Zobrazeno 1 - 10
of 357
pro vyhledávání: '"RAVISHANKER, NALINI"'
In this paper we propose univariate volatility models for irregularly spaced financial time series by modifying the regularly spaced stochastic volatility models. We also extend this approach to propose multivariate stochastic volatility (MSV) models
Externí odkaz:
http://arxiv.org/abs/2305.15343
In this paper we describe fast Bayesian statistical analysis of vector positive-valued time series, with application to interesting financial data streams. We discuss a flexible level correlated model (LCM) framework for building hierarchical models
Externí odkaz:
http://arxiv.org/abs/2206.05374
Energy usage monitoring on higher education campuses is an important step for providing satisfactory service, lowering costs and supporting the move to green energy. We present a collaboration between the Department of Statistics and Facilities Opera
Externí odkaz:
http://arxiv.org/abs/2102.03894
Autor:
Pais, Namitha1 (AUTHOR) namitha.pais@uconn.edu, Ravishanker, Nalini1 (AUTHOR) nalini.ravishanker@uconn.edu, Rajasekaran, Sanguthevar2 (AUTHOR) sanguthevar.rajasekaran@uconn.edu
Publikováno v:
Algorithms. Jul2024, Vol. 17 Issue 7, p275. 18p.
The burst of demand for TNCs has significantly changed the transportation landscape and dramatically disrupted the Vehicle for Hire (VFH) market that used to be dominated by taxicabs for many years. Since first being introduced by Uber in 2009, rides
Externí odkaz:
http://arxiv.org/abs/2008.00568
Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amoun
Externí odkaz:
http://arxiv.org/abs/2003.02353
Akademický článek
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Autor:
Ravishanker, Nalini, Chen, Renjie
The study of topology is strictly speaking, a topic in pure mathematics. However in only a few years, Topological Data Analysis (TDA), which refers to methods of utilizing topological features in data (such as connected components, tunnels, voids, et
Externí odkaz:
http://arxiv.org/abs/1909.10604