Results 31 to 40 of about 1,394,927 (329)
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
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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
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APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA
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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A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models [PDF]
Inference for spatial generalized linear mixed models (SGLMMs) for high-dimensional non-Gaussian spatial data is computationally intensive. The computational challenge is due to the high-dimensional random effects and because Markov chain Monte Carlo ...
Yawen Guan, M. Haran
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Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses.
Premika S. W. Boedhoe +106 more
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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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Minimization of Akaike's information criterion in linear regression analysis via mixed integer nonlinear program [PDF]
Akaike's information criterion (AIC) is a measure of evaluating statistical models for a given data set. We can determine the best statistical model for a particular data set by finding the model with the smallest AIC value. Since there are exponentially
K. Kimura, Hayato Waki
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Optimal Antibody Purification Strategies Using Data-Driven Models
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
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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The Essentials on Linear Regression, ANOVA, General Linear and Linear Mixed Models for the Chemist [PDF]
This text provides a brief and accessible guide for implementing general, ANOVA and linear mixed models for the analysis of real world data from chemistry, industrial, bio or life-sciences experiments. The reader is introduced to the main concepts and vocabulary of the subject and to the main statistical methods available for formalizing, estimating ...
Govaerts, Bernadette +4 more
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