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The result of the PACF correlogram shows that using an Autoregressive (AR) model with lag 8 (the first lag with significant correlation) is appropriate.
The result of the ACF correlogram shows that using a Moving Average (MA) model with lag 8 (the first lag with significant correlation) is appropriate.
In order to obtain these conditional probabilities, a multivariate autoregressive model with lag one [AR(1)] is chosen for extending multiple duration rainfall series.
For amoxicillin, amoxicillin-clavulanate, and cefaclor, a two-compartment pharmacokinetic model with lag time was used to simulate steady-state concentration time profiles.
A two-pollutant model with lag 0 24-hr averages of both personal NO2 and PM2.5 was consistent with these findings (not shown).
The absorption percentile times of the first-order model with lag time and the two IG-absorption models correspond quite good for the upswing.
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In contrast to FMOLS, augmentation of model with lags and leads of differenced regressand and regressors in DOLS helps it to overcome the problem of asymptotic endogeneity and serial correlation.
Therefore, a first-order absorption model with lag-time was applied to all data.
For example, a model with lags of 0 through 3 days from the maximum heat index yielded approximately the same deviance explained as a model with the same day and the average of 1-, 2-, and 3-day lags.
In addition to the results listed, models with lag times between independent and dependent variables were also computed.
Further, the R square statistic was greater for all age-specific models with lag compared to those without indicating better fitted estimates.
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