Irrational Exuberance: Correcting Bias in Probability Estimates

10/29/2019
by   Gareth M. James, et al.
0

We consider the common setting where one observes probability estimates for a large number of events, such as default risks for numerous bonds. Unfortunately, even with unbiased estimates, selecting events corresponding to the most extreme probabilities can result in systematically underestimating the true level of uncertainty. We develop an empirical Bayes approach "Excess Certainty Adjusted Probabilities" (ECAP), using a variant of Tweedie's formula, which updates probability estimates to correct for selection bias. ECAP is a flexible non-parametric method, which directly estimates the score function associated with the probability estimates, so it does not need to make any restrictive assumptions about the prior on the true probabilities. ECAP also works well in settings where the probability estimates are biased. We demonstrate through theoretical results, simulations, and an analysis of two real world data sets, that ECAP can provide significant improvements over the original probability estimates.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/27/2013

Objective Probability

A distinction is sometimes made between "statistical" and "subjective" p...
research
10/21/2021

Imprecise Subset Simulation

The objective of this work is to quantify the uncertainty in probability...
research
03/05/2019

A Prediction Tournament Paradox

In a prediction tournament, contestants "forecast" by asserting a numeri...
research
12/14/2021

Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning

Despite the empirical success of meta reinforcement learning (meta-RL), ...
research
12/02/2013

The Law of Total Odds

The law of total probability may be deployed in binary classification ex...
research
11/15/2013

On Estimating Many Means, Selection Bias, and the Bootstrap

With recent advances in high throughput technology, researchers often fi...

Please sign up or login with your details

Forgot password? Click here to reset