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Maximum likelihood estimation of parameters

Published on May 3, 2012
· DOI :10.1093/ACPROF:OSO/9780199641178.003.0007
James Durbin35
Estimated H-index: 35
,
Siem Jan Koopman46
Estimated H-index: 46
Abstract
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  • Citations (3)
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There has been a fast-growing demand for analysis tools for multivariate point-process data driven by work in neural coding and, more recently, high-frequency finance. Here we develop a true or exact as opposed to one based on time binning principal components analysis for preliminary processing of multivariate point processes. We provide a maximum likelihood estimator, an algorithm for maximization involving steepest ascent on two Stiefel manifolds, and novel constrained asymptotic analysis. Th...
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Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor model with vector autoregressive relations and time-varying cross-regression parameters at the factor level. Using techniques drawn from the state-space lit...
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