Results 21 to 30 of about 1,394,927 (329)

Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting [PDF]

, 2011
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed
Groll, Andreas, Tutz, Gerhard
core   +4 more sources

Analysis of neonatal clinical trials with twin births

BMC Medical Research Methodology, 2009
Background In neonatal trials of pre-term or low-birth-weight infants, twins may represent 10–20% of the study sample. Mixed-effects models and generalized estimating equations are common approaches for handling correlated continuous or binary data ...
Shaffer Michele L, Kunselman Allen R, Watterberg Kristi L   +2 more
doaj   +1 more source

Simultaneous Inference in General Parametric Models [PDF]

, 2008
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level ...
Bates, Bates, Bretz, Bretz, Bullinger, Bönsch, Garcia, Genz, Genz, Genz, Hochberg, Hothorn, Hothorn, Hsu, Marcus, R Development Core Team, Rousseeuw, Salib, Searle, Serfling, Shaffer, Stefanski, Todorov, Tong, Westfall, Westfall, Westfall, Yohai, Zeileis, Zeileis   +29 more
core   +3 more sources

Longitudinal beta regression models for analyzing health-related quality of life scores over time

BMC Medical Research Methodology, 2012
Background Health-related quality of life (HRQL) has become an increasingly important outcome parameter in clinical trials and epidemiological research. HRQL scores are typically bounded at both ends of the scale and often highly skewed.
Hunger Matthias, Döring Angela, Holle Rolf   +2 more
doaj   +1 more source

Combining the Box-Cox power and generalised log transformations to accommodate nonpositive responses in linear and mixed-effects linear models

South African Statistical Journal, 2022
Transformation of a response variable can greatly expand the class of problems for which the linear regression model or linear mixed-model is appropriate.
D. Hawkins, S. Weisberg
semanticscholar   +1 more source

Assessment of factors affecting flicker ERGs recorded with RETeval from data obtained from health checkup screening.

PLoS ONE, 2023
PurposeTo determine the factors significantly associated with the amplitudes and implicit times of the flicker electroretinograms (ERGs) recorded with the RETeval system by analyzing the comprehensive data obtained during a health checkup screening ...
Taiga Inooka, Taro Kominami, Shunsuke Yasuda, Yoshito Koyanagi, Junya Ota, Satoshi Okado, Ryo Tomita, Yasuki Ito, Takeshi Iwase, Hiroko Terasaki, Koji M Nishiguchi, Shinji Ueno   +11 more
doaj   +2 more sources

Utility of linear mixed effects models for event-related potential research with infants and children

Developmental Cognitive Neuroscience, 2022
Event-related potentials (ERPs) are advantageous for investigating cognitive development. However, their application in infants/children is challenging given children’s difficulty in sitting through the multiple trials required in an ERP task.
M. Heise, Serena K. Mon, L. Bowman
semanticscholar   +1 more source

On the mixed Kibria–Lukman estimator for the linear regression model

Scientific Reports, 2022
AbstractThis paper considers a linear regression model with stochastic restrictions,we propose a new mixed Kibria–Lukman estimator by combining the mixed estimator and the Kibria–Lukman estimator.This new estimator is a general estimation, including OLS estimator, mixed estimator and Kibria–Lukman estimator as special cases. In addition, we discuss the
Hongmei Chen, Jibo Wu
openaire   +3 more sources

MCMC methods for multi-response generalized linear mixed models

, 2010
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form.
J. Hadfield
semanticscholar   +1 more source

Mixed Lasso estimator for stochastic restricted regression models

Journal of Applied Statistics, 2021
Parameters of a linear regression model can be estimated with the help of traditional methods like generalized least squares and mixed estimator. However, recent developments increased the importance of big data sets, which have much more predictors than
Huseyin Guler, Ebru Ozgur Guler
semanticscholar   +1 more source