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 ...
Missing data imputation is a critical process in data analysis, enabling researchers to infer plausible values for absent observations. Over recent decades, a variety of methods have emerged, ranging ...
Missing data present a perennial challenge in scientific research, potentially undermining the validity of conclusions if not addressed rigorously. The analysis of missing data encompasses a broad ...
Missing rainfall data are a major limitation for distributed hydrological modeling and climate studies. Practitioners need reliable approaches that can be employed on a daily basis, often with too ...
Predictive mean matching (PMM) is a standard technique for the imputation of incomplete continuous data. PMM imputes an actual observed value, whose predicted value is among a set of k≥1 values (the ...
Haewon Jeong, an assistant professor in UC Santa Barbara’s Electrical and Computer Engineering (ECE) Department, experienced a pivotal moment in her academic career when she was a postdoctoral fellow ...
NEW YORK (Reuters) -U.S. bond firm DoubleLine said on Wednesday it is using a variety of official and private data sources to ...
A research briefing by DoubleLine Fixed Income Allocation Strategist Ryan Kimmel explores the growing challenges faced by the ...
When it comes to economic assessment, feelings are no substitute for hard data. A plurality of Americans say that we’re in a recession; the actual numbers on jobs and gross domestic product show an ...
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 ...
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