Asian Journal of Probability and Statistics最新文献

{"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":"32 1","pages":""},"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}

{"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":"39 1","pages":""},"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}

{"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":"31 1","pages":""},"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}

{"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":"23 1","pages":""},"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}

{"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":"111 1","pages":""},"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}

{"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":"15 1","pages":""},"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}

{"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":"37 1","pages":""},"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}

{"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":"100 1","pages":""},"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}

{"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":"24 1","pages":""},"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}

{"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":"IA-13 1","pages":""},"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}