Now showing items 1-5 of 5
Dynamic Characterization of Multivariate Time Series
The standard non-negative matrix factorization focuses on batch learning assuming that the fixed global latent parameters completely describe the observations. Many online extensions assume rigid constraints and smooth ...
Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods
A new approach to state filtering and parameter estimation for a class of stochastic volatility models for which the likelihood function is unknown is considered. The alpha-stable stochastic volatility model provides a ...
Dynamic Multivariate Wavelet Signal Extraction and Forecasting with Applications to Finance
Over the past few years, we have seen an increased need for analyzing the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary ...
Advances in the Analysis of Spatially Aggregated Data
An understanding of the spatial relationships in sociological and epidemiological applications is an important tool in the analysis of urban data. While point level data (e.g. observations at a given latitude/longitude) ...
Computational and Statistical Methodology for Highly Structured Data
Modern data-intensive research is typically characterized by large scale data and the impressive computational and modeling tools necessary to analyze it. Equally important, though less remarked upon, is the important ...