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Proportional rates models for multivariate panel count data

BIOMETRICS 2024年第1期80卷 ujad011页

作者： Xu, Yangjianchen Zeng, Donglin Lin, Dan-Yu Univ North Carolina Chapel Hill Dept Biostat Chapel Hill NC 27599 USA Univ N Carolina Dept Biostat Chapel Hill NC 27599 USA

Multivariate panel count data arise when there are multiple types of recurrent events, and the observation for each study subject consists of the number of recurrent events of each type between two successive examinations. We formulate the effects of potentially time-dependent covariates on multiple types of recurrent events through proportional rates models, while leaving the dependence structures of the related recurrent events completely unspecified. We employ nonparametric maximum pseudo-likelihood estimation under the working assumptions that all types of events are independent and each type of event is a nonhomogeneous Poisson process, and we develop a simple and stable em-type algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that can be estimated consistently by a sandwich estimator. In addition, we develop a class of graphical and numerical methods for checking the adequacy of the fitted model. Finally, we evaluate the performance of the proposed methods through simulation studies and analysis of a skin cancer clinical trial.

关键词： em algorithm interval censoring model checking proportional means model pseudo-likelihood recurrent events

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Estimation and inference for high dimensional factor model with regime switching

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JOURNAL OF ECONOMETRICS 2024年第2期241卷

作者： Urga, Giovanni Wang, Fa Bayes Business Sch Cass Fac Finance 106 Bunhill Row London EC1Y 8TZ England Peking Univ Sch Econ 5 Yiheyuan Rd Beijing 100871 Peoples R China

This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with regime switching in the loadings. The model parameters are estimated jointly by the em (expectation maximization) algorithm, which in the current context only requires iteratively calculating regime probabilities and principal components of the weighted sample covariance matrix. When regime dynamics are taken into account, smoothed regime probabilities are calculated using a recursive algorithm. Consistency, convergence rates and limit distributions of the estimated loadings and the estimated factors are established under weak cross-sectional and temporal dependence as well as heteroscedasticity. It is worth noting that due to high dimension, regime switching can be identified consistently after the switching point with only one observation period. Simulation results show good performance of the proposed method. An application to the FRED -MD dataset illustrates the potential of the proposed method for detection of business cycle turning points.

关键词： Factor model Regime switching Maximum likelihood High dimension em algorithm Turning points

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Imperfect and worse than old maintenances for a gamma degradation process

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2024年第3期40卷 620-639页

作者： Corset, Franck Fouladirad, Mitra Paroissin, Christian Univ Grenoble Alpes CNRS LJK Grenoble France Aix Marseille Univ UMR CNRS 7340 Cenrale Med M2P2 Marseille France Univ Pau & Pays Adour CNRS LMAP E2S UPPA Pau France

This article considers a condition-based maintenance for a system subject to deterioration. The deterioration is modeled by a non-homogeneous gamma process, more precisely the gamma process and the preventive maintenance are imperfect or worse than old. The corrective maintenance actions are as good as new. The maintenance efficiency or non-efficiency parameters as well as the deterioration parameters are considered to be unknown. The monitoring data under consideration give indirect information on the maintenance parameters. Therefore, an expected maximum algorithm is applied for parameter estimation.

关键词： ARD effect condition-based maintenance degradation em algorithm gamma process imperfect maintenance statistical inference worse than old maintenance

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emBRACE: An em-based bias reduction approach through Copas-model estimation for quantifying the evidence of selective publishing in network meta-analysis

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BIOMETRICS 2022年第2期78卷 754-765页

作者： Marks-Anglin, Arielle Luo, Chongliang Piao, Jin Gibbons, Mary Beth Connolly Schmid, Christopher H. Ning, Jing Chen, Yong Univ Penn Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Univ Southern Calif Dept Prevent Med Los Angeles CA 90007 USA Univ Penn Dept Psychiat Philadelphia PA 19104 USA Brown Univ Dept Biostat Providence RI 02912 USA Univ Texas MD Anderson Canc Ctr Dept Biostat Houston TX 77030 USA

Systematic reviews and meta-analyses synthesize results from well-conducted studies to optimize healthcare decision-making. Network meta-analysis (NMA) is particularly useful for improving precision, drawing new comparisons, and ranking multiple interventions. However, recommendations can be misled if published results are a selective sample of what has been collected by trialists, particularly when publication status is related to the significance of the findings. Unfortunately, the missing-not-at-random nature of this problem and the numerous parameters involved in modeling NMAs pose unique computational challenges to quantifying and correcting for publication bias, such that sensitivity analysis is used in practice. Motivated by this important methodological gap, we developed a novel and stable expectation-maximization (em) algorithm to correct for publication bias in the network setting. We validate the method through simulation studies and show that it achieves substantial bias reduction in small to moderately sized NMAs. We also calibrate the method against a Bayesian analysis of a published NMA on antiplatlet therapies for maintaining vascular patency.

关键词： Copas model em algorithm missing data multiarm trial network meta‐ analysis publication bias

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Rayleigh-Rice Mixture Parameter Estimation via em algorithm for Change Detection in Multispectral Images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第12期24卷 5004-5016页

作者： Zanetti, Massimo Bovolo, Francesca Bruzzone, Lorenzo Univ Trento Dept Informat & Commun Technol I-38123 Trento Italy Fdn Bruno Kessler Ctr Informat Technol I-38123 Trento Italy

The problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in the framework of change detection (CD) in multitemporal and multispectral images. One widely used approach to CD in multispectral images is based on the change vector analysis. Here, the distribution of the magnitude of the difference image can be theoretically modeled by a Rayleigh-Rice mixture density. However, given the complexity of this model, in applications, a Gaussian-mixture approximation is often considered, which may affect the CD results. In this paper, we present a novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm. The proposed technique, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines. Several numerical experiments on synthetic data demonstrate the effectiveness of the method, which is general and can be applied to any image processing problem involving the Rayleigh-Rice mixture density. In the CD context, the Rayleigh-Rice model (which is theoretically derived) outperforms other empirical models. Experiments on real multitemporal and multispectral remote sensing images confirm the validity of the model by returning significantly higher CD accuracies than those obtained by using the state-of-the-art approaches.

关键词： Parameter estimation em algorithm change detection Rayleigh distribution Rician distribution change vector analysis multispectral images remote sensing

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Effect of a correlated competing risk on marginal survival estimation in an accelerated failure time model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年

作者： Gares, Valerie Hudson, Malcolm Manuguerra, Maurizio Gebski, Val Univ Rennes INSA CNRS IRMAR UMR 6625 Rennes France Univ Sydney NHMRC Clin Trials Ctr Camperdown Australia Macquarie Univ Sch Math & Phys Sci FSE Sydney Australia

In problems with a time to event outcome, subjects may experience competing events, which censor the outcome of interest. Cox's partial likelihood estimator treating competing events as independent censoring is commonly used to examine group differences in clinical trials but fails to adjust for omitted covariates and can bias the assessment of marginal benefit. A bivariate normal linear model generating latent data with dependent censoring is used to assess this bias. Our R-package bnc provides maximum penalized likelihood (MPL) parameter estimation using a novel em algorithm. Using bnc, we study the properties of such MPL estimation. Simulation results for two-sample survival comparisons of time to an event of interest, with independent censoring accompanied by censoring from a correlated competing risk, are presented. Key parameters-means, hazard ratios, and correlation-are estimated. These results demonstrated that, despite ill-conditioning in models generating correlated competing risks, estimates of marginal effects are reliable. Bivariate normal models were fitted in a trial of head and neck cancer. Model fits help with clinical interpretation while also supplementing other standard methods for follow-up that are terminated by intervening risks.

关键词： Time-to-event analysis Accelerated failure time Bivariate normal Cox model Competing risks Dependent censoring em algorithm Ill-conditioning

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Variable selection for misclassified current status data under the proportional hazards model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年第3期53卷 1494-1503页

作者： Wang, Wenshan Fang, Lijun Li, Shuwei Sun, Jianguo Jilin Univ Ctr Appl Stat Res Sch Math Changchun Peoples R China Guangzhou Univ Sch Econ & Stat Guangzhou 10006 Peoples R China Univ Missouri Dept Stat Columbia MO 65211 USA

Misclassified current status data arise when the failure time of interest is observed or known only to be either smaller or larger than an observation time rather than observed exactly, and the failure status is examined by a diagnostic test with testing error. Such data commonly occur in various scientific fields, including clinical trials, demographic studies and epidemiological surveys. This paper discusses regression analysis of such data with the focus on variable selection or identifying predictable and important covariates associated with the failure time of interest. For the problem, a penalized maximum likelihood approach is proposed under the Cox proportional hazards model and the smoothly clipped absolute deviation penalty. More specifically, we develop a penalized em algorithm to relieve the computational burden in maximizing the resulting, complex penalized likelihood function. A simulation study is conducted to examine the empirical performance of the proposed approach in finite samples, and an illustration to a set of real data on chlamydia is also provided.

关键词： Current status data em algorithm Misclassification Penalized likelihood Proportional hazards model Variable selection

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A Class of Hierarchical Multivariate Wiener Processes for Modeling Dependent Degradation Data

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TECHNOMETRICS 2024年第2期66卷 141-156页

作者： Fang, Guanqi Pan, Rong Zhejiang Gongshang Univ Collaborat Innovat Ctr Stat Data Engn Technol & Ap Hangzhou Peoples R China Zhejiang Gongshang Univ Sch Stat & Math Hangzhou Peoples R China Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ USA Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA

In engineering practice, many products exhibit multiple and dependent degrading performance characteristics (PCs). It is common to observe that these PCs' initial measurements are nonconstant and sometimes correlated with the subsequent degradation rate, which typically varies from one unit to another. To accommodate the unit-wise heterogeneity, PC-wise dependency, and "initiation-growth" correlation, this article proposes a broad class of multi-dimensional degradation models under a framework of hierarchical multivariate Wiener processes. These models incorporate dual multi-normally distributed random effects concerning the initial values and degradation rates. To infer model parameters, expectation-maximization (em) algorithms and several tools for model validation and selection are developed. Various simulation studies are carried out to assess the performance of the inference method and to compare different models. Two case studies are conducted to demonstrate the applicability of the proposed methodology. The online supplementary materials of this article contain derivations of em estimators, additional numerical results, and R codes.

关键词： Degradation data em algorithm Hierarchical model Multivariate degradation Random effects Wiener process

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em-based likelihood inference for one-shot device test data under log-normal lifetimes and the optimal design of a CSALT plan

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2022年第2期38卷 780-799页

作者： Balakrishnan, Narayanaswamy Castilla, Elena McMaster Univ Dept Math & Stat Hamilton ON Canada Univ Complutense Madrid Dept Stat & OR Madrid Spain

One-shot devices result in an extreme case of interval censoring, wherein one can only know whether the failure time is either before or after the test time. The study of one-shot device testing has been developed considerably recently, both in terms of estimation and optimal design under different lifetime distributions. However, one-shot device testing analysis under lognormal lifetime distribution has not been studied yet. While the hazard function for exponential distribution is always a constant, and that of Weibull and gamma are either increasing or decreasing, the lognormal distribution has increasing - decreasing behavior of hazard which is encountered often in practice as units usually experience early failure and then stabilize over time in terms of performance. In this paper, we develop the em algorithm for the likelihood estimation based on one-shot device test data under lognormal distribution and also the design of optimal CSALTs (constant stress accelerated life tests) under this set up with budget constraints. A simulation study is carried out to assess the performance of the methods of inference developed here and some real-life data are analyzed for illustrative purpose.

关键词： accelerated life-test best test plan em algorithm lognormal distribution optimal design one-shot device reliability

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Parameter-expanded ECME algorithms for logistic and penalized logistic regression

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COMPUTATIONAL STATISTICS 2025年 1-27页

作者： Henderson, Nicholas C. Ouyang, Zhongzhe Univ Michigan Dept Biostat 1415 Washington Hts Ann Arbor MI 48109 USA

Parameter estimation in logistic regression is a well-studied problem with the Newton-Raphson method being one of the most prominent optimization techniques used in practice. A number of monotone optimization methods including minorization-maximization (MM) algorithms, expectation-maximization (em) algorithms and related variational Bayes approaches offer useful alternatives guaranteed to increase the logistic regression likelihood at every iteration. In this article, we propose and evaluate an optimization procedure that is based on a straightforward modification of an em algorithm for logistic regression. Our method can substantially improve the computational efficiency of the em algorithm while preserving the monotonicity of em and the simplicity of the em parameter updates. By introducing an additional latent parameter and selecting this parameter to maximize the penalized observed-data log-likelihood at every iteration, our iterative algorithm can be interpreted as a parameter-expanded expectation-conditional maximization either (ECME) algorithm, and we demonstrate how to use the parameter-expanded ECME with an arbitrary choice of weights and penalty function. In addition, we describe a generalized version of our parameter-expanded ECME algorithm that can be tailored to the challenges encountered in specific high-dimensional problems, and we study several interesting connections between this generalized algorithm and other well-known methods. Performance comparisons between our method, the em algorithm, Newton-Raphson, and several other optimization methods are presented using an extensive series of simulation studies based upon both real and synthetic datasets.

关键词： Convergence acceleration em algorithm MM algorithm P & oacute lya-Gamma augmentation Parameter expansion

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