Pub Date : 2024-02-09DOI: 10.9734/ajpas/2024/v26i2588
N. Halidias
.
.
{"title":"Asymptotic Theorems for Discrete Markov Chains","authors":"N. Halidias","doi":"10.9734/ajpas/2024/v26i2588","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i2588","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":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139850232","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 : 2024-02-09DOI: 10.9734/ajpas/2024/v26i2588
N. Halidias
.
.
{"title":"Asymptotic Theorems for Discrete Markov Chains","authors":"N. Halidias","doi":"10.9734/ajpas/2024/v26i2588","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i2588","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":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139790299","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 : 2024-02-07DOI: 10.9734/ajpas/2024/v26i1587
S. B. Atoyebi, Titi Obilade
A multilevel logistic regression model demonstrating high correlations among predictor variables is susceptible to multi-collinearity. Multi-collinearity significantly impacts the robustness and interpretability of multilevel non-linear models. In multilevel non-linear models, the effects of multi-collinearity can be amplified, leading to distorted parameter estimates and inflated standard errors. This phenomenon contributes to an escalation in the variances of parameter estimates, thereby resulting in inaccurate inferences regarding the relationships between the response and explanatory factors. The primary objective of this study is to investigate the impact of multi-collinearity on multilevel non-linear models. The research aims to assess whether the quantity of independent variables influences the Multilevel Variance Inflation Factor and to explore the effect of altering the correlation degree at one level on multi-collinearity within a multilevel non-linear model. Additionally, the research seeks to determine how multi-collinearity affects the standard errors of parameters in a multilevel non-linear model. In a 2-level logistic regression, a binary variable was the dependent variable, while pre-established standard variables functioned as regressors. The Monte Carlo analysis incorporated three distinct correlation strengths (0.2, 0.5, and 0.9) and sample sizes (500, 100, and 30). The Multilevel Variance Inflation Factor was employed for multi-collinearity diagnosis. The outcomes revealed that, within the logistic multilevel regression model, an increase in sample size correlated with a reduction in multi-collinearity. Notably, the influence of multi-collinearity on standard errors in a multilevel non-linear model was more pronounced. It was observed that increasing the sample size remains an effective strategy to mitigate multi-collinearity errors in a multilevel non-linear model. This approach is particularly crucial due to the reliance on maximum likelihood estimation in logistic regression, as opposed to ordinary least squares (OLS) regression, which contrasts with the methodology of OLS regression.
{"title":"Effect of Increasing Sample Size on Multi-Collinearity in Multilevel Non-Linear Model","authors":"S. B. Atoyebi, Titi Obilade","doi":"10.9734/ajpas/2024/v26i1587","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i1587","url":null,"abstract":"A multilevel logistic regression model demonstrating high correlations among predictor variables is susceptible to multi-collinearity. Multi-collinearity significantly impacts the robustness and interpretability of multilevel non-linear models. In multilevel non-linear models, the effects of multi-collinearity can be amplified, leading to distorted parameter estimates and inflated standard errors. This phenomenon contributes to an escalation in the variances of parameter estimates, thereby resulting in inaccurate inferences regarding the relationships between the response and explanatory factors. The primary objective of this study is to investigate the impact of multi-collinearity on multilevel non-linear models. The research aims to assess whether the quantity of independent variables influences the Multilevel Variance Inflation Factor and to explore the effect of altering the correlation degree at one level on multi-collinearity within a multilevel non-linear model. Additionally, the research seeks to determine how multi-collinearity affects the standard errors of parameters in a multilevel non-linear model. In a 2-level logistic regression, a binary variable was the dependent variable, while pre-established standard variables functioned as regressors. The Monte Carlo analysis incorporated three distinct correlation strengths (0.2, 0.5, and 0.9) and sample sizes (500, 100, and 30). The Multilevel Variance Inflation Factor was employed for multi-collinearity diagnosis. The outcomes revealed that, within the logistic multilevel regression model, an increase in sample size correlated with a reduction in multi-collinearity. Notably, the influence of multi-collinearity on standard errors in a multilevel non-linear model was more pronounced. It was observed that increasing the sample size remains an effective strategy to mitigate multi-collinearity errors in a multilevel non-linear model. This approach is particularly crucial due to the reliance on maximum likelihood estimation in logistic regression, as opposed to ordinary least squares (OLS) regression, which contrasts with the methodology of OLS regression.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139794990","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 : 2024-02-07DOI: 10.9734/ajpas/2024/v26i1587
S. B. Atoyebi, Titi Obilade
A multilevel logistic regression model demonstrating high correlations among predictor variables is susceptible to multi-collinearity. Multi-collinearity significantly impacts the robustness and interpretability of multilevel non-linear models. In multilevel non-linear models, the effects of multi-collinearity can be amplified, leading to distorted parameter estimates and inflated standard errors. This phenomenon contributes to an escalation in the variances of parameter estimates, thereby resulting in inaccurate inferences regarding the relationships between the response and explanatory factors. The primary objective of this study is to investigate the impact of multi-collinearity on multilevel non-linear models. The research aims to assess whether the quantity of independent variables influences the Multilevel Variance Inflation Factor and to explore the effect of altering the correlation degree at one level on multi-collinearity within a multilevel non-linear model. Additionally, the research seeks to determine how multi-collinearity affects the standard errors of parameters in a multilevel non-linear model. In a 2-level logistic regression, a binary variable was the dependent variable, while pre-established standard variables functioned as regressors. The Monte Carlo analysis incorporated three distinct correlation strengths (0.2, 0.5, and 0.9) and sample sizes (500, 100, and 30). The Multilevel Variance Inflation Factor was employed for multi-collinearity diagnosis. The outcomes revealed that, within the logistic multilevel regression model, an increase in sample size correlated with a reduction in multi-collinearity. Notably, the influence of multi-collinearity on standard errors in a multilevel non-linear model was more pronounced. It was observed that increasing the sample size remains an effective strategy to mitigate multi-collinearity errors in a multilevel non-linear model. This approach is particularly crucial due to the reliance on maximum likelihood estimation in logistic regression, as opposed to ordinary least squares (OLS) regression, which contrasts with the methodology of OLS regression.
{"title":"Effect of Increasing Sample Size on Multi-Collinearity in Multilevel Non-Linear Model","authors":"S. B. Atoyebi, Titi Obilade","doi":"10.9734/ajpas/2024/v26i1587","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i1587","url":null,"abstract":"A multilevel logistic regression model demonstrating high correlations among predictor variables is susceptible to multi-collinearity. Multi-collinearity significantly impacts the robustness and interpretability of multilevel non-linear models. In multilevel non-linear models, the effects of multi-collinearity can be amplified, leading to distorted parameter estimates and inflated standard errors. This phenomenon contributes to an escalation in the variances of parameter estimates, thereby resulting in inaccurate inferences regarding the relationships between the response and explanatory factors. The primary objective of this study is to investigate the impact of multi-collinearity on multilevel non-linear models. The research aims to assess whether the quantity of independent variables influences the Multilevel Variance Inflation Factor and to explore the effect of altering the correlation degree at one level on multi-collinearity within a multilevel non-linear model. Additionally, the research seeks to determine how multi-collinearity affects the standard errors of parameters in a multilevel non-linear model. In a 2-level logistic regression, a binary variable was the dependent variable, while pre-established standard variables functioned as regressors. The Monte Carlo analysis incorporated three distinct correlation strengths (0.2, 0.5, and 0.9) and sample sizes (500, 100, and 30). The Multilevel Variance Inflation Factor was employed for multi-collinearity diagnosis. The outcomes revealed that, within the logistic multilevel regression model, an increase in sample size correlated with a reduction in multi-collinearity. Notably, the influence of multi-collinearity on standard errors in a multilevel non-linear model was more pronounced. It was observed that increasing the sample size remains an effective strategy to mitigate multi-collinearity errors in a multilevel non-linear model. This approach is particularly crucial due to the reliance on maximum likelihood estimation in logistic regression, as opposed to ordinary least squares (OLS) regression, which contrasts with the methodology of OLS regression.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139854783","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 : 2024-02-06DOI: 10.9734/ajpas/2024/v26i1586
Orryza Oky Astrianka, Achmad Efendi
Aims: This research aims at grouping of cities/regencies on the island of Java, where the central government as well as the most densely populated island in Indonesia, using linear discriminant analysis (LDA) and Naïve Bayes Classifier (NBC). Study Design: Quantitative design. Place and Duration of Study: Sample: The data used in this study is secondary data from the Indonesian Central Statistics Agency (Badan Pusat Statistik, BPS) regarding the 2022 Human Development Index (HDI) from 119 cities/regencies on the island of Java. The data used are four HDI indicators as independent variables (long and healthy living, knowledge, and the dimensions of decent living standards) and the HDI value as the dependent variable. Methodology: The grouping was carried out using LDA and NBC. LDA is a type of multivariate analysis used in the dependency method where the relationship between variables can be distinguished between the independent variable and the dependent variable. It aims at obtaining discriminant function equations to group cases into certain groups and to determine differences between groups based on independent variables. Meanwhile, the NBC method is a simple probability-based prediction technique based on the application of Bayes' theorem (Bayes' rule) with a strong assumption of independence. Results: Both LDA and NBC can be used for prediction and classification. Based on the results of the discriminant analysis, three discriminant functions were formed to group cities/regencies on the island of Java into three HDI groups. In the NBC analysis, the prior probability value for the very high category HDI group was 0.211, the high category HDI group was 0.606, and the medium category HDI group was 0.183. The research results show that LDA is better than the NBC for grouping cities/regencies based on the 2022 HDI indicators with an accuracy rate of 72.92%. Meanwhile, the NBC analysis only provides an accuracy of 64.58%. Three discriminant functions have been obtained to group cities/regencies on the island of Java based on the largest discriminant score where life expectancy makes the largest contribution in distinguishing each group. Conclusion: As a result, in this case LDA is a better classification method than the NBC. It is also of important to note medium class regions for further actions from stakeholders.
{"title":"Classification of Java Cities/Regencies Based on Human Development Index Using Discriminant Analysis and Naïve Bayes Classifier","authors":"Orryza Oky Astrianka, Achmad Efendi","doi":"10.9734/ajpas/2024/v26i1586","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i1586","url":null,"abstract":"Aims: This research aims at grouping of cities/regencies on the island of Java, where the central government as well as the most densely populated island in Indonesia, using linear discriminant analysis (LDA) and Naïve Bayes Classifier (NBC). \u0000Study Design: Quantitative design. \u0000Place and Duration of Study: Sample: The data used in this study is secondary data from the Indonesian Central Statistics Agency (Badan Pusat Statistik, BPS) regarding the 2022 Human Development Index (HDI) from 119 cities/regencies on the island of Java. The data used are four HDI indicators as independent variables (long and healthy living, knowledge, and the dimensions of decent living standards) and the HDI value as the dependent variable. \u0000Methodology: The grouping was carried out using LDA and NBC. LDA is a type of multivariate analysis used in the dependency method where the relationship between variables can be distinguished between the independent variable and the dependent variable. It aims at obtaining discriminant function equations to group cases into certain groups and to determine differences between groups based on independent variables. Meanwhile, the NBC method is a simple probability-based prediction technique based on the application of Bayes' theorem (Bayes' rule) with a strong assumption of independence. \u0000Results: Both LDA and NBC can be used for prediction and classification. Based on the results of the discriminant analysis, three discriminant functions were formed to group cities/regencies on the island of Java into three HDI groups. In the NBC analysis, the prior probability value for the very high category HDI group was 0.211, the high category HDI group was 0.606, and the medium category HDI group was 0.183. The research results show that LDA is better than the NBC for grouping cities/regencies based on the 2022 HDI indicators with an accuracy rate of 72.92%. Meanwhile, the NBC analysis only provides an accuracy of 64.58%. Three discriminant functions have been obtained to group cities/regencies on the island of Java based on the largest discriminant score where life expectancy makes the largest contribution in distinguishing each group. \u0000Conclusion: As a result, in this case LDA is a better classification method than the NBC. It is also of important to note medium class regions for further actions from stakeholders.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139800248","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 : 2024-02-06DOI: 10.9734/ajpas/2024/v26i1586
Orryza Oky Astrianka, Achmad Efendi
Aims: This research aims at grouping of cities/regencies on the island of Java, where the central government as well as the most densely populated island in Indonesia, using linear discriminant analysis (LDA) and Naïve Bayes Classifier (NBC). Study Design: Quantitative design. Place and Duration of Study: Sample: The data used in this study is secondary data from the Indonesian Central Statistics Agency (Badan Pusat Statistik, BPS) regarding the 2022 Human Development Index (HDI) from 119 cities/regencies on the island of Java. The data used are four HDI indicators as independent variables (long and healthy living, knowledge, and the dimensions of decent living standards) and the HDI value as the dependent variable. Methodology: The grouping was carried out using LDA and NBC. LDA is a type of multivariate analysis used in the dependency method where the relationship between variables can be distinguished between the independent variable and the dependent variable. It aims at obtaining discriminant function equations to group cases into certain groups and to determine differences between groups based on independent variables. Meanwhile, the NBC method is a simple probability-based prediction technique based on the application of Bayes' theorem (Bayes' rule) with a strong assumption of independence. Results: Both LDA and NBC can be used for prediction and classification. Based on the results of the discriminant analysis, three discriminant functions were formed to group cities/regencies on the island of Java into three HDI groups. In the NBC analysis, the prior probability value for the very high category HDI group was 0.211, the high category HDI group was 0.606, and the medium category HDI group was 0.183. The research results show that LDA is better than the NBC for grouping cities/regencies based on the 2022 HDI indicators with an accuracy rate of 72.92%. Meanwhile, the NBC analysis only provides an accuracy of 64.58%. Three discriminant functions have been obtained to group cities/regencies on the island of Java based on the largest discriminant score where life expectancy makes the largest contribution in distinguishing each group. Conclusion: As a result, in this case LDA is a better classification method than the NBC. It is also of important to note medium class regions for further actions from stakeholders.
{"title":"Classification of Java Cities/Regencies Based on Human Development Index Using Discriminant Analysis and Naïve Bayes Classifier","authors":"Orryza Oky Astrianka, Achmad Efendi","doi":"10.9734/ajpas/2024/v26i1586","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i1586","url":null,"abstract":"Aims: This research aims at grouping of cities/regencies on the island of Java, where the central government as well as the most densely populated island in Indonesia, using linear discriminant analysis (LDA) and Naïve Bayes Classifier (NBC). \u0000Study Design: Quantitative design. \u0000Place and Duration of Study: Sample: The data used in this study is secondary data from the Indonesian Central Statistics Agency (Badan Pusat Statistik, BPS) regarding the 2022 Human Development Index (HDI) from 119 cities/regencies on the island of Java. The data used are four HDI indicators as independent variables (long and healthy living, knowledge, and the dimensions of decent living standards) and the HDI value as the dependent variable. \u0000Methodology: The grouping was carried out using LDA and NBC. LDA is a type of multivariate analysis used in the dependency method where the relationship between variables can be distinguished between the independent variable and the dependent variable. It aims at obtaining discriminant function equations to group cases into certain groups and to determine differences between groups based on independent variables. Meanwhile, the NBC method is a simple probability-based prediction technique based on the application of Bayes' theorem (Bayes' rule) with a strong assumption of independence. \u0000Results: Both LDA and NBC can be used for prediction and classification. Based on the results of the discriminant analysis, three discriminant functions were formed to group cities/regencies on the island of Java into three HDI groups. In the NBC analysis, the prior probability value for the very high category HDI group was 0.211, the high category HDI group was 0.606, and the medium category HDI group was 0.183. The research results show that LDA is better than the NBC for grouping cities/regencies based on the 2022 HDI indicators with an accuracy rate of 72.92%. Meanwhile, the NBC analysis only provides an accuracy of 64.58%. Three discriminant functions have been obtained to group cities/regencies on the island of Java based on the largest discriminant score where life expectancy makes the largest contribution in distinguishing each group. \u0000Conclusion: As a result, in this case LDA is a better classification method than the NBC. It is also of important to note medium class regions for further actions from stakeholders.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139860197","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 : 2024-02-02DOI: 10.9734/ajpas/2024/v26i1585
Shehu A., Adenomon M. O., Nweze N. O.
This study investigates the long-run and short-run impacts of economic growth on solid waste generation in Nigeria using a Vector Error Correction Model (VECM). Analyzing data from 1982 to 2022, the study reveals cointegration among solid waste, GDP, and real income, indicating a long-run equilibrium relationship. Key findings show that economic growth has a statistically significant and positive impact on waste generation in the long run, indicating a potential environmental trade-off associated with economic development. Conversely, resource intensity shows no significant long-run influence on waste generation. In the short run, past waste generation exhibits a positive and significant effect on current levels, highlighting the need for effective waste management practices to combat inertia and prevent further waste accumulation. Interestingly, the short-run impacts of both economic growth and resource intensity are found to be statistically insignificant. Based on these findings, we propose several policy recommendations for sustainable waste management in Nigeria: promoting environmentally friendly production processes, supporting resource recovery and waste-to-energy initiatives, implementing extended producer responsibility, expanding and improving waste collection infrastructure, investing in sorting and recycling facilities, and conducting public awareness campaigns. We further call for further research to explore the nuanced relationship between resource intensity and waste generation across different income groups and sectors.
{"title":"The Long Run and Short Run Impact of GDP and Real Income on Solid Waste in Nigeria Using Vector Error Correction Model","authors":"Shehu A., Adenomon M. O., Nweze N. O.","doi":"10.9734/ajpas/2024/v26i1585","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i1585","url":null,"abstract":"This study investigates the long-run and short-run impacts of economic growth on solid waste generation in Nigeria using a Vector Error Correction Model (VECM). Analyzing data from 1982 to 2022, the study reveals cointegration among solid waste, GDP, and real income, indicating a long-run equilibrium relationship. Key findings show that economic growth has a statistically significant and positive impact on waste generation in the long run, indicating a potential environmental trade-off associated with economic development. Conversely, resource intensity shows no significant long-run influence on waste generation. In the short run, past waste generation exhibits a positive and significant effect on current levels, highlighting the need for effective waste management practices to combat inertia and prevent further waste accumulation. Interestingly, the short-run impacts of both economic growth and resource intensity are found to be statistically insignificant. Based on these findings, we propose several policy recommendations for sustainable waste management in Nigeria: promoting environmentally friendly production processes, supporting resource recovery and waste-to-energy initiatives, implementing extended producer responsibility, expanding and improving waste collection infrastructure, investing in sorting and recycling facilities, and conducting public awareness campaigns. We further call for further research to explore the nuanced relationship between resource intensity and waste generation across different income groups and sectors.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809603","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 : 2024-02-02DOI: 10.9734/ajpas/2024/v26i1585
Shehu A., Adenomon M. O., Nweze N. O.
This study investigates the long-run and short-run impacts of economic growth on solid waste generation in Nigeria using a Vector Error Correction Model (VECM). Analyzing data from 1982 to 2022, the study reveals cointegration among solid waste, GDP, and real income, indicating a long-run equilibrium relationship. Key findings show that economic growth has a statistically significant and positive impact on waste generation in the long run, indicating a potential environmental trade-off associated with economic development. Conversely, resource intensity shows no significant long-run influence on waste generation. In the short run, past waste generation exhibits a positive and significant effect on current levels, highlighting the need for effective waste management practices to combat inertia and prevent further waste accumulation. Interestingly, the short-run impacts of both economic growth and resource intensity are found to be statistically insignificant. Based on these findings, we propose several policy recommendations for sustainable waste management in Nigeria: promoting environmentally friendly production processes, supporting resource recovery and waste-to-energy initiatives, implementing extended producer responsibility, expanding and improving waste collection infrastructure, investing in sorting and recycling facilities, and conducting public awareness campaigns. We further call for further research to explore the nuanced relationship between resource intensity and waste generation across different income groups and sectors.
{"title":"The Long Run and Short Run Impact of GDP and Real Income on Solid Waste in Nigeria Using Vector Error Correction Model","authors":"Shehu A., Adenomon M. O., Nweze N. O.","doi":"10.9734/ajpas/2024/v26i1585","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i1585","url":null,"abstract":"This study investigates the long-run and short-run impacts of economic growth on solid waste generation in Nigeria using a Vector Error Correction Model (VECM). Analyzing data from 1982 to 2022, the study reveals cointegration among solid waste, GDP, and real income, indicating a long-run equilibrium relationship. Key findings show that economic growth has a statistically significant and positive impact on waste generation in the long run, indicating a potential environmental trade-off associated with economic development. Conversely, resource intensity shows no significant long-run influence on waste generation. In the short run, past waste generation exhibits a positive and significant effect on current levels, highlighting the need for effective waste management practices to combat inertia and prevent further waste accumulation. Interestingly, the short-run impacts of both economic growth and resource intensity are found to be statistically insignificant. Based on these findings, we propose several policy recommendations for sustainable waste management in Nigeria: promoting environmentally friendly production processes, supporting resource recovery and waste-to-energy initiatives, implementing extended producer responsibility, expanding and improving waste collection infrastructure, investing in sorting and recycling facilities, and conducting public awareness campaigns. We further call for further research to explore the nuanced relationship between resource intensity and waste generation across different income groups and sectors.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139869419","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 : 2024-02-01DOI: 10.9734/ajpas/2024/v26i1584
Abifarin Modupe O., Isah Audu, Yakubu Yisa, Adeyemi Rasheed A.
In the presence of explosiveness of the adjustment term in the error correction model, the adjustment of the dependent variable Y was too large and overshoots the equilibrium, creating a divergent pattern. The error correction model fails to capture the deviation from equilibrium appropriately, thereby resulting in overshooting of the model. In this paper, a new model to stabilize the explosiveness in an Error Correction model called the stabilizing Error Correction Mechanism was proposed. Mathematical methodology for obtaining the estimate of the model using the Ordinal Least Square method was derived. Error Correction model was used to model the relationship among the variables and the result was compared with the Stabilizing Error Correction Mechanism using root mean square error. A Monte-Carlo simulation was performed, and the stimulation results showed that the error correction model exhibited some explosiveness, and the damping coefficient of the stabilizing model exerted a stabilizing effect on the error correction mechanism, thereby reducing the overshooting in the error correction model. The proposed model contributed to a smoother and more stable response to deviations from the long-run equilibrium. The root mean square error of the stabilizing Error Correction model was observed to be 1.30663, 1.04533, 12.55786, 10.49876, 10.0034, and 19.41545 as compared to the adjustment model in the Error Correction model (60.6888, 35.5929, 315238, 24.31958, 10.1485 and 19.7687) when the persistence is high and . Therefore, the Stabilizing Error Correction model performs better than the Error Correction model.
在误差修正模型中调整项存在爆炸性的情况下，因变量 Y 的调整幅度过大，超调了均衡状态，形成了背离模式。误差修正模型未能适当捕捉到均衡的偏离，从而导致模型的超调。本文提出了一种在误差修正模型中稳定爆炸性的新模型，称为稳定误差修正机制。并推导出了使用正序最小平方法获得模型估计值的数学方法。使用误差修正模型来模拟变量之间的关系，并使用均方根误差将结果与稳定误差修正机制进行比较。蒙特卡洛模拟结果表明，误差修正模型具有一定的爆炸性，而稳定模型的阻尼系数对误差修正机制起到了稳定作用，从而减少了误差修正模型中的过冲现象。所提出的模型有助于对偏离长期均衡做出更平滑、更稳定的反应。与误差修正模型中的调整模型（60.6888、35.5929、315238、24.31958、10.1485 和 19.7687）相比，稳定误差修正模型的均方根误差在持续性较高时分别为 1.30663、1.04533、12.55786、10.49876、10.0034 和 19.41545。因此，稳定误差修正模型优于误差修正模型。
{"title":"Stabilizing Error Correction Mechanism in the Presence of Explosiveness","authors":"Abifarin Modupe O., Isah Audu, Yakubu Yisa, Adeyemi Rasheed A.","doi":"10.9734/ajpas/2024/v26i1584","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i1584","url":null,"abstract":"In the presence of explosiveness of the adjustment term in the error correction model, the adjustment of the dependent variable Y was too large and overshoots the equilibrium, creating a divergent pattern. The error correction model fails to capture the deviation from equilibrium appropriately, thereby resulting in overshooting of the model. In this paper, a new model to stabilize the explosiveness in an Error Correction model called the stabilizing Error Correction Mechanism was proposed. Mathematical methodology for obtaining the estimate of the model using the Ordinal Least Square method was derived. Error Correction model was used to model the relationship among the variables and the result was compared with the Stabilizing Error Correction Mechanism using root mean square error. A Monte-Carlo simulation was performed, and the stimulation results showed that the error correction model exhibited some explosiveness, and the damping coefficient of the stabilizing model exerted a stabilizing effect on the error correction mechanism, thereby reducing the overshooting in the error correction model. The proposed model contributed to a smoother and more stable response to deviations from the long-run equilibrium. The root mean square error of the stabilizing Error Correction model was observed to be 1.30663, 1.04533, 12.55786, 10.49876, 10.0034, and 19.41545 as compared to the adjustment model in the Error Correction model (60.6888, 35.5929, 315238, 24.31958, 10.1485 and 19.7687) when the persistence is high and . Therefore, the Stabilizing Error Correction model performs better than the Error Correction model.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139687493","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 : 2024-01-22DOI: 10.9734/ajpas/2024/v26i1577
Uwaeme, O. R., Akpan, N. P.
Due to the ever growing demand for the development of new lifetime distributions to meet the goodness of fit demand of complex datasets, two-parameter distributions has been proposed in recent times. This study therefore aims to contribute to this demand. We propose a new two-parameter lifetime distribution known as the Remkan distribution. Important mathematical properties of the Remkan distribution such as the moments and other related measures, and moment generating function were derived and the model parameters estimated using the maximum likelihood estimate technique. Finally, the flexibility of the new Remkan distribution was illustrated using a real life dataset and the results showed that the new Remkan distribution was the best amongst other competing two parameter distributions.
{"title":"The Remkan Distribution and Its Applications","authors":"Uwaeme, O. R., Akpan, N. P.","doi":"10.9734/ajpas/2024/v26i1577","DOIUrl":"https://doi.org/10.9734/ajpas/2024/v26i1577","url":null,"abstract":"Due to the ever growing demand for the development of new lifetime distributions to meet the goodness of fit demand of complex datasets, two-parameter distributions has been proposed in recent times. This study therefore aims to contribute to this demand. We propose a new two-parameter lifetime distribution known as the Remkan distribution. Important mathematical properties of the Remkan distribution such as the moments and other related measures, and moment generating function were derived and the model parameters estimated using the maximum likelihood estimate technique. Finally, the flexibility of the new Remkan distribution was illustrated using a real life dataset and the results showed that the new Remkan distribution was the best amongst other competing two parameter distributions.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139607384","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：