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Thus, our model regression estimate is: bar{r} (vec{X}) = mathbb{E}_{Theta}bigl(r_{n} vec{X}, Theta bigr).
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Table 3 exhibits the model regression estimates using the different set of predictors on EQ-5D disutility.
Straight arrows in the model represent (causal) regression estimates, and curved arrows represent correlations (associations).
In particular, we employ an adaptive regression model to estimate the diversity of a diverse subset.
Toward this end, we used a quantile regression model to estimate risk.
In addition, we present the results of regression model to estimate location rating without division into two classes.
Then we fitted an ordinary least squares regression model to estimate b1.
We developed a non-parsimonious multivariable logistic regression model to estimate a propensity score for echocardiography.
Data were analysed using a logistic regression model to estimate the likelihood of long delay.
44 This technique uses a regression model to estimate missing values from known values.
Disease prevalence was modelled by population-averaged Poisson regression models estimated by generalised estimating equations.
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