检索结果-内蒙古大学图书馆

General Path Models for Degradation Data With Multiple Characteristics and Covariates

TECHNOMETRICS 2021年第3期63卷 354-369页

作者： Lu, Lu Wang, Bingxing Hong, Yili Ye, Zhisheng Univ S Florida Dept Math & Stat Tampa FL 33620 USA Zhejiang Gongshang Univ Sch Stat Hangzhou 310018 Peoples R China Virginia Tech Dept Stat Blacksburg VA USA Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore

Degradation data have been broadly used for assessing product and system reliability. Most existing work focuses on modeling and analysis of degradation data with a single characteristic. In some degradation tests, interest lies in measuring multiple characteristics of the product degradation process to understand different aspects of the reliability performance, resulting in degradation data with multiple characteristics. The literature on modeling such data is scarce. Motivated by the photodegradation process of polymeric material, we propose a multivariate general path model for analyzing degradation data with multiple degradation characteristics (DCs). The model incorporates covariates for modeling the nonlinear degradation path. It also includes random effects that are correlated among the multiple DCs to capture the unit-to-unit variation in the individual degradation paths and to model the interdependence among the multivariate measurements. An expectation-maximization algorithm combined with the Markov chain Monte Carlo simulation is developed for estimating the model parameters and predicting system reliability with quantified uncertainty. The performance of the developed method is evaluated and compared with existing methods through a simulation study. The implementation of the method is illustrated through two examples with different forms of reliability functions. The main motivating example analyzes the coating degradation data with a closed-form reliability function, while the second example on analyzing the Device-B data demonstrates a more general simulation approach for dealing with analytically intractable reliability functions.

关键词： mcem algorithm Multivariate degradation Photodegradation Polymeric materials Random effects Service life prediction

来源：评论

学校读者我要写书评

暂无评论

Estimating prevalence of post-war health disorders using multiple systems data

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-11页

作者： Bhuyan, Prajamitra Chatterjee, Kiranmoy Indian Inst Management Calcutta Kolkata 700104 India Bidhannagar Coll Kolkata 700064 India

Effective surveillance on the long-term public health impact due to war and terrorist attacks remains limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size or undercount of the population under the risk of physical and mental health hazards is of utmost necessity. A novel trivariate Bernoulli model is developed allowing heterogeneity among the individuals and dependence between the sources of information, and an estimation methodology using a Monte Carlo-based EM algorithm is proposed. Simulation results show the superiority of the performance of the proposed method over existing competitors and robustness under model mis-specifications. The method is applied to analyse two real case studies on monitoring amyotrophic lateral sclerosis (ALS) cases for the Gulf War veterans and the 9/11 terrorist attack survivors at the World Trade Center, USA. The average annual cumulative incidence rate for ALS disease increases by 33%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$33\%$$\end{document} and 16%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$16\%$$\end{document} for deployed and no-deployed military personnel, respectively, after adjusting the undercount. The number of individuals exposed to the risk of physical and mental health effects due to WTC terrorist attacks increased by 42%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$42\%

关键词： Amyotrophic lateral sclerosis Gulf war Humanitarian crisis mcem algorithm 9/11 terrorist attacks

来源：评论

学校读者我要写书评

暂无评论

Copula-frailty models for recurrent event data based on Monte Carlo EM algorithm

引用

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2021年第17期91卷 3530-3548页

作者： Bedair, Khaled F. Hong, Yili Al-Khalidi, Hussein R. Tanta Univ Fac Commerce Tanta Egypt Univ Dundee Sch Med Dundee Scotland Virginia Tech Dept Stat Blacksburg VA 24061 USA Duke Univ Dept Biostat & Bioinformat Durham NC USA

Multi-type recurrent events are often encountered in medical applications when two or more different event types could repeatedly occur over an observation period. For example, patients may experience recurrences of multi-type nonmelanoma skin cancers in a clinical trial for skin cancer prevention. The aims in those applications are to characterize features of the marginal processes, evaluate covariate effects, and quantify both the within-subject recurrence dependence and the dependence among different event types. We use copula-frailty models to analyze correlated recurrent events of different types. Parameter estimation and inference are carried out by using a Monte Carlo expectation-maximization (mcem) algorithm, which can handle a relatively large (i.e. three or more) number of event types. Performances of the proposed methods are evaluated via extensive simulation studies. The developed methods are used to model the recurrences of skin cancer with different types.

关键词： Clinical trial mcem algorithm multi-type recurrences multivariate frailty skin cancers survival models

来源：评论

学校读者我要写书评

暂无评论

Linear censored regression models with skew scale mixtures of normal distributions

引用

JOURNAL OF APPLIED STATISTICS 2021年第16期48卷 3060-3085页

作者： Guzman, Daniel C. F. Ferreira, Clecio S. Zeller, Camila B. Univ Fed Juiz de Fora Dept Stat Juiz De Fora Brazil

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (mcem) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The mcem algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset.

关键词： Censoring mcem algorithm linear regression models skew scale mixtures of normal distributions

来源：评论

学校读者我要写书评

暂无评论

Joint mean-covariance random effect model for longitudinal data

引用

BIOMETRICAL JOURNAL 2020年第1期62卷 7-23页

作者： Bai, Yongxin Qian, Manling Tian, Maozai Renmin Univ China Ctr Appl Stat Sch Stat Beijing 100872 Peoples R China Xinjiang Univ Finance & Econ Sch Stat & Informat Urumqi Peoples R China Lanzhou Univ Finance & Econ Sch Stat Lanzhou Gansu Peoples R China

In this paper, we consider the inherent association between mean and covariance in the joint mean-covariance modeling and propose a joint mean-covariance random effect model based on the modified Cholesky decomposition for longitudinal data. Meanwhile, we apply M-H algorithm to simulate the posterior distributions of model parameters. Besides, a computationally efficient Monte Carlo expectation maximization (mcem) algorithm is developed for carrying out maximum likelihood estimation. Simulation studies show that the model taking into account the inherent association between mean and covariance has smaller standard deviations of the estimators of parameters, which makes the statistical inferences much more reliable. In the real data analysis, the estimation of parameters in the mean and covariance structure is highly efficient.

关键词： joint mean-covariance model longitudinal data mcem algorithm random effect

来源：评论

学校读者我要写书评

暂无评论

A parameter estimation method for multivariate binned Hawkes processes

引用

STATISTICS AND COMPUTING 2022年第6期32卷 98-98页

作者： Shlomovich, Leigh Cohen, Edward A. K. Adams, Niall Imperial Coll London Dept Math South Kensington Campus London SW7 2AZ England

It is often assumed that events cannot occur simultaneously when modelling data with point processes. This raises a problem as real-world data often contains synchronous observations due to aggregation or rounding, resulting from limitations on recording capabilities and the expense of storing high volumes of precise data. In order to gain a better understanding of the relationships between processes, we consider modelling the aggregated event data using multivariate Hawkes processes, which offer a description of mutually-exciting behaviour and have found wide applications in areas including seismology and finance. Here we generalise existing methodology on parameter estimation of univariate aggregated Hawkes processes to the multivariate case using a Monte Carlo expectation-maximization (MC-EM) algorithm and through a simulation study illustrate that alternative approaches to this problem can be severely biased, with the multivariate MC-EM method outperforming them in terms of MSE in all considered cases.

关键词： Multivariate Hawkes processes Mutually exciting Aggregated data mcem algorithm

来源：评论

学校读者我要写书评

暂无评论

Regression analysis of interval-censored failure time data with time-dependent covariates

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2020年第0期144卷 106848-000页

作者： Yi, Fengting Tang, Niansheng Sun, Jianguo Yunnan Univ Yunnan Key Lab Stat Modeling & Data Kunming 650091 Yunnan Peoples R China Univ Missouri Dept Stat Columbia MO 65211 USA

Interval-censored failure time data often occur in many areas and their analysis has recently attracted a great deal of attention. On the other hand, most of the existing literature for them can only deal with time-independent covariates. Sometimes one may face time dependent covariates and furthermore the covariates could also suffer measurement errors. For the situation, one approach is to conduct a joint analysis for which many methods have been developed in the literature under various framework. One drawback of these methods is that they usually assume that there are no more measurements on the covariates after the failure time and it is apparent that this may not be true. In this paper, a new joint analysis approach is proposed that can take into account the extra observations. In particular, for estimation, a mcem algorithm is developed that is much more stable and converges much faster than the existing algorithms. To assess the finite sample performance of the proposed method, an extensive simulation study is conducted and suggests that it works well for practical situations. Also the method is applied to an AIDS study that motivated this investigation. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Interval censoring Joint analysis mcem algorithm Time-dependent covariate

来源：评论

学校读者我要写书评

暂无评论

A Wiener-based degradation model with logistic distributed measurement errors and remaining useful life estimation

引用

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2018年第6期34卷 1289-1303页

作者： Shen, Yan Shen, Lijuan Xu, Wangtu Xiamen Univ Sch Econ Dept Stat 422 South Siming Rd Xiamen 361005 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore Xiamen Univ Sch Architecture & Civil Engn Dept Urban Planning Xiamen Peoples R China

Degradation data analysis, which investigates degradation processes of products to extrapolate the lifetime properties, is an effective method for reliability analysis. But degradation data that reflect a product's inherent randomness of degradation are often contaminated by measurement errors. To deal with the problem, this paper proposes a Wiener-based model with an assumption of logistic distributed measurement errors and adopts the Monte Carlo expectation-maximization method together with the Gibbs sampling for parameter estimation. Based on the model and parameter estimates, an efficient algorithm is proposed for a quick calculation of maximum likelihood value. Also, the estimation of remaining useful lifetime is discussed. Simulation results show that the proposed model is relatively better and more robust in comparison with the Wiener process with Gaussian noises. Finally, the application of the proposed model is illustrated by an example.

关键词： Gibbs sampling logistic distribution maximum likelihood value measurement error mcem algorithm

来源：评论

学校读者我要写书评

暂无评论

Spatial models for non-Gaussian data with covariate measurement error

引用

ENVIRONMETRICS 2019年第3期30卷 e2545-e2545页

作者： Tadayon, Vahid Torabi, Mahmoud Shahid Chamran Univ Ahvaz Dept Stat Ahvaz Iran Higher Educ Ctr Eghlid Dept Stat Eghlid Iran Univ Manitoba Dept Community Hlth Sci Winnipeg MB R3E 0W3 Canada Univ Manitoba Dept Stat Winnipeg MB Canada

Spatial models have been widely used in the public health setup. In the case of continuous outcomes, the traditional approaches to model spatial data are based on the Gaussian distribution. This assumption might be overly restrictive to represent the data. The real data could be highly non-Gaussian and may show features like heavy tails and/or skewness. In spatial data modeling, it is also commonly assumed that the covariates are observed without errors, but for various reasons, such as measurement techniques or instruments used, uncertainty is inherent in spatial (especially geostatistics) data, and so, these data are susceptible to measurement errors in the covariates of interest. In this paper, we introduce a general class of spatial models with covariate measurement error that can account for heavy tails, skewness, and uncertainty of the covariates. A likelihood method, which leads to the maximum likelihood estimation approach, is used for inference through the Monte Carlo expectation-maximization algorithm. The predictive distribution at nonsampled sites is approximated based on the Markov chain Monte Carlo algorithm. The proposed approach is evaluated through a simulation study and by a real application (particulate matter data set).

关键词： mcem algorithm measurement error spatial modeling unified skew Gaussian

来源：评论

学校读者我要写书评

暂无评论

Analysis of bivariate zero inflated count data with missing responses

引用

JOURNAL OF MULTIVARIATE ANALYSIS 2016年 148卷 73-82页

作者： Yang, Miao Das, Kalyan Majumdar, Anandamayee Oregon State Univ Dept Stat Corvallis OR 97330 USA Univ Calcutta Dept Stat 35 Ballygunge Circular Rd Kolkata 700019 India Soochow Univ Ctr Adv Stat & Econometr Suzhou Peoples R China

Bivariate zero-inflated Poisson regression models have recently been used in various medical and biological settings to model excess zeros. However, there has not been any definite approach to deal with the same in the event of missing responses. The model itself is complex and as the responses are paired, missing values can occur in either or both coordinates. We propose a flexible Monte Carlo expectation maximization based approach to handle bivariate zero inflated count data with missing responses. We report the results of a simulation study designed to evaluate the performance of the proposed approach. To illustrate the application of our model and methodology, we consider a bivariate data concerning the demand for health care in Australia. (c) 2016 Elsevier Inc. All rights reserved.

关键词： Bivariate zero-inflated Poisson Generalized linear model mcem algorithm Missing data

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：