Mathematics

The ordinary least square (OLS) method is commonly used in regression analysis. But in the presence of outlier in the data, its results are unreliable. Hence, the robust regression methods have been suggested for a long time as alternatives to the OLS to solve the outliers problem. In the present study, new ratio type estimators of finite population mean are suggested using simple random sampling without replacement (SRSWOR) utilizing the supplementary information in Bowley's coefficient of skewness with quartiles. For these proposed estimators, we have used the OLS, Huber-M, Mallows GM-estimate, Schweppe GM-estimate, and SIS GM-estimate methods for estimating the population parameters. Theoretically, the mean square error (MSE) equations of various estimators are obtained and compared with the OLS competitor. Simulations for skewed distributions as the Gamma distribution support the results, and an application of real data set containing outliers is considered for illustration.

This paper deals with the Bayesian estimation of Shannon entropy for the generalized inverse exponential distribution. Assuming that the observed samples are taken from the upper record ranked set sampling (URRSS) and upper record values (URV) schemes. Formulas of Bayesian estimators are derived depending on a gamma prior distribution considering the squared error, linear exponential and precautionary loss functions, in addition, we obtain Bayesian credible intervals. The random-walk Metropolis-Hastings algorithm is handled to generate Markov chain Monte Carlo samples from the posterior distribution. Then, the behavior of the estimates is examined at various record values. The output of the study shows that the entropy Bayesian estimates under URRSS are more convenient than the other estimates under URV in the majority of the situations. Also, the entropy Bayesian estimates perform well as the number of records increases. The obtained results validate the usefulness and efficiency of the URV method. Real data is analyzed for more clarifying purposes which validate the theoretical results.

In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, called the exponentiated sine Weibull distribution, is highlighted; we analyze its skewness and kurtosis, moments, quantile function, residual mean and reversed mean residual life functions, order statistics, and extreme value distributions. Maximum likelihood estimation and Bayes estimation under the square error loss function are considered. Simulation studies are used to assess the techniques, and their performance gives satisfactory results as discussed by the mean square error, confidence intervals, and coverage probabilities of the estimates. The stress-strength reliability parameter of the exponentiated sine Weibull model is derived and estimated by the maximum likelihood estimation ...

One of the major issues confronting the ministry of higher education in the kingdom of Saudi Arabia is the procedures for taking managerial decision making on their institutions of higher learning. Thus, there has not been empirical study on the integration of institutions of higher learning’s repository to the ministry of higher education as the stakeholder. Since data warehouse is described as a system that capable of storing and exploring large amount of information from various data sources, this paper design a prototyping data warehouse for the ministry of higher institution of learning so as to facilitate searching and decision making on concerned institution. The XAMPP web server was used to design the data warehouse due to its embedded PHP and MySQL functions. Hence, the result presents a prototype that integrates the repositories of some higher learning institutions with the ministry of higher institution of learning in Saudi Arabia Kingdom.

In obesity modelling studies, researchers have been seeking to identify the effective indicators of obesity by using appropriate statistical or mathematical techniques. The main objective of the present study is addressed in three stages. First, a new framework for modelling obesity in university students is introduced. The second stage involves data analysis based on Bayesian Structural Equation Modelling (BSEM) for estimating the Body Mass Index (BMI) (representative of the obesity level) of students at three university levels: Bachelor, Master and PhD. In the third stage, the highest significant correlation is determined between the BMI and other variables in the research model that were found significant through the second phase. The data for this study were collected from students at selected Malaysian universities. The results indicate that unhealthy food intake (fast food and soft drinks), social media use and stress exhibit the highest weightage contributing to overweight an...

In this paper we have developed an alternative estimator for the Probability Proportional to Size (PPS) with replacement sampling scheme when certain characteristics under study are positively correlated with the selection probability. An analogue to the well-known superpopulation model for finite population is also suggested, using which, we compare the proposed estimator with Hansen and Hurwitz estimator. Finally, an empirical investigation of the performance of the propose estimator has also been made.

A new technique for feature withdrawal by neural response is going to be familiarized in this research work by merging an entropy measure with Squared Pearson correlation Coefficient (SPCC) method. The process of choosing effective models on the basis of entropy measures was proposed further to enhance the ability to select templates. For more accurate similarity measure we used the statistical significant relationship between functions. The research illustrate that the proposed method is proficiently compared with the state-of-the-art methods.

This work investigates that how the maximum likelihood estimate (MLE) of the phylogenetic tree works. MLE is one of the most standard method of estimation among all the methods used in estimation. It is obligatory to find ML before finding the core value of the parameters which proceed to figure out the likelihood function. Scholars used the MLE frequently to prove the notion of species trees from gene trees inspired by the incomplete lineage sorting. They have traditionally used rooted genes tree to understand rooted species tree or unrooted genes tree to realize unrooted species tree. Depending upon the knowledge of the topology of unrooted gene tree, Allman et al. (2011) gave a notion to relate the rooted species tree from unrooted genes trees. This theory was helpful to acknowledge the features of the rooted specie tree. Allman et al. (2011) discovered for the first time that the roots of species tree are using unrooted specie tree as the input data without assuming an outgroup....

The present article explains obtaining of the Bayesian prediction bounds at the maximum and minimum rate taking into account the results of future observation from a new version of a bathtub-shape failure rate distribution of life time type in the presence of outliers. The Type-II censored sample serves as a basis for the intervals of the prediction with the numerical examples as illustrations of the studied procedure.

Aim/Purpose: This study aims to propose and empirically validate a model and investigates the factors influencing acceptance and use of Software as a Services cloud computing services (SaaS) from individuals’ perspectives utilizing an integrative model of Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) with modifications to suit the objective of the study. Background: Even though SaaS cloud computing services has gained the acceptance in its educational and technical aspects, it is still expanding constantly with emerging cloud technologies. Moreover, the individual as an end-user of this technology has not been given the ample attention pertaining to SaaS acceptance and adoption (AUSaaS). Additionally, the higher education sector needs to be probed regarding AUSaaS perception, not only from a managerial stance, but also the individual. Hence, further investigation in all aspects, including the human factor, deserves deeper inspection. Methodology: A quantitat...

This paper analyzes the time to event data in the presence of collinearity. To address collinearity, the ridge regression estimator was applied in multiple and logistic regression as an alternative to the maximum likelihood estimator (MLE), among others. It has a smaller mean square error (MSE) and is therefore more precise. This paper generalizes the approach to address collinearity in the frailty model, which is a random effect model for the time variable. A simulation study is conducted to evaluate its performance. Furthermore, the proposed method is applied on real life data taken from the largest sample survey of India, i.e., national family health survey (2005–2006 ) data to evaluate the association of different determinants on infant mortality in India.

The study aims at getting the Bayesian predication intervals for some order statistics of future observations from the distribution of Gompertz (Gomp (α, β)). Doubly Type-II censored data has assisted obtaining in the presence of single outlier that arose from the different same family members of distribution. Single outlier of type β β0 and β+β0 are considered and bivariate independent prior density for α and β are used. The problem of solving the Double integral to obtain the closed form for α and β, leads us to use MCMC for calculating the Bayesian Predication Intervals. The use of numerical examples and statistical data has enable to properly present and describe the procedure. We conclude that the Bayesian predication intervals are shorter for y1 than y5 when we are increasing the β0 value.