Results 41 to 50 of about 1,982,495 (283)
Improved testing inference in mixed linear models
, 2009 Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit.
Barndorff-Nielsen +23 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Osteonecrosis (ON) is a debilitating complication of acute lymphoblastic leukemia (ALL) therapy. While numerous studies have explored its incidence and associated risk factors, investigations using large‐scale cohorts remain important to characterize ON across heterogeneous populations.
Noémie de Villiers +5 more
wiley +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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
G3: Genes, Genomes, Genetics, 2021 Genotype-by-environment (G × E) interactions are important for understanding genotype–phenotype relationships. To date, various statistical models have been proposed to account for G × E effects, especially in genomic selection (GS) studies.
Eiji Yamamoto, Hiroshi Matsunaga
doaj +1 more source
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
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background L‐asparaginase is a critical component in treatment protocols for pediatric acute lymphoblastic leukemia. Acute pancreatitis reactions can necessitate delays and, in some cases, discontinuation of L‐asparaginase, which compromises outcomes.
Edward J. Raack +39 more
wiley +1 more source
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
doaj +1 more source
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
core +1 more source