Model construction
In general, since there is no clear criterion for configuring the
optimal DLNM model8, various approaches can be taken
to construct this. Many studies use Akaike’s information criterion to
select the model with the best performance.9Therefore, we also considered a quasi Akaike information criterion
(QAIC) and a partial autocorrelation function (PACF) to choose the
optimal construction for the DLNM model. The quasi Akaike information
criterion (QAIC)10, the quasi-likelihood adjustments
of Akaike’s information criterion (AIC)11, provides
important information on the explanatory power of quasi Poisson models
that is used for overdispersed count data. The partial autocorrelation
function (PACF) evaluates the level of partial autocorrelation in model
residuals. We compared the mean of the absolute values of PACF (mPACF)
for the first 100 days of the models.
Table 1 shows the values of QAIC and mPACF for various models we
compared. Each model was selected by forward selection using a greedy
approach. The M5 model was selected because its QAIC was the lowest
(3289.498) while its PACF value was the second lowest (0.02849), which
was greater than the lowest by only 0.00002.