Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Estimation of Regression Coefficients under a Truncated Covariate with Missing Values
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

By means of a Monte Carlo study, this paper investigates the relative performance of Listwise Deletion, the EM-algorithm and the default algorithm in the MICE-package for R (PMM) in estimating regression coefficients under a left truncated covariate with missing values. The intention is to investigate whether the three frequently used missing data techniques are robust against left truncation when missing values are MCAR or MAR. The results suggest that no technique is superior overall in all combinations of factors studied. The EM-algorithm is unaffected by left truncation under MCAR but negatively affected by strong left truncation under MAR. Compared to the default MICE-algorithm, the performance of EM is more stable across distributions and combinations of sample size and missing rate. The default MICE-algorithm is improved by left truncation but is sensitive to missingness pattern and missing rate. Compared to Listwise Deletion, the EM-algorithm is less robust against left truncation when missing values are MAR. However, the decline in performance of the EM-algorithm is not large enough for the algorithm to be completely outperformed by Listwise Deletion, especially not when the missing rate is moderate. Listwise Deletion might be robust against left truncation but is inefficient. 

Place, publisher, year, edition, pages
2019. , p. 54
Keywords [en]
Key words: missing data handling, linear regression, truncated normal distribution, EM-algorithm, Listwise Deletion, MICE
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-385672OAI: oai:DiVA.org:uu-385672DiVA, id: diva2:1325332
Subject / course
Statistics
Educational program
Freestanding course
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-15 Last updated: 2019-06-24Bibliographically approved

Open Access in DiVA

fulltext(8364 kB)20 downloads
File information
File name FULLTEXT01.pdfFile size 8364 kBChecksum SHA-512
c022c34eccd82aa2eed1420019ed79dd361ddd864a79e330243816c2e09c7ea4ad87926e3bc28c175f360769eaede7d74cc96ea23d18e1d158b44bc0e66ed240
Type fulltextMimetype application/pdf

By organisation
Department of Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 20 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 43 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf