The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
, 2017 We propose a novel high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients subject to a budget on the maximal absolute correlation between the features and residuals ...
Mazumder, Rahul, Radchenko, Peter
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Non-linear Mixed Models in a Dose Response Modelling
InPrime, 2019 Study designs in which an outcome is measured more than once from time to time result in longitudinal data. Most of the methodological works have been done in the setting of linear and generalized linear models, where some amount of linearity is retained.
Madona Yunita Wijaya
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Clustering in linear mixed models with Dirichlet process mixtures using EM algorithm [PDF]
, 2011 In linear mixed models the assumption of normally distributed random effects is often inappropriate and unnecessary restrictive. The proposed Dirichlet process mixture assumes a hierarchical Gaussian mixture.
Heinzl, Felix, Tutz, Gerhard
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Meta-analysis of Proportions Using Generalized Linear Mixed Models
Epidemiology, 2020 Supplemental Digital Content is available in the text. Epidemiologic research often involves meta-analyses of proportions. Conventional two-step methods first transform each study’s proportion and subsequently perform a meta-analysis on the transformed ...
Lifeng Lin, H. Chu
semanticscholar +1 more source
Identifying genetically driven clinical phenotypes using linear mixed models
Nature Communications, 2016 Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait.
Jonathan D. Mosley +13 more
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Modeling Multiple Item Context Effects With Generalized Linear Mixed Models
Frontiers in Psychology, 2019 Item context effects refer to the impact of features of a test on an examinee's item responses. These effects cannot be explained by the abilities measured by the test. Investigations typically focus on only a single type of item context effects, such as
Norman Rose +6 more
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lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models
BMC Bioinformatics, 2022 Background Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity.
Brian E. Vestal +2 more
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D-optimal designs formulti-response linear mixed models [PDF]
, 2018 Linear mixed models have become popular in many statistical applications duringrecent years. However design issues for multi-response linear mixed models are rarelydiscussed.
Liu, Xin, Wong, Weng Kee, Yue, Rong-Xian
core
Two-period linear mixed effects models to analyze clinical trials with run-in data when the primary outcome is continuous: Applications to Alzheimer\u27s disease. [PDF]
, 2019 Introduction: Study outcomes can be measured repeatedly based on the clinical trial protocol before randomization during what is known as the run-in period.
Aschenbrenner, Andrew J +9 more
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Multivariate Generalized Linear Mixed Models for Count Data
Austrian Journal of StatisticsUnivariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of ...
Guilherme Parreira da Silva +4 more
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