PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 2765641

Point estimation following group sequential tests.

K Kim
View original source ↗Export metadata ↓

Abstract

Kim and DeMets (1987, Biometrics 43, 857-864) described an exact procedure for constructing confidence intervals for a normal mean following group sequential tests when the boundaries were generated based on the notion of use functions proposed by Lan and DeMets (1983, Biometrika 70, 659-663). In this article, three point estimators for a normal mean following group sequential tests are considered, and their properties are investigated by Monte Carlo simulation. Based on the simulation results, some suggestions are made as to the choice of group sequential designs and use function.

Explore related subjects

BiometryMonte Carlo MethodJournal ArticleResearch Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K Kim. 1989. Point estimation following group sequential tests.. https://pubmed.ncbi.nlm.nih.gov/2765641/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed1992

Coloured noise or low-dimensional chaos?

Devising a method capable of distinguishing a low-dimensional chaotic signal that might be embedded in a noisy stochastic process has become a major challenge for those involved in time-series analysis. Here a null hypothesis approach is used in conjunction with a known nonlinear predictive test, to probe for the presence of chaos in epidemiological data. A probabilistic set of rules is used to stimulate a historic record of New York City measles outbreaks, generally understood to be governed by a chaotic attractor. The simulated runs of 'surrogate data' are carefully constructed so as to be free from any underlying low-dimensional chaotic process. They therefore serve as a useful null model against which to test the observed time series. However, despite the assumed differences between the dynamics of measles outbreaks and the null model, a nonlinear predictive scheme is found to be unable to differentiate between their characteristic time series. The methodology confirms that, if there is in fact a chaotic signal in the measles data, it is extremely difficult to detect in time series of such limited length. The results have general relevance to the analysis of physical, ecological and environmental time series.

Biometry↗