JSTOR Daily

ADDRESSING MISSING DATA MECHANISM UNCERTAINTY USING MULTIPLE-MODEL MULTIPLE IMPUTATION: APPLICATION TO A LONGITUDINAL CLINICAL TRIAL

This is a preview. Log in through your library . Abstract We present a framework for generating multiple imputations for continuous data when the missing data mechanism is unknown. Imputations are ...
JSTOR Daily

Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs

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 ...

AI method recovers missing movement data to enhance behavioral analysis

Scientists learn about the brain's inner workings by studying what animals or people do, how they move, react, and make ...
CU Boulder News & Events

Data Analytics Best Practices: Best Machine Learning Model for Sparse Data

Sparse data can impact the effectiveness of machine learning models. As students and experts alike experiment with diverse datasets, sparse data poses a challenge. The Leeds Master’s in Business ...
Booth School of Business

A Better Way for Finance (and Others) to Handle Missing Data

There are data about practically everything these days, and they can be used to try to answer any number of questions. Do clinical trials really show a drug works? Can surveys really signal who’s ...