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Additive Hazards Regression for Misclassified Current Status Data

COMMUNICATIONS IN MATHemATICS AND STATISTICS 2025年第2期13卷 507-526页

作者： Wang, Wenshan Zhao, Shishun Li, Shuwei Sun, Jianguo Jilin Univ Ctr Appl Stat Res Sch Math Changchun Peoples R China Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Univ Missouri Dept Stat Columbia MO USA

We discuss regression analysis of current status data with the additive hazards model when the failure status may suffer misclassification. Such data occur commonly in many scientific fields involving the diagnosis test with imperfect sensitivity and specificity. In particular, we consider the situation where the sensitivity and specificity are known and propose a nonparametric maximum likelihood approach. For the implementation of the method, a novel em algorithm is developed, and the asymptotic properties of the resulting estimators are established. Furthermore, the estimated regression parameters are shown to be semiparametrically efficient. We demonstrate the empirical performance of the proposed methodology in a simulation study and show its substantial advantages over the naive method. Also an application to a motivated study on chlamydia is provided.

关键词： em algorithm Maximum likelihood estimation Misclassification Regression analysis

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Intelligent edge CDN with smart contract-aided local IoT sharing

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High-Confidence Computing 2024年第4期4卷 56-68页

作者： Jiamin Fan Daming Liu Guoming Tang Kui Wu Xun Shao Department of Computer Science University of VictoriaVictoria BC V8W 2Y2Canada College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Peng Cheng Laboratory Shenzhen 518000China Department of Electrical and Electronic Information Engineering Toyohashi University of TechnologyToyohashi Aichi 441-8580Japan

A content delivery network(CDN)aims to reduce the content delivery latency to end-users by using distributed cache ***,deploying and maintaining cache servers on a large scale is very *** solve this problem,CDN providers have developed a new content delivery strategy:allowing end-users’s IoT edge devices to share their storage/bandwidth *** new edge CDN platform must address two core questions:(1)how can we incentivize end users to share IoT devices?(2)how can we facilitate a safe and transparent content transaction environment for end users?This paper introduces SmartSharing,a new content delivery network solution to address these *** smartSharing,the over-the-top(OTT)IoT devices belonging to end-users are used as mini-cache *** motivate end users to share the idle devices and storage/bandwidth resources,SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms(specifically,a tailored expectation-maximization(em)algorithm).To facilitate content trading among end users,SmartSharing creates a secure and transparent transaction platform based on smart contracts in *** addition,SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing *** evaluation results show that CDN providers,end users and content providers can all benefit from our SmartSharing framework.

关键词： CDN over edge IoT Game theory em algorithm Smart contracts

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A robust approach for regression analysis of panel count data with time-varying covariates

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BERNOULLI 2024年第4期30卷 3251-3275页

作者： Sun, Dayu Guo, Yuanyuan Li, Yang Tu, Wanzhu Sun, Jianguo Emory Univ Dept Biostat & Bioinformat Atlanta GA 30322 USA Duke Univ Dept Biostat & Bioinformat Durham NC 27710 USA Indiana Univ Dept Biostat & Hlth Data Sci Indianapolis IN 46202 USA Univ Missouri Dept Stat Columbia MO 65211 USA

The validity of statistical inference for panel count data with time-varying covariates depends on the correct specification of within-subject correlation structures;misspecification often leads to questionable inference. To alleviate, robust inference has been proposed for mean models, which implicitly assume monotone mean functions. When covariate values fluctuate with time, however, the assumed monotonicity becomes unrealistic. In this research, we propose a robust inference based on rate models that are free of such constraints. Since the asymptotic variance has no closed form under the rate model, we further develop computationally efficient robust variance estimators using the Expectation-Maximization (em) algorithm, thus sidestepping the need for computationally intensive numerical methods, which could undermine the robustness. Rigorous theoretical development is provided in support of parameter estimation and inference. Extensive simulation studies demonstrate the superiority of the proposed method. We present a real clinical application to illustrate the use of the proposed method.

关键词： em algorithm panel count data robust variance estimation semiparametric model time-to-event data

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Compositional inverse Gaussian models with applications in compositional data analysis with possible zero observations

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2024年第2期94卷 248-278页

作者： Liu, Pengyi Tian, Guo-Liang Yuen, Kam Chuen Sun, Yuan Zhang, Chi Yunnan Univ Finance & Econ Dept Stat Kunming Peoples R China Southern Univ Sci & Technol Dept Stat & Data Sci Shenzhen Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China Shenzhen Univ Coll Econ Shenzhen Peoples R China Shenzhen Univ Coll Econ Shenzhen 518055 Guangdong Peoples R China

Compositional data (CoDa) often appear in various fields such as biology, medicine, geology, chemistry, economics, ecology and sociology. Although existing Dirichlet and related models are frequently employed in CoDa analysis, sometimes they may provide unsatisfactory performances in modelling CoDa as shown in our first real data example. First, this paper develops a multivariate compositional inverse Gaussian (CIG) model as a new tool for analysing CoDa. By incorporating the stochastic representation (SR), the expectation-maximization (em) algorithm (aided by a one-step gradient descent algorithm) can be established to solve the parameter estimation for the proposed distribution (model). Next, zero observations may be often encountered in the real CoDa analysis. Therefore, the second aim of this paper is to propose a new model (called as ZCIG model) through a novel mixture SR based on both the CIG random vector and a so-called zero-truncated product Bernoulli random vector to model CoDa with zeros. Corresponding statistical inference methods are also developed for both cases without/with covariates. Two real data sets are analysed to illustrate the proposed statistical methods by comparing the proposed CIG and ZCIG models with existing Dirichlet and logistic-normal models.

关键词： Compositional inverse Gaussian distribution em algorithm inverse Gaussian distribution zero-truncated product Bernoulli distribution >

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Sharp analysis of em for learning mixtures of pairwise differences 36

Sharp analysis of EM for learning mixtures of pairwise diffe...

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36th Annual Conference on Learning Theory (COLT)

作者： Dhawan, Abhishek Mao, Cheng Pananjady, Ashwin Georgia Inst Technol Sch Math Atlanta GA 30332 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA USA

We consider a symmetric mixture of linear regressions with random samples from the pairwise comparison design, which can be seen as a noisy version of a type of Euclidean distance geometry problem. We analyze the expectation-maximization (em) algorithm locally around the ground truth and establish that the sequence converges linearly, providing an l(infinity)-norm guarantee on the estimation error of the iterates. Furthermore, we show that the limit of the em sequence achieves the sharp rate of estimation in the l(2)-norm, matching the information-theoretically optimal constant. We also argue through simulation that convergence from a random initialization is much more delicate in this setting, and does not appear to occur in general. Our results show that the em algorithm can exhibit several unique behaviors when the covariate distribution is suitably structured.

关键词： Mixture of linear regressions pairwise comparison em algorithm Euclidean distance geometry problem

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Regression Analysis of Misclassified Current Status Data with Informative Observation Times

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Journal of Systems Science & Complexity 2023年第3期36卷 1250-1264页

作者： WANG Wenshan XU Da ZHAO Shishun SUN Jianguo Center for Applied Statistical Research School of MathematicsJilin UniversityChangchun 130012China Key Laboratory of Applied Statistics of MOE and School of Mathematics and Statistics Northeast Normal UniversityChangchun 130024China Department of Statistics University of MissouriColumbiaMO 65211USA

Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and *** the situation,another issue that may occur is that the observation time may be correlated with the interested failure time,which is often referred to as informative censoring or observation *** is well-known that in the presence of informative censoring,the analysis that ignores it could yield biased or even misleading *** this paper,the authors consider such data and propose a frailty-based inference *** particular,an em algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are *** numerical results show that the proposed method works well in practice and an application to a set of real data is provided.

关键词： Current status data em algorithm informative censoring misclassification proportional hazard model

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Divide and recombine approach for warranty database: estimating the reliability of an automobile component

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Data Science and Management 2024年第2期7卷 119-128页

作者： Md Rezaul Karim Department of Statistics University of RajshahiRajshahi6205Bangladesh

The continuously updated database of failures and censored data of numerous products has become large, and on some covariates, information regarding the failure times is missing in the database. As the dataset is large and has missing information, the analysis tasks become complicated and a long time is required to execute the programming codes. In such situations, the divide and recombine (D&R) approach, which has a practical computational performance for big data analysis, can be applied. In this study, the D&R approach was applied to analyze the real field data of an automobile component with incomplete information on covariates using the Weibull regression model. Model parameters were estimated using the expectation maximization algorithm. The results of the data analysis and simulation demonstrated that the D&R approach is applicable for analyzing such datasets. Further, the percentiles and reliability functions of the distribution under different covariate conditions were estimated to evaluate the component performance of these covariates. The findings of this study have managerial implications regarding design decisions, safety, and reliability of automobile components.

关键词： Weibull regression model Warranty database Reliability em algorithm Divide and recombine approach Managerial implications

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Maximum likelihood estimation for semiparametric regression models with interval-censored multistate data

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BIOMETRIKA 2024年第3期111卷 971-988页

作者： Gu, Yu Zeng, Donglin Heiss, Gerardo Lin, D. Y. Univ Hong Kong Dept Stat & Actuarial Sci Pokfulam Rd Hong Kong Peoples R China Univ Michigan Dept Biostat 1415 Washington Hts Ann Arbor MI 48109 USA Univ N Carolina Dept Epidemiol 137 East Franklin St Chapel Hill NC 27599 USA Univ N Carolina Dept Biostat 3101 E McGavran Greenberg Hall Chapel Hill NC 27599 USA

Interval-censored multistate data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We relate potentially time-dependent covariates to multistate processes through semiparametric proportional intensity models with random effects. We study nonparametric maximum likelihood estimation under general interval censoring and develop a stable expectation-maximization algorithm. We show that the resulting parameter estimators are consistent and that the finite-dimensional components are asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we demonstrate through extensive simulation studies that the proposed numerical and inferential procedures perform well in realistic settings. Finally, we provide an application to a major epidemiologic cohort study.

关键词： em algorithm Multistate model Nonparametric likelihood Proportional intensity Random effect Semiparametric efficiency Time-dependent covariate Transition probability

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Estimating the prevalence of osteoporosis using ranked-based methodologies and Manitoba's population-based BMD registry

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JOURNAL OF APPLIED STATISTICS 2024年第11期51卷 2090-2115页

作者： Omidvar, Sedigheh Jozani, Mohammad Jafari Nematollahi, Nader Leslie, Wiliam D. Allameh Tabatabai Univ Dept Stat Tehran Iran Univ Manitoba Dept Stat Winnipeg MB Canada Univ Manitoba Dept Med Winnipeg MB Canada

Osteoporosis is a metabolic bone disorder that is characterized by reduced bone mineral density (BMD) and deterioration of bone microarchitecture. Osteoporosis is highly prevalent among women over 50, leading to skeletal fragility and risk of fracture. Early diagnosis and treatment of those at high risk for fracture is very important in order to avoid morbidity, mortality and economic burden from preventable fractures. The province of Manitoba established a BMD testing program in 1997. The Manitoba BMD registry is now the largest population-based BMD registry in the world, and has detailed information on fracture outcomes and other covariates for over 160,000 BMD assessments. In this paper, we develop a number of methodologies based on ranked-set type sampling designs to estimate the prevalence of osteoporosis among women of age 50 and older in the province of Manitoba. We use a parametric approach based on finite mixture models, as well as the usual approaches using simple random and stratified sampling designs. Results are obtained under perfect and imperfect ranking scenarios while the sampling and ranking costs are incorporated into the study. We observe that rank-based methodologies can be used as cost-efficient methods to monitor the prevalence of osteoporosis.

关键词： Bone mineral density em algorithm finite mixture model osteoporosis stratified sampling unbalanced ranked set sampling

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Understanding Ability and Reliability Differences Measured with Count Items: The Distributional Regression Test Model and the Count Latent Regression Model

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MULTIVARIATE BEHAVIORAL RESEARCH 2024年第3期59卷 502-522页

作者： Beisemann, Marie Forthmann, Boris Doebler, Philipp TU Dortmund Univ Dept Stat Dortmund Germany Univ Munster Inst Psychol Educ Munster Germany TU Dortmund Univ Dept Stat Vogelpothsweg 87 D-44227 Dortmund Germany

In psychology and education, tests (e.g., reading tests) and self-reports (e.g., clinical questionnaires) generate counts, but corresponding Item Response Theory (IRT) methods are underdeveloped compared to binary data. Recent advances include the Two-Parameter Conway-Maxwell-Poisson model (2PCMPM), generalizing Rasch's Poisson Counts Model, with item-specific difficulty, discrimination, and dispersion parameters. Explaining differences in model parameters informs item construction and selection but has received little attention. We introduce two 2PCMPM-based explanatory count IRT models: The Distributional Regression Test Model for item covariates, and the Count Latent Regression Model for (categorical) person covariates. Estimation methods are provided and satisfactory statistical properties are observed in simulations. Two examples illustrate how the models help understand tests and underlying constructs.

关键词： Item response theory count data Conway-Maxwell-Poisson distribution item covariates person covariates 2PCMPM em algorithm

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