Non-Linear Mixed Models
Non linear mixed models for predictive modelling in actuarial science
... generalized linear model (the red points) can be viewed as the individual estimate for each job class level ignoring the other levels - indeed, fitting a GLM with only the job class as a predictor is equivalent to ...
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Non-linear mixed models in the analysis of mediated longitudinal data with binary outcomes
... link models in ...with linear models for continuous outcome data, the product of coefficients method is not equivalent to the “ difference of coeffi- cients ” method of determining the indirect or ...
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QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato
... usually non-linear trait responses over ...growth models are attractive candi- dates to play a central role in ...used models in biology are the linear and exponential growth ...
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On non-negative estimation of variance components in mixed linear models
... Conclusions In this article, two new estimators based on IAUE principle are introduced for estimating the variance components in the mixed linear model. The aim of this article was to evaluate the ...
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Recursive residuals for linear mixed models
... In the case where a model misspecification arises, under common circumstances the recursive residuals will have a mean non-zero difference and it is possible to detect changes over times that are sustained beyond ...
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Estimation for Multivariate Linear Mixed Models
... applied linear mixed model in Statistics for Biology Systems to calculate both covariates and correlations between signals which followed non-stationary time ...
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Topics On Generalized Linear Mixed Models
... Second, vectors z ij ’s sometimes have a sparse structure as well. For instance, in the salamander data example z ij ’s are twenty-dimensional but there are only two non-zero elements corresponding to a matching ...
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Statistical Inference For Non-linear Mixed Effects Models Involving Ordinary Differential Equations
... for this research consists of extending the proposed methodologies to the mixed effects model framework where data are subject to missingness and censoring. One of the main advantages of BEAM and SEAM is that ...
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Some problems in estimation in mixed linear models
... Figure 4.1 gives the parameter estimates for the species richness data, along with standard errors calculated according to the results in Section 4.4. Figure 4.2 displays the parameter estimates and standard errors for ...
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Multiple testing correction in linear mixed models
... power of GWAS [31, 34, 35]. Due to these desirable properties, LMM has become a widely used method in current GWAS [36–40]. However, the current approaches for multiple hypothe- sis testing correction cannot be applied ...
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Multivariate linear mixed models for statistical genetics
... The success of the first genome-wide association studies, which uncovered common variants with moderate effect sizes for traits such as type 2 diabetes (Scott et al., 2007) and coronary artery disease (Burton et al., ...
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Multiple testing correction in linear mixed models.
... power of GWAS [31, 34, 35]. Due to these desirable properties, LMM has become a widely used method in current GWAS [36–40]. However, the current approaches for multiple hypothe- sis testing correction cannot be applied ...
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Bayesian linear mixed models with polygenic effects
... 2009 ; Damgaard 2007 ) they have great difficulty in dealing with large genomic matrice(s). We therefore exploited matrix decomposition and parallel computation. We also compiled JAGS using both LAPACK and Intel MKL. ...
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Actuarial Statistics With Generalized Linear Mixed Models
... generalized linear models (GLMs) in actuarial statis- tics received a lot of attention, starting from the actuarial illustrations in the stan- dard text by McCullagh & Nelder ...generalized ...
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Model selection in linear mixed effect models
... In general, the proposed method enjoys many advantages over the classical likelihood based approaches. Compared to the classical likelihood approach, this procedure has greater computational stability since it avoids the ...
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Inference for generalised linear mixed models with sparse structure
... generalised linear mixed ...generalised linear mixed model, there is a parameter ψ controlling how the random effects enter into the linear ...a non-negligible proportion of the ...
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The performance of estimation methods for generalized linear mixed models
... Generalised linear models (GLMs) are a flexible class of non-linear models for non- normally distributed response ...encompass models for discrete response data which ...
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An efficient computing strategy for prediction in mixed linear models
... in linear mixed ...balanced linear mixed models with several random terms which can be analysed by ANOVA, is to replace predictions by treatment ...or non-orthogonal data ...
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Robust MM-Estimation and Inference in Mixed Linear Models
... ”worst-case” scenario. Differences with the first simulation can be noticed as the robust Wald test based on the Huber estimator is now also biased. One single extreme observation in the factor space can drive the level ...
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Generalized Linear Mixed Models
... This example shows that the significance of model terms can depend on the structure of the random effects. Thus, one must decide upon a reasonable model for the random effects as well as for the fixed effects. A commonly ...
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