The Originative Statistical Regression Models: Are They Too Old and Untenable?

Statistical ordinary least squares (OLS) regression, and logistic regression (LR) models are popular techniques for prediction (of a continuous dependent variable) or classification (of a categorical dependent variable). They are the workhorses of statistical modeling as their results are considered the gold standard. Moreover, they are used as the benchmark for assessing the superiority of newer techniques, such as a genetic logistic regression model (www.geniq.net/res/GeneticLRM.html).

OLS regression method was first published by Legendre on March 6, 1805. The LR model was developed by Berkson in 1944. Something old is not necessarily useless today, and something new is not necessarily better than something old is. Lest one forgets, "the wheel and writing." The statistical regression paradigm, which dictates "fitting the data to a pre-specified model," is old as it was developed, and tested within the small data setting of the day, and has been shown untenable for today's big data. I maintain the new machine-learning genetic paradigm, which "lets the data define the model," is especially effective for today's big data, and outdoes the originative statistical regression models.

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