Changing dynamics: Time-varying autoregressive models using generalized additive modeling.
Abstract
In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series....
Paper Details
Title
Changing dynamics: Time-varying autoregressive models using generalized additive modeling.
Published Date
Sep 1, 2017
Journal
Volume
22
Issue
3
Pages
409 - 425
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