Results 61 to 70 of about 4,106,343 (229)
Linear quantile mixed models [PDF]
, 2011 Dependent data arise in many studies. For example, children with the same parents or living in neighbouring geographic areas tend to be more alike in many characteristics than individuals chosen at random from the population at large; observations taken ...
Bottai, M, Geraci, M
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Best practice guidance for linear mixed-effects models in psychological science
Journal of Memory and Language, 2019 The use of Linear Mixed Effects Models (LMMs) is set to dominate statistical analyses in psychological science and may become the default approach to analyzing quantitative data. The rapid growth in adoption of LMMs has been matched by a proliferation of
L. Meteyard, Robert A. I. Davies
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G3: Genes, Genomes, Genetics, 2021 Genotype-by-environment (G × E) interactions are important for understanding genotype–phenotype relationships. To date, various statistical models have been proposed to account for G × E effects, especially in genomic selection (GS) studies.
Eiji Yamamoto, Hiroshi Matsunaga
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Efficient estimation of moments in linear mixed models
, 2012 In the linear random effects model, when distributional assumptions such as normality of the error variables cannot be justified, moments may serve as alternatives to describe relevant distributions in neighborhoods of their means.
Stute, Winfried, Wu, Ping, Zhu, Li-Xing
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Linear Mixed Models with Marginally Symmetric Nonparametric Random Effects
, 2016 Linear mixed models (LMMs) are used as an important tool in the data analysis of repeated measures and longitudinal studies. The most common form of LMMs utilize a normal distribution to model the random effects.
McLachlan, Geoffrey J., Nguyen, Hien D.
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RRPP: An r package for fitting linear models to high‐dimensional data using residual randomization
Methods in Ecology and Evolution, 2018 Residual randomization in permutation procedures (RRPP) is an appropriate means of generating empirical sampling distributions for ANOVA statistics and linear model coefficients, using ordinary or generalized least‐squares estimation.
M. Collyer, D. Adams
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Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models [PDF]
Journal of Sciences, Islamic Republic of IranWhen discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. However, the maximum likelihood methods are plagued with substantial calculations for large data sets ...
Mohsen Mohammadzadeh, Leyla Salehi
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An Optimization Model for Reducing Pumping Costs in Water Distribution Systems [PDF]
مجله مدل سازی در مهندسیThe architecture of water distribution networks is inherently intricate, encompassing the strategic management of pumping mechanisms, modulation of reservoir water levels, and ensuring the delivery of water to consumers with adequate flow and pressure. A
Abbasali Rezapour
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Fence methods for mixed model selection
, 2008 Many model search strategies involve trading off model fit with model complexity in a penalized goodness of fit measure. Asymptotic properties for these types of procedures in settings like linear regression and ARMA time series have been studied, but ...
Gu, Zhonghua +3 more
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Half-Normal Plots and Overdispersed Models in R: The hnp Package
Journal of Statistical Software, 2017 Count and proportion data may present overdispersion, i.e., greater variability than expected by the Poisson and binomial models, respectively. Different extended generalized linear models that allow for overdispersion may be used to analyze this type of
Rafael A Moral +2 more
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