Scholarly works

Permanent URI for this collectionhttps://repository.ui.edu.ng/handle/123456789/422

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Author: search.filters.author.Nja, M. E.

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    Alternative goodness-of-fit test in logistic regression models
    (Medwell Journals, 2011) Nja, M. E.; Enang, E. I.; Chukwu, A. U.; Udomboso, C. G.
    The Deviance and the Pearson chi-square are two traditional goodness-of-fit tests in generalized linear models for which the logistic model is a special case. The effort involved in the computation of either the Deviance or Pearson chi-square statistic is enormous and this provides a reason for prospecting an alternative goodness-of-fit test in logistic regression models with discrete predictor variables. The Deviance is based on the log likelihood function while the Pearson chi-square derives from the discrepancies between observed and predicted counts. Replacing observed and predicted counts with observed proportions and predicted probabilities, respectively in a cross-classification data arrangement, the standard error of estimate is proposed as an alternative goodness-of-fit test in logistic regression models. The illustrative example returns favourable comparisons with Deviance and the Pearson chi-square statistics.
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    The use of gamma and weibull distributions in modeling rainfall data in Nigeria (a comparative analysis)
    (2010) Udomboso, C. G.; Chukwu, A. U.; Nja, M. E.
    The distribution of rainfall in Nigeria is not uniform due to the slight differences in the climatic conditions from one geographical region to the other. The climatic conditions ranges from the ‘very wet’ mangrove forest zone of the coastal areas, especially the South-South, to the semi arid regions of the North- West and North-East that share boundaries with the ‘very hot’ desert zone of the North Africa subcontinent. Rainfall data are examples of environmental data, which generally can be modeled by the family of exponential distributions. The Weibull probability function is the most widely used in fitting the distribution of rainfall. This study compares the results obtained by this function with another distribution proposed to the African scientists, that is, the gamma probability function by employing the Kolmogorov- Smimov (K-S) one sample test in testing the goodness- of-fit of these distributions. The results provided are useful tools for decision makers in hydrological and related establishments.