Is it possible to do model selection in this way? Suppose I need to select a good (logistic) model among three variables (var1, var2, var3). The deviance D* (-2*log-likelihood) of this full model would be the minimum among all possible models. Then I could try all 6 combination of sub-models(1,2,3,12,13,23) and compute their deviance D1~D6. Next I compute the difference: deltaD_i=D_i-D*, this should follow chi-square distribution with df=differences_in_variables_numbers. The models with deltaD within 95% confidence interval of D* would be within the confidence interval of the full model, that is, the variance explained by the reduced model is not significantly different than the full model. Then we could accept these model as good models. By doing this, we could end of with several "good" models.
Is this somehow a possible way to do model selection?