Results 31 to 40 of about 301,037 (257)
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 +2 more
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Optimal Antibody Purification Strategies Using Data-Driven Models
Engineering, 2019 This work addresses the multiscale optimization of the purification processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of ...
Songsong Liu, Lazaros G. Papageorgiou
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Additive quantile mixed effects modelling with application to longitudinal CD4 count data
Scientific Reports, 2021 Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean.
Ashenafi A. Yirga +3 more
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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
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PLoS ONE, 2018 Epigenome-wide association studies seek to identify DNA methylation sites associated with clinical outcomes. Difference in observed methylation between specific cell-subtypes is often of interest; however, available samples often comprise a mixture of ...
Daniel W Kennedy +8 more
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Bayesian multimodel inference for geostatistical regression models. [PDF]
PLoS ONE, 2011 The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection ...
Devin S Johnson, Jennifer A Hoeting
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Quantile regression in linear mixed models: a stochastic approximation EM approach [PDF]
Statistics and Its Interface, 2017 This paper develops a likelihood-based approach to analyze quantile regression (QR) models for continuous longitudinal data via the asymmetric Laplace distribution (ALD). Compared to the conventional mean regression approach, QR can characterize the entire conditional distribution of the outcome variable and is more robust to the presence of outliers ...
Christian E, Galarza +2 more
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Variational Bayesian EM Algorithm for Quantile Regression in Linear Mixed Effects Models
MathematicsThis paper extends the normal-beta prime (NBP) prior to Bayesian quantile regression in linear mixed effects models and conducts Bayesian variable selection for the fixed effects of the model.
Weixian Wang, Maozai Tian
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A mixed-effects model for growth curves analysis in a two-way crossed classification layout
Revista de Matemática: TeorÃa y Aplicaciones, 2009 We propose a mixed-effects linear model for analyzing growth curves data obtained using a two-way classification experiment. The model combines an unconstrained means model and a regression model on the time, in which the coefficients are considered ...
Mario Miguel Ojeda, Hardeo Sahai
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A comparison of multiple imputation methods for missing data in longitudinal studies
BMC Medical Research Methodology, 2018 Background Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard)
Md Hamidul Huque +3 more
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