The R Journal: accepted article

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Profile Likelihood Estimation of the Correlation Coefficient in the Presence of Left, Right or Interval Censoring and Missing Data PDF download
Yanming Li, Brenda W. Gillespie, Kerby Shedden and John A. Gillespie

Abstract We discuss implentation of a profile likelihood method for estimating a Pearson correlation coefficient from bivariate data with censoring and/or missing values. The method is implemented in an R package clikcorr which calculates maximum likelihood estimates of the correlation coefficient when the data are modeled with either a Gaussian or a Student t-distribution, in the presence of left, right, or interval censored and/or missing data. The R package includes functions for conducting inference and also provides graphical functions for visualizing the censored data scatter plot and profile log likelihood function. The performance of clikcorr in a variety of circumstances is evaluated through extensive simulation studies. We illustrate the package using two dioxin exposure datasets.

Received: 2017-10-01; online 2018-08-17, supplementary material, (3 Kb)
CRAN packages: clikcorr, survival, mvtnorm
CRAN Task Views implied by cited CRAN packages: ClinicalTrials, Distributions, Econometrics, Finance, Multivariate, SocialSciences, Survival


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-040,
  author = {Yanming Li and Brenda W. Gillespie and Kerby Shedden and
          John A. Gillespie},
  title = {{Profile Likelihood Estimation of the Correlation Coefficient
          in the Presence of Left, Right or Interval Censoring and
          Missing Data}},
  year = {2018},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-040/index.html}
}