In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean square error (PMSE) in simulated ou-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and...

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or...

Der folgende Beitrag befasst sich mit dem Problem der Modellselektion im Finite Mixture Partial Least Squares (FIMIX-PLS)-Ansatz. Dieser Ansatz, welcher der Methodengruppe der Mischverteilungsmodelle zuzuordnen ist, ermöglicht eine simultane Schätzung der Modellparameter bei gleichzeitiger...

In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or...

Der folgende Beitrag befasst sich mit dem Problem der Modellselektion im Finite Mixture Partial Least Squares (FIMIX-PLS)-Ansatz. Dieser Ansatz, welcher der Methodengruppe der Mischverteilungsmodelle zuzuordnen ist, ermöglicht eine simultane Schätzung der Modellparameter bei gleichzeitiger...

Applications of exponential smoothing to forecast time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to select the method appropriate to a particular time series is based on...

This paper compares the performance of using an information criterion, such as the Akaike information criterion or the Schwarz (Bayesian) information criterion, rather than hypothesis testing in consideration of the presence of a unit root for a variable and, if unknown, the presence of a trend...

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or...