Statistics

Permanent URI for this communityhttps://repository.ui.edu.ng/handle/123456789/360

Browse

Author: search.filters.author.Dontwi, I. K.Has files: Yes

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    An adjusted network information criterion for model selection in statistical neural network models
    (JMASM, Inc., 2016) Udomboso, C. G.; Amahia, G. N.; Dontwi, I. K.
    In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC) criterion, based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The ANIC improves model selection in more sample sizes than does the NIC.
  • Thumbnail Image
    Item
    On the level of precision of the wavelet neural network in rainfall analysis
    (2014) Udomboso, C. G.; Amahia, G. N.; Dontwi, I. K.
    This research combines the efficiency of the artificial neural network and wavelet transform in modelling rainfall. The data used were decomposed into continuous wavelet signals on a scale of 48. Each of the decomposed series was subjected to correlation test with the original data. Instead of using all the series, ten series were selected on the basis of high correlation with die original data. These series included CWT 1, CWT 2, CWT 4, CWT 3, CWT 6, CWT 8, CWT 5, CWT 10, CWT 12, and CWT 7 (according to rank). The analysis showed that except in extremely rare cases, all the series performed optimally compared to the original data. The result of the study has been able to show' that using the continuous w'avelet transform in the ANN technique, a better performance of the network is observed.
  • Thumbnail Image
    Item
    Factors affecting learning in an open and distance learning programme
    (2010) Dontwi, I. K.; Amahia, G. N.; Chukwu, A. U.; Udomboso, C. G.
    There is bound to be a shift towards those courses that will provide the knowledge and skills for economic relevance and earning power. Commerce, science and technology are likely to be oversubscribed, once driven world, seems to be diminishing steadily. When designing instruction for distance education, attention is often focused on the cognitive domain, as it is in "traditional" (face-to-face) instruction. What do the students need to know? Which instructional strategies will be most appropriate? Upon what performance criteria will learners be evaluated?