Research data collected from single respondents may raise concerns regarding common method variance (CMV), which is believed to threaten the validity of findings. The primary concern that CMV can inflate substantive relationships, such they appear statistically significant when are not. Thus, understanding nature critical, especially one considers popularity—and sometimes necessity—of using sel...

In this paper we present an overview of classical results about the variance reduction technique of control variates. We emphasize aspects of the theory that are of importance to the practitioner, as well as presenting relevant applications.

Variance-based thresholding methods could be biased from the threshold found by expert and the underlying mechanism responsible for this bias is explored in this paper. An analysis on the minimum class variance thresholding (MCVT) and the Otsu method, which minimizes the within-class variance, is carried out. It turns out that the bias for the Otsu method is due to differences in class variance...

We show how the benefits of the pathwise sensitivity approach to computing Monte Carlo Greeks can be extended to discontinuous payoff functions through a combination of the pathwise approach and the Likelihood Ratio Method. With a variance reduction modification, this results in an estimator which for timestep h has a variance which is O(h−1/2) for discontinuous payoffs and O(1) for continuous ...

In this paper the performance of bagging in classification problems is theoretically analysed, using a framework developed in works by Tumer and Ghosh and extended by the authors. A bias-variance decomposition is derived, which relates the expected misclassification probability attained by linearly combining classifiers trained on N bootstrap replicates of a fixed training set to that attained ...