Data available in software engineering for many applications contains variability and it is not possible to say which variable helps the process of prediction. Most work present defect prediction focused on selection best techniques. For this purpose, deep learning ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning training data par...

For model selection in mixed effects models, Vaida and Blanchard (2005) demonstrated that the marginal Akaike information criterion is appropriate as to the questions regarding the population and the conditional Akaike information criterion is appropriate as to the questions regarding the particular clusters in the data. This article shows that the marginal Akaike information criterion is asymp...

The precise interpreting of RF data starts from retrieving or knowing the exact time instant at which moment sender is turned on, this challenge implies two important issues; prevent manipulating redundant information such as unavoidable background noise speed up processing and other issue to study behavior that sender. A method has been developed automatically catch onset in transient Bluetoot...

Using an innovations state space approach, it has been found that the Akaike information criterion (AIC) works slightly better, on average, than prediction validation on withheld data, for choosing between the various common methods of exponential smoothing for forecasting. There is, however, a puzzle. Should the count of the seed states be incorporated into the penalty term in the AIC formula?...