Considering most of our Universities that have a stationary campus map at their main entrance which in most cases is outdated; with a campus that is expanding in size and in its number of buildings, locating those buildings can be very tedious especially for someone new to the campus. Hence, this work addressed the issue of designing and implementing a mobile location-aware campus map application that aids navigation and location of landmarks within the campus. The work brought up a location-aware application which enables its user to locate himself or herself and landmarks within Bowen University Campus. The system was designed with the view of providing maximum simplicity, quality user experience, great user interface and most importantly accurate data.
Aims: To assess the validity and reliability of a measurement model in structural equation modeling (SEM).
Study Design: Short research article.
Place and Duration of Study: UiTM Kota Bharu Campus and International Islamic University Malaysia Kuantan Campus, between September and October 2014.
Methodology: A survey methodology using simple random sampling was carried out, covering the 220 students. A structured questionnaire was then distributed to 220 students. Then, the confirmatory factor analysis in structural equation modeling was employed to assess the validity and reliability of a measurement model.
Results: The results implied that the validity and reliability of the measurement model achieved the required level.
Conclusion: Based on this study, it revealed that all the fitness indexes achieved the level of acceptance. The validity and reliability of the measurement model was achieved. The measurement model is valid and reliable. It can be assembled into the structural model for further analysis.
A correction is brought to the opinion expressed in a previous note that the off critical line points indicated as being non trivial zeros of Davenport and Heilbronn function are affected of approximation errors and illustrations are presented which enforce the conclusion that they are true zeros. It is shown also that linear combinations of L-functions satisfying the same Riemann-type of functional equation do not offer counterexamples to RH, contrary to a largely accepted position.
In the logistic regression, it is known that multicollinearity affects the variance of Maximum Likelihood Estimator (MLE). To overcome this issue, several researchers proposed alternative estimators when exact linear restrictions are available in addition to sample model. In this paper, we propose a new estimator called Stochastic Restricted Ridge Maximum Likelihood Estimator (SRRMLE) for the logistic regression model when the linear restrictions are stochastic. Moreover, the conditions for superiority of SRRMLE over some existing estimators are derived with respect to Mean Square Error (MSE) criterion. Finally, a Monte Carlo simulation is conducted for comparing the performances of the MLE, Ridge Type Logistic Estimator (LRE) and Stochastic Restricted Maximum Likelihood Estimator (SRMLE) for the logistic regression model by using Scalar Mean Squared Error (SMSE).
This study compared the performance of some robust regression methods and the Ordinary Least Squares Estimator (OLSE). The estimators were compared using varied levels of leverages and vertical outliers in the predictors and the dependent variables. An anthropometric dataset on total body fat with height, Body Mass Index (BMI), Triceps Skin-fold(TS), and arm fat as percent composition of the body (parmfat), as the predictors. The effects of outliers and leverages on the estimators, were investigated at (5% leverages and 10% vertical outliers, 5% leverages with 15% vertical outliers). The criteria for the comparison: coefficients, Root Mean Square Error (RMSE), Relative Efficiencies (RE), coefficients of determination (R-squared) and power of the test. The findings from this study revealed that, OLSE was affected by both outliers and leverages whilst Huber Maximum likelihood Estimator (HME) was affected by leverages. The Least Trimmed Squares Estimator (LTSE) was slightly affected by high perturbations of outliers and leverages. The study also showed that Modified Maximum likelihood Estimator (MME) and S Estimator (SE) were robust to all levels of outliers and leverage perturbations. Therefore leverages and outliers in datasets do affect the post hoc power analysis of the methods which cannot resist them.
A technique is presented for obtaining an asymptotic solution of over damped nonlinear forced vibrating systems by general Struble’s technique and extended KBM method with varying coefficients. The implementation of the presented method is illustrated by an example. The first order analytical approximate solutions obtained by the method for different initial conditions show a good agreement with those obtains by numerical method.
This paper introduces and develops a novel variable importance score function in the context of ensemble learning, and demonstrates its appeal empirically. Our proposed score function is simple and more straightforward than its counterpart proposed in the context of random forest, and by avoiding permutations, it is by design computationally more efficient than the random forest variable importance function. Just like the random forest variable importance function, our score handles both regression and classification seamlessly. One of the distinct advantage of our proposed score is the fact that it offers a natural cut off at zero, with all the positive scores indicating importance and signifi cance, while the negative scores are deemed indications of insignificance. An extra advantage of our proposed score lies in the fact it works very well beyond ensemble of trees and can seamlessly be used with any base learners in the random subspace learning context. Our examples, both simulated and real, demonstrate that our proposed score does compete mostly favorably with the random forest score.