A Bayesian semiparametric latent variable model for mixed responses [PDF]
, 2006 In this article we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semiparametric predictor. We extend existing LVM with simple
Raach, Alexander, Fahrmeir, Ludwig
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Linear mixed-effects model for longitudinal complex data with diversified characteristics
Journal of Management Science and Engineering, 2020 The increasing richness of data encourages a comprehensive understanding of economic and financial activities, where variables of interest may include not only scalar (point-like) indicators, but also functional (curve-like) and compositional (pie-like ...
Zhichao Wang +4 more
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How to use live sampling tissues and archived specimens in cetacean stable isotope research
Water Biology and Security, 2023 Cetaceans are unique ecological engineers, and their restoration may have a crucial impact on the future structure of aquatic ecosystems, which calls for more investigations into their trophic ecology.
Tao Jin +9 more
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Genotype Selection for Grain Yield of Sorghum through Generalized Linear Mixed Model
Agronomy, 2023 The classical model only provides a correct analysis if all the effects are fixed. For experiments that include fixed and random effects, the general linear mixed model is appropriate for handling the non-normal distributed response variables. The aim of
Mulugeta Tesfa +4 more
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Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation [PDF]
, 2011 Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates.
Groll, Andreas, Tutz, Gerhard
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A variance shilf model for outlier detection and estimation in linear and linear mixed models [PDF]
, 2008 Includes abstract.Includes bibliographical references.Outliers are data observations that fall outside the usual conditional ranges of the response data.They are common in experimental research data, for example, due to transcription errors or faulty ...
Gumedze, Freedom Nkhululeko
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Estimating basis functions in massive fields under the spatial mixed effects model [PDF]
, 2021 Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values.
Pazdernik, Karl, Maitra, Ranjan
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Linear Mixed Model for Genotype Selection of Sorghum Yield
Applied Sciences, 2023 Data analysis using the General linear model assumes the factors to be fixed effects, and the BLUE method, which is based on their mean performance, is appropriate to select the best performing genotypes.
Mulugeta Tesfa +4 more
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Model Fitness and Predictive Accuracy in Linear Mixed-Effects Models with Latent Clusters
Journal of Nigerian Society of Physical Sciences, 2023 In clustered data, observations within a cluster show similarity between themselves because they share common features different from observations in the other clusters.
Waheed B. Yahya +2 more
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A mixed model approach for structured hazard regression [PDF]
, 2004 The classical Cox proportional hazards model is a benchmark approach to analyze continuous survival times in the presence of covariate information. In a number of applications, there is a need to relax one or more of its inherent assumptions, such as ...
Thomas Kneib +4 more
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