Correlation estimation with singly truncated bivariate data

Jongho Im, Eunyong Ahn, Namseon Beck, Jae Kwang Kim, Taesung Park

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Correlation coefficient estimates are often attenuated for truncated samples in the sense that the estimates are biased towards zero. Motivated by real data collected in South Sudan, we consider correlation coefficient estimation with singly truncated bivariate data. By considering a linear regression model in which a truncated variable is used as an explanatory variable, a consistent estimator for the regression slope can be obtained from the ordinary least squares method. A consistent estimator of the correlation coefficient is then obtained by multiplying the regression slope estimator by the variance ratio of the two variables. Results from two limited simulation studies confirm the validity and robustness of the proposed method. The proposed method is applied to the South Sudanese children's anthropometric and nutritional data collected by World Vision.

Original languageEnglish
Pages (from-to)1977-1988
Number of pages12
JournalStatistics in Medicine
Volume36
Issue number12
DOIs
Publication statusPublished - 2017 May 30

Bibliographical note

Funding Information:
We thank two anonymous referees and the AE for very helpful comments. The work was conducted when the fourth author, Kim, visited Taesung Park through the Brain Pool Program in Korea. This research was mainly supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI16C2037). The research of the fourth author was partially supported by the US National Science Foundation (MMS-1324922).

Publisher Copyright:
Copyright © 2017 John Wiley & Sons, Ltd.

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

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