On selecting a power transformation in time-series analysis
Article Abstract:
A practical Bayesian procedure was developed for estimating power transformation when using autoregressive moving average (ARMA) models in time-series analysis. The Gibbs sampler, a Markov Chain Monte Carlo approach, was employed to produce the posterior distributions that are relevant to the study. Two real data sets were used in the presentation of the proposed approach. The performance of the approach is contrasted with the various competing methods.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 1997
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One-sided simultaneous prediction intervals for AR(1) and MA(1) processes with exponential innovations
Article Abstract:
The simultaneous confidence regions for particular time series model types are determined. Recursive formulas are developed to determine the probability for an AR(1) stationary process based on exponential inputs covered by any sequence of constants during N steps. Probabilities of the same form are also obtained for an MA(1) process. Results show that the correlation structure of the models can be used to reduce the confidence regions area.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 1997
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Deletion diagnostics for transformations of time series
Article Abstract:
A study used a modified version of G.E.P. Box and D.R. Cox's (1969) power transformations to provide a family of transformations. Regression on a constructed variable was performed to make inference on the transformation parameter possible. The related deletion methods for the influence of individual observations on the estimated power transformation of a time series are developed.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 1996
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