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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.
After adjusting the admission data for yearly trends, day-of-week effects, ambient maximum temperature, and dew point temperature, we studied the associations of the pollutants in single-pollutant models with lag times of 0-4 days.
We then assessed the delayed effect through refitting the models using daily mean temperature on the day of death and the previous 13 days using distributed lag models with lag terms 0 1 days, 2 6 days, and 7 13 days.
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According to the sensitivity test of different lag days, in this study we quantified the "heat effect" as the extreme heat-related mortality risk with 0 3 lag days; and we used the models with lags of 0 21 days to calculate the extreme cold-related mortality risk as the "cold effect".
The model space included models with lagged values of apparent temperature, sea level pressure (up to lag 20), and an air pollutant (up to lag 5).
The Akaike Information Criterion AICC) was used to assess different models with lags up to three months, while smoothness parameters were determined using graphical visualization.
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.
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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