This paper examined the general trend of timing of breastfeeding initiation among nursing mothers in Nigeria. The time of initiating the first breast milk to an infant by his/her mother is measured as whether it is immediate (before the first hour of birth) or delayed (after the first hour of birth), and the impacts of some socioeconomic and maternal factors on this are determined. Results from this study showed that mother's age at birth, her enhanced educational status, mothers' domiciling in urban areas, singleton birth, and mother's frequent antenatal visits among others contributed positively to early initiation of breastfeeding by Nigerian nursing mothers ( < 0.05). In the contrary, delivery through caesarean operation, nursing mothers that delivers at homes instead of hospitals, and the current birth being the first from a mother are all found to militate against early initiation of breastfeeding ( < 0.05) among others. General results showed that early breastfeeding initiation experience among nursing mothers in Nigeria significantly improves over time between 1990 and 2008 ( < 0.05), although following a sinusoidal pattern. Four waves of national data from the Nigerian

The most important ingredient in Bayesian analysis is prior or prior distribution. A new prior determination method was developed under the framework of parametric empirical Bayes using bootstrap technique. By way of example, Bayesian estimations of the parameters of a normal distribution with unknown mean and unknown variance conditions were considered, as well as its application in comparing the means of two independent normal samples with several scenarios. A Monte Carlo study was conducted to illustrate the proposed procedure in estimation and hypothesis testing. Results from Monte Carlo studies showed that the bootstrap prior proposed is more efficient than the existing method for determining priors and also better than the frequentist methods reviewed.

In this work, a three-parameter Weibull Inverse Rayleigh (WIR) distribution is proposed. The new WIR distribution is an extension of a one-parameter Inverse Rayleigh distribution that incorporated a transformation of the Weibull distribution and Log-logistic as quantile function. The statistical properties such as quantile function, order statistic, monotone likelihood ratio property, hazard, reverse hazard functions, moments, skewness, kurtosis, and linear representation of the new proposed distribution were studied theoretically. The maximum likelihood estimators cannot be derived in an explicit form. So we employed the iterative procedure called Newton Raphson method to obtain the maximum likelihood estimators. The Bayes estimators for the scale and shape parameters for the WIR distribution under squared error, Linex, and Entropy loss functions are provided. The Bayes estimators cannot be obtained explicitly. Hence we adopted a numerical approximation method known as Lindley's approximation in other to obtain the Bayes estimators. Simulation procedures were adopted to see the effectiveness of different estimators. The applications of the new WIR distribution were demonstrated on three real-life data sets. Further results showed that the new WIR distribution performed credibly well when compared with five of the related existing skewed distributions. It was observed that the Bayesian estimates derived performs better than the classical method.

This paper proposes a weighted Support Vector Machine (w-SVM) method for efficient class prediction in binary response data sets. The proposed method was obtained by introducing weights which utilizes the point biserial correlation between each of the predictors and the dichotomized response variable into the standard SVM algorithm to maximize the classification accuracy. The optimal value of the proposed w-SVM cost and each of the kernels parameters were determined by grid search in a 10-fold cross validation resampling method. Monte-Carlo Cross Validation method was employed to examine the predictive power of the proposed method by partitioning the data into train and test samples using different sampling splitting ratios. Application of the proposed method on the simulated data sets yielded high prediction accuracy on the test sample. Results from other performance indices further gave credence to the efficiency of the proposed method. The performance of the proposed method was compared with three of the state-of-the art machine learning methods including the standard SVM and the result showed the superiority of this method over others. Finally, the results generally show that the modified algorithm with Radial Basis Function (RBF) Kernel perform excellently and achieved the best predictive performance than any of the existing classifiers considered.

Pharmacokinetics which describes the time course of drug absorption, distribution, metabolism, and excretion in the body is critical in formulating drug therapy. Nonlinear Mixed Effects (NLME) models are popularly used in many longitudinal studies, including human immunodeficiency viral dynamics, pharmacokinetic analyses, and studies of growth and decay.This work aimed to develop efficient NLME models for analyzing Theophylline concentration data within the pharmacokinetics framework. The data consisted of Theophylline concentration (mg/L) measurements of 12 asthmatic patients who were treated with oral Theophylline. The serum concentrations were measured at 11 times per subject over 25 hours periods. Hence, a total of 132 observations were obtained. Six different pharmacokinetics models were fitted in a step-wise manner to these data within the framework of NLME techniques. The best of these models that yielded the most efficient estimates of the physiological factors such as absorption rate ( ), elimination rate ( ), and clearance ( ) in the patients was determined using suitable model selection criteria. The results showed that the clearance and absorption rate have mixed effects with estimated values of = 0.0397 and = 1.54203 (for fixed effects) while the effect of the elimination rate in all the patients is fixed with the estimated value of =0.0860. Also, the low estimated standard deviations of the random effects components of (0.1699) and (0.6384)over the entire samples is a clear evidence that the fitted model was quite consistent and efficient.Results from this study would further serve as useful guides to clinicians and drug developers in the proper formulation and administration of Theophylline therapy on patients suffering from respiratory diseases.Keywords: Pharmacokinetics; theophylline concentration; nonlinear mixed effects model; compartmental model ÖZET Vücutta zamanla meydana gelen ilaç emilimi, dağılımı, metabolizması ve boşaltımını tanımlayan farmakokinetikler, ilaç terapi formüllerinde çok önemlidir. Doğrusal olmayan karışık etki (DOKE) modelleri, büyüme ve çürüme çalışmaları, farmakokinetik analizler ve insan immün yetmezliği viral dinamiklerini kapsayan birçok longitudinal çalışmada popüler olarak kullanılmaktadır. Bu çalışmada farmakokinetik yapı için teofilin konsantrasyon verisi analiz edilerek etkili DOKE modelleri geliştirmek amaçlanmaktadır. Veri seti, ağızdan teofilin ile tedavi edilen 12 astım hastasının teofilin konsantrasyon ölçümlerini içermektedir. Serum konsantrasyonları, 25 saatlik periyotlarla her hastadan 11 kez ölçülmüştür. Dolayısıyla 132 gözlem elde edilmiştir. DOKE teknikleri çerçevesinde bu veri seti için altı farklı farmakokinetik model aşamalı olarak tahmin edilmiştir. Hastalarda emilim hızı ( ), eleme hızı ( ) ve aralık ( ) gibi fizyolojik faktörlerin en etkili tahminini sağlayan en iyi modeller uygun model seçim kriteri kullanılarak belirlenmiştir. Sonuçlar, eleme hızının tüm hastalarda = 0.0860 tahmin değeri ile sabit olmasına rağmen, aralık ve emilim hız...