Výsledky vyhledávání - "Sundararajan, Raanju R."

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A multivariate heavy-tailed integer-valued GARCH process with EM algorithm-based inference

Autor: Jang, Yuhyeong, Sundararajan, Raanju R., Barreto-Souza, Wagner

A new multivariate integer-valued Generalized AutoRegressive Conditional Heteroscedastic process based on a multivariate Poisson generalized inverse Gaussian distribution is proposed. The estimation of parameters of the proposed multivariate heavy-ta

Externí odkaz: http://arxiv.org/abs/2306.17776

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Frequency Band Analysis of Nonstationary Multivariate Time Series

Autor: Sundararajan, Raanju R., Bruce, Scott A.

Information from frequency bands in biomedical time series provides useful summaries of the observed signal. Many existing methods consider summaries of the time series obtained over a few well-known, pre-defined frequency bands of interest. However,

Externí odkaz: http://arxiv.org/abs/2301.03664

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Student-t Stochastic Volatility Model With Composite Likelihood EM-Algorithm

Autor: Sundararajan, Raanju R., Barreto-Souza, Wagner

A new robust stochastic volatility (SV) model having Student-t marginals is proposed. Our process is defined through a linear normal regression model driven by a latent gamma process that controls temporal dependence. This gamma process is strategica

Externí odkaz: http://arxiv.org/abs/2105.13081

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Change Point Detection in Nonstationary Sub-Hourly Wind Time Series

Autor: Ariyarathne, Sakitha, Gangammanavar, Harsha, Sundararajan, Raanju R.

In this paper, we present a change point detection method for detecting change points in multivariate nonstationary wind speed time series. The change point method identifies changes in the covariance structure and decomposes the nonstationary multiv

Externí odkaz: http://arxiv.org/abs/2105.11353

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Principal Component Analysis using Frequency Components of Multivariate Time Series

Autor: Sundararajan, Raanju R.

Dimension reduction techniques for multivariate time series decompose the observed series into a few useful independent/orthogonal univariate components. We develop a spectral domain method for multivariate second-order stationary time series that li

Externí odkaz: http://arxiv.org/abs/2010.04515

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