We adopt the frequentist approach to statistical inference together with a recognition of the validity of conditional inference as described by Cox (1958). We describe a type of model which shows clearly the nature of a form of information which may be lost due to basing inference solely on a...

The Buehler 1-[alpha] upper confidence limit is as small as possible, subject to the constraints that (a) its coverage probability never falls below 1-[alpha] and (b) it is a non-decreasing function of a designated statistic T. We provide two new results concerning the influence of T on the...

Using an algorithm of Joffe (Ann. Probab. 2 (1974) 161-162) we generate a finite sequence of 4-independent random variables. Using this sequence we find a confidence interval with coverage probability exceeding a value close to 1-[alpha] for [theta]=E{g(U)} where U=(U1,...,Us) with U1,...,Us...

We present a new and simple method for constructing a 1-[alpha] upper confidence limit for [theta] in the presence of a nuisance parameter vector [psi], when the data is discrete. Our method is based on computing a P-value P{T[less-than-or-equals, slant]t} from an estimator T of [theta],...

We consider exact confidence limits obtained from discrete data by inverting a hypothesis test based on a studentized test statistic. We show that these confidence limits (a) are nesting and (b) have greater large sample efficiency than Buehler confidence limits that are required to be nesting.

Consider a two-treatment, two-period crossover trial, with responses that are continuous random variables. We find a large-sample frequentist 1-[alpha] confidence interval for the treatment difference that utilizes the uncertain prior information that there is no differential carryover effect.

We consider the following question: "Is there a confidence upper limit with given minimum coverage properties which is better than all other confidence upper limits with the given minimum coverage properties?". We prove that for discrete data this question is answered in the negative when...