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Now showing 1 - 5 of 13

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Kernel Function and Nonparametric Regression Estimation: Which Function is Appropriate?
(African Journal of Mathematics and Statistics Studies, 2020) Langat Reuben Cheruiyot; George O. Orwa; Odhiambo Romanus Otieno
In regression estimation, researchers have the option of using parametric or nonparametric regression estimation. Because of the challenges that one can encounter as a result of model misspecification in the parametric type of regression, the nonparametric type of regression has become popular. This paper explores this type of regression estimation. Kernel estimation usually forms an integral part in this type of regression. There are a number of functions available for such a use. The goal of this study is to find out an appropriate function that can be used for weighting in regression estimation. Though from the theoretical results epanechnikov function is the optimal one, there are situations where Gaussian function may be advantageous. Simulations show that the estimates inherit the smoothness of the kernel functions used.
The Power of Data Analytics on the Social Health Authority (SHA) for Sustainable Universal Health Coverage (UHC) in Kenya
(International Journal of Research and Innovation in Social Science (IJRISS), 2025-10) Reuben Cheruiyot Lang’at
Universal Health Coverage (UHC) remains a key goal in Kenya's national health agenda. With the introduction of the Social Health Authority (SHA) under the Social Health Insurance Act, 2023, there is an urgent need to develop evidence-driven strategies to ensure sustainability and equity in health service delivery. This paper explores how data analytics can be effectively leveraged by SHA to optimize provider management, beneficiary targeting, claims processing, and policy formulation. It proposes a framework for integrating advanced analytics into SHA operations to enhance transparency, efficiency, and long-term sustainability of UHC.
Modelling the Effects of Vaccination and Incubation on Covid-19 Transmission Dynamics
(Journal of Advances in Mathematics and Computer Science, 2025) Cheruiyot Kibii Peter; Kirui Wesley; Langat Reuben; Tonui Benard
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) is a strain of Coronavirus that causes Coronavirus Disease 2019 (COVID-19). The respiratory illness responsible for the COVID19 pandemic began in December 2019 in Wuhan city, China. Mathematical modeling has enabled the epidemiologist to understand the dynamics of the disease, its impact and future predictions in order to provide the governments with the best policies and strategies to curb the spread of the virus. Deterministic susceptible-vaccinated-asymptomaticinfectious-recovered (SVAIR) model was formulated incorporated with time delay. The delay accounts for the time lapsed between exposure and infectious period. Furthermore, time delay and vaccination inversely affects the basic reproduction number hence play a major role in stabilizing the rate of infection. In this study delay differential equations (DDE) were formulated for the purposes of determining the stability of both disease free equilibrium (DFE) and endemic equilibrium point (EEP). It was found out that the model was stable at both Disease Free Equilibrium (DFE) and Endemic Equilibrium Point (EEP) and was attained whenever 𝑅0 < 1 and 𝑅0 > 1 respectively. Calculations based on secondary data from various works of literature and the WHO dashboard was used. The basic reproduction number (𝑅0 ) was computed using the next generation matrix (NGM) approach. Finally, numerical simulations were carried out using MATLAB for validation of the analytical results. Graphical representation shows that stability is achieved when 𝜏 > 5 days and that 𝑅0 = 0.95 at DFE. Furthermore at EEP it was noted that 𝑅0 = 1.02 hence stability was guaranteed.
Leveraging Statistical Techniques in Religion and Decision-Making: Focus On the Catholic Church
(EPRA International Journal of Multidisciplinary Research (IJMR) - Peer Reviewed Journal, 2025-11) Reuben Cheruiyot Lang’at
This paper explores how statistical techniques can be leveraged in religion, with a particular focus on the Catholic Church. An overview of census from a biblical perspective has been given. While religion is often perceived as belonging to the realm of faith and spirituality, religious institutions increasingly depend on empirical data to inform their decision-making processes. Using both descriptive and inferential approaches, this study demonstrates the role of statistical methods in enhancing pastoral planning, resource allocation, financial stewardship, and youth engagement. The analysis begins with a broad overview of religion and statistics, moves into global and East African contexts, and narrows to Kenya, the Catholic Diocese level, and the administrative unit of Parish. Tables and illustrative data provide practical examples of how statistics can guide decisions. The paper concludes by emphasizing that integrating statistical methods in religion does not undermine faith but strengthens the capacity of institutions to fulfill their mission effectively.
Bridging the Gap between TVET Training and Industry Needs
(East African Journal of Interdisciplinary Studies eajis.eanso.org, 2025-12-26) Langat Reuben Cheruiyot
Aligning the Technical and Vocational Education and Training (TVET) programs to meet the industry requirements is a crucial matter if TVET is to foster economic growth through workforce development. TVET institutions globally have been known to bolster economic growth by rejuvenating small but key industrial sectors that cut across the formal and non-formal sectors. A disturbing fact, however, is that there still exist many TVET graduates who struggle to meet the expectations of the employers even after undergoing the full training as per the curriculum provided. This has been attributed to outdated curricula, limited practical exposure, and a lack of industry partnerships. This may arise as a result of the curriculum design that may not have been reviewed for quite a long period of time, and therefore may not have lived up to the dynamics of the varying job market demands. This paper explored how targeted reforms and collaboration between educational institutions, industries, and policymakers could help in bridging the skills gap. The study focused on TVET institutions and Curricula in use and the mode of delivery, extent of exposure of students and collaborations and linkages with the industry, as well as technological gaps occurring as a result of inadequacy of modern equipment that have become inhibitive to students who would rather be gaining practical skills with industrystandard tools. These challenges, as well as strategies for bridging the gap, have been reviewed. Best practices from developed economies have also been examined, and the gains are presented herein. A collaborative model incorporating current industrydriven curricula, skill-oriented learning, and policy interventions focusing on training a workforce that aligns with industry demands is recommended.