Guideline on Missing Data in Confirmatory Clinical Trials Discussion in the Efficacy Working Party June 1999/ November 2000 Transmission to CPMP January 2001 Released for consultation January 2001 Deadline for Comments April 2001 Discussion in the Efficacy Working Party October 2001 Transmission to CPMP November 2001 Adoption by CPMP November 2001 Draft Rev. 1 Agreed by Efficacy Working Party A...

This paper considers the problem of estimating the population mean using the ratio and product methods when some observations in the sample data are missing at random and the population mean of the auxiliary characteristic is not known. Besides an unbiased estimator arising from the total discard of incomplete pairs of observations, four generally biased estimators are presented. The rst two es...

0198-9715/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.compenvurbsys.2012.08.00 ⇑ Corresponding author. Tel.: +44 781 607 6958. E-mail addresses: [email protected] (J. Ha (T. Cheng). 1 Tel.: +44 781 56

The need to predict or fill-in missing data, often referred to as matrix completion, is a common challenge in today’s data-driven world. Previous strategies typically assume that no structural difference between observed and missing entries exists. Unfortunately, this assumption is woefully unrealistic in many applications. For example, in the classic Netflix challenge, in which one hopes to pr...

Incomplete information in a soft set restricts the usage of the soft set. To make the incomplete soft set more useful, in this paper, we propose a data filling approach for incomplete soft set in which missing data is filled in terms of the association degree between the parameters when stronger association exists between the parameters or in terms of the probability of objects appearing in the...