Search Results - "Monte Carlo expectation‐maximization algorithm"

A fast Monte Carlo expectation-maximization algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with atypical glandular cells by Kang, Le, Carter, Randy, Darcy, Kathleen, Kauderer, James, Liao, Shu-Yuan

“…In this article, we use a latent class model (LCM) with prevalence modeled as a function of covariates to assess diagnostic test accuracy in situations where…”
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Modeling the random effects covariance matrix for longitudinal data with covariates measurement error by Hoque, Md Erfanul, Torabi, Mahmoud

“… Generalized linear mixed models (GLMMs) are commonly used to analyze such data. It is typically assumed that the random effects covariance matrix is constant among subjects in these models…”
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Joint modeling of recurrent events and a terminal event adjusted for zero inflation and a matched design by Xu, Cong, Chinchilli, Vernon M., Wang, Ming

“… In some scenarios, a certain large portion of subjects may not have any recurrent events during the study period due to nonsusceptibility to events or censoring; thus, the zero…”
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Dynamic reliability assessment and prediction for repairable systems with interval-censored data by Peng, Yizhen, Wang, Yu, Zi, YanYang, Tsui, Kwok-Leung, Zhang, Chuhua

“… Due to instrument malfunctions, staff omissions and imperfect inspection strategies, field reliability data are often subject to interval censoring, making dynamic reliability assessment become a difficult task…”
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Joint analysis of left‐censored longitudinal biomarker and binary outcome via latent class modeling by Li, Menghan, Kong, Lan

“… We propose a modified likelihood function under the parametric assumption of biomarker distribution and develop a Monte Carlo expectation…”
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Augmented likelihood for incorporating auxiliary information into left-truncated data by Shi, Yidan, Zeng, Leilei, Thompson, Mary E., Tyas, Suzanne L.

“… A one-step Monte-Carlo Expectation-Maximization algorithm is developed to calculate an augmented likelihood through creating pseudo-data sets which extend the form and conditions of the observed sample. The method is illustrated by both a real dataset and simulation studies…”
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Mixed Mover-Stayer Model for Spatiotemporal Two-State Processes by Nathoo, F, Dean, C.B

“…Studies of recurring infection or chronic disease often collect longitudinal data on the disease status of subjects…”
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Semiparametric Analysis of Two‐Level Bivariate Binary Data by Naskar, Malay, Das, Kalyan

“… In the framework of the proposed hybrid method, estimation of parameters is done by implementing the Monte Carlo expectation…”
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Generalized linear mixed models for multi-reader multi-case studies of diagnostic tests by Liu, Wei, Pantoja-Galicia, Norberto, Zhang, Bo, Kotz, Richard M, Pennello, Gene, Zhang, Hui, Jacob, Jessie, Zhang, Zhiwei

“… (subjects with or without the disease in question) are examined by several readers using all tests to be compared…”
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A two-component nonlinear mixed effects model for longitudinal data, with application to gastric emptying studies by Kim, Inyoung, Cohen, Noah D., Roussel, Allen, Wang, Naisyin

“…‐effects of new or existing medications, diets, or procedures. Summarizing gastric emptying data is important to allow easier comparison between treatments or groups of subjects and comparisons of results among studies…”
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Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates - an application to Arctic data analysis by Das, Kalyan, Sarkar, Angshuman

“… into. The estimates are obtained by using Monte Carlo expectation-maximization algorithm. An approximate method which reduces the computational time to a great extent is also proposed…”
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Semiparametric Latent ANOVA Model for Event-Related Potentials by Cheng-Han, Yu, Li, Meng, Vannucci, Marina

“… A major goal is to link ERP characteristic components to subject-level covariates. Existing methods typically follow two-step approaches, first identifying ERP components using peak detection methods and then relating them to the…”
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Sufficient dimension reduction for clustered data via finite mixture modelling by Hui, F.K.C., Nghiem, L.H.

“…Summary Sufficient dimension reduction (SDR) is an attractive approach to regression modelling. However, despite its rich literature and growing popularity in…”
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A Latent Class Approach for Joint Modeling of Clinical Outcomes and Longitudinal Biomarkers Subject to Detection Limits by Li, Menghan

“…Biomarkers have played an important role in the biomedical studies for diagnosis and prognosis of acute and chronic diseases, and understanding the biological…”
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