Inference for the Lognormal Mean and Quantiles Based on Samples with Left and Right Type I Censoring

Details

• Personal Author:
  Krishnamoorthy, K. ; Mallick, Avishek ; Mathew, Thomas
• Description:
  Interval estimation of the mean and quantiles of a lognormal distribution is addressed based on a Type I singly censored sample. A special case of interest is that of a sample containing values below a single detection limit. Generalized inferential procedures which use maximum likelihood estimation based approximate pivotal quantities, and some likelihood based methods, are proposed. The latter include methodology based on the signed log-likelihood ratio test (SLRT) statistic and the modified signed log-likelihood ratio test (MSLRT) statistic. The merits of the methods are evaluated for a left-censored sample using Monte Carlo simulation. For inference concerning the lognormal mean, the SLRT is to be preferred for left-tailed testing, generalized inference for right-tailed testing, and all three approaches provide nearly the same performance for two-tailed testing. These conclusions hold even when the proportion of censored values is as large as 0.70. For inference concerning quantiles, both the generalized inference approach and the MSLRT approach are satisfactory. In view of its simplicity and ease of understanding and implementation, the generalized inference procedure is to be preferred. The results are illustrated with two examples. Technical derivations are given on the Technometrics website as supplementary material. http://www.tandfonline.com/doi/suppl/10.1198/TECH.2010.09189/suppl_file/utch_a_10714513_sm0001.pdf [Description provided by NIOSH]
• Subjects:
  Environmental Monitoring Epidemiology Occupational Health Risk Assessment Safety
• Keywords:
  Author Keywords: Detection Limit; Generalized Inference; Generalized Pivotal Quantity; Modified Signed Likelihood Ratio Test; Signed Likelihood Ratio Test; Tolerance Interval Statistical-analysis; Epidemiology; Exposure-assessment; Risk-analysis; Models; Mathematical-models;
• ISSN:
  0040-1706
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Louisiana ; Maryland ; OSHA Region 3 ; OSHA Region 6
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  6 pdf pages
• NIOSHTIC Number:
  nn:20047171
• Citation:
  Technometrics 2011 Feb; 53(1):72-83
• Contact Point Address:
  Thomas Mathew, Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD 21250
• Email:
  mathew@umbc.edu
• Federal Fiscal Year:
  2011
• Performing Organization:
  University of Maryland, Baltimore
• Peer Reviewed:
  True
• Start Date:
  20000501
• Source Full Name:
  Technometrics
• End Date:
  20150831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0f01664e2835e40f382312c7a8636b324fb493036177f03da9b987d7f0318dfb38cd7b957c88f9089294f5acb51df3d2df367cc0956435c176ed43f462cc11d9
• Download URL:
  https://stacks.cdc.gov/view/cdc/231581/cdc_231581_DS1.pdf
• File Type:
  [PDF - 102.27 KB ]