When researchers verify the effects of genes or gene–environment interactions on common diseases or age-related diseases, such as diabetes, cancer, or coronary heart disease, they usually regard age ...
When the response functions are the default generalized logits, then inclusion of the keyword _RESPONSE_ in every effect in the right-hand side of the MODEL statement induces a log-linear model. The ...
Abstract: Gaussian graphical models are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using high-throughput sequencing technologies to ...
Generalized linear mixed models (GLMM) are useful in a variety of applications. With surrogate covariate data, existing methods of inference for GLMM are usually computationally intensive. We propose ...
A hybrid method that combines Laplace's approximation and Monte Carlo simulations to evaluate integrals in the likelihood function is proposed for estimation of the parameters in nonlinear mixed ...
The correlations between the counts are modeled as , (exchangeable correlations). For comparison, the correlations are also modeled as independent (identity correlation matrix). In this model, the ...
Abstract: Based on the problem how to model Seemingly Unrelated linear models with different batches of samples, the paper raises the method of getting rid of the stale and taking in the fresh. The ...
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