Statistical Inference for the Difference Between the Best Treatment Mean and a Control Mean

Autor:	Chu-In Charles Lee, Lin Liu, Jianan Peng
Rok vydání:	2006
Předmět:	Statistics and Probability Linear inequality Linear programming Statistics Constrained optimization Statistical inference Isotonic regression Statistics Probability and Uncertainty Control (linguistics) Upper and lower bounds Selection (genetic algorithm) Mathematics
Zdroj:	Journal of the American Statistical Association. 101:1050-1058
ISSN:	1537-274X 0162-1459
DOI:	10.1198/016214506000000258
Popis:	In many experiments, researchers are interested in comparing several treatment means with a control mean. When there are some treatments significantly better than the control, it is often of interest to evaluate the difference between the best treatment mean and the control mean and to identify the best treatment. In this article we derive lower confidence bounds for the aforementioned difference for the case that treatments are at least as effective as the control and for the case that no restriction is placed on the treatment means and the control mean. The evaluation of the lower confidence bound for the difference between the best treatment mean and the control mean is a concave programming problem subject to homogeneous linear inequality constraints. We propose two efficient computation algorithms and discuss the connection between our procedures and Gupta's subset selection procedure. We compare the expected lower confidence bounds of the two procedures with that of Dunnett's procedure. An applicati...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c589435056cc3c5a2037a9fe950b7a65 https://doi.org/10.1198/016214506000000258 Zobrazit plný text záznamu