title: Nonparametric confidence intervals for the one- and two-sample problems.
abstract: Confidence intervals for the mean of one sample and the difference in means of two independent samples based on the ordinary-t statistic suffer deficiencies when samples come from skewed families. In this article we evaluate several existing techniques and propose new methods to improve coverage accuracy. The methods examined include the ordinary-t, the bootstrap-t, the biased-corrected acceleration and three new intervals based on transformation of the t-statistic. Our study shows that our new transformation intervals and the bootstrap-t intervals give best coverage accuracy for a variety of skewed distributions, and that our new transformation intervals have shorter interval lengths.
authors.affiliation: Department of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195, USA. azhou@u.washington.edu
authors.forename: Xiao Hua
authors.initials: XH
authors.lastname: Zhou
authors.forename: Phillip
authors.initials: P
authors.lastname: Dinh
mesh_terms: D003198:Computer Simulation; D016001:Confidence Intervals; D003863:Depression / Q000191:economics; D003955:Diagnostic Tests, Routine / Q000191:economics; D006801:Humans; D018709:Statistics, Nonparametric