Science Journal of Applied Mathematics and Statistics 

About: Science Journal of Applied Mathematics and Statistics is an academic journal published by Science Publishing Group. The journal publishes majorly in the area(s): Population & Estimator. It has an ISSN identifier of 2376-9491. It is also open access. Over the lifetime, 176 publications have been published receiving 552 citations. The journal is also known as: SJAMS.

Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models

Abstract: The Gross Domestic Product (GDP) is the market value of all goods and services produced within the borders of a nation in a year. In this paper, Kenya’s annual GDP data obtained from the Kenya National Bureau of statistics for the years 1960 to 2012 was studied. Gretl and SPSS 21 statistical softwares were used to build a class of ARIMA (autoregressive integrated moving average) models following the Box-Jenkins method to model the GDP. ARIMA (2, 2, 2) time series model was established as the best for modeling the Kenyan GDP according to the recognition rules and stationary test of time series under the AIC criterion. The results of an in-sample forecast showed that the relative and predicted values were within the range of 5%, and the forecasting effect of this model was relatively adequate and efficient in modeling the annual returns of the Kenyan GDP. Finally, we used the fitted ARIMA model to forecast the GDP of Kenya for the next five years.

Selection of the Samples with Probability Proportional to Size

Abstract: It is manifested to all that sample size varies from unit to unit. It goes without saying that large units contain more apropos information than the smaller units. So if the unit size is larger then there is a greater possibility to choose sample from the large unit than smaller one. It actually means the probability of selecting a unit is positively proportional to its sizes. The selection of unit is done corresponding to choose a number at random from the totality of numbers associated. My main aim is to prefer a method of selecting units on the basis of its size.

Robust Covariance Estimator for Small-Sample Adjustment in the Generalized Estimating Equations: A Simulation Study

Abstract: The robust or sandwich estimator is common to estimate the covariance matrix of the estimated regression parameter for generalized estimating equation (GEE) method to analyze longitudinal data. However, the robust estimator would underestimate the variance under a small sample size. We propose an alternative covariance estimator to the robust estimator to improve the small-sample bias in the GEE method. Our proposed estimator is a modification of the bias-corrected covariance estimator proposed by Pan (2001, Biometrika88, 901—906) for the GEE method. In a simulation study, we compared the proposed covariance estimator to the robust estimator and Pan's estimator for continuous and binominallongitudinal responses for involving 10—50 subjects. The test size of Wald-type test statistics for the proposed estimator is relatively close to the nominal level when compared with those for the robust estimator and the Pan's approach.

Modelling and Forecasting Malaria Mortality Rate using SARIMA Models (A Case Study of Aboh Mbaise General Hospital, Imo State Nigeria)

Abstract: This paper examined the modeling and forecasting malaria mortality rate using SARIMA Models. Among the most effective approaches for analysing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). In this paper, we employed Box-Jenkins methodology to build ARIMA model for malaria mortality rate for the period January 1996 to December 2013 with a total of 216 data points. The model obtained in this paper was used to forecast monthly malaria mortality rate for the upcoming year 2014. The forecasted results will help Government and medical professionals to see how to maintain steady decrease of malaria mortality in other to combat the predicted rise in mortality rate envisaged in some months.

Fractional Dynamics of Computer Virus Propagation

Abstract: This paper studies the fractional order model for computer virus in SEIR model. Firstly, the basic reproduction number R0, which determines the threshold of the spread of the virus is determined. The stability of equilibra was also determined and studied. The Adams-Bashforth-Moulton algorithm was employed to solve and simulate the system of differential equations. The results of the simulation depicts that by small change in α led to big change in the associated numerical results.