Browsing by Author "Afolayan, Razaq"

Now showing 1 - 8 of 8
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    Applications of some Exponential Related Distributions
    (Proceedings of the Professional Statisticians Society of Nigerian, 2019) Umar, M. A.; Jimoh, J.O.; Garba, Mohammed; Afolayan, Razaq; Yahya, Waheed
    Exponential family of distribution is a very popular family of distribution functions for analyzing any lifetime data with a lot of applications in different fields of knowledge. This family has distribution functions whose survival, hazard and mean residual life functions are simple and easy to study. As a result, this family has been generalized, modified and mixed with other density functions to give more flexible density functions to facilitate better modeling and analysis. This study is carried out to apply some of these distributions to real life datasets from clinical sciences, remission times of a Bladder cancer dataset. The behaviours of these distributions were illustrated graphically. The parameters of the distributions were estimated using the maximum likelihood method and their goodness-of-fits were examined. The distributions were found to provide satisfactory fits to the datasets considered. Exponentiated Exponential and Exponential-Gamma distributions were found to perform better than all the competing distributions.
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    Bayesian Estimation of Kumaraswamy Distribution under Different Loss Functions.
    (Professional Statisticians Society of Nigeria(PSSN)., 2019) Adegoke, T. M.; Nasiri, P; Adegoke, G. K.; Yahya, Waheed; Afolayan, Razaq
    In this study, the procedures of Bayesian estimation of the shape parameter of the Kumaraswamy distribution under different prior distributions are examined. The shape parameter of the Kumaraswamy distribution is assumed to follow noninformative prior distributions (such as the extension of Jeffrey’s prior distribution, Hartigan Prior distribution, and Uniform Prior distribution) and the informative prior distribution (such as the Gamma distribution and Inverted levy distribution) were adopted in this work. We compared the obtained estimates using their mean square errors under different loss functions (such as precautionary loss function, Squared error loss function, and Albayyati’s loss function). The results showed that the behaviour of the Bayesian estimations of the shape parameter of the Kumaraswamy distribution under the Squared Error loss function using Inverted levy prior distribution is the best among all the prior distributions considered.
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    Econometric Analysis of the Effects of Land Size on Cereals Production in Nigeria
    (Islamic University Multidisciplinary Journal, 2020) Akanni, Saheed B; Garba, Mohammed; Banjoko, Alabi; Afolayan, Razaq
    This study employed the techniques of unrestricted VAR to model and analyzed the causal effects of Land used for Cereals Production (LP) on Cereals Production (CP) in Nigeria for the period of 50 years (1966 to 2016). The data extracted from the repository of World Bank were used to obtain the time plots which depicted that the CP and LP series are not stationary at level. The unit root and cointegration tests carried out suggested that the series are integrated of order one I (1) and that the series are not co-integrated. This confirmed that the use of VAR(p) model was appropriate for analyzing the data. Based on the LR, FPE and AIC selection criteria, VAR (2) model was fitted to the data. Results from the fitted model estimates showed that both CP and LP series in immediate past periods t-1 (2015) and two previous periods t-2 (2014 and 2015) have significant impacts on CP series in current time period t (2016) while only LP in immediate past period t-1 (2015) has a significant effect on LP in current time period t (2016). The results of Granger causality test indicate a unidirectional relationship which runs from LP to CP. It is recommended that the Federal Government of Nigeria should re-visit the land tenure system policies and embrace those that will enhance easy acquisition of land by farmers for more cereals production.
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    Modeling Effect of Some Factor that Contribute to Cereals Yields in Nigeria using Toda- Yamamoto Techniques
    (Sule Lamido University Journal of Science and Technology (SLUJST), 2020-06) Garba, Mohammed; Akanni, Saheed B; Yahya, Waheed Babatunde; Kareem, K. Y.; Afolayan, Razaq
    This study aimed to examine the direction of causality among Cereals Production (CP), Land used for Cereals Production (LP) and Cereal Yields (CY) in Nigeria for a period of 50 years (1966 to 2016) using techniques of Toda-Yamamoto. The maximum order of integration and optimal lag order of the series confirmed that VAR(2+1) model best fitted the data. Results from the estimated model revealed that two year past values t-2 (2014 and 2015) of CP is the major determinant of CP series in current time period t (2016) while one year past value t-1 (2015) of CY and two year past values t-2 (2014 and 2015) of CP and LP are major determinants of CY series in current time period t (2016).The results of Toda-Yamamoto causality examination showed that CY is Granger caused by both CP and LP. Based on the sequence of analyses carried out in this study, it was concluded that cereals yields in Nigeria can be predicted by both cereal production and the size of farmland used for planting cereal crops. The study then recommended that adequate plots of land be allocated to farmers interested in cereals production in order to improve yields of cereals and ensure food security in the country.
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    Multivariate Time Series Analysis on the Prices of Staple Foodstuffs in Kwara State, Nigeria
    (Department of Science Education, Federal University of Technology Minna., 2016) Afolayan, Razaq; Yahya, Waheed; Garba, Mohammed; Adenuga, A. A.; Olatayo, T.
    Due to supplementary, complementary and substitute relationship between staple foodstuffs, the prices of one or more staple foodstuffs tend to influence and could be used to predict the prices of some others. This study was therefore aimed at establishing the co-movement between the prices of some major stable foodstuffs - Rice, Maize, Garri, Millet, Guinea-Corn and Beans - in Kwara State, Nigeria. Multivariate time series models were fitted to data on monthly prices of Rice, Maize, Garri, Millet, Guinea-Corn and Beans over a period of twelve years (from January 2000 to December 2012). The cointegration relations among the prices were established by applying Johansen’s cointegration tests. As a result, appropriate Vector Error Correction (VEC) model was fitted to the data. The unit root test for stationarity in the series reveals that all the series were non-stationary but they were only made to be stationary at first difference. The results from the analysis showed that there exist short term adjustments and long-term dynamics among the prices of Rice, Maize, Garri, Millet, Guinea-Corn and Beans in Nigeria over the study period. Further results showed that a Vector Error Correction (VEC) model of lag two with one cointegration equation best fits the data. The forecasting accuracy of the fitted model was determined by out-of-sample forecasts of the future prices of the selected staple foodstuffs. Suitable model’s assessment criteria such as root mean square error, mean absolute error and the like were employed to determine the efficiency of the fitted model. The data employed for the study were collected from the Kwara State office of National Bureau of Statistics, Nigeria. All analyses were performed in the environment of R Statistical package.
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    On Statistical Analysis and Modeling of Rare Events with Autoregressive integrated Moving Average (Arima) Model.
    (Journal of the Nigerian Association of Mathematical Physics, 2014-11) Olatayo, T. O.; Mayor, Andrew; Alabi, O. O.; Afolayan, Razaq
    Forecasting methods to produced numerical estimates range from relative simple techniques to complex and sophisticated techniques are discussed in this paper. Among forecasting methods were extrapolative or projective techniques i.e. moving averages and exponential smoothing. A moving average is a trend method wherein each point of a moving average of a time series is the arithmetic mean of a number of consecutive observations of the series. The number of observations in the moving average computation is chosen to minimize the effect of seasonality or other disturbances in the series [l-4]. Exponential smoothing is a flexible trend whereby past data observations arc given different weights in computing the forecast. It has the advantages of providing a simple up-to-date forecast where the new forecast is equal to the previous one plus some stated proportion of the previous periods of a forecasting error. Exponential smoothing methods are adaptable to adjustment to include trend and seasonal projections with adaptive types of optimum weighting procedures. Exponential smoothing methods are used to forecast large numbers of items. Time series decomposition methods are widely used to identify the systematic components of a time scries, trend cycle and seasonal pattern and the non systematic or random component. The seasonal Pattern is identified by first determining the seasonal indexes for each month or quarter of year and in turn these patterns arc projected ahead. The cyclical forecast may be prepared by other systematic projection or by economic judgment. Non systematic or irregular variation is usually assumed zero in a forecast but irregular adjustments may be needed for an anticipated stoppage in production or some other casual factors in the time period of the forecast. A highly analytical method for measuring seasonal fluctuations is called census I l or X-l l variant. In Regression models express the past relationships among the item being forecasted. These models are useful when adequate history of data are available on the major factors associated with variations in the item being forecasted [5,6].
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    On the Strength of Agreement between Initial and Final Academic performances in a Nigerian University System
    (ABACUS, Mathematical Association of Nigeria, 2018) Banjoko, Alabi; Yahya, Waheed; Abiodun, H. S.; Afolayan, Razaq; Garba, Mohammed; Olorede, K. O.; Dauda, K.A.; Adeleke, Mariam O.
    This paper examines the strength of agreement between academic performances of students after their first and final years in the University. Academic performances of a total of 886 students that were admitted into various academic programs in the Faculty of Science, University of Ilorin, during the 2008/2009 academic session were followed-up to their year of graduation in 2012. Information on the grade point average (GPA) of students at the end of their first year in 2008, their final cumulative grade point average (CGPA) at the end of their studies in 2012 among others were collected. Results from this study generally showed a fair agreement between students’ initial and final academic performances in Nigeria University system (p < 0.001). It was also found that about 50% of students maintained the classes of degrees they had in their first year till graduation, about 40% of them improved on their performances while the performances of about 7% of them dropped from what they had at their first year. Further results showed that students’ performance is gender sensitive. Specifically, about 45% and 60% of female and male students maintained the classes of degrees they had during their first year in the University, about 50% and 30% of them improved on theirs while about 5% and 10% of them dropped from their initial academic performances at the end of their studies respectively. Finally, students in the Biological Sciences improved on their initial academic performances more than their counterparts in the Physical Sciences. Also, female students improved on their initial academic performances more than their male counterparts. This work will serve as useful counselling guide to prospective admission seekers into the Universities and all the stakeholders at enhancing students’ academic performances in the University system.
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    Statistical Modeling and Prediction of Rainfall Time Series Data.
    (Journal of the Nigerian Association of Mathematical Physics, 2014-07) Olatayo, T. O.; Taiwo, A. I.; Afolayan, Razaq
    Climate and rainfall are highly non-linear and complicated phenomena, which requires classical , modern and detailed models to obtain accurate prediction. In this paper, we present tools for modelling and predicting the behavioural pattern in rainfall phenomena based on past observations. The paper introduces three fundamentally different approaches for designing a model , the statistical method based on autoregressive Integrated Moving Average(ARIMA), the emerging fuzzy time series(FTS) model and non-parametric method(Theil's regression). In order to evaluate prediction efficiency, we made use of 31 years annual rainfall data from year 1982 to 2012 of Ibadan, Oyo state, Nigeria. The Fuzzy time series model has its universe of discourse divided into 13 intervals and the interval with the largest number of rainfall data is divided into 4 sub intervals of equal length. Three rules were used to determine if the forecast value under FTS is upward 0.75 -point, middle or downward 0.25-point. ARIMA(1,2,1) was used to derive the weights and the regression coefficients, while the Theil's regression was used to fit a linear model. The performance of the model was evaluated using mean squared forecast error(MAE) and root mean square forecast error (RMSE) and coefficient of determination. The study reveals that FTS model can be used as an appropriate forecasting tool to predict the rainfall, since it outperformed the ARIMA and Theil's models.