One of the most common approaches of regression coefficient estimation is the Least Squares (LS) method.
In this paper, however, we consider a machine-learning approach of logistic regression for location classification.
Afterwards, the models developed using the new approach were compared with the models obtained using only the common modelling approach of the regression analysis.
The table shows the comparison of the Bayesian ZIP and Classical ZIP approach of count regression model with their respective error and with a number of significant variables.
Our multilocus analysis strategy makes use of the traditional parametric approach of logistic regression and the nonparametric method of multifactor dimensionality reduction (MDR) [27].
We therefore adopted the statistical approach of linear regression with a generalized least-squares (GLS) extension [ 24- 26], which allows heteroscedastic variances (unequal variances among treatment combinations) to be modelled as a variance covariance matrix [ 25, 26].
This finding is supported by the STEPP analysis and we observed the same effect with the classical approach of logistic regression, which also showed a significant effect of HER2 mRNA levels (measured as a continuous variable) on pCR only in the ESR1-positive/HER2-positive group.
Given randomized assignment, no confounding is expected except due to dropout, which is accommodated by using baseline variables as covariates and including all randomized children into the outcome analyses (intention-to-treat analysis) using the direct Likelihood approach of mixed regression, which requires no imputation of missing outcome values.
Our primary intention in this paper is to identify the strength of these effects, but the reduced-form approach of these regressions may naturally be subject to criticisms despite being a common practice in the related literature on the economics of suicide.
