The analysis of missing data encompasses a broad spectrum of statistical methods designed to assess, mitigate, and rigorously quantify the uncertainty that arises when data points are absent.

In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression ...

Next, we will briefly cover early methods for handling missing data, such as complete case analysis and single imputation techniques (mean, hot deck, etc.), and why in practice they can produce ...

Fourth, the preferred methods of analysis (e.g., weighted estimating equations and multiple imputation) often involve complex modeling. It will be important for Journal authors who use these ...

During his doctoral studies, Rubin grew interested in the missing data problem. Though single imputation avoided the bias of complete case analysis, Rubin saw that it had its own flaw: overconfidence.

Multiple imputation is a principled approach to account for missing data in analyses where valid results depends on careful construction of the imputation model. The potential for misspecification of ...

Indeed, gut-based analysis may be even worse these days than it was in the past, given how much economic views are affected by partisanship: Republicans rate the economy worse now than they did in ...