Dissertation/Thesis Abstract

A Mixed Model for Variance of Successive Difference of Stationary Time Series: Modeling Temporal Instability in Intensive Longitudinal Data
by Jahng, Seungmin, M.A., University of Missouri - Columbia, 2008, 49; 1504621
Abstract (Summary)

Temporal instability of a stochastic process has been of interest in many areas of behavioral and social science. Recent development in data collection techniques in behavioral and health sciences, such as Ecological Momentary Assessment (EMA) enables researchers in these areas to get direct assessment on temporal fluctuations over time for many individuals. Although many researchers have used variance and autocorrelation as a temporal instability measure, their utility and interpretation are limited to index temporal instability. I propose variance of successive difference (VSD) of stationary time series as an overall index of temporal instability such that it is a function of variance and first order autocorrelation of time series. A version of variance of successive difference of unequally spaced time series is also presented as well as distinction of within-day and between-day instability measures. Given that VSD is an individual difference measure, it is proposed that group differences on these indices be explored using a mixed variance model proposed by Hedeker et al. (2008). To illustrate, we present EMA data from a study of negative mood in borderline personality disorder (BPD) and major depressive disorder (MDD) patients, resulting that BPD patients showed more negative affective instability than MDD patients.

Indexing (document details)
Advisor: Kolenikov, Stanislav
Commitee:
School: University of Missouri - Columbia
School Location: United States -- Missouri
Source: MAI 50/03M, Masters Abstracts International
Source Type: DISSERTATION
Subjects: Statistics
Keywords:
Publication Number: 1504621
ISBN: 9781267005588
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest