In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression — cannot ...
Feed-forward neural networks (FFNNs) have received considerable attention due to their successful use in a wide variety of statistical applications, including regression and classification problems. A ...
Missing data can plague researchers in many scenarios, arising from incomplete surveys, experimental objects broken or destroyed, or data collection/computational errors. This short course will ...
Data is almost always incomplete. Patients drop out of clinical trials and survey respondents skip questions; schools fail to report scores, and governments ignore elements of their economies. When ...
Multiple imputation (MI) can be used to address missing data at Level 2 in multilevel research. In this article, we compare joint modeling (JM) and the fully conditional specification (FCS) of MI as ...
We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS ...
The US government has reopened following its longest-ever shutdown, setting the stage for the eventual release of the ...
At a crucial moment for the US economy, Federal Reserve officials are preparing to decide whether to keep lowering interest rates or hold them steady — but they will have to do so with limited fresh ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback