The -level Conditional Tail Expectation (CTE) of a continuous random variable X is defined as its conditional expectation given the event {X q }, where q represents its -level quantile. It is well known that the empirical CTE (the average of the n(1 ) largest order statistics in a sample of size n) is a negatively biased estimator of the CTE. This bias vanishes as the sample size increases but ...

This paper presents a Bayesian additive “correction” to the familiar quadratic loss biasplus-variance formula. It then discusses some other loss-function-specific aspects of supervised learning. It ends by presenting a version of the bias-plus-variance formula appropriate for log loss, and then the Bayesian additive correction to that formula. Both the quadratic loss and log loss correction ter...

We analyze the bias correction methods using jackknife, bootstrap, and Taylor series. We focus on the binomial model, and consider the problem of bias correction for estimating f(p), where f ∈ C[0, 1] is arbitrary. We characterize the supremum norm of the bias of general jackknife and bootstrap estimators for any continuous functions, and demonstrate the in deleted jackknife, different values o...

Recent research findings combined with the theoretical laws of biological similarity make the compelling case that all physical fitness test items for the Army, Air Force, and Navy impose a 15-20% physiologic bias against heavier, not fatter, men and women. Using the published findings that actual scores of muscle and aerobic endurance scale by body mass raised to the 1/3 power, correction fact...

OBJECTIVE The aim was to examine whether design features of Wave 1, 1980 National Incidence Study (NIS) data resulted in sample selection bias when certain victims of maltreatment were excluded. METHOD Logistic regression models for the probability of child abuse reports to the child protective services (CPS) were estimated using maximum likelihood methods for Black (n = 511) and White (n = 2...

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By constructio...

When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during...