Pub Date : 2023-08-09DOI: 10.9734/ajpas/2023/v23i4511
Miriam Sitienei, A. Anapapa, A. Otieno
Artificial Intelligence is the discipline of making computers behave without explicit programming. Machine learning is a subset of artificial Intelligence that enables machines to learn autonomously from previous data without explicit programming. The purpose of machine learning in agriculture is to increase crop yield and quality in the agricultural sector. It is driven by the emergence of big data technologies and high-performance computation, which provide new opportunities to unravel, quantify, and comprehend data-intensive agricultural operational processes. Random Forest is an ensemble technique that reduces the result's overfitting. This algorithm is primarily utilized for forecasting. It generates a forest with numerous trees. The random forest classifier predicts that the model's accuracy will increase as the number of trees in the forest increases. All through the training phase, multiple decision trees are constructed. It generates subsets of data from randomly selected training samples with replacement. Each data subset is employed to train decision trees. It utilizes multiple trees to reduce the possibility of overfitting. Maize is a staple food in Kenya and having it in sufficient amounts in the country assures the farmers' food security and economic stability. This study predicted maize yield in the Kenyan county of Uasin Gishu using the machine learning algorithm Random Forest regression. The regression model employed a mixed-methods research design, and the survey employed well-structured questionnaires containing quantitative and qualitative variables, which were directly administered to 30 clustered wards' representative farmers. The questionnaire encompassed 30 maize production-related variables from 900 randomly selected maize producers in 30 wards. The model was able to identify important variables from the dataset and predicted maize yield. The prediction evaluation used machine learning regression metrics, Root Mean Squared error-RMSE=0.52199, Mean Squared Error-MSE =0.27248, and Mean Absolute Error-MAE = 0.471722. The model predicted maize yield and indicated the contribution of each variable to the overall prediction.
人工智能是一门让计算机在没有明确编程的情况下运行的学科。机器学习是人工智能的一个子集，它使机器能够在没有显式编程的情况下从先前的数据中自主学习。农业中机器学习的目的是提高农业部门的作物产量和质量。它是由大数据技术和高性能计算的出现推动的，这些技术为解开、量化和理解数据密集型农业操作过程提供了新的机会。随机森林是一种减少结果过拟合的集成技术。该算法主要用于预测。它形成了一片树木繁茂的森林。随机森林分类器预测模型的精度将随着森林中树木数量的增加而增加。在整个训练阶段，构建了多个决策树。它通过替换从随机选择的训练样本中生成数据子集。每个数据子集被用来训练决策树。它利用多个树来减少过拟合的可能性。玉米是肯尼亚的主食，在肯尼亚拥有足够数量的玉米可以确保农民的粮食安全和经济稳定。这项研究使用机器学习算法随机森林回归预测肯尼亚瓦辛吉舒县的玉米产量。回归模型采用混合方法研究设计，调查采用结构合理、包含定量和定性变量的问卷，直接对30个集聚区具有代表性的农户进行问卷调查。问卷包含30个玉米生产相关变量，来自30个省900个随机选择的玉米生产者。该模型能够从数据集中识别重要变量并预测玉米产量。预测评估使用机器学习回归指标，均方根误差- rmse =0.52199，均方误差- mse =0.27248，平均绝对误差- mae = 0.471722。该模型预测了玉米产量，并指出了各变量对总体预测的贡献。
{"title":"Random Forest Regression in Maize Yield Prediction","authors":"Miriam Sitienei, A. Anapapa, A. Otieno","doi":"10.9734/ajpas/2023/v23i4511","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i4511","url":null,"abstract":"Artificial Intelligence is the discipline of making computers behave without explicit programming. Machine learning is a subset of artificial Intelligence that enables machines to learn autonomously from previous data without explicit programming. The purpose of machine learning in agriculture is to increase crop yield and quality in the agricultural sector. It is driven by the emergence of big data technologies and high-performance computation, which provide new opportunities to unravel, quantify, and comprehend data-intensive agricultural operational processes. Random Forest is an ensemble technique that reduces the result's overfitting. This algorithm is primarily utilized for forecasting. It generates a forest with numerous trees. The random forest classifier predicts that the model's accuracy will increase as the number of trees in the forest increases. All through the training phase, multiple decision trees are constructed. It generates subsets of data from randomly selected training samples with replacement. Each data subset is employed to train decision trees. It utilizes multiple trees to reduce the possibility of overfitting. Maize is a staple food in Kenya and having it in sufficient amounts in the country assures the farmers' food security and economic stability. This study predicted maize yield in the Kenyan county of Uasin Gishu using the machine learning algorithm Random Forest regression. The regression model employed a mixed-methods research design, and the survey employed well-structured questionnaires containing quantitative and qualitative variables, which were directly administered to 30 clustered wards' representative farmers. The questionnaire encompassed 30 maize production-related variables from 900 randomly selected maize producers in 30 wards. The model was able to identify important variables from the dataset and predicted maize yield. The prediction evaluation used machine learning regression metrics, Root Mean Squared error-RMSE=0.52199, Mean Squared Error-MSE =0.27248, and Mean Absolute Error-MAE = 0.471722. The model predicted maize yield and indicated the contribution of each variable to the overall prediction.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87059054","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 : 2023-08-07DOI: 10.9734/ajpas/2023/v23i4510
J. Acquah, B. Odoi, Abdulzeid Yen Anafo, Bosson-Amedenu Senyea
In this study, a new three parameter extension of the Chen distribution was proposed and called the New Extended Chen distribution. Some statistical properties of the proposed distribution are presented. The proposed distribution exhibits varied complex and hazard shapes. Parameters of the distribution are estimated using the maximum likelihood estimation method and a simulation study is conducted to evaluate the performance of the estimators. The New Extended Chen distribution is applied to two real data set and compared to other modifications of the Chen distribution to emphasise the applicability of the the distribution.
{"title":"An Extension of the Chen Distribution: Properties, Simulation Study and Applications to Data","authors":"J. Acquah, B. Odoi, Abdulzeid Yen Anafo, Bosson-Amedenu Senyea","doi":"10.9734/ajpas/2023/v23i4510","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i4510","url":null,"abstract":"In this study, a new three parameter extension of the Chen distribution was proposed and called the New Extended Chen distribution. Some statistical properties of the proposed distribution are presented. The proposed distribution exhibits varied complex and hazard shapes. Parameters of the distribution are estimated using the maximum likelihood estimation method and a simulation study is conducted to evaluate the performance of the estimators. The New Extended Chen distribution is applied to two real data set and compared to other modifications of the Chen distribution to emphasise the applicability of the the distribution.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84427565","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 : 2023-08-05DOI: 10.9734/ajpas/2023/v23i4509
Odom Conleth Chinazom, Nduka Ethelbert Chinaka, I. M. Azubuike
This article introduces a new family of Generalized Exponentiated Exponential distribution. Using the T-R{Y} framework, a new family of T-Exponentiated Exponential{Y} distributions named T-Exponentiated Exponential{Frechet} family of distributions is proposed. Some general properties of the family such as hazard rate function, quantile function, non-central moment, mode, mean absolute deviations and Shannon’s entropy are discussed. A new continuous univariate probability distribution which is a member of the T-Exponentiated Exponential{Frechet} family of distributions is introduced. From the general properties of the family, expressions are derived for some specific properties of the new distribution. To show the usefulness of the T-Exponentiated Exponential{Frechet} family of distributions, the new distribution is fitted to two real life data sets and the results are compared with the results of some other existing distributions.
{"title":"The T-Exponentiated Exponential{Frechet} Family of Distributions: Theory and Applications","authors":"Odom Conleth Chinazom, Nduka Ethelbert Chinaka, I. M. Azubuike","doi":"10.9734/ajpas/2023/v23i4509","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i4509","url":null,"abstract":"This article introduces a new family of Generalized Exponentiated Exponential distribution. Using the T-R{Y} framework, a new family of T-Exponentiated Exponential{Y} distributions named T-Exponentiated Exponential{Frechet} family of distributions is proposed. Some general properties of the family such as hazard rate function, quantile function, non-central moment, mode, mean absolute deviations and Shannon’s entropy are discussed. A new continuous univariate probability distribution which is a member of the T-Exponentiated Exponential{Frechet} family of distributions is introduced. From the general properties of the family, expressions are derived for some specific properties of the new distribution. To show the usefulness of the T-Exponentiated Exponential{Frechet} family of distributions, the new distribution is fitted to two real life data sets and the results are compared with the results of some other existing distributions.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78827607","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 : 2023-08-05DOI: 10.9734/ajpas/2023/v23i4508
Sisti Nadia Amalia, S. Saragih, Zul Amry
Indonesia is known for its excessive rainfall. Rainfall trends in an area have different characteristics. Differences in latitude, apparent motion of the sun, geographical position, topography, and the interaction of many forms of air circulation all contribute to this. Rainfall time series is essential for engineering planning, particularly for water infrastructure like irrigation, dams, urban drainage, ports, and wharves. Although meteorological technologies provide short-term rainfall predictions, long-term rainfall prediction is difficult and fraught with uncertainty. Unpredictability and seasonality can cause complex behavior in rainfall time series. This research utilizes the Singular Spectrum Analysis approach to extract trends; seasonality, cyclists, and noise can all be identified with potentially high accuracy.
{"title":"Singular Spectrum Analysis to Identify Excessive Rainfall","authors":"Sisti Nadia Amalia, S. Saragih, Zul Amry","doi":"10.9734/ajpas/2023/v23i4508","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i4508","url":null,"abstract":"Indonesia is known for its excessive rainfall. Rainfall trends in an area have different characteristics. Differences in latitude, apparent motion of the sun, geographical position, topography, and the interaction of many forms of air circulation all contribute to this. Rainfall time series is essential for engineering planning, particularly for water infrastructure like irrigation, dams, urban drainage, ports, and wharves. Although meteorological technologies provide short-term rainfall predictions, long-term rainfall prediction is difficult and fraught with uncertainty. Unpredictability and seasonality can cause complex behavior in rainfall time series. This research utilizes the Singular Spectrum Analysis approach to extract trends; seasonality, cyclists, and noise can all be identified with potentially high accuracy.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82721628","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 : 2023-08-02DOI: 10.9734/ajpas/2023/v23i3507
M. E. Archibong, O. R. Uwaeme
This study proposed a four-parameter continuous distribution, called the Dagum-Cauchy{Exponential} Distribution (DCED) for modelling financial time series returns using the generalized family of Cauchy distribution by Alzaatreh et al. [1]. Some structural properties of this new distribution such as quantile function, reliability measures and hazard function, and order statistics are obtained. The method of maximum likelihood estimation was proposed in estimating its parameters. Finally, the distribution was used to model some financial datasets adequately.
{"title":"A New Dagum-Cauchy{Exponential} Distribution","authors":"M. E. Archibong, O. R. Uwaeme","doi":"10.9734/ajpas/2023/v23i3507","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i3507","url":null,"abstract":"This study proposed a four-parameter continuous distribution, called the Dagum-Cauchy{Exponential} Distribution (DCED) for modelling financial time series returns using the generalized family of Cauchy distribution by Alzaatreh et al. [1]. Some structural properties of this new distribution such as quantile function, reliability measures and hazard function, and order statistics are obtained. The method of maximum likelihood estimation was proposed in estimating its parameters. Finally, the distribution was used to model some financial datasets adequately.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76316428","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 : 2023-07-29DOI: 10.9734/ajpas/2023/v23i3506
L. E. Ebakpa, I. Amadi, R. G. Nchelem, P. A. Azor
The key importance of asset values and it return rates are geared towards investment funds which grows wealth over time. This paper considered stochastic models where asset values were examined. A twelve (12) months (2022) initial closing stock price data of Oando, PLC, were used in the study. The problems were accurately solved analytical by means of Ito’s theorem and a closed form solutions were obtained which governed asset price return rates through multiplicative effects series. The empirical illustrations between Stochastic Differential Equations (SDEs) and Stochastic Delay Differential Equations (SDDEs) asset values were compared to inform Oando PLC in terms of decision making. However, the behaviour on the value of asset prices were analysed using Kolmogorov-Smirnov (KS). To this end, graphical solutions and the effects of the relevant stock variables were conferred accordingly.
{"title":"Stochastic Analysis of Asset Returns Which Follows Multiplicative Effects Series","authors":"L. E. Ebakpa, I. Amadi, R. G. Nchelem, P. A. Azor","doi":"10.9734/ajpas/2023/v23i3506","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i3506","url":null,"abstract":"The key importance of asset values and it return rates are geared towards investment funds which grows wealth over time. This paper considered stochastic models where asset values were examined. A twelve (12) months (2022) initial closing stock price data of Oando, PLC, were used in the study. The problems were accurately solved analytical by means of Ito’s theorem and a closed form solutions were obtained which governed asset price return rates through multiplicative effects series. The empirical illustrations between Stochastic Differential Equations (SDEs) and Stochastic Delay Differential Equations (SDDEs) asset values were compared to inform Oando PLC in terms of decision making. However, the behaviour on the value of asset prices were analysed using Kolmogorov-Smirnov (KS). To this end, graphical solutions and the effects of the relevant stock variables were conferred accordingly.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81107457","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 : 2023-07-24DOI: 10.9734/ajpas/2023/v23i3505
Okim I. Ikpan, F. Nwobi
D-optimality is a design criterion that seeks to maximize the determinant of the information matrix, or equivalently minimize the determinant of the inverse information matrix of the design. This design criterion results in maximizing the differential Shannon information content of the parameter estimates. Cycling, a phenomenal problem associated with the construction of optimal designs, impedes the rate of convergence to such desired optimum, whenever it occurs in a variance exchange process. Different polynomial functions may have varying effects on the pattern of convergence due to cycling. This paper seeks to determine the nature and extent to which the influence of cycling affects the pattern of convergence on Linear, Interactive, and Quadratic order effect designs. The variance exchange algorithmic search method was adopted based on the philosophy of numerically searching the design space for optimum designs. Two and three-variable response functions are used in the investigation of even and odd-sized point designs. Generated data from designs of sizes 10 and 11 were employed in the investigation. Numerical illustrations were given to ascertain the pattern of convergence on each of the degree polynomial designs. The computations and graphs were conducted in R version 4.1.1 (2021). The results show that cycling patterns differ with respect to the degree of the response function whether it is of even or odd-sized design, or has two or three variables. The result will enable researchers to find appropriate measures to accommodate the challenge posed by cycling.
d -最优性是一种设计准则，旨在最大化信息矩阵的行列式，或等效地最小化设计的逆信息矩阵的行列式。该设计准则使参数估计的差分香农信息含量最大化。循环是与最优设计构建相关的一个现象性问题，无论何时它发生在方差交换过程中，都会阻碍收敛到理想最优的速度。由于循环，不同的多项式函数对收敛模式有不同的影响。本文试图确定循环对线性、交互和二次阶效应设计的收敛模式的影响的性质和程度。基于数值搜索设计空间的思想，采用方差交换算法搜索方法进行优化设计。在奇偶点设计的研究中，采用了二变量和三变量响应函数。从尺寸为10和11的设计中生成的数据被用于调查。通过数值实例验证了各次多项式设计的收敛规律。计算和图表在R 4.1.1版本(2021)中进行。结果表明，无论是偶数或奇数设计，还是有两个或三个变量，循环模式在响应函数的程度上都是不同的。研究结果将使研究人员能够找到适当的措施来适应骑自行车带来的挑战。
{"title":"On the Difference in Cycling Pattern on Linear and Higher-Order Effect Designs","authors":"Okim I. Ikpan, F. Nwobi","doi":"10.9734/ajpas/2023/v23i3505","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i3505","url":null,"abstract":"D-optimality is a design criterion that seeks to maximize the determinant of the information matrix, or equivalently minimize the determinant of the inverse information matrix of the design. This design criterion results in maximizing the differential Shannon information content of the parameter estimates. Cycling, a phenomenal problem associated with the construction of optimal designs, impedes the rate of convergence to such desired optimum, whenever it occurs in a variance exchange process. Different polynomial functions may have varying effects on the pattern of convergence due to cycling. This paper seeks to determine the nature and extent to which the influence of cycling affects the pattern of convergence on Linear, Interactive, and Quadratic order effect designs. The variance exchange algorithmic search method was adopted based on the philosophy of numerically searching the design space for optimum designs. Two and three-variable response functions are used in the investigation of even and odd-sized point designs. Generated data from designs of sizes 10 and 11 were employed in the investigation. Numerical illustrations were given to ascertain the pattern of convergence on each of the degree polynomial designs. The computations and graphs were conducted in R version 4.1.1 (2021). The results show that cycling patterns differ with respect to the degree of the response function whether it is of even or odd-sized design, or has two or three variables. The result will enable researchers to find appropriate measures to accommodate the challenge posed by cycling.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86778967","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 : 2023-07-19DOI: 10.9734/ajpas/2023/v23i3503
P. Dalatu, Asabe Ibrahim
Lassa Fever (LF) is an acute viral haemorrhagic zoonotic disease, is endemic in some parts of Nigeria. The disease alert and outbreak threshold are known; however, there has been a shift from the previous seasonal transmission pattern to an all year-round transmission. The aim of this study was to carry out the analysis on LF and highlight the magnitude of the disease over a five-year period. We described data on Lassa fever and highlighted the magnitude of the disease over a five-year period. We conducted a secondary data analysis of LF with specific surveillance data from the NCDC for five years period (January 2019 to June 2023). A total of 29347 suspected cases were reported within the study period; of these, 4469 were confirmed cases, 861 were dead cases by NCDC. However, highest percentage for the case fatality rate/ratio was recorded in the year 2019 with 20.9% and lowest was recorded in the year 2023 with 17.3%. The highest percentage case positive rate was recorded in the year 2023 with 18.7% and lowest was recorded in the year 2021 with 11.0%.
{"title":"An Epidemiological Analysis of Lassa Fever Outbreak in Nigeria from January 2019 to June 2023","authors":"P. Dalatu, Asabe Ibrahim","doi":"10.9734/ajpas/2023/v23i3503","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i3503","url":null,"abstract":"Lassa Fever (LF) is an acute viral haemorrhagic zoonotic disease, is endemic in some parts of Nigeria. The disease alert and outbreak threshold are known; however, there has been a shift from the previous seasonal transmission pattern to an all year-round transmission. The aim of this study was to carry out the analysis on LF and highlight the magnitude of the disease over a five-year period. We described data on Lassa fever and highlighted the magnitude of the disease over a five-year period. We conducted a secondary data analysis of LF with specific surveillance data from the NCDC for five years period (January 2019 to June 2023). A total of 29347 suspected cases were reported within the study period; of these, 4469 were confirmed cases, 861 were dead cases by NCDC. However, highest percentage for the case fatality rate/ratio was recorded in the year 2019 with 20.9% and lowest was recorded in the year 2023 with 17.3%. The highest percentage case positive rate was recorded in the year 2023 with 18.7% and lowest was recorded in the year 2021 with 11.0%.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83335489","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 : 2023-07-19DOI: 10.9734/ajpas/2023/v23i3504
Manish Kumar, G. Vishwakarma
The present paper is an extension of the work published in Kumar and Vishwakarma (Proceedings of theNational Academy of Sciences, India, Section A: Physical Sciences, 90(5): 933-939, 2020). In this paper,various sample allocation schemes are utilized to derive the mathematical expressions for mean square errors(MSEs) of several well-known estimators of population mean in stratified random sampling. Moreover, theeffects of various allocation schemes on the estimation of mean, are demonstrated theoretically as well asempirically. The findings of the study reveal that the Neyman allocation provides a smaller variance (or MSE,as the case may be) as compared to that of Equal and Proportional allocation schemes for the concernedestimators. Moreover, the proposed classes of estimators are dominant over the pre-existing estimators underthe various allocation schemes considered in the study.
{"title":"Efficient Classes of Estimators of Population Mean under Various Allocation Schemes in Stratified Random Sampling","authors":"Manish Kumar, G. Vishwakarma","doi":"10.9734/ajpas/2023/v23i3504","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i3504","url":null,"abstract":"The present paper is an extension of the work published in Kumar and Vishwakarma (Proceedings of theNational Academy of Sciences, India, Section A: Physical Sciences, 90(5): 933-939, 2020). In this paper,various sample allocation schemes are utilized to derive the mathematical expressions for mean square errors(MSEs) of several well-known estimators of population mean in stratified random sampling. Moreover, theeffects of various allocation schemes on the estimation of mean, are demonstrated theoretically as well asempirically. The findings of the study reveal that the Neyman allocation provides a smaller variance (or MSE,as the case may be) as compared to that of Equal and Proportional allocation schemes for the concernedestimators. Moreover, the proposed classes of estimators are dominant over the pre-existing estimators underthe various allocation schemes considered in the study.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90470607","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 : 2023-07-18DOI: 10.9734/ajpas/2023/v23i2502
R. Verma
In the literature on fuzzy information theory, there are numerous divergence metrics and fuzzy information. Disparities are crucial for determining relationships. Here, we'll discuss some fresh information inequalities related to fuzzy measures and how they apply to the detection of patterns. With the aid of the fuzzy f-divergence measure and Jensen's inequality, links between new and well-known fuzzy divergence measures were also created.
{"title":"A Novel Set of Fuzzy f-Divergence Measure-Related Intuitionistic Fuzzy Information Equalities and Inequalities","authors":"R. Verma","doi":"10.9734/ajpas/2023/v23i2502","DOIUrl":"https://doi.org/10.9734/ajpas/2023/v23i2502","url":null,"abstract":"In the literature on fuzzy information theory, there are numerous divergence metrics and fuzzy information. Disparities are crucial for determining relationships. Here, we'll discuss some fresh information inequalities related to fuzzy measures and how they apply to the detection of patterns. With the aid of the fuzzy f-divergence measure and Jensen's inequality, links between new and well-known fuzzy divergence measures were also created.","PeriodicalId":8532,"journal":{"name":"Asian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84591042","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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