Browsing by Author "Kareem, A. O."
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Item Combined Exponential-Type Estimators for Finite Population Mean in Two-Phase Sampling(Asian Journal of Probability and Statistics, 2023) Oyeyemi, G. M.; Muhammad, I.; Kareem, A. O.A family of exponential-type estimator for estimating population mean in two-phase sampling when the population proportion of the auxiliary character is available is proposed in this paper. Theoretically, the bias and minimum mean square error (MSE) for the proposed estimator are obtained. The expression for MSE of the proposed exponential-type of estimator is compared with the existing estimators in the literature. The optimum values of the parameters are determined. An empirical study was carried out by comparing the proposed estimators with some of the existing estimators reviewed in the literature based on the criteria of bias, mean square error (MSE) and relative efficiency using life datasets. The result of the comparisons showed that the proposed exponential-type estimators produce a better estimate of finite population mean than the existing estimators in the sense of having higher percentage relative efficiency which implies lesser mean square error and bias. Furthermore, the realistic conditions under which the proposed class of exponential-type estimators is more efficient were also presented. Thus, the proposed estimators can be considered as significant alternatives to estimating population characteristics of real life datasets.Item A Comparison of some Discriminant Techniques in Predicting Blood Pressure(Faculty of Science, University of Ibadan, Nigeria, 2019) Yussuf, T; Adeleke, B. L.; Oyeyemi, G. M.; Adeleke, M. O.; Kareem, A. O.The paper compared the performance of the Logistic Regression (LR), Fishers Discriminant Analysis (FDA) and the Support Vector Machines (SVM) in predicting high blood pressure. The variables used in the study are Age, Gender, BMI, Cholesterol and the smoking status. The SVM model was tuned to get the best parameter combination and cost function to avoid over fitting and under fitting of the model. The tuning model was used to compare the performance of the SVM model for sample sizes of 100, 500, 5000 and 7900. This approach was carried out for both the LR and FDA. The Data was divided into train and test data sets in the ratio 80:20 for all sample sizes considered to test the performance of the fitted models. The results from the sample sizes considered showed that for sample size of 100, the FDA performed better than the LR and SVM. But for sample sizes of 500, 5000 and 7900, the SVM performed better than both the LR and LDA. The area under the receiver operating curve showed 81.6% for the test data. This means that about 81.6% of the dataset was correctly predicted. The confusion matrix for the three approaches was computed. The result obtained showed the superiority of SVM to the other two methods.Item Comparison of some Robust Estimators in Multiple Regression in the Presence of Outliers(Akamai University, U.S.A, 2021) Oyeyemi, G. M.; Aji, D. A.; Ibraheem, B. A.; Kareem, A. O.Outlier results are one of the problems of Ordinary Least Squares (OLS) in regression analysis. Some estimators have been suggested as alternatives to the Ordinary Least Squares (OLS) estimator to improve the accuracy of the parameter estimates in the linear regression model in the presence of outliers. In this study, six robust estimators of handling the problem of outliers: Robust-M, Robust-MM; Robust-S; Least Trimmed Squares (LTS); Least Median Squares (LMS); and Least Absolute Deviation (LAD) were compared with OLS using Variance criterion. The multiple linear regression model considered, had 4 predictor variables (p = 5) and one dependent variable and there were four levels each of percentage of outliers (10%, 20%, 30%, 40%), variance of outliers (σ_outlier^2=1,50,100,200) and sample sizes (n = 20, 50, 100, 200) were considered through Monte Carlo experiments. The experiment was carried out 1000 times. The results showed that when the variance of outlier is 1, that is, the outliers and variables have standard normal distribution, OLS had the least variance at all sample sizes. But as the variance increases and at all sample sizes, the robust estimators outperformed the OLS. The robust MM had least variance more consistently as the sample size increases at all variance level of the outlier and also as the sample size increases. Therefore, the Robust MM Estimator performed more consistently than the other robust estimators considered.Item An Improvement on Some Approximate Solutions to Dalenius Equation(College of Natural Sciences, Al-Hikmah University, Ilorin, Nigeria, 2016) Kareem, A. O.; Oshungade, I. O.; Oyeyemi, G. M.Dalenius researched into the problem of strata boundary determination and came up with some sets of general equations that must be satisfied to reach optimum points of stratification (OPS). These equations met a lot of criticism in terms of its difficulty and time involved in solving them as well as its practical adaptability. Thus, for easy application, sets of approximate solutions were suggested. Deficiencies in the suggested solutions include unavailability of a theory to guide the choice of class interval in their application, use of Approximate Boundary Value (ABV) and overlapping within strata. This study developed the Exact Boundary Value (EBV) approach which places the boundaries at their exact value, eliminates overlapping within strata and produces more strata formation than the ABV. In terms of the precision of the two approaches, the EBV approach was found to be much more precise than the ABV approach for both optimum and proportional allocation.Item Moving Average Stratification Algorithm for Strata Boundary Determination in Skewed Populations(Central Bank of Nigeria (CBN), 2016) Kareem, A. O.; Oshungade, I. O.; Oyeyemi, G. M.; Adejumo, A. O.Moving Average Stratification (MAS) is a new competing and simple algorithm for strata boundary determination in Stratified Sampling. It eliminates arbitrary choice of class interval associated with cumulative square root of frequency method (Dalenius and Hodges Rule (DHR) 1959) and the inherent geometric gaps created within strata by Geometric Stratification (GMS) of Gunning & Horgan (2004). It competes favorably well with DHR and GMS in terms of its precision, simplicity and speeds and therefore recommended for use in strata boundaries determination especially in skewed populations.Item A Note on the Efficiency of Geographic Stratification(Invention Journals, 2015) Kareem, A. O.; Oyeyemi, G. M.; Aiyelabegan, A. B.Often times, administrative convenience or the need for estimates for the domain of study dictates the use of Stratified Sampling using Geographic Stratification (GS). This method of strata boundary determination is ill adapted in practice due to its less amenability to mathematical approach. Despite its poor performance in terms of Precision, empirical investigation using four sets of real-life data with varying degrees of skewness shows that GS yields minimum Mean Square Error (MSE) value when compared with popular strata construction methods like cumulative square root of frequency method Dalenius and Hodges, DHR (1959) and Geometric Stratification of Gunning and Horgan, GMS, (2004) using optimum allocation.Item A Note on the Precision of Stratified Systematic Sampling(Scientific Research Publishing, 2015) Kareem, A. O.; Oshungade, I. O.; Oyeyemi, G. M.in stratified sampling in terms of the precision of the population mean base on the inherent characteristics of the population. These conflicting views were analyzed using Cochran data (1977, p.211) [1]. When the population units are ordered, variance of systematic sampling for all possible systematic samples provides equal, non-negative and most precise estimates for all the variance functions considered i.e. 1 ( sy ) 2 ( sy ) 3 ( sy ) V y = V y = V y , unlike when a single systematic sample is used and when variance of simple random sampling is used to estimate selected systematic samplesItem On Linear Stratification of Skewed and Normal Populations(Faculty of Science, University of Ibadan, Nigeria, 2016) Kareem, A. O.; Oshungade, I. O.; Oyeyemi, G. M.the literature in the appraisal of the performance of methods of strata construction which fails to account for the bias associated with each method because the most precise method may not actually be the most efficient. This study develops Linear Stratification (LS) as a new and simple approach to strata boundary determination. Strata boundaries were established with LS, cumulative square root of frequency method and Geometric Stratification. Samples were selected randomly without replacement from each stratum and estimates of the population parameters obtained. These estimates were compared i.e. LS with that of the two existing methods using four sets of real life data with varying degrees of skewness. With the Mean Square Error (MSE) value rather than minimum variance commonly used for appraisal, the results show that LS provides minimum MSE value in both skewed and normal populations, hence the most efficient when compared with the two competing methods in strata boundary determination.Item On the Choice of an Efficient Sampling Scheme within Strata(International Center for Advance Studies, India, 2015) Kareem, A. O.; Adewara, A. A.; Oyeyemi, G. M.The choice of an efficient sampling scheme within strata is one of the technical operations involved with the use of Stratified Sampling design in Survey Sampling. Literature had concentrated on the use of Simple Random Sampling (SRS) as a choice scheme within the strata. This study examined systematic sampling scheme within the strata and proved to be more precise than SRS for both optimum and proportional allocation using any method of strata boundaries determination.Item Spatial Analysis of Distribution of Inmates in Nigeria Prisons(Faculty of Physical Sciences, University of Ilorin, Nigeria, 2022) Oyeyemi, G. M.; Kareem, A. O.; Alemika, E. E. O.; Ibraheem, B. A.; Oladuti, O. M.The number of prison facilities available in Nigeria differs from state to state as well as across the regions. Hence the distribution of the inmates across these correctional facilities differs. This study investigates the distribution of the inmates across Nigeria correctional facilities (prisons) and the likely factors that may influence the distribution considering the spatial effect of the distribution. The considered factors are; year, month, state, region, gender and state population. Ordinary Least Square (OLS) was used to obtain the multiple regression model and Moran’s I test of spatial autocorrelation showed that there is presence of spatial autocorrelation in the distribution of inmates across Nigeria prisons. The Spatial Autocorrelation Regression Model (SARM) was therefore adopted to fit the regression model. While OLS approach indicated that all factors are significant except state, the SARM approach found both state and region not to be significant among the factors considered in the model. The SARM did not only give good compact spatial distribution of the inmates across Nigeria prisons it also gave a better predicted (forecast) values of inmates’ distribution than the OLS approach.