Akaike Information Criterion value

The Akaike Information Criterion is used to select the model which minimises the AIC(0) function for a specified value of (j). In the original formulation of the above equation, Akaike used a value of (j) = 2 but an alternative selection criterion proposed by Leontaritis and Billings [Leontaritis and Billings, 1987] is based on a value of (j) = 4. 

The selection of the appropriate population pharmacokinetic base model was guided by the following criteria a significant reduction in the objective function value (p < 0.01,6.64 points) as assessed by the Likelihood Ratio Test the Akaike Information Criterion (AIC) a decrease in the residual error a decrease in the standard error of the model parameters randomness of the distribution of individual weighted residuals versus the predicted concentration and versus time post start of cetuximab administration randomness of the distribution of the observed concentration versus individual predicted concentration values around the line of identity in a respective plot. 

If the highest substrate concentration shows a linear increase in velocity, the last component of the rate equation should be V/K, i.e., vn = (V/K)n. Inclusion of additional rate components should be justified by statistical methods, such as comparing F values for the regression analyses or the minimum Akaike information criterion estimation (MAICE) (12,13). 

Figures 11 and 12 illustrate the performance of the pR2 compared with several of the currently popular criteria on a specific data set resulting from one of the drug hunting projects at Eli Lilly. This data set has IC50 values for 1289 molecules. There were 2317 descriptors (or covariates) and a multiple linear regression model was used with forward variable selection the linear model was trained on half the data (selected at random) and evaluated on the other (hold-out) half. The root mean squared error of prediction (RMSE) for the test hold-out set is minimized when the model has 21 parameters. Figure 11 shows the model size chosen by several criteria applied to the training set in a forward selection for example, the pR2 chose 22 descriptors, the Bayesian Information Criterion chose 49, Leave One Out cross-validation chose 308, the adjusted R2 chose 435, and the Akaike Information Criterion chose 512 descriptors in the model. Although the pR2 criterion selected considerably fewer descriptors than the other methods, it had the best prediction performance. Also, only pR2 and BIC had better prediction on the test data set than the null model.

With GAM the data (covariate and individual Bayesian PM parameter estimates) would be subjected to a stepwise (single-term addition/deletion) modeling procedure. Each covariate is allowed to enter the model in any of several functional representations. The Akaike information criterion (AIC) is used as the model selection criterion (22). At each step, the model is changed by addition or deletion of a covariate that results in the largest decrease in the AIC. The search is stopped when the AIC reached a minimum value. 

When the uncertainty in the parameter values becomes too large, the analyst should consider reducing the model. The correlation matrix between parameters can be useful in selecting the parameters that can be removed to make the model smaller. There are statistical criteria that can be used to select the better model. These include the Akaike Information Criterion (AIC) value and the F-test. The AIC value is calculated using the WSS, the number of parameters in the model, and the number of data points. The model with the lower AIC values is usually selected as the better model. The statistical F-test involves the calculation of an F value from the WSS and degrees of freedom from two analyses. The calculated F value is compared with the tabled values and a decision can be made whether the more complex model provides a significant improvement in the fit to the data. The analyst using a combination of subjective and objective criteria can make an educated decision about the best model. 

Prom (2.62) it can be taken that the model s goodness of fit and the number of parameters used are counterbalanced. Among a set of model specifications, the specification with minimal IC value is recommended. In other words, information criteria aim at minimizing the residuals variance with as few parameters as possible. Often used information criteria for time series models are the Akaike information criterion (AIC)," the Schwarz information criterion (SIC) or the Hannan-Quinn information criterion (HQIC) with the following k functions ... 

When the value of a is 2 in the expression for GIC, it takes the form of the Akaike information criterion (AIC). The optimum model order p is one that minimizes the generalized or Akaike information criterion. More recent methods to estimate model order for signal sequences with a finite number of sample points include predictive least squares (PLS) and finite sample information criteria (FSIC). ... 

The ARIMA method uses historical values to set the parameters described above. These datasets can only be used when they have specific properties like homoscedasticity and uniformity. The datasets of inflation do not have these properties, therefore some deformations on the dataset need to be performed. The number of deformations needed is described by the letter d. The theory speaks of ARIMA (p,d,q) models to describe the lag structure. In this research, different ARIMA lag structures are used to model the different indexes. The choices of the p d and q are based on the Akaike Information Criterion (AIC). The model-hng is executed with Palisade s RISK software as plug-in in Microsoft Excel. 

If the calculated F value is higher than the tabled F at the chosen level of significance (often 0.05), the use of the more detailed model is supported. Another commonly used parameter is the Akaike s Information Criterion (AIC) value. It is calculated for each model, and the model producing the lowest value (most negative value) is considered the better model. The AIC value is calculated using the number of data (n), WSS, and the number of parameters (m). 

Mean absolute percent error between observed and predicted values in the prediction Akaike s Information Criterion Mean square error Adjusted r square... 

The Akaike s Information Criterion (AIC) can be used for model selection and is applicable to compare models. The AIC value is calculated according to equation [6.2] ... 

---