Zobrazeno 1 - 10
of 193
pro vyhledávání: '"Parzen, Emanuel"'
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, finance, econometrics, genomics, social science, machine learning, heal
Externí odkaz:
http://arxiv.org/abs/1912.05503
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 statistical measures, methods, and exploratory tools. This article outlines
Externí odkaz:
http://arxiv.org/abs/1405.2601
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 novel statistical methods for nonparametric exploratory data modeling, a
Externí odkaz:
http://arxiv.org/abs/1311.0562
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 Polynomial (LP) like nonlinear transformations of the original time series
Externí odkaz:
http://arxiv.org/abs/1308.0642
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 traditional and novel statistical methods for modeling Small Data and B
Externí odkaz:
http://arxiv.org/abs/1308.0641
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, pioneered by machine learning research. Parzen (2001), as a part of discu
Externí odkaz:
http://arxiv.org/abs/1204.4699
This paper outlines a unified framework for high dimensional variable selection for classification problems. Traditional approaches to finding interesting variables mostly utilize only partial information through moments (like mean difference). On th
Externí odkaz:
http://arxiv.org/abs/1112.3373