umu.sePublications
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
Inference for partial correlation when data are missing not at random
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)ORCID iD: 0000-0003-2135-9963
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)ORCID iD: 0000-0003-3187-1987
2018 (English)In: Statistics and Probability Letters, ISSN 0167-7152, E-ISSN 1879-2103, Vol. 141, p. 82-89Article in journal (Refereed) Published
Abstract [en]

We introduce uncertainty regions to perform inference on partial correlations when data are missing not at random. These uncertainty regions are shown to have a desired asymptotic coverage. Their finite sample performance is illustrated via simulations and real data example. (C) 2018 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2018. Vol. 141, p. 82-89
Keywords [en]
Nonignorable dropout, Uncertainty region, Change-change analysis, Brain markers, Cognition
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-151035DOI: 10.1016/j.spl.2018.05.027ISI: 000440961600011Scopus ID: 2-s2.0-85048717692OAI: oai:DiVA.org:umu-151035DiVA, id: diva2:1245086
Available from: 2018-09-04 Created: 2018-09-04 Last updated: 2019-04-26Bibliographically approved
In thesis
1. Methods for longitudinal brain imaging studies with dropout
Open this publication in new window or tab >>Methods for longitudinal brain imaging studies with dropout
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Metoder för longitudinella hjärnavbildningsstudier med bortfall
Abstract [en]

One of the challenges in aging research is to understand the brain mechanisms that underlie cognitive development in older adults. Such aging processes are investigated in longitudinal studies, where the within-individual changes over time are observed. However, several methodological issues exist in longitudinal analyses.  One of them is loss of participants to follow-up, which occurs when individuals drop out from the study. Such dropout should be taken into account for valid conclusions from longitudinal investigations, and this is the focus of this thesis. The developed methods are used to explore brain aging and its relation to cognition within the Betula longitudinal study of aging.

Papers I and II consider the association between changes in brain structure and cognition. In the first paper, regression analysis is used to establish the statistical significance of brain-cognition associations while accounting for dropout. Paper II develops interval estimators directly for an association as measured by partial correlation, when some data are missing. The estimators of Paper II may be used in longitudinal as well as cross-sectional studies and are not limited to brain imaging. 

Papers III and IV study functional brain connectivity, which is the statistical dependency between the functions of distinct brain regions. Typically, only brain regions with associations stronger than a predefined threshold are considered connected. However, the threshold is often arbitrarily set and does not reflect the individual differences in the overall connectivity patterns.  Paper III proposes a mixture model for brain connectivity without explicit thresholding of associations and suggests an alternative connectivity measure. Paper IV extends the mixture modeling of Paper III to a longitudinal setting with dropout and investigates the impact of ignoring the dropout mechanism on the quality of the inferences made on longitudinal connectivity changes.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2019. p. 20
Series
Statistical studies, ISSN 1100-8989 ; 54
Keywords
Missing data, nonignorable dropout, sensitivity analysis, uncertainty intervals, pattern-mixture models, aging, cognition, MRI, brain structure, resting-state functional connectivity
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-155680 (URN)978-91-7855-011-1 (ISBN)
Public defence
2019-02-22, Hörsal 1031, Norra Beteendevetarhuset, Umeå University, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2019-02-01 Created: 2019-01-25 Last updated: 2019-04-26Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Gorbach, Tetianade Luna, Xavier

Search in DiVA

By author/editor
Gorbach, Tetianade Luna, Xavier
By organisation
Statistics
In the same journal
Statistics and Probability Letters
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 239 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