A Note on Confidence Intervals in Regression Problems

A Note on Teaching Binomial Confidence Intervals

On confidence intervals for semiparametric expectile regression

In regression scenarios there is a growing demand for information on the conditional distribution of the response beyond the mean. In this scenario quantile regression is an established method of tail analysis. It is well understood in terms of asymptotic properties and estimation quality. Another way to look at the tail of a distribution is via expectiles. They provide a valuable alternative s...

Confidence Intervals for Nonparametric Regression

In non-parametric function estimation, providing a confidence interval with the right coverage is a challenging problem. This is especially the case when the underlying function has a wide range of unknown degrees of smoothness. Here we propose two methods of constructing an average coverage confidence interval built from block shrinkage estimation methods. One is based on the James-Stein shrin...

A note on bootstrap confidence intervals for proportions

We first show that any 1−α bootstrap percentile confidence interval for a proportion based on a binomial random variable has an infimum coverage probability zero for any sample size. This result is then extended to intervals for the difference, the relative risk and the odds ratio of two proportions as well as other types of bootstrap intervals. © 2013 Elsevier B.V. All rights reserved.

A Note on Teaching Large–Sample Binomial Confidence Intervals

This paper addresses the question of which large–sample binomial confidence interval method to teach in elementary courses. Recently, Goodall (1995), Simon (1996), and the references therein, have discussed the merits of several large–sample systems of binomial intervals. Three of the systems considered these articles are the z-interval, a “t-based interval,” and a continuitycorrected interval,...