Mathematical Theory and Modeling
http://www.iiste.org/Journals/index.php/MTM
<p><span id="internal-source-marker_0.04939836589619517"><span id="internal-source-marker_0.04939836589619517">Mathematical Theory and Modeling </span>is a peer reviewed journal published by IISTE. The journal publishes original papers at the forefront of mathematical theories, modelings, and applications. The journal is published in both printed and online versions. The online version is free access and download.</span></p><p><span>IISTE is member of <a href="http://www.crossref.org/01company/17crossref_members.html">CrossRef</a>.<br /></span></p>en-USMathematical Theory and Modeling2224-5804Modelling Age at First Marriage among Ghanaians in Urban Southern Ghana
http://www.iiste.org/Journals/index.php/MTM/article/view/41397
<p>The paper obtains models for determining age at first marriage (AFM) among Ghanaians in urban southern Ghana. Logistic regression models are developed for determining marriage under various circumstances and socio-economic changes that are necessitated by marriage. It also determines distributions that fit AFM and intended age of marriage (IAM) among various sub-populations in the study area. Generally, the distribution fit of AFM for males and females are Frechet and Cauchy, respectively, with corresponding expected ages of 30.8 and 28.0. Distributions have also been determined for IAM for males and females. It is found that both sexes have almost the same average IAM of about 27.5 years but with different distributions. Thus, respondents generally experience delayed marriages. The results show that in all models, one’s religion is an overriding predictor of marrying under various circumstances. Other variables that are influential regarding issues of marriage are gender, level of extended responsibility, and level of education. One may therefore be guided in these variables in order to achieve the desired AFM.</p> <p><strong>Keywords:</strong> Age at First Marriage, Intended Age of Marriage</p>N. HowardR. SackeyB. K. Nkansah8High Order Penalty Functions in Calibration Estimators
http://www.iiste.org/Journals/index.php/MTM/article/view/41398
<p>Use of penalty functions in calibration estimators has severally been considered by this author. A calibration problem is transformed to an unconstrained optimization problem by constructing a penalty function. To guarantee convergence in the minimization of the penalty function by the Newton method, the order of the penalty function is usually restricted to 2. In this paper, we consider use of more flexible higher order penalty functions by applying the variable metric method. We report on the results of the resulting population total estimator for a cubic penalty function.</p> <p><strong>Keywords: </strong>variable metric<strong> </strong>method<strong>, </strong>calibration, penalty function, model calibration, unconstrained optimization.</p>Pius Nderitu Kihara8The Correspondence between Composite Functions in Modern Algebra and Calculus
http://www.iiste.org/Journals/index.php/MTM/article/view/41399
<p>The study of maps which act on a finite set of objects is of special importance in modern algebra. The main objective of this paper is to compare between the structures of groups that yield from the definition of composite functions in group theory (permutation groups) and those in calculus.</p> <p>In calculus there no detailed study for groups, but we took the definition of composite functions as a binary operation to define a group. This study can be considered as an essential model for isomorphic groups.</p>Abdelrahim B. Hamid8Exact Solution of Coupled Nonlinear PDEs Via Sumudu Decomposition Method
http://www.iiste.org/Journals/index.php/MTM/article/view/41400
<p>In this paper, we apply the Sumudu Decomposition Method on system of coupled nonlinear partial differential equations to calculate the analytical solutions in closed form. The nonlinear term can easily be handled with the help of He’s polynomials. The proposed technique is tested on four problems. Calculated results show the potential of the technique.</p> <p><strong>Keyword:</strong> Nonlinear PDEs, He’s polynomials, Sumudu transform, Adomian decomposition method</p>Sundas Rubab8On Performance of Confidence Interval Estimate of Mean for Skewed Populations: Evidence from Examples and Simulations
http://www.iiste.org/Journals/index.php/MTM/article/view/41727
<p>The performances of confidence interval (CI) estimates of mean for skewed distributions are compared for three traditional methods and two newly proposed methods using coverage probability and confidence length for varying levels of skewness via simulations. Two real-life examples are incorporated to justify the applicability of the two newly proposed methods (trimmed <em>t</em> and modified trimmed <em>t</em> CIs), compared to the traditional methods (Student’s <em>t</em> mad <em>t</em> and median <em>t</em> CIs). From the results of examples and simulation study, it appears that with skewed distribution, the proposed trimmed <em>t</em> and modified trimmed <em>t</em> CIs are as good as mad <em>t</em> or median <em>t</em> CIs in coverage probability consideration. With lower % trimmed, trimmed and modified trimmed<em> t</em> CIs are identical or close to the Student’s <em>t</em> CI, and with increased % trimmed, they are identical or close to the median <em>t</em> CI.</p> <p><strong>Keywords: </strong>Student’s <em>t</em>, Mad <em>t</em>, Median <em>t</em>, Modified trimmed <em>t</em>, Coverage probability, Length of confidence interval.</p> <p> </p>Khairul IslamTanweer J Shapla8