Pub Date : 2024-07-06DOI: 10.9734/ajpas/2024/v26i7633
Shivangee Misra, Rajeev Pandey
.
.
{"title":"Bayesian Sequential Updation and Prediction of Currency in Circulation Using a Weighted Prior","authors":"Shivangee Misra, Rajeev Pandey","doi":"10.9734/ajpas/2024/v26i7633","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i7633","url":null,"abstract":"<jats:p>.</jats:p>","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141672693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-15DOI: 10.9734/ajpas/2024/v26i7629
S. Garren, Brooke A. Cleathero
When estimating a population proportion p within margin of error m, a preliminary sample of size n is taken to produce a preliminary sample proportion y/n, which is then used to determine the required sample size (y/n)(1-y/n)(z/m)2, where z is the critical value for a given level of confidence. The population is assumed to be infinite, so these Bernoulli(p) observations are mutually independent. Upon taking a new sample based on the required sample size, the coverage probabilities on p are determined exactly for various values of m, n, p, and z, using a commonly-used formula for a confidence interval on p. The coverage probabilities tend to be somewhat smaller than their nominal values, and tend to be a lot smaller when np or n(1 - p) is small, which would result in anti-conservative confidence intervals. As a more minor conclusion, since the given margin of error m is not relative to the population proportion p, then the required sample size is larger for values of p nearest to 0.5. The mean and standard deviation of the required sample size are also computed exactly to provide prospective, regarding just how large or how small these required sample sizes need to be.
在误差范围 m 内估计人口比例 p 时，需要抽取规模为 n 的初步样本，得出初步样本比例 y/n，然后用它来确定所需的样本规模 (y/n)(1-y/n)(z/m)2，其中 z 是给定置信度的临界值。假设总体是无限的，因此这些伯努利（p）观测结果是相互独立的。根据所需的样本量重新抽取样本后，使用常用的 p 置信区间公式，可以精确地确定 m、n、p 和 z 的不同值时 p 的覆盖概率。一个更次要的结论是，由于给定的误差范围 m 不是相对于人口比例 p 而定的，因此当 p 值接近 0.5 时，所需的样本量会更大。我们还精确计算了所需样本量的平均值和标准偏差，以提供关于所需样 本量需要多大或多小的前瞻性信息。
{"title":"Assessment of Required Sample Sizes for Estimating Proportions","authors":"S. Garren, Brooke A. Cleathero","doi":"10.9734/ajpas/2024/v26i7629","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i7629","url":null,"abstract":"When estimating a population proportion p within margin of error m, a preliminary sample of size n is taken to produce a preliminary sample proportion y/n, which is then used to determine the required sample size (y/n)(1-y/n)(z/m)2, where z is the critical value for a given level of confidence. The population is assumed to be infinite, so these Bernoulli(p) observations are mutually independent. Upon taking a new sample based on the required sample size, the coverage probabilities on p are determined exactly for various values of m, n, p, and z, using a commonly-used formula for a confidence interval on p. The coverage probabilities tend to be somewhat smaller than their nominal values, and tend to be a lot smaller when np or n(1 - p) is small, which would result in anti-conservative confidence intervals. As a more minor conclusion, since the given margin of error m is not relative to the population proportion p, then the required sample size is larger for values of p nearest to 0.5. The mean and standard deviation of the required sample size are also computed exactly to provide prospective, regarding just how large or how small these required sample sizes need to be.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141337743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.9734/ajpas/2024/v26i7628
A. Langat, John Kamwele Mutinda
Understanding the pattern of rainfall in Kenya is crucial for a range of sectors, including agriculture, water management, and disaster risk reduction. In this research, we propose a Bayesian non-parametric approach to model the rainfall patterns in Kenya. Specifically, we use a hierarchical Dirichlet process mixture model to cluster the rainfall stations and identify groups of stations with similar rainfall patterns. We then model the rainfall distribution within each group using a Bayesian non-parametric model based on the normalized generalized gamma process. We apply our method to a dataset of daily rainfall measurements from 150 stations across Kenya for the period 1980-2021. Our results reveal distinct regional patterns of rainfall, with some regions experiencing bimodal rainfall patterns while others have unimodal patterns. We also find that the rainfall distribution within each region exhibits heavy tails and skewedness, which cannot be accurately captured by parametric models. In conclusion, our approach provides a flexible and interpretable framework for modeling complex spatio-temporal data such as rainfall patterns, and can inform decision-making in various sectors.
{"title":"Rainfall Pattern in Kenya: Bayesian Non-parametric Model Based on the Normalized Generalized Gamma Process","authors":"A. Langat, John Kamwele Mutinda","doi":"10.9734/ajpas/2024/v26i7628","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i7628","url":null,"abstract":"Understanding the pattern of rainfall in Kenya is crucial for a range of sectors, including agriculture, water management, and disaster risk reduction. In this research, we propose a Bayesian non-parametric approach to model the rainfall patterns in Kenya. Specifically, we use a hierarchical Dirichlet process mixture model to cluster the rainfall stations and identify groups of stations with similar rainfall patterns. We then model the rainfall distribution within each group using a Bayesian non-parametric model based on the normalized generalized gamma process. We apply our method to a dataset of daily rainfall measurements from 150 stations across Kenya for the period 1980-2021. Our results reveal distinct regional patterns of rainfall, with some regions experiencing bimodal rainfall patterns while others have unimodal patterns. We also find that the rainfall distribution within each region exhibits heavy tails and skewedness, which cannot be accurately captured by parametric models. In conclusion, our approach provides a flexible and interpretable framework for modeling complex spatio-temporal data such as rainfall patterns, and can inform decision-making in various sectors.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141347701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O'Neill [1] introduces the concept of dualistic partial metric space. In this study, we prove some common fixed-point theorems for dualistic expanding mappings defined on a dualistic partial metric space. Some famous conclusions of [2] and [3] are extended and generalized by our result. Additionally, we offer an example that demonstrates the value of these dualistic expanding mappings.
{"title":"Common Fixed-Point Theorem for Expansive Mappings in Dualistic Partial Metric Spaces","authors":"Shiva Verma, Rahul Gourh, Manoj Ughade, Sheetal Yadav","doi":"10.9734/ajpas/2024/v26i7627","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i7627","url":null,"abstract":"O'Neill [1] introduces the concept of dualistic partial metric space. In this study, we prove some common fixed-point theorems for dualistic expanding mappings defined on a dualistic partial metric space. Some famous conclusions of [2] and [3] are extended and generalized by our result. Additionally, we offer an example that demonstrates the value of these dualistic expanding mappings.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.9734/ajpas/2024/v26i7626
Cyril Neba C., Gerard Shu F., Gillian Nsuh, Philip Amouda A., Adrian Neba F., F. Webnda, Victory Ikpe, Adeyinka Orelaja, Nabintou Anissia Sylla
In the rapidly evolving landscape of retail analytics, the accurate prediction of sales figures holds paramount importance for informed decision-making and operational optimization. Leveraging diverse machine learning methodologies, this study aims to enhance the precision of Walmart sales forecasting, utilizing a comprehensive dataset sourced from Kaggle. Exploratory data analysis reveals intricate patterns and temporal dependencies within the data, prompting the adoption of advanced predictive modeling techniques. Through the implementation of linear regression, ensemble methods such as Random Forest, Gradient Boosting Machines (GBM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), this research endeavors to identify the most effective approach for predicting Walmart sales. Comparative analysis of model performance showcases the superiority of advanced machine learning algorithms over traditional linear models. The results indicate that XGBoost emerges as the optimal predictor for sales forecasting, boasting the lowest Mean Absolute Error (MAE) of 1226.471, Root Mean Squared Error (RMSE) of 1700.981, and an exceptionally high R-squared value of 0.9999900, indicating near-perfect predictive accuracy. This model's performance significantly surpasses that of simpler models such as linear regression, which yielded an MAE of 35632.510 and an RMSE of 80153.858. Insights from bias and fairness measurements underscore the effectiveness of advanced models in mitigating bias and delivering equitable predictions across temporal segments. Our analysis revealed varying levels of bias across different models. Linear Regression, Multiple Regression, and GLM exhibited moderate bias, suggesting some systematic errors in predictions. Decision Tree showed slightly higher bias, while Random Forest demonstrated a unique scenario of negative bias, implying systematic underestimation of predictions. However, models like GBM, XGBoost, and LGB displayed biases closer to zero, indicating more accurate predictions with minimal systematic errors. Notably, the XGBoost model demonstrated the lowest bias, with an MAE of -7.548432 (Table 4), reflecting its superior ability to minimize prediction errors across different conditions. Additionally, fairness analysis revealed that XGBoost maintained robust performance in both holiday and non-holiday periods, with an MAE of 84273.385 for holidays and 1757.721 for non-holidays. Insights from the fairness measurements revealed that Linear Regression, Multiple Regression, and GLM showed consistent predictive performance across both subgroups. Meanwhile, Decision Tree performed similarly for holiday predictions but exhibited better accuracy for non-holiday sales, whereas, Random Forest, XGBoost, GBM, and LGB models displayed lower MAE values for the non-holiday subgroup, indicating potential fairness issues in predicting holiday sales. The study also highlights the importance of model selection
{"title":"Advancing Retail Predictions: Integrating Diverse Machine Learning Models for Accurate Walmart Sales Forecasting","authors":"Cyril Neba C., Gerard Shu F., Gillian Nsuh, Philip Amouda A., Adrian Neba F., F. Webnda, Victory Ikpe, Adeyinka Orelaja, Nabintou Anissia Sylla","doi":"10.9734/ajpas/2024/v26i7626","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i7626","url":null,"abstract":"In the rapidly evolving landscape of retail analytics, the accurate prediction of sales figures holds paramount importance for informed decision-making and operational optimization. Leveraging diverse machine learning methodologies, this study aims to enhance the precision of Walmart sales forecasting, utilizing a comprehensive dataset sourced from Kaggle. Exploratory data analysis reveals intricate patterns and temporal dependencies within the data, prompting the adoption of advanced predictive modeling techniques. Through the implementation of linear regression, ensemble methods such as Random Forest, Gradient Boosting Machines (GBM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), this research endeavors to identify the most effective approach for predicting Walmart sales. \u0000Comparative analysis of model performance showcases the superiority of advanced machine learning algorithms over traditional linear models. The results indicate that XGBoost emerges as the optimal predictor for sales forecasting, boasting the lowest Mean Absolute Error (MAE) of 1226.471, Root Mean Squared Error (RMSE) of 1700.981, and an exceptionally high R-squared value of 0.9999900, indicating near-perfect predictive accuracy. This model's performance significantly surpasses that of simpler models such as linear regression, which yielded an MAE of 35632.510 and an RMSE of 80153.858. \u0000Insights from bias and fairness measurements underscore the effectiveness of advanced models in mitigating bias and delivering equitable predictions across temporal segments. Our analysis revealed varying levels of bias across different models. Linear Regression, Multiple Regression, and GLM exhibited moderate bias, suggesting some systematic errors in predictions. Decision Tree showed slightly higher bias, while Random Forest demonstrated a unique scenario of negative bias, implying systematic underestimation of predictions. However, models like GBM, XGBoost, and LGB displayed biases closer to zero, indicating more accurate predictions with minimal systematic errors. Notably, the XGBoost model demonstrated the lowest bias, with an MAE of -7.548432 (Table 4), reflecting its superior ability to minimize prediction errors across different conditions. Additionally, fairness analysis revealed that XGBoost maintained robust performance in both holiday and non-holiday periods, with an MAE of 84273.385 for holidays and 1757.721 for non-holidays. \u0000Insights from the fairness measurements revealed that Linear Regression, Multiple Regression, and GLM showed consistent predictive performance across both subgroups. Meanwhile, Decision Tree performed similarly for holiday predictions but exhibited better accuracy for non-holiday sales, whereas, Random Forest, XGBoost, GBM, and LGB models displayed lower MAE values for the non-holiday subgroup, indicating potential fairness issues in predicting holiday sales. \u0000The study also highlights the importance of model selection ","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-24DOI: 10.9734/ajpas/2024/v26i5618
M. Ekum, Sheriffdeen Taiwo Oyeyemi, T. O. Alakija, Saduwa Akpoviri Francis, Azeez Olabisi Omodasola, O. M. Akinmoladun
The ongoing volatility of crude oil prices on the international market has harmed every sector of the Nigerian economy. Every Nigerian government regime experiences fluctuations in the price of petroleum. Thus, this research studied the effect of change in government regime on change in petroleum prices using a moving index with a constant and moving base year. Data on government regime and prices of petroleum were collected (1960 to 2021) from the Office of the Secretary to the Government of the Federation, Central Bank of Nigeria (CBN) Statistical Bulletin, and National Bureau of Statistics (NBS), spanning 62 years, and the changes in these prices over all the regime were observed via the time plot. The results of the analysis showed that regime change in Nigeria has significantly impacted the price of petroleum. The trend of change in petroleum prices using 1960 as a constant base year showed that regime change has a significant effect on change in petroleum price, while the moving index with varying base years showed no significant effect on the change in the petroleum price. Therefore, it can be concluded that variations in the price of petroleum in Nigeria are caused by both changes in time and regime. The estimated trend of change in the prices of petroleum with the period under study showed an upward trend. The movement showed that the price of petroleum is not likely to reduce shortly but rather will increase if nothing is done to stabilize it.
{"title":"Measuring the Effect of Regime Change on Petroleum Price in Nigeria Using Moving Index","authors":"M. Ekum, Sheriffdeen Taiwo Oyeyemi, T. O. Alakija, Saduwa Akpoviri Francis, Azeez Olabisi Omodasola, O. M. Akinmoladun","doi":"10.9734/ajpas/2024/v26i5618","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i5618","url":null,"abstract":"The ongoing volatility of crude oil prices on the international market has harmed every sector of the Nigerian economy. Every Nigerian government regime experiences fluctuations in the price of petroleum. Thus, this research studied the effect of change in government regime on change in petroleum prices using a moving index with a constant and moving base year. Data on government regime and prices of petroleum were collected (1960 to 2021) from the Office of the Secretary to the Government of the Federation, Central Bank of Nigeria (CBN) Statistical Bulletin, and National Bureau of Statistics (NBS), spanning 62 years, and the changes in these prices over all the regime were observed via the time plot. The results of the analysis showed that regime change in Nigeria has significantly impacted the price of petroleum. The trend of change in petroleum prices using 1960 as a constant base year showed that regime change has a significant effect on change in petroleum price, while the moving index with varying base years showed no significant effect on the change in the petroleum price. Therefore, it can be concluded that variations in the price of petroleum in Nigeria are caused by both changes in time and regime. The estimated trend of change in the prices of petroleum with the period under study showed an upward trend. The movement showed that the price of petroleum is not likely to reduce shortly but rather will increase if nothing is done to stabilize it.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141098771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.9734/ajpas/2024/v26i5617
Shashank Kirti, Rajeev Pandey
Outlier identification is a crucial field within data mining that focuses on identifying data points that significantly depart from other patterns in the data. Outlier identification may be categorized into formal and informal procedures. This article discusses informal approaches, sometimes known as labelling methods. The study focused on the analysis of real-time medical data to identify outliers using outlier labelling techniques. Various labelling approaches are used to calculate realistic situations in the dataset. Ultimately, using the anticipated outcomes of the outliers is a more suitable approach for addressing the needs of the larger populations.
{"title":"Methods of Assigning Labels to Detect Outliers","authors":"Shashank Kirti, Rajeev Pandey","doi":"10.9734/ajpas/2024/v26i5617","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i5617","url":null,"abstract":"Outlier identification is a crucial field within data mining that focuses on identifying data points that significantly depart from other patterns in the data. Outlier identification may be categorized into formal and informal procedures. This article discusses informal approaches, sometimes known as labelling methods. The study focused on the analysis of real-time medical data to identify outliers using outlier labelling techniques. Various labelling approaches are used to calculate realistic situations in the dataset. Ultimately, using the anticipated outcomes of the outliers is a more suitable approach for addressing the needs of the larger populations.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-11DOI: 10.9734/ajpas/2024/v26i5616
C. A. Ugomma, Samuel Chimuanya Chijioke
This study evaluated the impact of inflation and exchange rate on the Nigerian Gross Domestic Product (GDP) from 1981 to 2022. The data for this study were obtained from Central Bank of Nigeria Statistical Bulletin. Multiple Linear Regression model was adopted for the study to determine the relationship between the GDP, the inflation and exchange rates and the result showed that there is a significant relationship with the p-value (0.005). The result also showed with Ordinary Least Square (OLS) method that inflation rate has a negative impact on the Nigerian GDP while exchange rate is significant with (p-value <0,005) over the years of study. The value of the coefficient of variation R2 for this research is 92.2% indicating that inflation and exchange rate account for about 92% of the variation in the GDP over the years of study. It was observed there was an increase in exchange rate and price level is also detrimental to the economic growth, this means it contributes to the growth of Nigerian GDP over the period of study.
{"title":"Assessing the Impact of Inflation and Exchange Rate on Nigerian Gross Domestic Product (1981-2022)","authors":"C. A. Ugomma, Samuel Chimuanya Chijioke","doi":"10.9734/ajpas/2024/v26i5616","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i5616","url":null,"abstract":"This study evaluated the impact of inflation and exchange rate on the Nigerian Gross Domestic Product (GDP) from 1981 to 2022. The data for this study were obtained from Central Bank of Nigeria Statistical Bulletin. Multiple Linear Regression model was adopted for the study to determine the relationship between the GDP, the inflation and exchange rates and the result showed that there is a significant relationship with the p-value (0.005). The result also showed with Ordinary Least Square (OLS) method that inflation rate has a negative impact on the Nigerian GDP while exchange rate is significant with (p-value <0,005) over the years of study. The value of the coefficient of variation R2 for this research is 92.2% indicating that inflation and exchange rate account for about 92% of the variation in the GDP over the years of study. It was observed there was an increase in exchange rate and price level is also detrimental to the economic growth, this means it contributes to the growth of Nigerian GDP over the period of study.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140988367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.9734/ajpas/2024/v26i5615
John K. Njenga, E. N. Irungu
This study sought to analyze the underlying financial inclusion determinants in Kenya. The study applies ordinal logit regression to examine the effect of the residential area, gender, education level, marital status, and employment type on financial inclusion. Financial inclusion is measured by developing a financial inclusion index for ten binary financial services variables. From the index, three financial inclusion levels are designed. These include low financial inclusion with scores of zero to three, medium with scores of four to six, and high level with scores of seven to ten. The estimates of the ordinal model are statistically significant for all factors considered except gender. Area of residence, age, education type, income, and marital status positively affect the log odds of financial inclusion, while employment is negatively linked. Education, employment, and marital status have interaction effects on financial inclusion. This study recommends that the Kenyan government formulate and strengthen policies to tackle challenges such as gender disparity, rural bank infrastructure development, fostering an environment conducive for entrepreneurship to address unemployment and income disparities, advocating for secondary school completion, and addressing social issues impacting family stability, including separation or the absence of marriage.
{"title":"Determinants of Financial Inclusion in Kenya: A Demand-Side Perspective","authors":"John K. Njenga, E. N. Irungu","doi":"10.9734/ajpas/2024/v26i5615","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i5615","url":null,"abstract":"This study sought to analyze the underlying financial inclusion determinants in Kenya. The study applies ordinal logit regression to examine the effect of the residential area, gender, education level, marital status, and employment type on financial inclusion. Financial inclusion is measured by developing a financial inclusion index for ten binary financial services variables. From the index, three financial inclusion levels are designed. These include low financial inclusion with scores of zero to three, medium with scores of four to six, and high level with scores of seven to ten. The estimates of the ordinal model are statistically significant for all factors considered except gender. Area of residence, age, education type, income, and marital status positively affect the log odds of financial inclusion, while employment is negatively linked. Education, employment, and marital status have interaction effects on financial inclusion. This study recommends that the Kenyan government formulate and strengthen policies to tackle challenges such as gender disparity, rural bank infrastructure development, fostering an environment conducive for entrepreneurship to address unemployment and income disparities, advocating for secondary school completion, and addressing social issues impacting family stability, including separation or the absence of marriage.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of the paper is to propose an almost unbiased ratio estimator for the finite coefficient of variation (CV). In this paper, we have proposed an exponential ratio type and log ratio type estimators for estimating population coefficient of variation. Two real data sets and one simulation study is carried out in support of the theoretical results. Mean squared error and Percent relative efficiency criteria is used to assess the performance of the estimators. It has been shown that the proposed class of estimators are almost unbiased up to the first order of approximation. Also proposed estimators are better in efficiency to other estimators consider in this study.
{"title":"Almost Unbiased Estimators for Population Coefficient of Variation Using Auxiliary Information","authors":"Rajesh Singh, Rohan Mishra, Anamika Kumari, Sunil Kumar Yadav","doi":"10.9734/ajpas/2024/v26i5614","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i5614","url":null,"abstract":"The objective of the paper is to propose an almost unbiased ratio estimator for the finite coefficient of variation (CV). In this paper, we have proposed an exponential ratio type and log ratio type estimators for estimating population coefficient of variation. Two real data sets and one simulation study is carried out in support of the theoretical results. Mean squared error and Percent relative efficiency criteria is used to assess the performance of the estimators. It has been shown that the proposed class of estimators are almost unbiased up to the first order of approximation. Also proposed estimators are better in efficiency to other estimators consider in this study.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141001727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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