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Data were analyzed using a multivariate generalized additive model accounting for the sampling design and the sampling weights.
Relative excess risks of death (RER) were estimated using a multivariate generalized linear model with a Poisson distribution, based on collapsed relative survival data, using exact survival times.
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To overcome these limitations and add to existing knowledge on the effects of MNA on cost, we used a multivariate, generalized, linear, mixed model (mGLMM) approach (which accounts for the shared correlation among cost variables) to estimate the inpatient, outpatient, and pharmacy-related costs in a longitudinal cohort of 740,195 U.S. veterans with type 2 diabetes.
saplings were analysed using a multivariate method.
4) Statistical classification using a multivariate Gaussian classifier.
Independent risk factors were analysed using a multivariate logistic regression.
In particular, we employ a multivariate generalized autoregressive conditionally heteroskedastic (MVGARCH) model involving time-varying settings and multivariate Markov switching autoregressive conditionally heteroskedastic (MVSWARCH) model with regime-switching techniques and compare them with a conventional linear regression-based (LRB) model.
This matrix was used to specify the variance structure in a multivariate generalized linear model.
RERs were estimated by a multivariate generalized linear model with a Poisson error structure, which was based on collapsed relative survival data, by using exact survival times.
Methodologically, we linked a multivariate generalized autoregressive conditional heteroscedasticity model (mGARCH (1,1)) with three global land use scenarios that are of strategic importance to the forest bioeconomy.
Therein, Lange et al. [10] proposed a multivariate generalized estimating equations (GEEs) based method, termed FBAT-GEE.
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