نتایج جستجو برای: akaike information criterion
تعداد نتایج: 1214690 فیلتر نتایج به سال:
The Modified Information Criterion (MIC) is an Akaike-like criterion which allows performance control by means of a simple a priori defined parameter, the upper-bound on the error of the first kind (false alarm probability). The criterion MIC is for example used to estimate the order of Auto-Regressive (AR) processes. The criterion can only be used to test pairs of composite hypotheses; in an A...
We examine the problem of jointly selecting the number of components and variables in finite mixture regression models. We find that the Akaike information criterion is unsatisfactory for this purpose because it overestimates the number of components, which in turn results in incorrect variables being retained in the model. Therefore, we derive a new information criterion, the mixture regressio...
BACKGROUND AND OBJECTIVES: The classification of marine animals as protected species makes data and information on them to be very important. Therefore, this led to the need to retrieve and understand the data on the event counts for stranded marine animals based on location emergence, number of individuals, behavior, and threats to their presence. Whales are g...
This study aims at better understanding the long-term relationships among the software engineering capabilities and business performance of the representative IT firms in Japan. We conducted longitudinal analyses on standardized software engineering capability scores of three surveys and tenyear business performance from 151 firms. Through panel analyses of the best Akaike Information Criteria ...
A statistical model of gear mesh vibration signals is proposed in this paper. It is seen that the sample mean is first removed from the gear mesh vibration signal, and the remaining signal components are approximated with a set of sinusoidal functions in the signal model. The signal model parameters can be estimated by the least-square method and the optimal model order is determined based on t...
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