Article ID Journal Published Year Pages File Type
5060360 Economics Letters 2013 4 Pages PDF
Abstract
► The correspondence between learning algorithms and the Kalman filter is generalized. ► Both least squares and stochastic gradient are covered in the same united framework. ► Our approach derives these correspondences from the algorithms' non-recursive forms. ► The algorithms' gains are allowed to be (unrestrictedly) time-varying. ► Our correspondences hold exactly, instead of asymptotically approximated.
Related Topics
Social Sciences and Humanities Economics, Econometrics and Finance Economics and Econometrics
Authors
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