Proposed by Tibshirani (1996), the LASSO (least absolute shrinkage and selection operator) estimates a vector of regression coefficients by minimising the residual sum of squares subject to a… (More)

A new test of model misspecification is proposed, based on the ratio of in-sample and out-of-sample likelihoods. The test is broadly applicable, and in simple problems approximates well known,… (More)

A Functional Data—Analytic Approach to Signal Discrimination Peter Hall, D S Poskitt & Brett Presnell a Centre for Mathematics and Its Applications Australian National University Canberra ACT 0200… (More)

A class of weighted-bootstrap techniques, called biased-bootstrap methods, is proposed. It is motivated by the need to adjust more conventional, uniform-bootstrap methods in a surgical way, so as to… (More)

The 2000 presidential election was the most controversial U.S. election in recent history, mainly due to the disputed outcome of the election in Florida. Elsewhere in this issue, Richard Smith… (More)

Ranked set sampling has attracted considerable attention as an efficient sampling design, particularly for environmental and ecological studies. A number of authors have noted a gain in efficiency… (More)

We suggest a general method for tackling problems of density estimation under constraints. It is, in effect, a particular form of the weighted bootstrap, in which resampling weights are chosen so as… (More)

The IOS test of Presnell and Boss (J. Am. Stat. Assoc. 2004; 99(465):216-227) is a general-purpose goodness-of-fit test based on the ratio of in-sample and out-of-sample likelihoods. For large… (More)

Contamination of a sampled distribution, for example by a heavy-tailed distribution, can degrade the performance of a statistical estimator. We suggest a general approach to alleviating this problem,… (More)

Jackknife and bootstrap bias corrections are based on a diierencing argument which does not necessarily respect the sign of the true parameter value. Depending on sampling variability they can… (More)