Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models

This article develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the number of missing entries is small enough compared to the panel size, then they can be estimated w...

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Bibliographische Detailangaben

Hauptverfasser:	Choi, Jungjun, Yuan, Ming
Format:	Dataset
Sprache:	eng
Schlagworte:	Biotechnology Computational Biology Developmental Biology FOS: Health sciences FOS: Sociology Infectious Diseases Medicine Sociology
Online-Zugang:	Volltext bestellen
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