by Abdel Nasser S.A.A. Hassan, Ahmed M. Gad and Wafaa M. Ibrahim
Original Research
Radio channel signals are heavily used tool in telecommunications. A suitable probability distribution is needed to model signals. Many probability distributions have been introduced for this purpose. The α-μ probability distribution is a general channel signal fading model that encompasses many applied important distributions as a special case. This distribution is also known as generalized gamma, Stacy distribution. This distribution is used to describe the fading mobile radio signal under a general diffuse scattering. The main advantage of this probability distribution is that it is flexible and mathematically tractable. Also, many other distributions can be considered as a special case of α-μ probability distribution. In this article we discuss the model parameters' estimation. Two new maximum likelihood (ML) and Psi-inverse (PI) estimators for the α-μ channel signal fading distribution have been proposed. Simulation study is finally conducted to evaluate the performance of the proposed estimators. Simulation results show that the proposed methods perform well comparable to the existing estimators. This behavior is valid for limited sample size; n<1000 or large sample size; n≥1000.
American Journal of Applied Mathematics and Statistics. 2020, 8(1), 28-38. DOI: 10.12691/ajams-8-1-4
Pub. Date: May 05, 2020
3946 Views617 Downloads
by Qiansheng Zhang, Wenjie Wang, Maoxiang Huang, Xi Tian, Jiao Zhang, Donghui Zhao and Xuye Gao
Original Research
Based on technology acceptance model, perceived risk, perceived ease of use, perceived usefulness, subjective norm and behavioral attitude are extracted as important factors affecting residents to sign the family physician. The close relationship between the factors is studied by constructing structural equation model (SEM). And the empirical test of fitted SEM shows that all the above factors have impact on residents' signing of family physicians directly or indirectly and the subjective norm plays the most important role in signing family physicians. In response to the above influencing factors of signing family doctor the related government department can take relevant measure to implement family doctor services efficiently.
American Journal of Applied Mathematics and Statistics. 2020, 8(1), 21-27. DOI: 10.12691/ajams-8-1-3
Pub. Date: March 18, 2020
4645 Views1015 Downloads
by Elebe E. Nwezza, Chinonyerem V. Ogbuehi, Uchenna U. Uwadi and C.O. Omekara
Original Research
In this paper, we propose a new class of Gumbel generated distributions called Gumbel-Marshall-Olkin family of distributions. The new family of distributions is represented as linear mixture of exponentiated-G distribution. Some of the sub-models are presented. We derived some characterizations such as the quantile, moments, moment generating function, entropy and order statistics of the proposed family of distributions. The estimation of the unknown parameters of the new class of distribution is through the maximum likelihood. The consistency of the MLEs of the sub-model is assessed by means of simulation. Furthermore, we derive the bivariate density function of the new class of distributions. Two real life data sets are used to illustrate the potential usefulness of the sub-models of the proposed class of distributions. The results of the applications clearly indicate that the sub-models of the proposed class of distribution provided better fit among the other competing models.
American Journal of Applied Mathematics and Statistics. 2020, 8(1), 9-20. DOI: 10.12691/ajams-8-1-2
Pub. Date: January 19, 2020
7064 Views967 Downloads1 Likes
by N. M. Eze, O. C. Asogwa, A. O. Obetta, K. C. Ojide and C. I. Okonkwo
Review Article
This work is carried out to statistically analyze federal budgetary allocations to the education sector in Nigeria (1970-2018). Time series analysis is used to analyse the data using the Box and Jenkins modeling approach. This involved identification of model, estimation of parameters, diagnostic checking of the model and forecasting. ARIMA (1, 1, 1) was identified in the course of identification of the model. ARIMA (1, 1, 0) was selected as a parsimonious model using the Bayesian Information Criterion (BIC). Also, the diagnostic check was carried out and it was found that the model is adequate. Forecasts are equally made for the year 2019, 2020, 2021 and 2022 using the model obtained. It was observed that there is an upward trend from 2019-2022 forecasts, hence using the model will bring about increment in the future budgets.
American Journal of Applied Mathematics and Statistics. 2020, 8(1), 1-8. DOI: 10.12691/ajams-8-1-1
Pub. Date: January 10, 2020
9902 Views1192 Downloads1 Likes