Interval estimation of the mean response in a log-regression model.

by: J Wu, AC Wong, W Wei

Statistics in medicine, Vol. 25, No. 12. (30 June 2006), pp. 2125-2135.

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Abstract

A standard approach to the analysis of skewed response data with concomitant information is to use a log-transformation to normalize the distribution of the response variable and then conduct a log- regression analysis. However, the mean response at original scale is often of interest. El-Shaarawi and Viveros developed an interval estimation of the mean response of a log-regression model based on large sample theory. There is however very little information available in the literature on constructing such estimates when the sample size is small. In this paper, we develop a small-sample corrected interval by using the likelihood-based inference method developed by Barndorff-Nielson and Fraser et al. Simulation results show that the proposed interval provides almost exact coverage probability, even for small samples.

@article{citeulike:2878430, abstract = {A standard approach to the analysis of skewed response data with concomitant information is to use a log-transformation to normalize the distribution of the response variable and then conduct a log- regression analysis. However, the mean response at original scale is often of interest. El-Shaarawi and Viveros developed an interval estimation of the mean response of a log-regression model based on large sample theory. There is however very little information available in the literature on constructing such estimates when the sample size is small. In this paper, we develop a small-sample corrected interval by using the likelihood-based inference method developed by Barndorff-Nielson and Fraser et al. Simulation results show that the proposed interval provides almost exact coverage probability, even for small samples.}, address = {Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN 38105, USA. jianrong.wu@stjude.org}, author = {Wu, J. and Wong, A. C. and Wei, W. }, citeulike-article-id = {2878430}, doi = {http://dx.doi.org/10.1002/sim.2329}, issn = {0277-6715}, journal = {Statistics in medicine}, keywords = {log-normal, transformation}, month = {June}, number = {12}, pages = {2125--2135}, posted-at = {2008-06-10 03:05:52}, priority = {2}, title = {Interval estimation of the mean response in a log-regression model.}, url = {http://dx.doi.org/10.1002/sim.2329}, volume = {25}, year = {2006} }

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TY - JOUR ID - citeulike:2878430 L3 - citeulike-article-id:2878430 TI - Interval estimation of the mean response in a log-regression model. JF - Statistics in medicine VL - 25 IS - 12 SP - 2125 EP - 2135 AD - Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN 38105, USA. jianrong.wu@stjude.org SN - 0277-6715 N2 - A standard approach to the analysis of skewed response data with concomitant information is to use a log-transformation to normalize the distribution of the response variable and then conduct a log- regression analysis. However, the mean response at original scale is often of interest. El-Shaarawi and Viveros developed an interval estimation of the mean response of a log-regression model based on large sample theory. There is however very little information available in the literature on constructing such estimates when the sample size is small. In this paper, we develop a small-sample corrected interval by using the likelihood-based inference method developed by Barndorff-Nielson and Fraser et al. Simulation results show that the proposed interval provides almost exact coverage probability, even for small samples. KW - log-normal KW - transformation AU - Wu, J AU - Wong, AC AU - Wei, W PY - 2006/06/30/ UR - http://dx.doi.org/10.1002/sim.2329 ER -

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