Besides, another challenge regarding the QG task is the correlation between question types and their corresponding answers. Subsequently, current research supports the idea of including a classifier to improve
The predictive performance of habitat models developed using logistic regression needs to be evaluated in terms of two components: reliability or calibration (the agreement between predicted probabilities of occurrence and observed proportions of sites occupied), and discrimination capacity (the ability o...
The CaViaR quantile regression models of Engle and Manganelli (2004) have shown some success in predicting the VaR risk measure for various periods, generally more stable than those that invert a distribution function. Overall, the forecasting performance of the VaR models considered varies over the...
Although the proposed methods are theoretically feasible, their empirical performance in revealing a spurious moderated mediation effect with sample data is unclear. Since both methods count on the estimate of the regression coefficients in the model (viz., a1̂, a3̂, b1̂, and b3̂), th...
The objective is to construct a model \(f : \mathbb {X} \rightarrow \mathbb {Y}\), where f denotes the regression function. Summarizing, we generate the following matrix: $$\begin{aligned} Y_{[n,p]} = \left[ \begin{array}{ccccc|c} y_{1} &{} y_{2} &{} \dots &{} y_...
We present a soft computing method for evaluating economic performance. To avoid computational explosion, we utilize intervals. This will reduce the number attributes in the dataset. Utilizing intervals allows us to overcome difficult modeling problems such as large quantity of missing data, substantial...
Therefore, a rigorous assessment of prediction performance is performed on various statistical and machine learning techniques in an attempt to determine the ‘best’ predictive model. The modeling techniques include logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE)...
Machine learning models are basically mathematical functions that represent the relationship between different aspects of data. For instance, a linear regression model uses a line to represent the relationship between “features” and “target.” The formula looks like this: ...
A panel regression of activity in the primary auditory cortex on time showed no significant linear slope (b1 = 0.0003, z = 0.56, p > 0.5). This supports the notion that a bias in attention began almost immediately (i.e. 4 sec) after the presentation of the framing ...
model predictions are under changes to the policy environment. The second way to gauge a model’s performance is out-of-sample tests, where the fit to other time periods (or other empirical contexts more generally) is used as a test of the model. This is a more demanding measure of ...