Journal of Probability and Statistics最新文献_第2页

New Test for the Comparison of Survival Curves to Detect Late Differences 比较生存曲线以发现晚期差异的新试验

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-03-01 DOI: 10.1155/2023/9945446

Ildephonse Nizeyimana, S. Mwalili, G. Orwa

{"title":"New Test for the Comparison of Survival Curves to Detect Late Differences","authors":"Ildephonse Nizeyimana, S. Mwalili, G. Orwa","doi":"10.1155/2023/9945446","DOIUrl":"https://doi.org/10.1155/2023/9945446","url":null,"abstract":"Background. Survival analysis attracted the attention of different scientists from various domains such as engineering, health, and social sciences. It has been widely exploited in clinical trials when comparing different treatments looking at their survival probabilities. Kaplan–Meier curves plotted from the Kaplan–Meier estimates of survival probabilities are used to depict the general image for such situations. Methods. The weighted log-rank test has been dealt with by suggesting different weight functions which give specific strength in specific situations. In this work, we proposed a new weight function comprising all numbers at risk, i.e., the overall number at risk and the separate numbers at risk in the groups under study, to detect late differences between survival curves. Results. The new test has been found to be a good alternative after the FH (0, 1) test in detecting late differences, and it outperformed all tests in case of small samples and heavy censoring rates according to the simulation studies. The new test kept the same strength when applied to real data where it showed itself to be among the powerful ones or even outperforms all other tests under consideration. Conclusion. As the new test stays stronger in the case of small samples and heavy censoring rates, it may be a better choice whenever targeting the detection of late differences between the survival curves.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48477253","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

Using ORRT Models for Mean Estimation under Nonresponse and Measurement Errors in Stratified Successive Sampling 用ORRT模型估计分层连续抽样中无响应和测量误差下的均值

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-02-16 DOI: 10.1155/2023/1340068

M. Choudhary, S. P. Kour, Sunil Kumar, C. Bouza, Agustín Santiago

引用次数: 0

Fitting the Distribution of Linear Combinations of t − Variables with more than 2 Degrees of Freedom 具有2个以上自由度的t变量线性组合的分布拟合

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-02-01 DOI: 10.1155/2023/9967290

O. L. Alcaraz López, E. M. García Fernández, M. Latva-aho

{"title":"Fitting the Distribution of Linear Combinations of \u0000 t\u0000 −\u0000 Variables with more than 2 Degrees of Freedom","authors":"O. L. Alcaraz López, E. M. García Fernández, M. Latva-aho","doi":"10.1155/2023/9967290","DOIUrl":"https://doi.org/10.1155/2023/9967290","url":null,"abstract":"The linear combination of Student’s \u0000 \u0000 t\u0000 \u0000 random variables (RVs) appears in many statistical applications. Unfortunately, the Student’s \u0000 \u0000 t\u0000 \u0000 distribution is not closed under convolution, thus, deriving an exact and general distribution for the linear combination of \u0000 \u0000 K\u0000 \u0000 Student’s \u0000 \u0000 t\u0000 \u0000 RVs is infeasible, which motivates a fitting/approximation approach. Here, we focus on the scenario where the only constraint is that the number of degrees of freedom of each \u0000 \u0000 t\u0000 −\u0000 \u0000 RV is greater than two. Notice that since the odd moments/cumulants of the Student’s \u0000 \u0000 t\u0000 \u0000 distribution are zero and the even moments/cumulants do not exist when their order is greater than the number of degrees of freedom, it becomes impossible to use conventional approaches based on moments/cumulants of order one or higher than two. To circumvent this issue, herein we propose fitting such a distribution to that of a scaled Student’s \u0000 \u0000 t\u0000 \u0000 RV by exploiting the second moment together with either the first absolute moment or the characteristic function (CF). For the fitting based on the absolute moment, we depart from the case of the linear combination of \u0000 \u0000 K\u0000 =\u0000 2\u0000 \u0000 Student’s \u0000 \u0000 t\u0000 \u0000 RVs and then generalize to \u0000 \u0000 K\u0000 ≥\u0000 2\u0000 \u0000 through a simple iterative procedure. Meanwhile, the CF-based fitting is direct, but its accuracy (measured in terms of the Bhattacharyya distance metric) depends on the CF parameter configuration, for which we propose a simple but accurate approach. We numerically show that the CF-based fitting usually outperforms the absolute moment-based fitting and that both the scale and number of degrees of freedom of the fitting distribution increase almost linearly with \u0000 \u0000 K\u0000 \u0000 .","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46839023","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

An Improved Measurement Error Model for Analyzing Unreplicated Method Comparison Data under Asymmetric Heavy-Tailed Distributions 非对称重尾分布下非复制方法比较数据分析的改进测量误差模型

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-12-15 DOI: 10.1155/2022/3453912

Jeevana Duwarahan, Lakshika S. Nawarathna

{"title":"An Improved Measurement Error Model for Analyzing Unreplicated Method Comparison Data under Asymmetric Heavy-Tailed Distributions","authors":"Jeevana Duwarahan, Lakshika S. Nawarathna","doi":"10.1155/2022/3453912","DOIUrl":"https://doi.org/10.1155/2022/3453912","url":null,"abstract":"Method comparison studies mainly focus on determining if the two methods of measuring a continuous variable are agreeable enough to be used interchangeably. Typically, a standard mixed-effects model uses to model the method comparison data that assume normality for both random effects and errors. However, these assumptions are frequently violated in practice due to the skewness and heavy tails. In particular, the biases of the methods may vary with the extent of measurement. Thus, we propose a methodology for method comparison data to deal with these issues in the context of the measurement error model (MEM) that assumes a skew-\u0000 \u0000 t\u0000 \u0000 (ST) distribution for the true covariates and centered Student’s \u0000 \u0000 t\u0000 \u0000 (cT) distribution for the errors with known error variances, named STcT-MEM. An expectation conditional maximization (ECM) algorithm is used to compute the maximum likelihood (ML) estimates. The simulation study is performed to validate the proposed methodology. This methodology is illustrated by analyzing gold particle data and then compared with the standard measurement error model (SMEM). The likelihood ratio (LR) test is used to identify the most appropriate model among the above models. In addition, the total deviation index (TDI) and concordance correlation coefficient (CCC) were used to check the agreement between the methods. The findings suggest that our proposed framework for analyzing unreplicated method comparison data with asymmetry and heavy tails works effectively for modest and large samples.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43032382","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

On Random Dynamical Systems Generated by White Noise Time Change of Deterministic Dynamical Systems 确定性动力系统白噪声时变产生的随机动力系统

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-12-09 DOI: 10.1155/2022/3881486

M. Hmissi, F. Mokchaha

引用次数: 0

Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance 随机森林计数数据建模:数据特征和过度分散对回归性能的影响分析

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-12-01 DOI: 10.1155/2022/2833537

C. A. Mushagalusa, A. B. Fandohan, R. G. Glèlè Kakaï

{"title":"Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance","authors":"C. A. Mushagalusa, A. B. Fandohan, R. G. Glèlè Kakaï","doi":"10.1155/2022/2833537","DOIUrl":"https://doi.org/10.1155/2022/2833537","url":null,"abstract":"Machine learning algorithms, especially random forests (RFs), have become an integrated part of the modern scientific methodology and represent an efficient alternative to conventional parametric algorithms. This study aimed to assess the influence of data features and overdispersion on RF regression performance. We assessed the effect of types of predictors (100, 75, 50, and 20% continuous, and 100% categorical), the number of predictors (p = 816 and 24), and the sample size (N = 50, 250, and 1250) on RF parameter settings. We also compared RF performance to that of classical generalized linear models (Poisson, negative binomial, and zero-inflated Poisson) and the linear model applied to log-transformed data. Two real datasets were analysed to demonstrate the usefulness of RF for overdispersed data modelling. Goodness-of-fit statistics such as root mean square error (RMSE) and biases were used to determine RF accuracy and validity. Results revealed that the number of variables to be randomly selected for each split, the proportion of samples to train the model, the minimal number of samples within each terminal node, and RF regression performance are not influenced by the sample size, number, and type of predictors. However, the ratio of observations to the number of predictors affects the stability of the best RF parameters. RF performs well for all types of covariates and different levels of dispersion. The magnitude of dispersion does not significantly influence RF predictive validity. In contrast, its predictive accuracy is significantly influenced by the magnitude of dispersion in the response variable, conditional on the explanatory variables. RF has performed almost as well as the models of the classical Poisson family in the presence of overdispersion. Given RF’s advantages, it is an appropriate statistical alternative for counting data.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44361782","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

Mathematical Modeling of Concentration Risk under the Default Risk Charge Using Probability and Statistics Theory 违约风险收费下集中风险的概率统计数学建模

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-11-01 DOI: 10.1155/2022/3063505

Badreddine Slime

引用次数: 1

Extreme Value Distributions: An Overview of Estimation and Simulation 极值分布：估计与模拟综述

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-10-19 DOI: 10.1155/2022/5449751

Bashir Ahmed Albashir Abdulali, Mohd Aftar Abu Bakar, K. Ibrahim, N. M. Ariff

{"title":"Extreme Value Distributions: An Overview of Estimation and Simulation","authors":"Bashir Ahmed Albashir Abdulali, Mohd Aftar Abu Bakar, K. Ibrahim, N. M. Ariff","doi":"10.1155/2022/5449751","DOIUrl":"https://doi.org/10.1155/2022/5449751","url":null,"abstract":"The generalized extreme value distribution (GEVD) and various extreme value distributions are commonly applied in air pollution, telecommunications, operational risk management, finance, insurance, material sciences, economics, and hydrology, among many other industries that deal with extreme events. Extreme value distributions (EVDs) typically limit the distribution of maximum and minimum values for many random observations drawn from the same arbitrary distribution. Besides that, it is a crucial method for forecasting future events and emerged as critical method for predicting future events. As a result, prior research is required to select the best estimation method to obtain a reliable value for the parameters of extreme value distributions. This study provides an overview of three-parameter estimation methods based on goodness-of-fit statistics and root mean square error (RMSE). This paper reviewed and compared three estimation methods used to approximate values of parameters for simulated observations taken from the EVD and GEVD. The method of moments (MOMs), maximum likelihood estimator (MLE), and maximum product of spacing (MPS) were the methods investigated in this study. Our findings indicated that the MPS performed better based on the mean square errors (MSEs); meanwhile, the MPS had similar goodness-of-fit statistic values compared to the MLE.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45984192","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: 3

NetDA: An R Package for Network-Based Discriminant Analysis Subject to Multilabel Classes 一个基于网络的多标签类判别分析的R包

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-09-27 DOI: 10.1155/2022/1041752

Li‐Pang Chen

引用次数: 2

Some Improved Classes of Estimators in Stratified Sampling Using Bivariate Auxiliary Information 基于二元辅助信息的分层抽样中的一些改进估计类

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-08-31 DOI: 10.1155/2022/2660114

Shashi Bhushan, Anoop Kumar, Rodney Onyango, Saurabh Singh

引用次数: 4