Journal article
Estimation of the parameters of first order autoregressive process for balanced repeated measures designs
Journal of statistical computation and simulation, v 51(1), pp 57-69
01 Dec 1994
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Abstract
A first order autoregressive process has been proposed as a possible model for the error structure of repeated measures data. In this paper, we give new method of moment estimates of the autocorrelation and the within subject variance for balanced repeated measures designs with arbitrary between subject fixed effects and interactions but with time as the only within subject effect. Interactions among the between subjects effects and time may also be included in the model. The results of simulations show that these new estimates of the parameters of the autoregressive process are less biased and more efficient than those given by Pantula and Pollock (1985).
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Details
- Title
- Estimation of the parameters of first order autoregressive process for balanced repeated measures designs
- Creators
- Marcia Polansky - Hahnemann University Hospital
- Publication Details
- Journal of statistical computation and simulation, v 51(1), pp 57-69
- Publisher
- Gordon and Breach Science Publishers
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:A1994QR61900006
- Scopus ID
- 2-s2.0-84948504390
- Other Identifier
- 991019184041704721
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- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Statistics & Probability