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Near-Vector Spaces Constructed over Zp, for p a Prime
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作者 Kelone Tefoetsile Jeromy Kalunga +3 位作者 Mathews Zimba james muchinga Saviour Chibeti Henry M. Phiri 《Advances in Pure Mathematics》 2023年第1期11-33,共23页
The purpose of this paper is to construct near-vector spaces using a result by Van der Walt, with Z<sub>p</sub> for p a prime, as the underlying near-field. There are two notions of near-vector spaces, we ... The purpose of this paper is to construct near-vector spaces using a result by Van der Walt, with Z<sub>p</sub> for p a prime, as the underlying near-field. There are two notions of near-vector spaces, we focus on those studied by André [1]. These near-vector spaces have recently proven to be very useful in finite linear games. We will discuss the construction and properties, give examples of these near-vector spaces and give its application in finite linear games. 展开更多
关键词 Vector Spaces Near Vector Spaces REGULARITY Compatibility Fields Near-Fields F-Group and Quasi-Kernel
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Modelling HIV/AIDS Cases in Zambia: A Comparative Study of the Impact of Mandatory HIV Testing
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作者 Edwin Moyo james C. Shakalima +2 位作者 Gilbert Chambashi james muchinga Levy K. Matindih 《Open Journal of Statistics》 2021年第3期409-419,共11页
In this study, a time series modeling approach is used to determine an<span style="font-family:Verdana;"> ARIMA model and advance counterfactual forecasting at a point of policy intervention. We consid... In this study, a time series modeling approach is used to determine an<span style="font-family:Verdana;"> ARIMA model and advance counterfactual forecasting at a point of policy intervention. We consider monthly data of HIV/AIDS cases from the Ministry of Health (Copperbelt province) of Zambia, for the period 2010 to 2019 and ha</span><span style="font-family:Verdana;">ve</span><span style="font-family:Verdana;"> a total of 120 observations. Results indicate that ARIMA (1,</span><span style="font-family:""> </span><span style="font-family:Verdana;">0,</span><span style="font-family:""> </span><span style="font-family:Verdana;">0) is an adequate model which best fits the HIV/AIDS time series data and is, therefore, suitable for forecasting cases. The model predicts a reduction from an average of 3500 to 3177 representing 14.29% in HIV/AIDS cases from 2017 (year of policy activation) to 2019, but the actual recorded cases dropped from 3500 to 1514 accounting for 57.4% in the same time frame.</span> 展开更多
关键词 Counterfactual Forecasting Box-Jenkins Methodology ARIMA Model Auto-correlation Function Partial Autocorrelation Function
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