For the staged regression model, the genome was divided into consecutive windows of size (2 + 1) = 129 base pairs.
We first construct a staged regression model using a large set of human ENCODE [ 29] ChIP-seq datasets (123 in total) from the three Tier 1 cell lines to quantify the ChIP-seq signal in terms of five predictors: Mappability, GC-content, chromatin accessibility, input DNA control, and IgG control.
Researchers could address this methodological gap of underestimating the endogeneity in context items by resorting more often to multiple-stage regression models, using instrumental variables to secure the correct model specifications.
Importantly, however, it is necessary to include instrumental variables in the first-stage regression (Model (1)).
This regression is described in Eq. (3 1): First-stage regression model: Zi=beta 0+beta 1. INCOMEi+varSigma Xi+varepsilon i (3 1).
A comprehensive framework composed of a carefully designed two-stage regression model is employed to estimate both the direct and indirect effects of demographic changes on CO2 emissions.
In order to take into account the pre-existing abilities of exposed and reference workers, and investigate the neurobehavioral changes over time, longitudinal data were analyzed by a two-stage regression model and analysis of variance for repeated measures (MANOVA).
The sign, magnitude and significance of almost all variables are similar in all models and databases, implying that any educational policy recommendations derived from them would be basically the same regardless the second-stage regression model finally chosen adding robustness to these findings.
Specifically, we design three types of low-level statistical features in both spatial and frequency domains to quantify super-resolved artifacts, and learn a two-stage regression model to predict the quality scores of super-resolution images without referring to ground-truth images.
Our model, incorporating MMP9, age, gender, weight loss, blood in the stool, harder stools, anal pain, white blood count, smoking and alcohol, achieved a sensitivity of 79% and a specificity of 70%, yet this sensitivity and specificity was achievable only via a relatively complex two-stage regression model.
We used the Expectation-Maximization algorithm as a part of a two-stage regression model to handle diverse age data and overall incidence rate variation by study to generate country specific incidence rates for low- and middle-income countries for 1990 and 2010.
