Results 31 to 40 of about 1,409,985 (359)

Simultaneous Inference in General Parametric Models [PDF]

open access: yes, 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   +29 more
core   +3 more sources

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

open access: yesBMC 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   +2 more
doaj   +1 more source

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

open access: yesDevelopmental 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

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

open access: yesPLoS 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   +11 more
doaj   +2 more sources

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

open access: yesSouth 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

MCMC methods for multi-response generalized linear mixed models

open access: yes, 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

open access: yesJournal 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

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

open access: yesScientific 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

Bayesian variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration.

open access: yesPLoS ONE, 2020
This paper presents a Bayesian analysis of linear mixed models for quantile regression based on a Cholesky decomposition for the covariance matrix of random effects.
Yonggang Ji, Haifang Shi
doaj   +1 more source

A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

open access: yesEnvironmental Health, 2020
Background Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes.
Yuyan Wang   +6 more
doaj   +1 more source

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