This paper introduces a rank-based test for the instrumental variables regression model that dominates the Anderson-Rubin test in terms of Þnite sample size and asymptotic power in certain circumstances. The test has correct size for any distribution of the errors with weak or strong instruments. The test has noticeably higher power than the Anderson-Rubin test when the error distribution has t...

We describe an interesting application of the principle of local learning to density estimation. Locally weighted fitting of a Gaussian with a regularized full covariance matrix yields a density estimator which displays improved behavior in the case where much of the probability mass is concentrated along a low dimensional manifold. While the proposed estimator is not guaranteed to integrate to...

Methods for choosing an appropriate sample size in animal experiments have received much attention in the statistical and biological literature. Due to ethical constraints the number of animals used is always reduced where possible. However, as the number of animals decreases so the risk of obtaining inconclusive results increases. By using a more efficient experimental design we can, for a giv...

This paper considers specification testing for instrumental variables estimation in the presence of many instruments. The test is similar to the overidentifying restrictions test of Sargan (1958) but the test statistic asymptotically follows the standard normal distribution under the null hypothesis when the number of instruments is proportional to the sample size. It turns out that the new tes...

A challenge when interpreting replications is determining whether the results of a replication "successfully" replicate the original study. Looking for consistency between two studies is challenging because individual studies are susceptible to many sources of error that can cause study results to deviate from each other and the population effect in unpredictable directions and magnitudes. In t...

Selecting the number of different classes which will be assumed to exist in the population is an important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) provides a data-driven way to evaluate the relative adequacy of a (K -1)-class model compared to a K-class model. However, very little is known about how to predict the power or the required sample size for the...

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results t...

Over the last decade, considerable interest has focused on sample size estimation in the design of clinical trials. The resulting literature is scattered over many textbooks and journals. This paper presents these methods in a single review and comments on their application in practice.

The behaviour of group sequential tests in the two-sample problem is investigated if one replaces the classical non-robust estimators in the t-test statistic by modern robust estimators of location and scale. Hampel's 3-part redescending M-estimator 25A used in the Princeton study and the robust scale estimators length of the shortest half proposed by Rousseeuw & Leroy and Q proposed by Roussee...

We present a definition for the effective sample size of a parametric prior distribution in a Bayesian model, and propose methods for computing the effective sample size in a variety of settings. Our approach first constructs a prior chosen to be vague in a suitable sense, and updates this prior to obtain a sequence of posteriors corresponding to each of a range of sample sizes. We then compute...