results concerning the correct weights for bias reduction under various sampling schemes are provided and the asymptotic … reported and it is found that the jackknife can deliver substantial reductions in bias in autoregressive models. This finding …

better than some existing methods in terms of bias and root mean squared error (RMSE). Finally, two examples based on real …

data analysis. This article compares the two procedures with respects to bias and efficiency of parameter estimates. It …, estimates of variance -covariance parameters by MI may contain severe bias even at relative large sample sizes. Although … performing a lot better, ML parameter estimates may also contain substantial bias at smaller sample sizes. The results also …

identifies unconscious bias as a source of inaccuracy in the credit ratings process. It examines prior behavioral research on … unconscious bias, and uses this research to identify structural issues within the credit ratings industry that give rise to biased … offers additional reforms aimed at combating unconscious bias. Findings – Recent government reforms will be most effective in …

A contest model is constructed to examine the existence of conference bias in college basketball's Ratings Percentage … Index (RPI). Though a general RPI bias has been identified in previous literature, this is the first study to address … whether the bias is random or systematic in nature. Within the theoretical model, the RPI is shown to be systematically biased …

of pro-incumbent bias can also be useful for maintaining high levels of control. When the equilibrium level of control is …