Browsing by Author "Akeyede, I."

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    Comparison of Outlier Detection Procedures in Multiple Linear Regressions
    (Scientific and Academic Publishing, 2015) Oyeyemi, G. M.; Bukoye, A.; Akeyede, I.
    Regression analysis has become one of most widely used statistical tools for analyzing multifactor data. It is appealing because it provides a conceptually simple method for investigating functional relationship among variables. A relationship is expressed in the form of an equation or a model connecting the response or dependent variable and one or more explanatory or predictor variables. The major problem that statisticians have been confronted with, while dealing with regression analysis, is presence of outliers in data. An outlier is an observation that lies outside the overall pattern of a distribution. In other words it is a point which falls more than 1.5 times the interquartile range above the third quartile or below the first quartile. Several statistics are available to detect whether or not outlier(s) are present in data. Therefore, in this study, a simulation study was conducted to investigate the performance of Deffits, Cooks distance and Mahalanobis distance at different proportion of outliers (10%, 20% and 30%) and for various sample sizes (10, 30 and 100) in first, second or both independent variables. The data were generated using R software from normal distribution while the outliers were from uniform distribution. Findings: For small and medium sample sizes and at 10% level of outliers, Mahalanobis distance should be employed for her accuracy of detection of outliers. For small, medium and large sample size with higher percentage of outliers, Deffits should be employed. For small, medium and large sample sizes, Deffits should be used in detecting outlier signal irrespective of the percentage levels of outliers in the data set. For small sample and low percent of outliers Mahalanobis distance should be employed for easy computation.
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    On Performance of some Methods of Detecting Nonlinear Stationary and Non-stationary Time Series Data
    (Faculty of Physical Sciences, Federal University of Lafia, Nigeria., 2016) Akeyede, I.; Oyeyemi, G. M.
    There has been growing interest in exploiting potential forecast gains from the nonlinear structure of autoregressive time series. Several models are available to fit nonlinear time series data. However, before investigating specific nonlinear models for time series data, it is desirable to have a test of nonlinearity in the data. And since most of real life data collected are non-stationary data. Statistical tests have been proposed in the literature to help analysts to check for the presence of nonlinearities in observed time series, these tests include Keenan and Tsay tests, and they have been used under the assumption that data is stationary. The effect of the stationarity and non-stationarity were studied on simulated data based on general class of linear and nonlinear autoregressive structure using R-software. The powers of the tests were compared at different sample sizes for the two cases. It was observed that the Tsay F-test performed better than Keenan’s tests with little order of autoregressive and increase in sample size when data is non-stationary and vice-versa when data is stationary. Finally, we provided illustrative examples by applying the statistical tests to real life datasets and results obtained were desirable.
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    On the Symmetric Balanced Incomplete Block Designs from Mutually Orthogonal Latin Squares
    (Islamic University, Uganda, 2020-05-06) Saka, A.J.; Adeleke, M.O.; Adeleke, B.L; Akeyede, I.
    A set of Mutually Orthogonal Latin Squares (MOLS) of order 7 gives rise to series of incomplete block designs, such as; balanced incomplete block designs (BIBD), and partially balanced incomplete block designs of two, three, four, five and six associate classes, that is PBIBD(k) with k =2,3,4,5 and 6. Two distinct Near-Resolvable BIBDs that are symmetric are equally obtained. All the aforementioned designs are constructed via orderly combinations of off-diagonal elements of a complete set of mutually orthogonal Latin squares (MOLS).