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+44 28 3718 0055Journal of Applied Bioinformatics & Computational BiologyISSN: 2329-9533
Kendra K. Schmid Author
Subjects of specialization
Affiliation
Statistical Shape Analysis, Biostatistics, Landmark Data, Face Recognition Applications
Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, 68198, USA
Biography
Kendra K. Schmid Works as a Assistant Vice Chancellor of Academic Affairs and Assistant Dean of Graduate Studies, Professor from Department of Biostatistics. He has received his PhD Statistics from University of Nebraska-Lincoln. He persude MA Educational Administration from University of Nebraska-Lincoln.
Publications
Editorial Subscription
Missing Data: A Non-ignorable Issue in Modern Biostatistics
Author(s): Baojiang Chen and Kendra K. Schmid
Missing Data: A Non-ignorable Issue in Modern Biostatistics
Missing data is a common feature in modern biostatistics. For most cases, the easily implemented method such as complete case analysis will yield biased conclusions if data are Missing At Random (MAR) or Missing Not At Random (MNAR). The vast majority of articles in the literature dealing with incomplete data make the unrealistic assumption that data are available for the response but incomplete for the covariates or risk factors, or alternatively, they assume that information on risk factors is complete, but the data on response are incomplete.
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