DETECTING OVERDISPERSION IN DATA WITH CENSORING by
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چکیده
HSING-VI CHANG. Testing Overdispersion in Data With Censoring. (Under the direction of Chirayath M. Suchindran.) The term overdispersion refers to the situation that the variance of the outcome exceeds the nominal variance. Overdispersion in general has two effects. The first effect is that summary statistics have a larger variance than anticipated under the simple model. The second is a possible loss of efficiency in using statistics appropriate for the assumed distribution. Censoring is common in medical experiments, and hence estimation methods must allow for it if they are to be generally useful. The review of the statistical literature shows that many models and test statistics for overdispersion do not deal with
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تاریخ انتشار 1996