11 citations · 34 across the 6 of their papers we have counts for
7 papers
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…
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…
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…