11 citations · 42 across the 15 of their papers we have counts for
20 papers
Breiman's "Two Cultures" Revisited and Reconciled
Subhadeep, Mukhopadhyay, Kaijun Wang
In a landmark paper published in 2001, Leo Breiman described the tense standoff between two cultures of data modeling: parametric statistical and algorithmic machine learning. The…
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…
Spectral Graph Analysis: A Unified Explanation and Modern Perspectives
Subhadeep Mukhopadhyay, Kaijun Wang
Complex networks or graphs are ubiquitous in sciences and engineering: biological networks, brain networks, transportation networks, social networks, and the World Wide Web, to nam…
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…
Statistics Educational Challenge in the 21st Century
Subhadeep Mukhopadhyay
What do we teach and what should we teach? An honest answer to this question is painful, very painful--what we teach lags decades behind what we practice. How can we reduce this `g…
Unified Statistical Theory of Spectral Graph Analysis
Subhadeep Mukhopadhyay
The goal of this paper is to show that there exists a simple, yet universal statistical logic of spectral graph analysis by recasting it into a nonparametric function estimation pr…
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…
Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications
Scott Bruce, Zeda Li, Hsiang-Chieh Yang +1
Dramatic increases in the size and complexity of modern datasets have made traditional "centralized" statistical inference prohibitive. In addition to computational challenges asso…
Large Scale Signal Detection: A Unified Perspective
Subhadeep Mukhopadhyay
There is an overwhelmingly large literature and algorithms already available on `large scale inference problems' based on different modeling techniques and cultures. Our primary go…
Strength of Connections in a Random Graph: Definition, Characterization, and Estimation
Subhadeep Mukhopadhyay
How can the `affinity' or `strength' of ties of a random graph be characterized and compactly represented? How can concepts like Fourier and inverse-Fourier like transform be devel…
LP Approach to Statistical Modeling
Subhadeep Mukhopadhyay, Emanuel Parzen
We present an approach to statistical data modeling and exploratory data analysis called `LP Statistical Data Science.' It aims to generalize and unify traditional and novel statis…
LP Mixed Data Science : Outline of Theory
Emanuel Parzen, Subhadeep Mukhopadhyay
This article presents the theoretical foundation of a new frontier of research-`LP Mixed Data Science'-that simultaneously extends and integrates the practice of traditional and no…
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…
United Statistical Algorithm, Small and Big Data: Future OF Statistician
Emanuel Parzen, Subhadeep Mukhopadhyay
This article provides the role of big idea statisticians in future of Big Data Science. We describe the `United Statistical Algorithms' framework for comprehensive unification of t…
Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application
Subhadeep Mukhopadhyay, Emanuel Parzen
A new comprehensive approach to nonlinear time series analysis and modeling is developed in the present paper. We introduce novel data-specific mid-distribution based Legendre Poly…
Modeling, dependence, classification, united statistical science, many cultures
Emanuel Parzen, Subhadeep Mukhopadhyay
Breiman (2001) proposed to statisticians awareness of two cultures: 1. Parametric modeling culture, pioneered by R.A.Fisher and Jerzy Neyman; 2. Algorithmic predictive culture, pio…
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…