Exchange rate volatility poses significant challenges for economic planning, risk management, and policy formulation in emerging economies such as Kenya. This study models the volatility of the United States Dollar/ Kenyan Shillings (USD/KES) exchange rate using the nonparametric Nadaraya-Watson kernel regression estimator. Daily buying rates from the Central Bank of Kenya spanning January 2, 2003, to December 29, 2023 (4,764 observations) were used. The Nadaraya-Watson estimator smooths the data using kernel functions and bandwidth parameters, enabling it to adapt to localized features in the volatility pattern that conventional parametric models may overlook. The conditional mean and conditional variance functions were estimated using a Gaussian kernel with cross-validated fixed bandwidths. The optimal bandwidth for the conditional mean was 0.02750928 and for the conditional variance was 0.1349632, with a bandwidth ratio of 4.905869, indicating that the volatility function requires smoother estimation. The conditional variance function exhibited a distinct U-shape, showing higher volatility following extreme positive or negative lagged returns. The model achieved a Root Mean Squared Error (RMSE) of 1.919 for variance, capturing major volatility episodes including the 2008-2009 financial crisis, the 2011 currency crisis, and the 2016 reserves depletion shock. The study concludes that the Nadaraya-Watson kernel regression estimator successfully captures nonlinear volatility dynamics without imposing rigid parametric assumptions, making it suitable for emerging market currencies like the Kenyan shilling.
Kenya is very weather sensitive, and the seasonality and variability of rainfall has a significant impact on agricultural productivity, water resource management and food security. Therefore, precise rainfall prediction is a key requirement for climate risk management and decision-making. Most Kenyan rainfall studies have been of monthly and/or annual rainfall, which may not be sensitive enough to the intra-seasonal variability in rainfall seen at dekadal (10-day) time scales. The aim of this study was to model and forecast dekadal rainfall dynamics in selected Sub-national regions of Kenya using Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The quantitative time series research design adopted involved Box–Jenkins methodology with data on dekadal rainfall from 2021 to 2025 from the Humanitarian Data Exchange (HDX). The five regions namely Manyatta, Mbeere North, Igembe Central, Marsabit and Isiolo were chosen for analysis. Data was analyzed by R statistical software. Logarithmic transformation and differencing were used to stabilize the variance and to make the data stationary as verified by the Augmented Dickey–Fuller test. Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) were used to identify the candidate SARIMA models, while Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to select the models. Model adequacy was checked with residual diagnostics and Ljung–Box test, and the forecasting performance was judged based on RMSE, MAE, MAPE, and MASE. The results showed that SARIMA (1,0,0) (1,1,1) 36 was the most suitable model for regions51348,51352, and51364, whereas SARIMA (1,0,0) (0,1,1) 36 provided the best fit for regions 51357 and 51363. Residual diagnostics also showed that the models estimated were appropriate to represent the temporal dependence in the rainfall series, as the residuals were in the form of white noise. High MAPE values were observed, which were mostly related to the intermittent nature of the rainfall data, but satisfactory forecasting performance was shown by RMSE, MAE and MASE. Forecasts for the coming year reproduced the observed seasonal rainfall pattern and showed greater uncertainty in the longer-term forecasts. In general, the chosen SARIMA models were successful in modelling dekadal rainfall patterns and can be a valuable tool for agricultural planning, water resource management, and climate risk preparedness for Kenya.
Drug addiction remains a critical public health challenge, particularly in developing countries like Kenya, where access to effective rehabilitation services is limited. Understanding the factors that influence recovery time is essential for improving treatment outcomes and informing evidence-based interventions. This study aimed to model recovery time among drug-addicted individuals using survival analysis techniques and to identify key determinants influencing the rate of recovery. This study focuses on applying the Cox PH model to estimate recovery hazard rates and identify significant predictors among individuals receiving treatment. A retrospective cohort design was employed using secondary data obtained from a rehabilitation facility in Kericho County, Kenya, covering the period 2021 to 2025. The key findings indicate that education level (HR 1.18, p=0.049) and Substance Type (Multiple Drugs vs. Alcohol, HR 0.67, p=0.027) are significant predictors of recovery from drug addiction. Specifically, higher education levels are associated with a higher hazard of recovery, likely due to enhanced health literacy. At the same time, individuals using multiple substances face a lower hazard of recovery compared to those using alcohol only, reflecting the clinical complexity of polysubstance use. The Cox PH model satisfied the proportional hazards assumption (Global p=0.24), confirming its adequacy for the data. These results highlight the importance of socio-demographic and substance-related factors in recovery from drug addiction. In practice, treatment facilities/rehabilitation centers should integrate health literacy enhancement programs specifically for individuals with lower education levels to improve treatment adherence and accelerate recovery. Besides, more intensive and prolonged interventions, such as enhanced counseling, specialized therapy, and closer monitoring, are recommended for polysubstance users to address their greater clinical needs. For policy, the study’s results underscore the need to develop substance-specific treatment guidelines, increase resource allocation for polysubstance addiction programs, and implement standardized drug-user data recording systems in rehabilitation facilities across Kenya. These strategies will improve recovery outcomes, optimize service delivery, and strengthen national efforts for reducing the burden of drug addiction.
Students’ attitudes toward physics play a crucial role in shaping their engagement, interest, and disposition toward learning the subject at the secondary school level. Despite the importance of physics to scientific and technological development, negative attitudes toward the subject persist among many students. This study examined the extent to which school type, school location, and gender predict secondary school students’ attitudes toward physics in Akwa Ibom State, Nigeria. The study adopted an ex-post facto descriptive survey design. A multistage sampling technique was used to select a sample of senior secondary school students offering physics. Data were collected using a validated Students’ Attitude Toward Physics Questionnaire (SATPQ), which yielded a reliability coefficient of 0.82 using Cronbach’s Alpha method. Mean and standard deviation were used to answer the research questions, while Analysis of Variance (ANOVA) was employed to test the hypotheses at 0.05 level of significance. The findings revealed that school type and school location significantly influenced students’ attitudes toward physics, while gender had no significant influence. There was no significant interaction effect among school type, school location, and gender on students’ attitudes toward physics. The study concluded that school-related factors are more critical than gender in shaping students’ attitudes toward physics. It was recommended that efforts be made to improve physics learning environments, particularly in public and rural schools.
Change-point detection is the point or location in the series where the observations of that series are shifted to another point. Inflation is considered as one of the most important determinants of economic growth and also a key macroeconomic indicator which shows how prices of goods change from one period to another and this plays a critical role in economic stability and growth. The study therefore, aimed to determine structural change-point(s) of the inflation rate in Ghana, which will serve as an essential source of information to guide policy direction. Annual data on Ghana’s inflation rate were sourced from the World Bank website covering the years 1965-2025. To remove the effect of serial correlation since the inflation data was collected over time, an ARIMA model was considered and the errors which were independent and identically distributed, were extracted for multiple change point procedures. Change point methods considered were the Cumulative Sum (CUSUM) Test, the Binary Segmentation (BS) Method and the Pruned Exact Linear Time (PELT) Algorithm. We sought to determine change points in mean, variance (risk) and mean-variance jointly since they are the basic measured quantities for econometric analysis. Results show that the mean change point was detected at time (index) 12, which represents the year 1976, corresponding to Ghana’s mid-1970s macroeconomic instability. Variance (risk) change points were detected at time points 37 and 56, which represent the years 2001 and 2020, respectively corresponding to times of fiscal stress (Ghana joining the Heavily Indebted Poor Countries (HIPC)), electoral spending and COVID-19 shock. The mean-variance change points were also detected at time points 10 and 20, which represent the years 1974 and 1984, respectively aligning with the oil shock era and the economic recovery programme (ERP) regime. The study showed that Ghana’s inflation process has experienced multiple structural shifts associated with major economic shocks and policy transitions. It is highly recommended that credible macroeconomic management and fiscal discipline be adhered to during structural changes.
Malaria continues to pose a major threat to public health across the world, particularly in African countries where infection rate s remain high. Nearly half of the global population is exposed to the risk of contracting the disease. Malaria is caused by parasites belonging to the Plasmodium family and affects both humans and other warm-blooded animals. Over the years, researchers have explored different causes of malaria transmission using techniques such as spatial analysis, time series methods and regression models. Although these methods are useful, they are less suitable when the data set involved are categorical or count variables. This study used Poisson Generalized Linear Model to investigate factors associated with malaria incidence in Mbita Sub-county. The variables considered included treated mosquito bed net use, age group, educational level of household heads and access to healthcare services. The Poisson Generalized Linear Model was fitted having estimated its parameters. The findings showed that treated mosquito net usage, age group, and access to healthcare facilities were statistically significant. The educational background of the household head was not significant. The association between the exploratory variables and the response variable was determined by use of the Chi-square test.The results indicated that there was an association between mosquito bed net use and malaria, age group and malaria, education level of family head and malaria and finally healthcare access and malaria. The goodness of fit was conducted by the use of deviance statistic. A comparison between the null model and the full model was done and this resulted into a p-value of 0.001871, which was below the 0.05 significance threshold. As a result, the null hypothesis was rejected, indicating that additional exploratory variable improve the model. This suggests that the full model together with additional parameters significantly improves the fit of the model to the data. The study’s findings reinforce existing evidence that the use of treated mosquito bed net use plays an important role in lowering malaria infections. organizations that handle matters in relation to health and environment such as World Health Organization and United Nations may apply the outcome to aid in developing mechanisms to lower the spread of malaria within Mbita Sub-county and other parts of the world with similar settings.
The study conducts an assessment of the spatial distribution of cardiovascular diseases (CVD) in Kenya by integrating spatial modeling techniques and spatial autocorrelation measures. CVDs, which refer to disorders of the heart and blood vessels, have surpassed communicable diseases as the leading cause of morbidity and mortality worldwide, posing a critical public health concern, especially in low- and middle-income countries (LMICs) where resources remain limited. A growing body of global evidence has revealed marked geographical disparities in CVD incidence, prompting investigations into small-area spatial distribution patterns. This study employed both global and local spatial autocorrelation measures to analyze CVD prevalence across Kenyan counties. The Global Moran’s I statistic was used to assess the overall degree of spatial clustering, while the Local Moran’s I identified significant clusters of high and low prevalence, alongside spatial outliers. Additionally, the Getis-Ord Gi* statistic was applied to detect statistically significant hotspots and coldspots, revealing important spatial patterns in disease prevalence. Spatial regression models were compared using the Lagrange Multiplier (LM) test, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) for model selection. The Spatial Lag Model (SLM) demonstrated superior performance over the Spatial Error Model (SEM) and the Spatial Durbin Model (SDM), achieving a Rao’s score (RSlag) of 16.449 and an adjusted score (adjRSlag) of 12.181, both statistically significant at the 5% level. The SLM also recorded the lowest AIC and BIC values at -380.09 and -361.80, respectively, confirming its suitability in capturing spatial dependence in the data. The findings revealed significant spatial clustering of CVD prevalence, with distinct high-risk and low-risk regions across the country. High body mass index (HBMI), tobacco use, and poor dietary habits emerged as major risk factors driving CVD prevalence, while urbanization and economic development were associated with lower disease burdens. The study highlights the importance of incorporating spatial analysis in public health planning to inform targeted interventions, optimize resource allocation, and enhance community health education campaigns aimed at promoting heart-healthy lifestyles.
We examine the statistical properties of nearest neighbor regression function estimation beyond the classical i.i.d assumption. Second-order properties of this estimator for uniformly mixing processes have been derived in previous studies. Nevertheless, uniform mixing is a very strong form of dependence that is difficult to achieve. In contrast, strong mixing conditions are satisfied by a broad class of stochastic processes commonly encountered in theoretical and applied time series modeling. This paper focuses on the analysis of the second-order properties of the nearest neighbor regression estimator under strong mixing dependence measure. Under appropriate regularity conditions, including strong mixing assumptions and smoothness of the underlying density functions, we derive expressions for both the bias and the quadratic mean squared error (QMSE) of the nearest neighbor regression estimator with a uniform weighting scheme for estimating the unknown conditional mean function. Our results demonstrate that the QMSE of the nearest neighbor estimator attains the minimax-optimal rate for estimating a p-smooth regression function in a d-dimensional embedding space. The theoretical analysis integrates the dependence structure specific to strongly mixing processes with the geometric characteristics of k-nearest neighborhoods. This combination enables the identification of an optimal choice for the number of nearest neighbors that effectively balances the trade-off between bias and variance. Overall, these findings provide a rigorous theoretical foundation for the application of nearest neighbor regression methods to short-range dependent data. Furthermore, the explicit bias and variance characterizations lay the groundwork for establishing asymptotic normality, thereby enabling the construction of valid confidence intervals and supporting reliable statistical inference in dependent data settings.
Malaria is a major public health challenge in sub-Saharan Africa, with transmission patterns that vary significantly across space and time due to environmental, socioeconomic, and epidemiological factors. These variations complicate efforts to design effective and targeted interventions, making it crucial to understand the dynamics of disease spread. This study employed Bayesian spatio-temporal random effects modeling framework to analyze malaria incidence and mortality ratio across Kenya. The approach incorporated spatial and temporal dependencies to provide a detailed understanding of malaria incidence and mortality risk patterns. Spatial random effects were modeled using conditional autoregressive (CAR) priors to account for correlations among neighboring counties, while temporal dependence was captured using autoregressive processes of order two (AR2), reflecting trends over multiple time periods. An evaluation was on the performance of Spatio-Temporal Poisson Linear Trend Model (STPLM), Spatio-Temporal Poisson ANOVA Model (STPAM), Spatio-Temporal Poisson Separable Model (STPSM) and Poisson Temporal Model for Spatio-Temporal Effects (PTSTN)using the Deviance Information Criterion (DIC), the effective number of parameters (p.d) and the Log Marginal Pseudo-Likelihood (LMPL). The Spatio-Temporal Poisson ANOVA Model (STPAM) was found as the best Poissson Spatial-Temporal Model and was used to develop a multivariate spatio-temporal model for the joint modeling of malaria incidence and mortality. Using the developed model, the study identified significant spatial clustering of malaria, with persistent high-risk zones in western and coastal counties. Temporal trends indicated an overall decline in transmission, though progress was uneven across counties, reflecting differences in intervention coverage, healthcare access, and local epidemiology. These findings underscored the value of multivariate spatio-temporal modeling of malaria incidence and mortality for guiding malaria control strategies. This study thus demonstrates that Bayesian Spatial-Temporal modeling is essential for understanding heterogeneous malaria incidence and mortality risk and informing strategies aimed at reducing disease burden and advancing toward malaria elimination in Kenya.
The COVID-19 pandemic caused a strong impact on the young population in Kerala with increased cases of depressive, anxious and stress-related conditions. The objective of the current exploration was to determine the mental health condition of children between the ages of 5 and 15 years in the state during the strict lockdown that was in place between April and June 2021. In this direction, a cross-sectional online survey with the Depression Anxiety Stress Scales-21 (DASS-21), which is reasonably adjusted to the assessment of pediatric respondents, was conducted, and about 400 answers were gained by parents and guardians 383 responses taken for this study. One-way analysis of variance conducted on STATA -13 was used to explain the main trends and determine the statistical significance. The DASS-21 results analysis showed that the test group of participants had moderate means on such indicators as depression (M = 9.85), anxiety (M = 9.19), and stress (M = 9.18). According to ANOVA, the statistically significant value of residential area was not revealed in relation to child depression (p=0.783), anxiety (p=0.471) and stress (p=0.130). The level of psychological distress was not higher in rural households than in semi-urban or urban households, and hence, the residential location did not have effects on the mental-health outcomes of participants. According to the data the mild stress was observed in 4 children only (1.04%), and no moderate, severe, or extremely severe stress symptoms were found. The general results were that 98.95% of the sample was in the normal stress range, meaning that children had emotional and mental symptoms (depression and anxiety), but responses of physiological stress were much rarer. Regardless of the levels of distress, the proportion of respondents who had used professional help was only 10%, which highlights a dire need to have stigma reduction efforts and policy-level interventions. The results defined salient correlations between demographic factors, namely, educational level, pre-existing mental health history, and levels of severity with the emotional wellbeing of the children.
This study focuses on the recursive nonparametric estimation of the intensity function associated with a nonhomogeneous Poisson process. Accurately estimating the intensity function is crucial for understanding the dynamics of events in fields such as finance, neuroscience, and environmental monitoring. While traditional nonparametric estimators are theoretically robust, their reliance on the entire dataset for every update makes them impractical for real-time applications. To overcome this limitation, we introduce a recursive estimator that supports efficient, online updates as new data becomes available. This approach significantly lowers computational overhead while maintaining strong statistical reliability. We thoroughly analyze the asymptotic behavior of the proposed estimator, paying particular attention to the Asymptotic Mean Integrated Squared Error (AMISE), a key measure of estimation accuracy. Additionally, we compare the performance of our recursive estimator with Cucala’s non-recursive method. The results reveal that our approach achieves equivalent or superior accuracy in terms of AMISE, particularly in large-sample scenarios. A computational performance comparison further underscores the advantages of the proposed method, demonstrating its substantial reduction in execution time and its suitability for applications requiring rapid processing.
The paper provides an analysis of how income distribution in Senegal changed during the pandemic. It takes data from the wider FES-IDOS-IlO household surveys on informal employment in Sub-Saharan Africa and captures the income situation of people in informal employment in 2019 and 2022. Using nationally representative samples of 1200 households in both years, it shows that agriculture, rural households, and male peasants benefitted from income increases, while urban workers and women in farming lagged behind. The redistribution of income inequality was realized when the government implemented a substantive food aid project during COVID-19 and based its social relief programme on a ‘buying local’ strategy, thereby transferring budget expenditures into local producer income. Linking social relief to local production may be the single most cause to explain why poverty alleviation and income redistribution took place on such a massive scale.
Stationarity plays a crucial role in time series analysis, significantly influencing model performance and the reliability of forecasts. Despite its importance, many real-world datasets exhibit non-stationary behaviour, which can lead to misleading or spurious forecasting outcomes. This study explores the impact of stationarity on the performance of the Prophet Model, a scalable time series forecasting tool developed by Facebook, by comparing forecasts from both stationary and non-stationary versions of the same dataset. The monthly international airline passenger data was downloaded from the Kaggle website. We applied the Prophet to generate forecasts from raw (non-stationary) data and its transformed (stationary) version, obtained through first differencing. Three metrics, including the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coverage, were used to evaluate both versions of the forecasts. The findings reveal that forecasted values from stationary and non-stationary data exhibit strong correlations with actual values, as confirmed by T-tests and Pearson correlation coefficients. However, the Prophet model demonstrated notably better performance on stationary data compared to the non-stationary version, with the forecast for stationary data showing lower RMSE and MAE values and higher coverage percentages. The study shows the importance of ensuring stationarity before forecasting, even when using advanced models like the Prophet Model. We suggest that integrating stationarity considerations into future iterations of the Prophet algorithm could further enhance its predictive capabilities.
This study aims to evaluate public safety perceptions and street crime patterns in Vadodara, a major city in Gujarat, India. Data was collected from both primary and secondary sources to provide a comprehensive understanding of safety concerns across the city’s four zones: North, East, West, and South. The primary data was obtained through field surveys conducted within these zones, capturing citizen perceptions and experiences related to safety. Secondary data was sourced from the Office of the Commissioner of Police, Vadodara, which provided official records of street crimes over the past four years, categorized zone-wise. The study employed various statistical tools, including Microsoft Excel for data management and visualization, R programming for advanced statistical analysis, and SPSS software for in-depth data interpretation. Findings from the analysis reveal a significant variation in perceived safety between day and night, with the majority of residents reporting a greater sense of security during daylight hours. Furthermore, certain zones displayed higher incidences of street crimes such as theft, chain snatching, and assault, indicating spatial disparities in crime concentration. The study highlights the importance of targeted policing strategies and enhanced night-time surveillance in high-risk areas. These insights can assist local authorities in formulating evidence-based policies to improve urban safety and strengthen community trust in law enforcement.
This study looked at the interconnections among trade openness, national income (GDP), investment, exchange rate, government spending, financial development and inflation in Ghana employing the autoregressive distributed lag (ARDL) model. The bounds cointegration, as applicable within the ARDL modelling was carried out to check the existence or otherwise of long run relationships among the economic variables while the error correction model (ECM) was also used to capture the short-term relationships among the variables. Unlike known previous studies in Ghana, this study made use of three trade openness proxies including ratio of imports plus exports to GDP (OPEN1), ratio of export to GDP (OPEN2) and the ratio of import to GDP (OPEN3). With GDP growth as endogenous variable, the bound co-integration analysis results showed that there exists co-integration among GDP, openness to trade and the macroeconomic variables included in the study. In this study, the short-run results found that openness to trade and government spending had significant positive effects on Ghana’s GDP growth. However, investment, exchange rate and financial development were found to be impacting growth in GDP significantly but negatively. In the long term, trade openness and government spending again had positive influence on GDP growth whiles investment, real effective exchange rate and financial development still impacted GDP growth significantly and negatively at 5 percent significance level. This study recommends among others things that the government of Ghana should reduce trade barriers and streamline regulations in critical sectors to promote GDP growth in Ghana.
This study investigates the broad economic, behavioral, and psychological impacts of rising fuel prices on urban residents in Vadodara, India. Primary data was collected from two key areas Fatehgunj and Tarsali using structured questionnaires to capture citizens’ responses to the ongoing increase in fuel costs. The study had multiple objectives: to evaluate the direct consequences of rising fuel prices on household expenditure, assess the readiness of people to adopt alternative transportation methods, analyze fuel spending across different age groups and income levels, and understand how individuals are adjusting to manage this financial pressure. Furthermore, the study explores the impact of fuel inflation on mental health and seeks citizens’ suggestions on how this issue might be addressed. An additional comparative objective was introduced to examine whether similar fuel pricing challenges are experienced in another developing country, specifically Kenya. A range of statistical tools were employed for data processing and analysis, including SPSS, R, Python, Microsoft Excel, and Power BI. These tools were used to conduct descriptive and inferential analysis, create data visualizations, and draw correlations between demographic variables and spending behaviors. The results reveal a significant shift in travel habits, increased financial stress among middle- and lower-income households, and growing public concern about long-term affordability. Many respondents reported cutting back on non-essential expenses, while others expressed willingness to switch to fuel-efficient vehicles or public transportation. These findings provide valuable insights for policymakers, economists, and urban planners aiming to develop effective strategies to ease the burden of rising fuel prices on the general public.
We propose a novel theoretical framework in which energy is generalized to a bicomplex quantity, significantly extending previous formalisms that treated energy as a complex number. In this bicomplex approach, energy comprises two distinct imaginary components arranged orthogonally, providing a richer algebraic structure. By carefully defining arithmetic operations within this bicomplex space, we demonstrate that division naturally introduces a geometric scaling factor identified explicitly as the fine-structure constant α. The emergence of α within this algebraic structure provides new insights into its fundamental geometric interpretation and underscores its role as a universal scaling factor connecting quantum-scale interactions to larger-scale phenomena. We present rigorous algebraic derivations and systematically define the arithmetic rules governing bicomplex quantities. Additionally, we clarify how these algebraic properties facilitate novel connections across various domains, including quantum mechanics, holographic theories, and theoretical physics frameworks aimed at unification. Specifically, the introduction of bicomplex energy allows us to interpret quantum mechanical processes and holographic projections in a unified mathematical context, offering fresh perspectives on longstanding theoretical challenges. The proposed framework not only deepens theoretical understanding but also generates experimentally testable predictions. These include unique signatures that could manifest in high-precision quantum electrodynamics experiments, as well as potential observable effects in advanced holographic or quantum-gravity-inspired setups. The framework invites further exploration into how higher-dimensional algebraic structures might underlie physical constants and fundamental interactions, providing a robust mathematical foundation for future theoretical and experimental investigations.
Human trafficking negatively impacts individuals and national development, yet its root causes are poorly understood. This study aimed to investigate the socioeconomic and demographic factors influencing irregular migration from Shashogo Woreda, Hadiyya Zone, Central Ethiopia to South Africa. Data from 346 respondents across eight Kebeles were analyzed using bivariate and Bayesian logistic regression models. The findings revealed that about 50L. 57% of household heads plan to send a family member abroad, while 49.42% do not. Female-headed households are significantly less likely to plan irregular migration than male-headed ones (Coeff = -1.527, OR = 0.217, P = 0.001). The odds of planning migration rise by 45.7% per additional household member (Coeff = 0.784, OR = 1.457, P = 0.000) and by 21.2% for each year increase in the household head’s age (Coeff = 0.193, OR = 1.212, P = 0.000). Education negatively correlates with migration plans, as those with primary education (Coeff = -2.652, OR = 0.816, P = 0.001) or a diploma and above (Coeff = -3.228, OR = 0.040, P = 0.001) are less likely to plan migration compared to those with secondary education, while uneducated respondents show no significant difference. Non-agricultural employment such as trade (Coeff = -2.781, OR = 0.062, P = 0.001), formal jobs (Coeff = -1.549, OR = 0.212, P = 0.020), or other work (Coeff = -2.453, OR = 0.086, P = 0.002) also lowers migration plans compared to agricultural work. Urban residents are more likely to plan migration than rural ones (Coeff = 1.309, OR = 3.704, P = 0.001), and those unaware of migration risks are significantly more likely to plan migration than those who are aware (Coeff = 1.623, OR = 5.066, P = 0.001). In conclusion, irregular migration from Shashogo Woreda is driven by structural socio-economic challenges and the allure of better opportunities abroad. Key predictors include age, sex, family size, education, employment type, residence, and risk awareness. Despite awareness of migration risks, economic hardships remain dominant drivers. Effective policy responses should focus on rural development, youth employment, education access, and safe migration alternatives to address the root causes.
The study of causality in multivariate relationships in scientific studies involves the application of stochastic models in quantifying complex relationships. Stochastic models are becoming increasingly significant in health research due to their adaptability in practical situations and their ability to capture randomness, assess uncertainty, and inform decision-making. The models also provide reliable performance in capturing the complex determinants of malnutrition, demonstrating prediction precision and explanatory power. This study applies iterative parameter estimation methods for the multinomial regression model to investigate factors influencing childhood malnutrition in Kenya. Using data from the 2022 Kenya Demographic and Health Survey (KDHS), the research applies Newton-Raphson, Fisher’s Scoring, and Reweighted Least Squares methods to estimate parameters of the model and assess their classification performance. The study evaluates classification accuracy, goodness of fit, computational time, and predictive power of each method to identify the most reliable approach for modeling multinomial outcomes of childhood malnutrition, including stunting, wasting, underweight, and overweight. The methodological novelty of this study is the systematic comparison of iterative estimation methods, and the practical implications of selecting a method consistent with study objectives. By revealing causal relationships between malnutrition outcomes and significant demographic, socioeconomic, and environmental factors, the study aims to improve the analysis of multinomial datasets, provide accurate estimates, and support evidence-based decision-making for public health interventions in Kenya. The study results therefore, demonstrate practical policy implications for interventions toward high-risk children, prioritizing resource allocation and ensuring stronger credibility of evidence that supports nutritional policy decisions.
Practical problems in mixture experiments are usually associated with the investigation of mixture of m ingredients, which are assumed to influence the response through the proportions in which they are blended together. Mixture experiments are modeled using Scheffe’ models or Kronecker models whichever that is applicable. In such problems, the response of mixture experiments may also be affected by the conditions under which the mixture in conducted. This creates a shift in the blending characteristics of the mixture ingredients hence affecting the end product hence the need for inclusion of these conditions during modeling of mixture experiments. The objective of this study is to construct D-optimal designs for mixture experiments in the presence of process variables. In order to achieve this, first, a combined model of the second-degree Kronecker model for mixture experiments and the second-degree polynomial in the process variables in developed. The D-optimal designs are constructed using a Monte Carlo algorithmic approach in the AlgDesign of the R-packages. The designs constructed in this study were augmented with two replications of a level of the process variable. The D-optimal designs are evaluated using their D-optimal values and their relative D-efficiencies. The results of this study illustrate the existence of two alternate designs; one replicating at (-1, -1) and (-1, 1) in Table 2 and the other replicating at (0, 0) in Table 1. In conclusion the results of this study indicate that a design replicating at different levels of the process variable performs better than the one replicating at the overall centroid.
