European Journal of Statistics最新文献_第3页

Multinomial Naïve Bayes Classifier: Bayesian versus Nonparametric Classifier Approach 多项Naïve贝叶斯分类器:贝叶斯与非参数分类器方法

European Journal of Statistics Pub Date : 2022-02-22 DOI: 10.28924/ada/stat.2.8

R. O. Olanrewaju, S. A. Olanrewaju, L. Nafiu

{"title":"Multinomial Naïve Bayes Classifier: Bayesian versus Nonparametric Classifier Approach","authors":"R. O. Olanrewaju, S. A. Olanrewaju, L. Nafiu","doi":"10.28924/ada/stat.2.8","DOIUrl":"https://doi.org/10.28924/ada/stat.2.8","url":null,"abstract":"This paper proposes a Naïve Bayes Classifier for Bayesian and nonparametric methods of analyzing multinomial regression. The Naïve Bayes classifier adopted Bayes’ rule for solving the posterior of the multinomial regression via its link function known as Logit link. The nonparametric adopted Gaussian, bi-weight kernels, Silverman’s rule of thumb bandwidth selector, and adjusted bandwidth as kernel density estimation. Three categorical responses of information on 78 people using one of three diets (Diet A, B, and C) that consist of scaled variables: age (in years), height (in cm), weight (in kg) before the diet (that is, pre-weight), weight (in kg) gained after 6 weeks of diet were subjected to the classifier multinomial regression of Naïve Bayes and nonparametric. The Gaussian and bi-weight kernel density estimation produced the minimum bandwidths across the three categorical responses for the four influencers. The Naïve Bayes classifier and nonparametric kernel density estimation for the multinomial regression produced the same prior probabilities of 0.3077, 0.3462, and 0.3462; and A prior probabilities of 0.3077, 0.3462, and 0.3462 for Diet A, Diet B, and Diet C at different smoothing bandwidths.","PeriodicalId":153849,"journal":{"name":"European Journal of Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126976485","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}

引用次数: 1

The Effect of Sample Size on Random Component in Multilevel Modeling 多层模型中样本量对随机分量的影响

European Journal of Statistics Pub Date : 2022-02-22 DOI: 10.28924/ada/stat.2.7

Asadullah, M. Husain

{"title":"The Effect of Sample Size on Random Component in Multilevel Modeling","authors":"Asadullah, M. Husain","doi":"10.28924/ada/stat.2.7","DOIUrl":"https://doi.org/10.28924/ada/stat.2.7","url":null,"abstract":"In cluster-correlated data arise when there exists any condition for that individual are grouped among themselves. Data of this kind arise frequently in social science, behavioral, and medical sciences since individuals can be grouped in so many different ways. Multilevel modeling (MLM) is an approach that can be used to handle cluster or grouped data. Analyzing of correlated data is different from the usual way for independent data since we have to consider the correlation structure among individuals within cluster. In random effects models’ correlation structure can be estimated by considering the models parameters are allowed to vary across the cluster. Random effect models have two components, within cluster components, cluster-specific response is described by a regression model with a population-level intercept and slope, other is between-cluster component: variation in cluster-intercepts and slopes is captured. In a multilevel model, cluster level variance component is more affected by no. of cluster as well as cluster size. So, this is important to aware the researcher about no. of cluster and cluster size in estimating the random components of random effect models for correlated continuous and discrete outcome respectively in MLM since it produces bias estimate for few no. of cluster and cluster size. The parameters of random effects models can be estimated by Maximum Likelihood and Restricted Maximum Likelihood (REML) estimation for correlated continuous outcome, on the contrast besides REML, Penalized Quassi Likelihood (PQL) and Adaptive Gaussian Quadrature (AGQ) estimation techniques are applied for correlated discrete outcome. In this thesis, using the simulation procedure we would be compared among these estimation techniques by exploring the influence of no. of cluster and cluster size on estimated random parameters from random effect models of two-level and three levels for continuous and discrete outcome respectively. Relative bias, mean square error and coverage probability would be used for comparison purpose among the estimation techniques.","PeriodicalId":153849,"journal":{"name":"European Journal of Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130412954","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}

引用次数: 0

A Novel Discrete Distribution: Properties and Application Using Nipah Virus Infection Data Set 一种新的离散分布:尼帕病毒感染数据集的性质及其应用

European Journal of Statistics Pub Date : 2022-01-09 DOI: 10.28924/ada/stat.3.3

Fatma Zohra Seghier, Halim zeghdoudi, Vinoth Raman

引用次数: 0

Analyzing the Best Fitted Probabilistic Model for the Seasonal Rainfall Data in Khulna Region of Bangladesh 孟加拉国库尔纳地区季节性降雨资料的最佳拟合概率模型分析

European Journal of Statistics Pub Date : 2021-12-10 DOI: 10.28924/ada/stat.2.5

K. Fatema, Muhammad Habibulla Alamin, M. Z. Hasan, M. Hossain

{"title":"Analyzing the Best Fitted Probabilistic Model for the Seasonal Rainfall Data in Khulna Region of Bangladesh","authors":"K. Fatema, Muhammad Habibulla Alamin, M. Z. Hasan, M. Hossain","doi":"10.28924/ada/stat.2.5","DOIUrl":"https://doi.org/10.28924/ada/stat.2.5","url":null,"abstract":"There are several pieces of research on the statistical modeling of rainfall data in Bangladesh. Since all the seasons of a year do not receive a similar amount of rainfall, hence one single statistical model might not be able to explain the pattern of rainfall at any season of a year. According to the climatologists, Bangladesh has four seasons which are Monsoon, Post-monsoon, Summer, and Winter based on the geographical characteristics of this country. This paper aims to determine the best-fitted probability distribution model for the monthly rainfall data of each particular season in the Khulna district of Bangladesh using the rainfall data of the Khulna region from 1951 to 2018. Very commonly used seven continuous distributions- Normal, Weibull, Gamma, Log-normal, Exponential, Cauchy, and Logistic distributions were used to model the data and to evaluate the performances of the distributions, three non-parametric goodness-of-fit tests were conducted, and AIC, BIC values were calculated. Parameters of the distributions were estimated by the maximum likelihood method. The best-fit result of each season was taken as the distribution with the lowest AIC and BIC values. Among the seven distributions, the Gamma distribution showed the best-fit results of the monthly rainfall data for the Monsoon, Post-Monsoon, and Winter Season, and the Weibull distribution showed the best-fit result for Summer Season.","PeriodicalId":153849,"journal":{"name":"European Journal of Statistics","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036206","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}

引用次数: 0

Some Resultats on Optimal Allocation of Policy Limits and Deductibles: Mixture Model 保险限额与免赔额最优分配的若干结果:混合模型

European Journal of Statistics Pub Date : 2021-11-29 DOI: 10.28924/ada/stat.2.4

Bouhadjar Meriem, Halim zeghdoudi, Abdelali Ezzebsa

引用次数: 0

On the T-X Class of Topp Leone-G Family of Distributions: Statistical Properties and Applications Topp Leone-G族分布的T-X类:统计性质及应用

European Journal of Statistics Pub Date : 2021-11-22 DOI: 10.28924/ada/stat.2.2

Femi Samuel Adeyinka

引用次数: 0

Mixingale Estimation Function for SPDEs with Random Sampling 随机抽样SPDEs的混合估计函数

European Journal of Statistics Pub Date : 2021-11-22 DOI: 10.28924/ada/stat.2.3

J. Bishwal

引用次数: 2

A Novel Chen Extension: Theory, Characterizations and Different Estimation Methods 一种新的Chen可拓:理论、表征和不同的估计方法

European Journal of Statistics Pub Date : 2021-11-22 DOI: 10.28924/ada/stat.2.1

H. Yousof, M. C. Korkmaz, G. Hamedani, M. Ibrahim

引用次数: 5

A Size-Biased Poisson-Gamma Lindley Distribution with Application 尺寸偏置Poisson-Gamma Lindley分布及其应用

European Journal of Statistics Pub Date : 2021-10-22 DOI: 10.28924/ada/stat.1.132

Fatma Zohra Seghier, Halim zeghdoudi, A. Benchaabane

引用次数: 1

The Alpha Power Transformed Dagum Distribution: Properties and Applications 阿尔法能量转换的达格姆分布:性质和应用

European Journal of Statistics Pub Date : 2021-10-22 DOI: 10.28924/ada/stat.1.108

Hesham Reyad, Farrukh Jamal, G. Hamedani, Soha Othman

引用次数: 1