The “Continuous Monotone Convexity (CMC)” Property and its ‎Implications to Statistical Modeling

In a previous post in this series, I have discussed reasons for the effectiveness of the Box-Cox (BC) transformation, particularly when applied to a response variable within linear regression analysis. The final conclusion was that this effectiveness could probably be attributed to the “Continuous Monotone Convexity (CMC)” property, owned by the inverse BC transformation. It was emphasized that the latter, comprising the three most fundamental monotone convex functions, the “linear-power-exponential” trio, delivers only partial representation to a whole host of models of monotone convex relationships, which can be arranged in a hierarchy of monotone convexity. This hierarchy had been denoted the “Ladder of Monotone Convex Functions.”

In this post (and Article #2, linked below), I address in more detail the nature of the CMC property. I specify models included in the Ladder, and show how one can deliver, via a single model, representation to all models belonging to the Ladder (analogously with the inverse BC transformation, a special case of that model). Furthermore, I point to published evidence demonstrating that models of the Ladder may often substitute, with negligible loss in accuracy, published models of monotone convexity, which had been derived from theoretical discipline-specific considerations.

This blog entry (with the linked article given below) was originally posted on the site of the American Statistical Association (ASA), where the linked article was visible to members only.

Haim Shore_2_ASA_Dec 2013

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