AIC is commonly used for model selection in time series studies of the association between PM and mortality (Goldberg et al. 2006; Samoli et al. 2008).
Also, Tsay [19], Hurvich and Tsai [20] and Pötscher [21] have studied model selection methods in time series models.
In this paper, we propose a new approach called ensemble active learning for solving the problems of active learning and model selection at the same time.
The results of model fitting, AIC and AICc model selection suggest that the time series of the recording rate of smaller and larger eruptions can be described by double exponential decay model and exponential decay model, respectively (Fig. 2, Table 3).
The entire process, from the model selection to bootstrapping, is time consuming and computationally intractable, especially when handling large data sets.
Analytical predictions for Psoft,2 t, s) and Psoft,10 t, s) in Figure 4 and Figure 5 were obtained by the following procedure: For the given demographic model, selection coefficient, and starting time of the sweep, we first calculated the fixation probability of the adaptive allele via Equation 9 using Monte Carlo integration routines from the GNU Scientific Library (Galassi et al. 2009).
Only gene-profile accuracy is considered at model selection stage, as the time-profile accuracy depends on the set of genes selected, and therefore may not be a good indicator for the accuracy of the real prediction targets, which may have been selected because of their special roles under these experimental conditions.
In our examples, we repeat 50 times 10-fold nested cross-validation which means that for nine examples we performed 500 times full model selection process, where each model selection consists of 50 times repeated 10-fold cross-validation.
We have previously defined two mathematical functions, the information density function and information function, which are useful for model selection and optimization of scan time in PET [13].
Bank of Spain TRAMO-SEATS (Gómez and Maravall 1994, 2001a, b) has used a more powerful automatic model selection (AMS) procedure called TRAMO (Time series Regression with ARIMA noise, Missing values and Outliers), see below, before a very different signal extraction-based procedure called SEATS (Signal Extraction in ARIMA Time Series).
