Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the method of pivoting the CDF for exact confidence intervals with
  illustration for exponential mean under life-test with time constraints   Two requirements for pivoting a cumulative distribution function (CDF) in
order to construct exact confidence intervals or bounds for a real-valued
parameter $\theta$ are the monotonicity of this CDF with respect to $\theta$
and the existence of solutions of some pertinent equations for $\theta$. The
second requirement is not fulfilled by the CDF of the maximum likelihood
estimator of the exponential scale parameter when the data come from some
life-testing scenarios such as type-I censoring, hybrid type-I censoring, and
progressive type-I censoring that are subject to time constraints. However, the
method has been used in these cases probably because the non-existence of the
solution usually happens only with small probability. Here, we illustrate the
problem by giving formal details in the case of type-I censoring and by
providing some further examples. We also present a suitable extension of the
basic pivoting method which is applicable in situations wherein the considered
equations have no solution.

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The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph