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Effect of Re-entry Policy Implementation on Readmitted Girls’ Academic Performance in Mathematics in Selected Secondary Schools of Mufulira District in Zambia
Nsalamba Gladys,
Simpande Alex
Issue:
Volume 5, Issue 5, October 2019
Pages:
73-85
Received:
13 July 2019
Accepted:
22 August 2019
Published:
5 October 2019
Abstract: Zambia is one of the countries in the Sub-Sahara Africa that has an established Re-Entry Policy. The policy was declared in 1997 and allows pregnant school girls to go to school. The aim is to create academically healthy learning institutions in which both girls and boys are free. As per findings of this research, the Re-Entry Policy has helped reduce gender discrepancies in terms of equity in education. Sad though, the paper has reviewed that most reentered girls fail mathematics, a thing that disadvantages them because mathematics is used as criteria of purity for admission into university and well-paid jobs. As such, the purpose of this study was to investigate the effect of Re-Entry Policy implementation on readmitted girl’s performance in mathematics. In order to achieve this aim, a qualitative research approach guided by some research questions and objectives was undertaken. Data relating to the research was collected through interview guides and questionnaires, and analyzed using narrative techniques. Furthermore, purposive sampling technique was used because the study targeted a specific group of people and characteristics. The conclusion made through the findings of this paper were that the perceived poor performance in mathematics by reentered girls is a ‘socio construct’ and not solely due to the Re-Entry Policy. This implies that the implementation of the policy is what is key; hence, the variations in performance for reentered girls in individual schools.
Abstract: Zambia is one of the countries in the Sub-Sahara Africa that has an established Re-Entry Policy. The policy was declared in 1997 and allows pregnant school girls to go to school. The aim is to create academically healthy learning institutions in which both girls and boys are free. As per findings of this research, the Re-Entry Policy has helped red...
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Joint Survival Model of CD4 Outcome for HIV/TB Coinfected: Data from Kenya AIDS Indicator Survey
Bernadette Ikandi,
Samuel Musili Mwalili,
Anthony Wanjoya
Issue:
Volume 5, Issue 5, October 2019
Pages:
86-91
Received:
24 September 2019
Accepted:
22 October 2019
Published:
28 October 2019
Abstract: HIV infection leads to immune deficiency, increasing the risk of TB in people with HIV. HIV/TB co-infection increases the risk of death from TB or other opportunist infections. CD4 cell counts (cells/mm3) along with viral load are measures of treatment failure. This study purposed to apply shared frailty model in analyzing the survival and hazard rates of the TB/HIV co-infected persons. This work is very important because co-morbidity with TB and HIV is a rambling cause of death in Africa. The research employed a bivariate Gamma Frailty model to get the correlation amongst the HIV/TB outcomes to necessitate valid and reliable statistical inferencing. A survival frailty model on the CD4 counts is developed and fitted to factor in the unobserved heterogeneity that might occur in some observations. Ignoring some unobserved or unmeasured effects gives misguided estimates of survival. Thus, correcting these overdispersion or under-dispersion helps adjust these frailties. Frailty model provided a solid statistical analysis to CD4 data accounting for TB/HIV co-infection. The study also carried out some simulations along with the standard errors to compare the true values of the parameters. From the simulation findings, it is evident that precision and coverage improves with increase in sample size. Data used in this paper is from Kenya AIDS Indicator Survey (2012) which comprised of 648 HIV-positive patients, 10978 HIV-negative, and 2094 whose status was unknown. From the results, it is evident that the survival rate for the HIV positive individuals who are TB negative, with CD4 ≤ 310 is higher, at 0.9963 than that of the TB positive persons, at 0.975. The research finding points TB/HIV co-infection as a key factor for predicting immunological failure as measured by CD4 counts. The Kenyan government, and in particular the ministry of health should develop policies that mandate TB diagnosis among the PLHIV and linkage to TB treatment for the positive cases.
Abstract: HIV infection leads to immune deficiency, increasing the risk of TB in people with HIV. HIV/TB co-infection increases the risk of death from TB or other opportunist infections. CD4 cell counts (cells/mm3) along with viral load are measures of treatment failure. This study purposed to apply shared frailty model in analyzing the survival and hazard r...
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Valuation of European Call Options Using Wavelet-Based Pricing Model and Black-Scholes Pricing Model
Sigei Sheila Chepkorir,
Anthony Gichuhi Waititu,
Jane Aduda Akinyi
Issue:
Volume 5, Issue 5, October 2019
Pages:
92-98
Received:
2 October 2019
Accepted:
12 October 2019
Published:
28 October 2019
Abstract: The Black- Scholes model is a well-known model for hedging and pricing derivative securities. However, it exhibits some systematic biases or unrealistic assumptions like the log-normality of asset returns and constant volatility. A number of studies have attempted to reduce these biases in different ways. The objective of this study is to value a European call option using a non-parametric model and a parametric model. Amongst the non-parametric approaches used to improve the accuracy of the model in this study is the Wavelet-based pricing model. This model is found as promising alternative as far as pricing of European options is concerned, due to its varied volatility of the underlying security and estimation of the risk neutral MGF. This study made an attempt to improve the accuracy of option price estimation using Wavelet method and it improves the accuracy due to its ability to estimate the risk neutral MGF. The MSE and RMSE of Wavelet model is 0.208546 and 0.456669 respectively which is much lower than that of Black-Scholes model and therefore in conclusion, Wavelet model outperforms the other model. The study was carried out using simulated stock prices of 1024 observations.
Abstract: The Black- Scholes model is a well-known model for hedging and pricing derivative securities. However, it exhibits some systematic biases or unrealistic assumptions like the log-normality of asset returns and constant volatility. A number of studies have attempted to reduce these biases in different ways. The objective of this study is to value a E...
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Penalized Poisson Regression Model Using Elastic Net and Least Absolute Shrinkage and Selection Operator (Lasso) Penality
Josephine Mwikali,
Samuel Mwalili,
Anthony Wanjoya
Issue:
Volume 5, Issue 5, October 2019
Pages:
99-103
Received:
5 October 2019
Accepted:
22 October 2019
Published:
29 October 2019
Abstract: Variable selection in count data using Penalized Poisson regression is one of the challenges in applying Poisson regression model when the explanatory variables are correlated. To tackle both estimate the coefficients and perform variable selection simultaneously, Lasso penalty was successfully applied in Poisson regression. However, Lasso has two major limitations. In the p > n case, the lasso selects at most n variables before it saturates, because of the nature of the convex optimization problem. This seems to be a limiting feature for a variable selection method. Moreover, the lasso is not well-defined unless the bound on the L1-norm of the coefficients is smaller than a certain value. If there were a group of variables among which the pairwise correlations are very high, then the lasso tends to select only one variable from the group and does not care which one is selected. To address these issues, we propose the elastic net method between explanatory variables and to provide the consistency of the variable selection simultaneously. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in the model together.
Abstract: Variable selection in count data using Penalized Poisson regression is one of the challenges in applying Poisson regression model when the explanatory variables are correlated. To tackle both estimate the coefficients and perform variable selection simultaneously, Lasso penalty was successfully applied in Poisson regression. However, Lasso has two ...
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Bayesian Finite Mixture Negative Binomial Model for Over-dispersed Count Data with Application to DMFT Index Data
Kipngetich Gideon,
Anthony Wanjoya,
Samuel Mwalili
Issue:
Volume 5, Issue 5, October 2019
Pages:
104-110
Received:
8 October 2019
Accepted:
23 October 2019
Published:
30 October 2019
Abstract: To establish viable statistical model for modelling and analyzing DMFT index data which is important in oral health studies, difficulty arise when DMFT index data is characterized by over-dispersion. Over-dispersion caused by unobserved heterogeneity in the data pose a problem in fitting more common models to this data. and failure to account on such heterogeneity in the model can undermine the validity of the empirical results. The limitations of other count data models to account for overdispersion in DMFT index data due to existence of heterogeneity in the data, this paper formulated alternative model that captures heterogeneity in the data, that is Bayesian Finite mixture negative binomial regression model and the model applied to simulated overdispersed count data to determine the exact number of negative binomial components to be mixed and finally apply the model to DMFT index data. Bayesian finite mixture Negative Binomial (BFMNB-3) regression model is useful since the data were collected from heterogenous population. simulation results shows that 3-component Bayesian finite mixture of NB regression model converges and was quite enough to model the overdispersed simulated count data, applying BFMNB-3 model to DMFT index data, the model capability to capture heterogeneity in the data identifies that the methods; all the treatment (all methods together), mouth wash with 0.2% sodium fluoride and Oral hygiene were the best methods in preventing tooth decay in children in Belo Horizonte (Brazil) aged seven years this shows that BFMNB-3 performs better than BNB model were due to heterogeneity present in methods it only identifies methods; all the treatment (all methods together) and mouth wash with 0.2% sodium fluoride to be the best methods for preventing tooth decay for children in Belo Horizonte (Brazil) aged seven while this two methods were not the only significant methods, therefore from results there is complete superiority of BFMNB-3 over BNB model. R statistical software was used to accomplish the objectives of this paper.
Abstract: To establish viable statistical model for modelling and analyzing DMFT index data which is important in oral health studies, difficulty arise when DMFT index data is characterized by over-dispersion. Over-dispersion caused by unobserved heterogeneity in the data pose a problem in fitting more common models to this data. and failure to account on su...
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