The R Journal: article published in 2018, volume 10:2

Profile Likelihood Estimation of the Correlation Coefficient in the Presence of Left, Right or Interval Censoring and Missing Data
Yanming Li, Brenda W. Gillespie, Kerby Shedden and John A. Gillespie , The R Journal (2018) 10:2, pages 159-179.

Abstract We discuss implementation of a profile likelihood method for estimating a Pearson correla tion 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 KiB)
CRAN packages: clikcorr, survival, mvtnorm
CRAN Task Views implied by cited CRAN packages: ClinicalTrials, Distributions, Econometrics, Finance, Multivariate, SocialSciences, Survival

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}},
  doi = {10.32614/RJ-2018-040},
  url = {https://doi.org/10.32614/RJ-2018-040},
  pages = {159--179},
  volume = {10},
  number = {2}
}