Interval estimation for the ratio in means of log-normally distributed medical costs with zero values

Xiao Hua Zhou, Wanzhu Tu

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

We consider the problem of constructing confidence intervals for the ratio in the means of two independent populations which contain both log-normal and zero observations. We propose a maximum likelihood (ML)-based method and a two-stage bootstrap approach. We also conduct an extensive simulation study to evaluate coverage accuracy, interval width, and relative bias of the proposed methods. The simulation results indicate that when the two population skewness coefficients are the same, the ML-based interval has better coverage accuracy but is more biased than the bootstrap-based interval; when the two population skewness coefficients are different, the bootstrap-based interval has better coverage accuracy and is less biased than the ML-based interval. Finally, we analyze the charges for diagnostic tests in a study that assesses the relationship between the excess charges among older patients and the burden of their medical illness, and we find that these two are related.

Original languageEnglish
Pages (from-to)201-210
Number of pages10
JournalComputational Statistics and Data Analysis
Volume35
Issue number2
DOIs
StatePublished - Dec 28 2000

Fingerprint

Interval Estimation
Maximum likelihood
Interval
Bootstrap
Costs
Zero
Maximum Likelihood
Coverage
Skewness
Biased
Charge
Diagnostic Tests
Coefficient
Excess
Confidence interval
Interval estimation
Medical costs
Simulation Study
Evaluate
Coefficients

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Numerical Analysis
  • Statistics and Probability

Cite this

Interval estimation for the ratio in means of log-normally distributed medical costs with zero values. / Zhou, Xiao Hua; Tu, Wanzhu.

In: Computational Statistics and Data Analysis, Vol. 35, No. 2, 28.12.2000, p. 201-210.

Research output: Contribution to journalArticle

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