title: Likelihood-based confidence intervals for a log-normal mean.
abstract: To construct a confidence interval for the mean of a log-normal distribution in small samples, we propose likelihood-based approaches - the signed log-likelihood ratio and modified signed log-likelihood ratio methods. Extensive Monte Carlo simulation results show the advantages of the modified signed log-likelihood ratio method over the signed log-likelihood ratio method and other methods. In particular, the modified signed log-likelihood ratio method produces a confidence interval with a nearly exact coverage probability and highly accurate and symmetric error probabilities even for extremely small sample sizes. We then apply the methods to two sets of real-life data.
authors.affiliation: Department of Biostatistics, St Jude Children's Research Hospital, 332 North Lauderdale Street, Memphis, TN 38105, USA. jianrong.wu@stjude.org
authors.forename: Jianrong
authors.initials: J
authors.lastname: Wu
authors.forename: A C M
authors.initials: AC
authors.lastname: Wong
authors.forename: Guoyong
authors.initials: G
authors.lastname: Jiang
mesh_terms: D035843:Biomedical Research; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006302:Health Services Research; D006801:Humans; D016013:Likelihood Functions; D009010:Monte Carlo Method; D011336:Probability; D014481:United States