Journal article
Modeling time series data with semi-reflective boundaries
Journal of applied statistics, Vol.46(9), pp.1636-1648
07/04/2019
DOI: 10.1080/02664763.2018.1561834
Abstract
Time series data are increasingly common in many areas of the health sciences, and in some instances, may have natural boundaries serving as performance guidelines or as thresholds associated with adverse outcomes. Such boundaries may be labeled as semi-reflective, in that the time series values have an increased chance of returning towards middle levels as the boundaries are approached, but boundaries can still be breached. In this paper we review a model that was previously proposed for such data and we investigate its statistical properties. Specifically, this model consists of a third-order auto-regressive projection component, parameterized as a constrained linear combination of linear, flat, and quadratic trends, and an error term that uses a logistic regression model for its sign. We describe and compare a previously-proposed estimation method with a modified version thereof, using computer simulations, as well as data examples from heart monitoring and from a driving simulator. We find that the two methods tend to give different results, with the modified technique having lower bias and more accurate confidence intervals than the previously-proposed method.
Details
- Title: Subtitle
- Modeling time series data with semi-reflective boundaries
- Creators
- Amy M. J O'Shea - Center for Comprehensive Access and Delivery Research and Evaluation, Iowa City VA Health Care SystemJeffrey D Dawson - Department of Biostatistics, University of Iowa College of Public Health
- Resource Type
- Journal article
- Publication Details
- Journal of applied statistics, Vol.46(9), pp.1636-1648
- DOI
- 10.1080/02664763.2018.1561834
- ISSN
- 0266-4763
- eISSN
- 1360-0532
- Publisher
- Taylor & Francis
- Grant note
- AG17177, AG15071 / NIH / NIA NS044930 / NIH / NINDS
- Language
- English
- Date published
- 07/04/2019
- Academic Unit
- Public Health Administration; Biostatistics; General Internal Medicine; Internal Medicine
- Record Identifier
- 9984094618302771
Metrics
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