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

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

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

D-Optimal Design for a Causal Structure for Completely Randomized and Random Blocked Experiments 完全随机和随机阻塞实验因果结构的d -最优设计

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-08-30 DOI: 10.1155/2022/7299086

Zaher Kmail, K. Eskridge

引用次数: 0

On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment 空间偏差下二值数据的层次贝叶斯空间小面积模型

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-06-30 DOI: 10.1155/2022/3865626

Kindie Fentahun Muchie, A. Wanjoya, S. Mwalili

{"title":"On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment","authors":"Kindie Fentahun Muchie, A. Wanjoya, S. Mwalili","doi":"10.1155/2022/3865626","DOIUrl":"https://doi.org/10.1155/2022/3865626","url":null,"abstract":"Small area models have become popular methods for producing reliable estimates for sub-populations (small geographic areas in this study). Small area modeling may be carried out via model-assisted approaches within the model-based approaches or design-based paradigm. When there are medium or large samples, a model-assisted approach may be reliable. However, when data are scarce, a model-based technique may be required. Model-based Bayesian analysis is popular for its ability to combine information from several sources as well as taking account uncertainties in the analysis and spatial prediction of spatial data. Nevertheless, things become more complex when the geographic boundaries of interest are misaligned. Some authors have addressed the problem of misalignment under hierarchical Bayesian approach. In this study, we developed non-trivial extension of existing hierarchical Bayesian model for a binary outcome variable under spatial misalignment with three contributions. First, the model uses unit-level survey data and area-level auxiliary data to predict the posterior mean proportion spatially at the second geographic area level. Second, the linking model is changed to logit-normal model in the proposed model. Lastly, the mean process was considered to overcome the multicollinearity between the true predictors and the spatial random effect. Sensitivity analysis was also done via simulation.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47651594","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

Nonstationary Generalised Autoregressive Conditional Heteroskedasticity Modelling for Fitting Higher Order Moments of Financial Series within Moving Time Windows 运动时间窗内金融序列高阶矩拟合的非平稳广义自回归条件异方差模型

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-05-20 DOI: 10.1155/2022/4170866

Luke De Clerk, Sergey Savel’ev

{"title":"Nonstationary Generalised Autoregressive Conditional Heteroskedasticity Modelling for Fitting Higher Order Moments of Financial Series within Moving Time Windows","authors":"Luke De Clerk, Sergey Savel’ev","doi":"10.1155/2022/4170866","DOIUrl":"https://doi.org/10.1155/2022/4170866","url":null,"abstract":"Here, we present a method for a simple GARCH (1,1) model to fit higher order moments for different companies’ stock prices. When we assume a Gaussian conditional distribution, we fail to capture any empirical data when fitting the first three even moments of financial time series. We show instead that a mixture of normal distributions is needed to better capture the higher order moments of the data. To demonstrate this point, we construct regions (parameter diagrams), in the fourth- and sixth-order standardised moment space, where a GARCH (1,1) model can be used to fit moment values and compare them with the corresponding moments from empirical data for different sectors of the economy. We found that the ability of the GARCH model with a double normal conditional distribution to fit higher order moments is dictated by the time window our data spans. We can only fit data collected within specific time window lengths and only with certain parameters of the conditional double Gaussian distribution. In order to incorporate the nonstationarity of financial series, we assume that the parameters of the GARCH model can have time dependence. Furthermore, using the method developed here, we investigate the effect of the COVID-19 pandemic has upon stock’s stability and how this compares with the 2008 financial crash.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48217396","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

Mean Estimation of a Sensitive Variable under Nonresponse Using Three-Stage RRT Model in Stratified Two-Phase Sampling 分层两阶段抽样中三阶段RRT模型在无响应情况下的敏感变量均值估计

IF 1.1

Journal of Probability and Statistics Pub Date : 2022-04-22 DOI: 10.1155/2022/4530120

Rodney Onyango, Brian Oduor, F. Odundo

引用次数: 2