Journal of the Royal Statistical Society Nigeria Group (JRSS-NIG Group) ISSN NUMBER: 1116-249X

MODELLING IMPACT OF RENEWABLE ENERGY CONSUMPTION TRADE OPENNESS AND FOREIGN DIRECT INVESTMENT ON ECONOMIC GROWTH IN NIGERIA

2025-05-13T10:36:15+01:00

This aimed at modelling the impact of renewable energy consumption trade openness and foreign direct investment on economic growth in Nigeria. The study used secondary data which were collected from World Development Indicator (WDI) covering a period of thirty-four (34) years spanning from 1990 - 2023. The data collected were analyzed using Vector Error Correction Model (VECM). Unit root test was carried out using Augmented Dickey Fuller (ADF)Test and
Phillips Perron (PP) test. The results of the root test revealed that Renewable Energy Consumption (REC), Trade Openness (TO), Foreign Direct Investment (FDI) and Gross Domestic Product Per Capita (GDPPC) were stationary after first difference (P< 0.05). The variables were further tested for existence of co-integration using Johansen and Joserius. The results revealed the existence of one co-integrating equation. Thus, the VECM long and short run
were estimated. The results revealed an Error Correction Term (ECT) of -0.235917 implies that an impulse to gross domestic product per capita in the current period will be restored at a speed of adjustment of about 23.6% in the next period. The long run estimated revealed that FDI and TO have negative and significant impact on GDPPC while REC has positive and significant impact on GDPPC in the long run. However, only REC have significant impact on GDPPC in the
short run. Based on these findings, it was recommended among others that the government and general public should priorities renewable energy consumption as it contributed positively to gross domestic product per capita both in short and long run.

Analyzing Exchange Rates and Inflation in Nigeria with ARDL Models

2025-05-13T10:53:21+01:00

Changes in foreign exchange rates and rising costs for products and services are the main causes of fluctuations in economic development. Despite numerous attempts by the government to reduce inflation in Nigeria, prices of goods and services continue to rise. Thus, this study analyzed the exchange rate-inflation relationship in Nigeria using autoregressive distributive lag (ARDL) approach. The data for the study were collected from National Bureau of statistics (NBS) for the
period1 1981 to 2023. The data collected were analyzed using Augmented Dickey Fuller test and autoregressive distributive lag (ARDL) model. The result of the data analysis revealed that there is no long relationship between inflation and exchange rate in Nigeria for the period under study.
The results of the ARDL short run estimates revealed that there is a significant relationship between inflation rate and official exchange rate ((p<0.05). However, there is no significant relationship between inflation rate and bureau de change in the short run (p>0.05). Based on these findings, it was recommended among others that there is need for adjusting exchange rate stabilization policy by the government that will help businesses and individuals to invest more,
which in turn, decrease importation thereby reducing inflation in the country.

SOME DETERMINANT OF DELAY PARAMETER OF TRANSITION VARIABLE IN THE SMOOTH TRANSITION AUTOREGRESSIVE (STAR) MODEL

2025-05-13T11:03:32+01:00

This study investigates some determinant of appropriate delay parameter of the transition variable in the smooth transition autoregressive (STAR) models, with emphasize on the model type and data characteristics through a refine method. Daily share price data were sourced from the Nigerian Exchange Limited, covering 10 years (January 2, 2014 to December 29, 2023), comprising 2,472 observations for each of the selected stocks: GTCO and STANBIC from
the financial sector, and DANGCEM, BETAGLASS, and WAPCO from the industrial sector. The correlation matrix revealed significant associations among STANBIC, BETAGLASS, DANGCEM, GTCO and WAPCO stock indices. Linearity tests demonstrated that DANGCEM, GTCO, and STANBIC share returns exhibit nonlinear characteristic of financial time series (FTS), whereas WAPCO returns remain linear and the most suitable delay parameter for each nonlinear stock returns was determined. The Escribano-Jorda procedure was employed to select appropriate transition function. An asymmetric transition function was specified for DANGCEM and GTCO stock returns, while a symmetric transition function was identified for STANBIC and BETAGLASS stock returns. Consequently, asymmetric STAR models were fitted to DANGCEM and GTCO stock returns, and symmetric STAR models were fitted to BETAGLASS and STANBIC stock returns using delay lengths within the range ( ). The results indicated that the APLSTAR, LSTAR, and SPLSTAR models with the initially chosen delay lengths were optimal for
DANGCEM, GTCO, and BETAGLASS stock returns, respectively. However, for STANBIC stock returns, the optimal SPLSTAR model was associated with a delay length different from the initially selected delay parameter. This study concludes that while the delay parameter of the transition variable is typically determined from the characteristics of the FTS, STAR model type also significantly influences the selection of an appropriate delay parameter. The findings contribute to improving the precision and reliability of STAR model for modeling and forecasting financial time series.

ASSESSING THE EFFICIENCY OF CLASSICAL AND BAYESIAN APPROACHES IN ADDRESSING HETEROSCEDASTICITY UNDER KNOWN FUNCTIONAL FORMS.

2025-05-13T14:24:38+01:00

This study provides a robust comparison of the traditional and Hierarchical Bayesian approaches for addressing heteroscedasticity, evaluated under known functional forms where the variance of errors is modeled as a function of exogenous variables. Using simulated data generated through Gibbs Sampling in a Monte Carlo framework, the study examines the performance of hierarchical Bayesian (HB), ordinary least squares (OLS), and generalized least squares (GLS)
approaches across different sample sizes and replications. The findings indicate that the HB demonstrates superior efficiency in addressing heteroscedasticity compared to the traditional approaches, consistently outperforming them across various scenarios. These results underscore the advantage of the HB approach in modeling relationships involving predictor variables and a dependent variable exhibiting heteroscedasticity, offering a robust alternative for researchers and
practitioners.

DYNAMIC CONNECTEDNESS OF OIL TO AGRICULTURAL COMMODITIES: COMPARISM OF TIME-VARYING VAR AND DIEBOLD-YILMAZ METHODS.

2025-05-13T14:46:43+01:00

By using Time-Varying Parameter VAR (TVP-VAR), Diebold-Yılmaz, and Partial Correlation Network methodologies to analyze the time-varying variance-covariance mechanism of daily data for the period 20 May 1987 to 13 December 2023. This study investigates the dynamic connectivity of oil to agricultural commodities. Both West Texas Intermediate (WTI) oil and Brent oil were considered since they are two most popular oil markets in the world. Global
agricultural commodities are considered, and these are as wheat, corn, soyabean, cotton, sugar, coffee, cocoa, live cattle, and lean hogs. The results show that the assets under study exhibit distinct patterns of volatility interdependence. It is found that, using all available techniques, cotton, sugar, cocoa, and lean hogs are truly identified as net shock receivers. Findings showed that, the result of Diebold-Yılmaz were larger own-forecast errors (90.00, 61.59, 97.67,…….,89.84) and that of TVP-VAR were (58.49, 61.45, 68.98,…, 80.30); The fact that WTI and Brent crude oil are not listed convincingly as shock transmitters or shock receivers based on these different methods imply that researcher should be careful when rendering policies on them using one approach. It was discovered that, most of the agricultural commodities were shock recipients; investors should diversify their portfolio across commodities to minimize risk, as the connectedness between commodities varies across methods.

DISCRETIZATION OF CLIMATIC CHARACTERISTICS AND MARKOV CHAIN MODELLING OF CROP GROWTH

2025-05-15T11:42:04+01:00

Climate change is impacted by multiple variables, and modeling the joint impact of climatic
variables is of paramount interest; hence, this study presents a unique method that uses the logical
operator to map bivariate data series to the univariate sequence. Each of the bivariate random
variables can take only categorical values. The logical “AND” and “OR” were used for mapping
these sequences and, subsequently, the Markov chain analysis. The method was applied to climatic
variables (Rainfall and Temperature) to obtain favourable and unfavourable climate conditions for
the growth of the yam crop. The Markov chain analysis indicates that the sequence of state for the
yam crop is ergodic and thus, the favourable and unfavourable climatic conditions has a stable
distribution. The logical “AND” has a low probability of favorability of 0.36 compared to the
logical “OR”, 0/75. The climate change impact (CCI) revealed that climate change adversely
affects the growth of the yam crop. The mean recurrent time for favourable climate gave an insight
into how to adapt to avoid losses. The study recommends that farmers invest more in the crop in
question, considering climate change adaptation (CCA). This is because there is high climatic
favorability during this period.

DEVELOPMENT OF NOVEL DATA MINING ALGORITHM FOR THE PREDICTION OF RECURRENCE AND SURVIVABILITY OF BREAST CANCER PATIENTS.

2025-05-13T15:19:52+01:00

Globally, breast cancer is currently the most common cancer, accounting for one-eighth of all
new annual cancer cases, and it is one of the leading causes of cancer-related death in women,
second only to lung cancer. The prediction of the recurrence and the survivability of breast
cancer patients is important as it will assist patients in knowing about the recurrence and
survivability pattern, and thereby encourage them to visit doctors promptly, so more lives can be
saved. This study developed an ensemble learning model, ANN-SVM, that can predict breast
cancer patients' recurrence and survivability. A total of 2,469 patients with breast cancer dataset
were obtained from Barau-Dikko Teaching Hospital (BDTH), Kaduna, Cancer Registry
Department. The results showed that the conventional Machine learning (ML) models- Support
Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and
the proposed model- ANN-SVM could predict the recurrence of breast cancer respectively with
82.29%, 94.84%, 90.49%, and 95.65% accuracy, also they could predict survivability of breast
cancer patients respectively with 63.29%, 90.46%, 81.93%, and 91.47% accuracy in the tested
dataset. The ANN-SVM model outperformed the conventional ML models regarding recurrence
and survival prediction of breast cancer patients. In this study, family history and chemotherapy,
respectively, turned out to be the most important features for recurrence and survivability of
breast cancer patients. The outstanding performance of the proposed model in terms of precision,
recall and F1 score highlights the model's effectiveness in accurately predicting both “yes” and
“no” for recurrence prediction and both “alive” and “dead” for survivability prediction. Both
conventional ML models and the proposed ensemble learning model predict the recurrence of
breast cancer and the survivability of breast cancer patients with high accuracy.

CAUSALITY BETWEEN KEY MACROECONOMIC FACTORS AND NIGERIA’S ECONOMIC GROWTH

2025-05-13T15:33:09+01:00

Globally, macroeconomic factors or variables such as interest rates, inflation rates, exchange
rates, export of goods and services, consumer price index, etc. play fundamental roles on the
economic performance of any country, especially the developing countries. This work therefore
investigates the causal dynamics between the Nigeria’s economy growth proxy as Gross
Domestic Product (GDP) and some vital macroeconomic factors such as Exchange Rate (EXR),
Consumer Price Index (CPI) and Export of Goods and Services (EGS) using the unrestricted
Vector Autoregressive (VAR) modeling techniques. Pre-examinations of the time series
variables using the Augmented Dickey-Fuller (ADF) and cointegration tests confirmed that the
series are not only difference stationary series of order ones {I(1)s} but are also not cointegrated;
which means that the VAR (p) model is appropriate for analyzing the series. However, the
Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and Hann-Quinn
Information Criteria (HQC) selected the optimal order p to be 5 (i.e. p = 5). This means VAR (5)
will be fitted to the datasets. Model stability diagnosis of the VAR (5) model revealed that: all
inverse roots of characteristic AR polynomial have modulus less than one and lie inside the
circle; majority of the spikes of the residual correlogram are laying inside two standard error
bounds, and there is no serial correlation in the residuals of the fitted model. In other words,
VAR (5) is stable. Findings from the study established that there is no causation or prediction
running from the EXR, EGS and CPI to GDP. Conversely, there are unilateral causalities
running from the GDP to EXR, EGS to EXR while bilateral causality exists between CPI and
EXR. Finally, there is no causation or prediction running from GDP to EGS, EXR to EGS, CPI
to EGS, GDP to CPI, and EGS to CPI.

SPATIAL IDENTIFICATION OF HIGH-RISK OF HIV/AIDS IN KEFFI LGA USING SPATIAL AUTOCORRELATION AND KRIGING INTERPOLATION.

2025-05-13T15:44:22+01:00

Quite often, authorities and policies maker are confronted with the challenges to serve
society with the little resources at her disposal. More so, the need to distribute the limited
resources correctly to the needed persons and location remain nonnegotiable in the
present dispensation. Indeed, it became very expedient to sort for means of allocating
these minimum resources to the needy against all odds. The study seek to identify
communities with severe cases of HIV virus across Karu Local Government Area of
Nasarawa State and communities at high risk to this peril. The study used secondary data
from the Keffi General hospital, which covered a period of ten (10) years, from 2013 to
2023. The study made used of Moran’s I Statistics, Kriging Model and Semivariogram
model, and employed the ArcGIS software to analyzed the data. The finding shows that
the Moran’s I statistics recorded a positive value of 0.154147, z-score of 5.062777 and p-
value 0.0000 which is statistically significant and cluster. The Semivariogram showed
that spatial autocorrelation flatten out at the range of 0.605 while the kriging model give
the prediction of communities with high risk of HIV virus. This study conclude that
resources should be allocated to the identified communities alongside with intervention
program such as, campaign programs and medical awareness to stop further prevalence
of this virus.

A NEW CLASS OF POISSON BIASING RIDGE‑TYPE ESTIMATOR: SIMULATION AND THEORY APPROACH

2025-05-15T10:07:17+01:00

Multicollinearity poses a significant challenge to the accuracy and reliability of Poisson
regression models, leading to inflated variance and biased estimates. This study proposes a novel
estimation approach, leveraging a modified version of the Liu estimator, to mitigate the adverse
effects of multicollinearity in Poisson regression models. A comprehensive simulation study is
conducted to evaluate the performance of the proposed estimator against traditional estimators,
including the Maximum Likelihood Estimator (MLE), Ridge Regression Estimator (RRE), Liu
Regression Estimator (RRE) and Modified Ridge Type Regression Estimator (MRTE). The
results demonstrate the superior performance of the proposed estimator both in theoretical and
simulation approach, particularly in simulation approach scenarios characterized by large sample
sizes, from small to large number of explanatory variables and different levels of
multicollinearity. Also, a biasing parameter k of the median version also accounted for the
smallest mean square error (MSE), under different experimental design used in the study. The
findings of this study contribute to the ongoing discussion on multicollinearity in Poisson
regression models and provide a valuable estimation approach for researchers and practitioners
dealing with multicollinearity count data.

A MULTI-STATE ASSESSMENT OF THE RECOVERY PROCESS OF HIV PATIENTS UNDER ANTIRETROVIRAL THERAPY

2025-05-15T12:04:59+01:00

The human immunodeficiency virus (HIV) remains a significant global public health
challenge. This study employs multi-state models to assess the recovery process in HIV
patients under antiretroviral therapy (ART). A retrospective study design with 1948 HIV
patients using Secondary data. Based on probability transition matrix ( ) of the Markov chain,
HIV patients on antiretroviral therapy were able to transit from the unsuppressed viral load
state () to target not detected () with a transition probability of 0.9059, with the lowest
transition from low level viremia () to unsuppressed viral load () with a transition probability
of 0.0402. The limiting distribution () of the states is 0.9079, 0.0730, and 0.0190 respectively
indicating that HIV patients are likely to remain in recovery target not detected state () in the
long run. The binary logistic regression analysis demonstrates significant factors influencing
the recovery process of HIV patients under antiretroviral therapy. Specifically, Regimen1
shows a notable odds ratio with favorable outcomes compared to the reference group
Additionally, age is a significant factor, with an odds ratio which suggest an increase in the
odds of adverse outcomes for each additional year of age Furthermore, FirstCD4 is
associated with a reduction in the odds of unfavorable outcomes, with an odds ratio with
(p<0.05). This study employs multi-state models to evaluate HIV recovery under ART using
retrospective data from 1,948 patients. Results indicate a high transition probability (0.9059)
from unsuppressed viral load to undetectable status and long-term stability in recovery (π₁ =
0.9079). Logistic regression identifies regimen type, age, and initial CD4 count as
significant factors influencing recovery (p<0.05), highlighting the effectiveness of multi-state
models in assessing HIV treatment outcomes.

METROPOLIS-HASTINGS BAYESIAN POISSON REGRESSION ANALYSIS WITH APPLICATION TO THE NATALITY OF MOTHERS IN LAGOS METROPOLIS

2025-05-15T12:10:01+01:00

This study modelled the natality of mothers in the Lagos metropolis of Lagos State using the
Bayesian Poisson Regression Analysis with the Metropolis-Hastings algorithm to sample the
expected posterior mean natality. The specific objectives were to compare the natality of mothers
by different predictors incorporating the prior knowledge about natality with a Poisson
distributed likelihood to obtain the posterior distribution. The study used nine different
categorical predictors to model the natality of mothers vis mother's age, highest education
qualification, religious affiliation, residence, use of contraceptives in between births, length of
breastfeeding babies, length of child spacing (birth gaps), mother age at first marriage, and the
Local government of residence. The prior distribution used was the normal prior on a Poisson
likelihood and obtained the posterior distributions. The data used comprised 2000 mothers
selected purposively and was extracted from Abe (2013), a city-wide study on infant mortality in
the presence of child spacing and migration and the data were analysed using the Bayesian
Poisson Regression with the help of code written in R programme environment. The study found
that the expected natality of mothers in the Lagos metropolis is 2.68 (95% CI 2.46 – 2.79). Also,
it found that the highest educational qualification, child spacing (birth gaps), age at first marriage
and Local Government of residence has a positive impact on the natality of mothers in Lagos
metropolis and while mothers’ age, residence, religious affiliation, use of contraceptives in
between birth, and breastfeeding length have a positive impact on natality of mothers. Also, it
found that the highest educational qualification, child spacing (birth gaps) and LGA of residence
have a significant impact on natality while the other predictors do not. The study therefore
concludes that the Bayesian Poisson Regression Model was a good model for the natality of
mothers in Lagos metropolis using the Metropolis-Hastings algorithm. It also concluded that the
model determined that the expected natality of mothers falls around 3 children.

AN IMPROVED ESTIMATION PROCEDURE FOR TWO-OCCASION SUCCESSIVE SAMPLING

2025-05-15T12:19:12+01:00

Surveys are frequently conducted repeatedly throughout the course of the years or seasons in order to
track changes in characteristics of interest. Estimation in current occasion is possible by utilizing facts
from prior occasion, we have spent time in working on the subject of population mean estimation in two
occasion successive sampling. An estimator T is Proposed by using the convex linear combination of
tow estimators
Tu
, based on u units of sample drawn afresh at the current occasion, and
Tm
, based on m
units, which is retained from the previous occasion. The mean square error expressions and bias of the
proposed estimator is calculated, and the best replacement strategy for the specified scenario is also
explained. An empirical study is carried out to evaluate the estimator’s efficiency. The coefficient of
variation (CV) and mean square error estimates shows that the suggested estimator is more efficient than
the current estimators taken into account in this study.

MATHEMATICAL MODELING OF COVID-19 TRANSMISSION DYNAMICS IN NIGERIA

2025-05-15T12:51:44+01:00

The coronavirus disease (COVID-19) pandemic caused by Severe Acute Respiratory
Syndrome Corona virus-2 SARS-CoV-2, has posed significant health and socio-economic
challenges worldwide, including Nigeria. Understanding the disease's dynamics is essential
for effective public health interventions. This study develops a mathematical model to
analyze COVID-19 transmission in Nigeria, considering vaccination,
isolation/hospitalization, and recovery processes. A compartmental SVEIHR (Susceptible,
Vaccinated, Exposed, Infected asymptomatic, Infected symptomatic, Hospitalized, and
Recovered model was formulated, dividing the population into susceptible, vaccinated,
exposed, asymptomatic, symptomatic, hospitalized, and recovered groups. The model's
equilibrium points were analyzed mathematically for stability. Key epidemiological
parameters including the basic reproduction number R0, were derived to assess disease
progression. Numerical simulations were conducted using MAPLE 18.0 software to evaluate
vaccination and hospitalization impacts. The model demonstrated that solutions remained
non-negative and bounded under epidemiologically realistic conditions. A disease-free
equilibrium was stable when R0< 1, indicating the potential for eradication under controlled
conditions. Simulations showed that increased vaccination rates reduced susceptible and
infectious populations, while hospitalization effectively curtailed symptomatic and
asymptomatic cases. The SVEIHR model underscores the critical role of vaccination and
hospitalization in controlling COVID-19. These findings provide valuable insights for
policymakers to optimize intervention strategies and mitigate the pandemic's impact in
Nigeria.

A MODIFIED BIASING RIDGE ESTIMATORS FOR ADDRESSING MULTICOLLINEARITY PROBLEM IN LINEAR REGRESSION MODEL

2025-05-15T13:00:15+01:00

This study conduct an extensive analysis of various biasing estimators in the context of multiple
explanatory variable (p) and varying degree of multicollinearity, a number of approaches have
been developed for deriving biasing estimators. In this study, a new approach to obtain the ridge
biasing parameter k is suggested and then evaluated by Monte Carlo simulations. A number of
different models are investigated for different number of observations, the strength of correlation
between the explanatory variables, and distribution of the error terms. The mean squared error
(MSE) criterion is used to examine the performance of the proposed estimators when compared
with other well-known estimators. Accordingly, the analysis revealed that generally, mean
square error (MSE) value of the estimators decrease as the degree of correlation among
explanatory variables increased with a few exceptions. In conclusion, kibria’s biasing estimator
proposed in 2022 exhibit effectiveness across multicollinearity level, error terms, sample sizes
and correlation levels. These results provide valuable insight for researcher and practitioner
seeking to choose appropriate biasing estimators in similar statistical scenarios.

DEVELOPMENT OF ALMOST UNBIASED RATIO TYPE ESTIMATOR USING THE STANDARD DEVIATION WHEN AUXILIARY VARIABLE IS UNKNOWN

2025-05-15T13:10:01+01:00

Some authors had proposed modified ratio estimators of population mean using the population
parameter of the auxiliary when the information on the auxiliary is known. These estimators
are biased though with smaller mean square error compared to the classical ratio estimator.
However, the information on the auxiliary variable may not be available in all cases. In this
paper, the use of double sampling strategy was employed to obtain more information on the
auxiliary variable and then the almost unbiased ratio estimator of population mean using
standard deviation
x S
ˆ
is proposed. The mean square error and bias of the developed estimator
were derived, as well as the condition under which the developed estimator performs better
than the classical ratio estimator. To validate the merits of the proposed estimator over other
estimators, an empirical study carried out revealed that the proposed estimator has the smallest
bias among the existing estimators considered (0.0645 and 0.0175) for population 1 and
population 2 respectively. Similarly, with respect to mean square errors, the proposed
estimator has the least among the existing estimators considered (1.076 and 15.6953) for
population 1 and population 2 respectively.

Appraising Obesity among Female Undergraduate Students of Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria: A Discriminant and Principal Component Analysis Approach

2025-05-15T15:33:31+01:00

The prevalence of obesity and its negative consequences is on the increase globally especially West Africa and Nigeria. This menace is fast increasing even among university students and if not properly checked it will have a far-reaching implication on the student’s health and academic performance. Thus, this study measured the weight (Wt), Height (Ht), Waist Circumference, hip, body fat, systolic and diastolic blood pressure and pulse pressure of female students living in Block D of Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria.

The variables were all measured using appropriate measuring instruments and 250 samples were collected. After validating the data for the necessary assumptions of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) approaches, these methods were employed for the data analysis. Employing the WHO criteria for classifying obesity into Normal weight (N), Obese (O) and Overweight (W) students, a comparison of group means shows that the obese group has a higher mean value for body mass index (BMI) than the other two groups. The prior percentage probabilities of an individual being in the non-obese, obese and overweight group is 67.8%, 8%, and 24.1% respectively. Indicatively, the PCA approach was able to reduce the dimension of the data with the first principal component (LD1) explaining 98.8% of the variation in the data while the second principal component (LD2) explains 1.2% of the variation. The model is used to predict obesity and was shown to possess 96.05% accuracy which implies that the error of mis-classification is 0.04%. It was concluded that there is only 8% chance of a female student being in the obese group as against 67.8% chance for the non-obese group.

STATISTICAL EVALUATION OF WASTE MANAGERMENT AND RECYCLING PRACTICES IN IREE COMMUNITY, NIGERIA.

2025-05-15T16:17:00+01:00

There have been ongoing search to reduce, reuse, recycle and recover waste management. The data
obtained were mainly primary data sourced among residents of Iree Community in Boripe Local
Government, Osun State, Nigeria, with a focus on identifying effective strategies for sustainable
waste management. The study examined the relationship between demographic factors, awareness
levels, and waste disposal methods using statistical tools such as Chi-square tests and ordinal
logistic regression. Results revealed that while 56% of residents engage in sustainable waste
disposal practices, a significant portion (44%) still relies on unsustainable methods such as burning
and illegal dumping. Awareness of recycling programs was a significant predictor of sustainable
practices (p < 0.01), highlighting the need for public education campaigns. Recycling programs
(30%) and composting (28.5%) emerged as the most preferred waste management strategies. The
study underscores the importance of increasing public awareness, improving access to waste
management facilities, and fostering community-led initiatives to address the challenges of waste
management. By identifying key factors that drive sustainable waste disposal, the findings provide
actionable insights for policymakers and stakeholders aiming to enhance recycling rates and reduce
landfill dependence in developing countries.

A TYPE I HALF LOGISTIC TOPP-LEONE INVERSE WEIBULL DISTRIBUTION: STATISTICAL PROPERTIES AND APPLICATIONS

2025-05-15T16:58:00+01:00

Researchers in the area of statistical distribution have made efforts on generalizing the existing
probability distributions to improve their modeling flexibility by adding extra parameters. In this
paper, a new continuous probability distribution called type I Half Logistic Topp-Leone Inverse
Weibull distribution with four parameters was derived. The nature of the new distribution with
the help of its mathematical and statistical properties such as quantile function, ordinary
moments, moment generating function and reliability were studied. The probability density
function of the minimum and maximum order statistics for the new distribution was also
obtained. A classical estimation of the unknown parameters of the model was done using the
technique of maximum likelihood estimation. Monte Carlo simulation study was carried out to
see the performance of maximum likelihood estimation method. The proposed model was
applied to two real datasets and the results showed that the new model provides a better fit than
the competing distributions considered.

A MIXED DATA SAMPLING (MIDAS) APPROACH TO MODELING CRUDE OIL PRODUCTION IN NIGERIA: ACCOUNTING FOR MIXED—FREQUENCY DATA

2025-05-15T17:06:04+01:00

This research work attempt to establish an efficient method of forecasting Nigeria’ Crude Oil
Production and the Nigeria Gross Domestic Product applying Mixed Data Sampling approach
(MIDAS) from 2010 to 2022. It combined data set of different frequencies; quarterly and
monthly in the same regression. It was observed that based on the Root Mean Square Error the
MIDAS Almon (PDL) regression model provided a better model estimation than the MIDAS
Step weighting and the MIDAS Beta. The monthly Crude oil production has a positive effect on
GDP as the slope coefficient is statistically significant 0.849895 (Prob. 0.0000). It is therefore
important for appropriate policy formulation and implementation of such policies to encourage
and boost these variables for effective management of Crude oil production in Nigeria. Hence,
direct relationship between Crude oil production and GDP is needed to diversify the economy
base to enhance productive activities in Nigeria and better crude oil production.

SENSITIVITY OF BAYESIAN DYNAMIC MIXED LOGIT MODELS TO PRIOR DISTRIBUTIONS

2025-05-15T17:17:14+01:00

This research work investigated the performance of various prior distributions on the Bayesian
Dynamic Mixed Logistic Regression Model (BDML). The data set used was a Public datasets
gotten from UCI Machine Learning Repository. The study compared the performance of
Uniform, Jeffrey’s, Exponential, Gamma, Cauchy, Normal, and Beta prior distributions in
capturing the heterogeneity in customer preferences. The result of the Bank marketing data
showed that Jeffery’s prior outperforms other priors used in terms of MAE, RMSE, and Log
Likelihood this showed that the choice of prior distribution significantly affects the model
estimates and predictions.