Oluwafemi S Oyamakin PhD

Efficiency and the unbiasedness structure of three method of estimation namely: the Weighted Least Square, Maximum Likelihood Estimation and the Method of Moment were examined in this study. Using three scenarios labelled as Case I, II & III; where Case I is model simulation based on the distributional properties of the Lee Carter model, Case II and III were majorly data contamination by self-inclusion of Outliers to the simulated dataset for High (Case II) and Low (Case III), respectively.  The behavioral patterns of the estimators were assessed at different sample sizes. Under Case I, it was noted that the larger the sample size n, the more the distribution tends to Normal but for Case II and III, the data gets inflated. The performance of each method of estimation was tested in accordance to the properties of estimators. MLE was best among the three estimators for the Lee Carter Model. The estimated parameters \(a_{x}\)  \(b_{x}\) and \(k_{t}\) were observed to approach the true ...

In this study, we sought to model the time to the first birth interval from marriage for women in Nigeria, and identify the various factors affecting this timing. The study was also set to determine the average/median survival time for marriage to first birth interval among Nigerian women, to provide enlightenment in such areas and possibly reduce anxiety levels of women who have little or no knowledge of the median survival time to first birth who might be vulnerable to the exploitations of illicit religious and medical practitioners. Data obtained from the Nigerian Demographic Health Survey (NDHS) 2018 was used for the purpose of this research. Information on the following variables was obtained: Time to first birth from marriage, Age Women's education, Wealth index, Place of residence, Employment, Contraceptive, Religion, and Region. The Kaplan-Meier estimator was used to estimate the median survival time, while the log-rank test was used to test the significance of the categ...

Efficiency and the unbiasedness structure of three method of estimation namely: the Weighted Least Square, Maximum Likelihood Estimation and the Method of Moment were examined in this study. Using three scenarios labelled as Case I, II & III; where Case I is model simulation based on the distributional properties of the Lee Carter model, Case II and III were majorly data contamination by self-inclusion of Outliers to the simulated dataset for High (Case II) and Low (Case III), respectively. The behavioral patterns of the estimators were assessed at different sample sizes. Under Case I, it was noted that the larger the sample size n, the more the distribution tends to Normal but for Case II and III, the data gets inflated. The performance of each method of estimation was tested in accordance to the properties of estimators. MLE was best among the three estimators for the Lee Carter Model. The estimated parameters and were observed to approach the true parameter as the sample size increases.

In this study, we sought to model the time to the first birth interval from marriage for women in Nigeria, and identify the various factors affecting this timing. The study was also set to determine the average/median survival time for marriage to first birth interval among Nigerian women, to provide enlightenment in such areas and possibly reduce anxiety levels of women who have little or no knowledge of the median survival time to first birth who might be vulnerable to the exploitations of illicit religious and medical practitioners.

SARS coronavirus cases have been reported all across the world in the previous two years. The rate of transmission has been steadily increasing over time. This study examines residents' knowledge, attitudes, and preventive activities for SARS-CoV-2 prevention in five local government areas in Ibadan's urban zone (total population: 1,886,800 inhabitants, census 2016).During the months of November and December, 2021, a cross-sectional survey based on the health belief model was conducted with a simple random sample of 355 respondents from densely populated regions of urban Ibadan. SARS-CoV-2 was known by a total of 262 people. Only 33.5% of the total respondents were concerned about SARS coronavirus. A small percentage (4.5%) had previously been diagnosed with SARS coronavirus, 66.7% considered a SARS-CoV-2 infection to be serious, yet only 33.5% were concerned about SARS coronavirus. The results are inconsistent in reporting preventive practices, either community-level interventions (e.g., quarantining/self-isolating after returning from travel, 23.9%; actively maintaining a certain distance between myself and people outside, 31.8%) or personal preventive behaviours (e.g., use of alcohol-based hand sanitizers, 53.2%). Participants who reported willingness to accept a SARS-CoV-2 vaccine were more likely to perceive the risk of contracting it as higher (aOR = 1.02, 95% confidence interval [CI] = 1.01-1.04), as well as knowing if a friend had previously been diagnosed with SARS coronavirus (aOR = 2.92, 95% confidence interval [CI] = 1.62-5.55), according to a multivariate analysis using stepwise binary logistic regression which had a 77.27% predictive accuracy, using a test-train, confusion matrix split. In conclusion, the findings of this study suggest that future interventions promoting SARS-CoV-2 prevention among inhabitants of urban Ibadan should focus on individuals' views of SARS coronavirus vulnerability, highlighting the benefits of personal protective behaviors.

This study examined how six estimation methods of a simultaneous equation model cope with varying degrees of correlation between pairs of random deviates using the Variance and Total Absolute Bias (TAB). A two-equation simultaneous system was considered with assumed covariance matrix. The model was structured to have a mutual correlation between pairs of random deviates which is a violation of the assumption of mutual independence between pairs of such random deviates. The correlation between the pairs of normal deviates were generated using three scenarios of r = 0.0, 0.3 and 0.5. The performances of various estimators considered were examined at various sample sizes, correlation levels and 50 replications. The sample size, 30 , 25 , 20 = N each replicated 50 times was considered. OLS is performed best when the variance is used to study the finite sample properties of the estimators in that it produces the least variances in all the cases considered and at all sample sizes. All the...

Modeling of the relationship between diameter and height of trees generally employs one of several possible nonlinear growth models, with Richards and Schnute among the most commonly used in the literature. The recently defined hyperbolastic models are nonlinear models of sigmoidal growth noted for their flexibility in representation of biological phenomena. We apply these models to a set of data representing the relationship between diameter and height for a stand of Gmelina arborea in Ibadan, Nigeria and compare the performance with several other nonlinear models. We also compare these nonlinear models with a standard allometric relation, as well as an expanded version. In addition we display the use of the multivariable form of these hyperbolastic models for use in models representing height as a function of diameter as well as other additional explanatory variables.

Probability distributions and their generalisations have contributed greatly in the modeling and analysis of random variables. However, due to the increased introduction of new distributions there has been a major problem with choosing and applying the right distribution for a given set of data. In most cases, it is discovered that the data set in question fits two or more probability distributions and hence one must be chosen among others. The Lomax-Weibull and Lomax-Log-Logistic distributions introduced in an earlier study using a Lomax-based generator were found to be positively skewed and may be victims of this situation especially when modelling positively skewed datasets. In this article, we apply the two distributions to some selected datasets to compare their performance and provide useful insight on how to select the most fit among them when dealing with a real-life situation. We used the log-likelihood value, AIC, CAIC, BIC, HQIC, Cramér-Von Mises (W*) and Anderson Darling...

In this paper, a compound continuous distribution (i.e. Exponentiated length biased exponential (E-LBE) distribution) is given. Also, the statistical properties of the proposed distribution are examined, and the parameters are obtained by maximum likelihood estimation method. The flexibility, adequacy, and superiority of the proposed model were investigated by means of applications to dataset. The result indicates that the E-LBE distribution outperformed the competing distributions considered.

Length-biased and Area-biased distributions arise in many forestry applications, as well as other environmental, econometric, and biomedical sampling problems. We examine the Length-biased and Area-biased distributions versions of the Exponentiated Weibull distribution (EW). This study introduced a new distribution based on Length-biased Exponentiated Weibull distribution (LBEW) and Area-biased Exponentiated Weibull distribution (ABEW). Some characteristics of the new distributions were obtained. Plots for the cumulative distribution function, pdf and tables with values of skewness and kurtosis were also provided. Height-Diameter (H-D) data on Bombax and Pines (Pinus caribaea) were used to demonstrate the application of the distributions. Estimation of parameters of EW, LBEW and ABEW distributions were done using the maximum likelihood approach and compared across the distributions using criteria like AIC and Loglikelihood. We therefore proposed that similar to Exponentiated Weibull distribution (EW), a better fitting of Bombax and Pines H-D data are possible by LBEW and ABEW distributions. We hope in numerous fields of theoretical and applied sciences, the findings of this study will be useful for the practitioners.

Aim: To examine the suitability of Artificial Neural Network (ANN) in predicting infant mortality and compare its performance with Logistic Regression (LR) model. Methodology: Data were partitioned into training and testing sets with ratio 7:3. Logistic and ANN models were fitted on the training set and were validated using the testing sample. Akaike Information Criterion (AIC) and Area under curve (AUC) were used as criteria for comparing the two models. The discriminative ability was measured using sensitivity and specificity. Variable importance analysis was also conducted to determine the magnitude of contribution of each predictor to the outcome. Results: The sensitivity of the classification model was 67% and 76% for the LR and the ANN models Original Research Article Jaiyeola et al.; BJMCS, 19(5): 1-14, 2016; Article no.BJMCS.28870 2 respectively. Specificity of the prediction was 94% for the two models. Overall accuracy was approximately 81% and 83% for LR and ANN respectively. The AIC values were 9462 and 9614 for ANN model and LR model respectively. Area under curve was 0.621 and 0.637 for the LR model and the ANN model respectively. The variable importance analysis showed that preceding birth interval less than 24 months and not receiving tetanus toxoid injection during pregnancy had the highest positive contribution to infant mortality. Conclusion: The artificial neural network model had a higher sensitivity than the logistic regression model. Preceding birth interval of less than 24 months and non-reception of tetanus toxoid injection by mothers' during pregnancy were important predictors of infant mortality in Nigeria.

Richrads growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richards growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richards growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richards nonlinear growth models better than the classical Richards growth model.

In other to study the complex simultaneous relationships existing in forest/tree growth modeling, six estimation methods of a simultaneous equation model are examined to determine how they cope with varying degrees of correlation between pairs of random deviates using average parameter estimates. A two-equation simultaneous system assumed covariance matrix was considered. The model was structured to have a mutual correlation between pairs of random deviates: a violation of the assumption of mutual independence between pairs of such random deviates. The correlation between the pairs of normal deviates were generated using three scenarios r = 0.0, 0.3 and 0.5. The performances of estimators considered were examined at various sample sizes (N = 20, 25, 30) and correlation levels with 50 replications for each. Using the average of parameter estimates criterion, 2 3SLIML were the best estimators followed by FIML and OLS for the three cases studied. Also, as sample size increases from 20 to 25 to 30, 2-3SLIML performed best and was most consistent.

In other to give an objective explanation to the effect of some natural phenomenon in the study of climate change, especially global warming, monthly recorded agro-meteorological data was collected from the Forestry Research Institute of Nigeria's (FRIN) agro-meteorological station. Least square method was used to estimate the trend in the series and the trend equation for rainfall and temperature. Time series method using additive model for the deterministic approach was employed in estimating the seasonal variation/index and also to make elementary forecast of the future values of temperature and rainfall. However, before the analysis was carried out, the time plots were plotted for the two variables. The plots of the AC and PAC neither decay exponentially to zero or cut off which is a characteristic of autoregressive (AR) model. The reverse does not suggest moving average (MA) also. The two corellograms exhibit characteristics of autoregressive moving average (ARMA) based on the inability to display a characteristic pattern, which suggests it to be a mixed model of AR and MA. The minimum AIC value is 10.90464 which is less than the initial one (10.95916). There is no serial correlation in the residuals as indicated by the Durbin Watson statistic (DW=2.034172) and Breusch-Godfrey Serial correlation test and the model parameters are significant at 1% and 5% level.

This study examined how six estimation methods of a simultaneous equation model cope with varying degrees of correlation between pairs of random deviates using the Variance and Total Absolute Bias (TAB). A two-equation simultaneous system was considered with assumed covariance matrix. The model was structured to have a mutual correlation between pairs of random deviates which is a violation of the assumption of mutual independence between pairs of such random deviates. The correlation between the pairs of normal deviates were generated using three scenarios of r = 0.0, 0.3 and 0.5. The performances of various estimators considered were examined at various sample sizes, correlation levels and 50 replications. The sample size, 30 , 25 , 20 = N each replicated 50 times was considered. OLS is performed best when the variance is used to study the finite sample properties of the estimators in that it produces the least variances in all the cases considered and at all sample sizes. All the estimators revealed an asymptotic pattern under CASE I.

Richrads growth equation being a generalized logistic growth equation was improved upon by introducing an
allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richards growth
model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was
compared with the classical Richards growth model an approach which mimicked the natural variability of heights/diameter
increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of
determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov Smirnov test and
Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh
over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richards
nonlinear growth models better than the classical Richards growth model.

In other to give an objective explanation to the effect of some natural phenomenon in the study of climate change, especially global warming, monthly recorded agro-meteorological data was collected from the Forestry Research Institute of Nigeria’s (FRIN) agro-meteorological station. Least square method was used to estimate the trend in the series and the trend equation for rainfall and temperature. Time series method using additive model for the deterministic approach was employed in estimating the seasonal variation/index and also to make elementary forecast of the future values of temperature and rainfall. However, before the analysis was carried out, the time plots were plotted for the two variables.
The plots of the AC and PAC neither decay exponentially to zero or cut off which is a characteristic of autoregressive (AR) model. The reverse does not suggest moving average (MA) also. The two corellograms exhibit characteristics of autoregressive moving average (ARMA) based on the inability to display a characteristic pattern, which suggests it to be a mixed model of AR and MA. The minimum AIC value is 10.90464 which is less than the initial one (10.95916). There is no serial correlation in the residuals as indicated by the Durbin Watson statistic (DW=2.034172) and Breusch-Godfrey Serial correlation test and the model parameters are significant at 1% and 5% level.

In other to study the complex simultaneous relationships existing in forest/tree growth modeling, six
estimation methods of a simultaneous equation model are examined to determine how they cope with
varying degrees of correlation between pairs of random deviates using average parameter estimates. A
two-equation simultaneous system assumed covariance matrix was considered. The model was structured
to have a mutual correlation between pairs of random deviates: a violation of the assumption of mutual
independence between pairs of such random deviates. The correlation between the pairs of normal
deviates were generated using three scenarios r = 0.0, 0.3 and 0.5. The performances of estimators
considered were examined at various sample sizes (N = 20, 25, 30) and correlation levels with 50
replications for each. Using the average of parameter estimates criterion, 2 3SLIML were the best
estimators followed by FIML and OLS for the three cases studied. Also, as sample size increases from 20
to 25 to 30, 2-3SLIML performed best and was most consistent.

This study examined how six estimation methods of a simultaneous equation
model cope with varying degrees of correlation between pairs of random
deviates using the Variance and Total Absolute Bias (TAB). A two-equation
simultaneous system was considered with assumed covariance matrix. The
model was structured to have a mutual correlation between pairs of random
deviates which is a violation of the assumption of mutual independence
between pairs of such random deviates. The correlation between the pairs of
normal deviates were generated using three scenarios of r = 0.0, 0.3 and 0.5.
The performances of various estimators considered were examined at various
sample sizes, correlation levels and 50 replications. The sample size,
N = 20,25,30 each replicated 50 times was considered. OLS is performed best
when the variance is used to study the finite sample properties of the
estimators in that it produces the least variances in all the cases considered and
at all sample sizes. All the estimators revealed an asymptotic pattern under
CASE I.

A new tree growth model called the hyperbolic exponential nonlinear growth model is
suggested. Its ability in model prediction was compared with the Malthus or exponential
growth model an approach which mimicked the natural variability of heights/diameter
increment with respect to age and therefore provides more realistic height/diameter
predictions as demonstrated by the results of the Kolmogorov Smirnov test and Shapiro-
Wilk test. The mean function of top height/Dbh over age using the two models under
study predicted closely the observed values of top height/Dbh in the Hyperbolic
exponential nonlinear growth models better than the ordinary exponential growth model
without violating most of the assumptions about the error term.