$(k - 1)$-Mean Significance Levels of Nonparametric Multiple Comparisons Procedures
نویسندگان
چکیده
منابع مشابه
Multiple comparisons procedures.
In biomedical research, a common question posed by investigators is whether or not an outcome of interest differs significantly between multiple independent groups of subjects in the study sample. For example, in a randomized clinical trial focusing on differences in a parameter of cardiovascular health such as systolic blood pressure or heart rate that is measured on a continuum, one might mak...
متن کاملNonparametric Predictive Multiple Comparisons of Lifetime Data
We consider lifetime experiments to compare units from different groups, where the units’ lifetimes may be right-censored. Nonparametric predictive inference for comparison of multiple groups is presented, in particular lower and upper probabilities for the event that a specific group will provide the largest next lifetime. We include the practically relevant consideration that the overall life...
متن کاملNonparametric Predictive Inference for Multiple Comparisons
This thesis presents Nonparametric Predictive Inference (NPI) for several multiple comparisons problems. We introduce NPI for comparison of multiple groups of data including right-censored observations. Different right-censoring schemes discussed are early termination of an experiment, progressive censoring and competing risks. Several selection events of interest are considered including selec...
متن کاملNonparametric Predictive Multiple Comparisons with Censored Data and Competing Risks
This paper provides an overview of nonparametric predictive inference for comparison of multiple groups of data including right-censored observations. Different right-censoring schemes discussed are early termination of an experiment, progressive censoring and competing risks. Theoretical results are briefly stated, detailed justifications are presented elsewhere. The methods are illustrated an...
متن کاملSignificance levels for multiple tests
Let X 1 . . . . . X, be n random variables, with cumulative distribution functions F1, . . . , F,. Define ¢i := Fi(Xi) for all i, and let ~(1) ~< ... ~< ~(,) be the order statistics of the (~)~. Let cq ~< ... ~< ~. be n numbers in the interval [0, 1]. We show that the probability of the event R := {(") ~< ct~ for all 1 ~< i ~< n} is at most min~n~Ji }. Moreover, this bound is exact: for any giv...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1980
ISSN: 0090-5364
DOI: 10.1214/aos/1176344892