the frequentist Benjamini and Hochbergs False Discovery Rate method (Benjamini and Hochberg,1995). This article attempts…
Quantile Based Variable Mining : Detection, FDR based Extraction and Interpretation
S. Mukhopadhyay, Emanuel Parzen, S. N. Lahiri
This paper outlines a unified framework for high dimensional variable selection for classification problems. Traditional approaches to finding interesting variables mostly utilize…