Recursive integration methodologies with statistical applications

Recursive confidence band construction for an unknown distribution function

Given a sample X1,...,Xn of independent observations from an unknown continuous distribution function F, the problem of constructing a confidence band for F is considered, which is a fundamental problem in statistical inference. This confidence band provides simultaneous inferences on all quantiles and also on all of the cumulative probabilities of the distribution, and so they are among the most important inference procedures that address the issue of multiplicity. A fully nonparametric approach is taken where no assumptions are made about the distribution function F. Historical approaches to this problem, such as Kolmogorov's famous () procedure, represent some of the earliest inference methodologies that address the issue of multiplicity. This is because a confidence band at a given confidence level 1-α allows inferences on all of the quantiles of the distribution, and also on all of the cumulative probabilities, at that specified confidence level. In this paper it is shown how recursive methodologies can be employed to construct both one-sided and two-sided confidence bands of various types. The first approach operates by putting bounds on the cumulative probabilities at the data points, and a recursive integration approach is described. The second approach operates by providing bounds on certain specified quantiles of the distribution, and its implementation using recursive summations of multinomial probabilities is described. These recursive methodologies are illustrated with examples, and R code is available for their implementation.

Pooling batches in drug stability study by using constant-width simultaneous confidence bands

One important study objective in drug stability studies is to estimate the shelf-life of a drug. A key statistical problem involved in this is how to assess the practical equivalence of different batches of the same drug so that different batches can be subgrouped to produce a single shelf-life for the drug. In this paper constant-width simultaneous confidence bands are proposed to quantify the magnitude of difference between different batches, with a particular view to establish the practical equivalence of different batches. This approach is suitable for the situation that the intercepts and slopes of the regression lines for the batches cannot be assumed to be equal. It is shown how constant-width simultaneous confidence bands can be easily constructed for the multiple comparison of several general linear regression models. In particular, it is shown that constant-width simultaneous confidence bands have a better chance to establish the equivalence than, and so are preferable to, the hyperbola-shaped simultaneous confidence bands considered.