Estimating the number of constraints an the prediction function
Shahar Boneh, Arnon Boneh, Richard J. Caron · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1994
After N iterations of the stand and hit algorithm we are interested in estimating the expected number of as yet undetected necessary constraints, and the expected number of new necessary that will be detected if an additional tN, t a positive real, iterations are completed. Viewed as a function of t, the latter is known as the prediction function. Such estimates can be used to obtain a stopping rule for the stand and hit algorithm. We provide a new estimator for the prediction function, and show why it is an improvement over the Efron and Thisted estimate.