Results 31 to 40 of about 1,319,586 (359)

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

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

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

On the estimation of variance parameters in non-standard generalised linear mixed models: application to penalised smoothing [PDF]

Statistics and computing, 2018
We present a novel method for the estimation of variance parameters in generalised linear mixed models. The method has its roots in Harville (J Am Stat Assoc 72(358):320–338, 1977)’s work, but it is able to deal with models that have a precision matrix ...
M. Rodríguez-Álvarez, M. Durbán, Dae-Jin Lee, P. Eilers   +3 more
semanticscholar   +1 more source

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

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

Environmental 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, Yinxiang Wu, Melanie H. Jacobson, Myeonggyun Lee, Peng Jin, Leonardo Trasande, Mengling Liu   +6 more
doaj   +1 more source

APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA

Barekeng, 2023
Spatial regression analysis is a statistical method used to perform modeling by considering spatial effects. Spatial models generally use a parametric approach by assuming a linear relationship between explanatory and response variables.
Nindi Pigitha, Anik Djuraidah, Aji Hamim Wigena   +2 more
doaj   +1 more source

Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India

Ecological Indicators, 2021
Over the past several decades, ecologists have been striving to develop models that accurately describe species-habitat relationships across ecological communities.
Rubina Mondal, Anuradha Bhat
doaj   +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