Statistical Evidence Measured on a Properly Calibrated Scale across Nested and Non-nested Hypothesis Comparisons

Entropy

Veronica J. Vieland²  Sang-Cheol Seok²  Carlos Alberto De Bragan๺ Pereira¹ 

[1] Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children’s Hospital, 575 Children’s Crossroad, Columbus, OH 43215, USA; E-Mail;Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children’s Hospital, 575 Children’s Crossroad, Columbus, OH 43215, USA; E-Mail:

关键词: statistical evidence;    information dynamics;    thermodynamics;   

DOI  :  10.3390/e17085333

来源: mdpi

【 摘 要 】

Statistical modeling is often used to measure the strength of evidence for or against hypotheses about given data. We have previously proposed an information-dynamic framework in support of a properly calibrated measurement scale for statistical evidence, borrowing some mathematics from thermodynamics, and showing how an evidential analogue of the ideal gas equation of state could be used to measure evidence for a one-sided binomial hypothesis comparison (“coin is fair” vs. “coin is biased towards heads”). Here we take three important steps forward in generalizing the framework beyond this simple example, albeit still in the context of the binomial model. We: (1) extend the scope of application to other forms of hypothesis comparison; (2) show that doing so requires only the original ideal gas equation plus one simple extension, which has the form of the Van der Waals equation; (3) begin to develop the principles required to resolve a key constant, which enables us to calibrate the measurement scale across applications, and which we find to be related to the familiar statistical concept of degrees of freedom. This paper thus moves our information-dynamic theory substantially closer to the goal of producing a practical, properly calibrated measure of statistical evidence for use in general applications.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.