1996

最新文章

• Sequential iterated bootstrap confidence intervals AI翻译
  作者：Lee, SMS; Young, GA
  作者单位：University of Cambridge; University of Hong Kong
  摘要：A version of the sequential probability ratio test for testing simultaneously a set of nested hypotheses is developed. This procedure is then applied to define a sequential procedure of sampling at the inner level of the two nested levels of resampling required by Monte Carlo construction of an iterated bootstrap percentile method confidence interval. The sequential resampling scheme reduces very significantly the computational demands of construction of the iterated bootstrap confidence inter...
• Regression with doubly censored current status data AI翻译
  作者：Rabinowitz, D; Jewell, NP
  作者单位：University of California System; University of California Berkeley
  摘要：Data from settings in which an initiating event and a subsequent event occur in sequence are called doubly censored current status data if the time of neither event is observed directly, but instead it is determined at a random monitoring time whether either the initiating or subsequent event has yet occurred. This paper is concerned with using doubly censored current status data to estimate the regression coefficient in an accelerated failure time model for the length of time between the init...
• On pseudodata methods for removing boundary effects in kernel density estimation AI翻译
  作者：Cowling, A; Hall, P
  作者单位：Australian National University
  摘要：We suggest a method for boundary correcting kernel density estimators, based on generating pseudodata beyond the extremities of the density's support. The estimator produced in this way enjoys optimal orders of bias and variance right up to the ends of the support, and it may be used with kernels of arbitrary order. Our method is considerably more adaptive than the common data reflection approach, which is not really appropriate for kernels of order 2 or more since it does not adequately corre...
• Regression shrinkage and selection via the Lasso AI翻译
  作者：Tibshirani, R
  摘要：We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models...
• The usefulness of optimum experimental designs AI翻译
  作者：Atkinson, AC
  摘要：Optimum experimental designs were originally developed by Kiefer, mainly for response surface models. This survey of recent developments emphasizes potential or actual usefulness. For linear models the construction of exact designs, particularly over irregular design regions, is stressed, as is the blocking of response surface designs. Other important areas include systematic designs that are robust against trend and designs for mixtures with irregular design regions: several industrial exampl...
• Discriminant analysis by Gaussian mixtures AI翻译
  作者：Hastie, T; Tibshirani, R
  作者单位：University of Toronto
  摘要：Fisher-Rao linear discriminant analysis (LDA) is a valuable tool for multigroup classification. LDA is equivalent to maximum likelihood classification assuming Gaussian distributions for each class. In this paper, we fit Gaussian mixtures to each class to facilitate effective classification in non-normal settings, especially when the classes are clustered. Low dimensional views are an important by-product of LDA - our new techniques inherit this feature. We can control the within-class spread ...
• Experimental design and observation for large systems - Discussion AI翻译
  作者：Jones, B; Sebastiani, P; Lewis, SM; Giovagnoli, A; Torsney, B; Gilmour, SG; Abraham, I; Firth, D; Atkinson, AC; Chan, PLY; Muller, WG; Bogacka, B; Box, G; Donev, AN; Fang, KT; Hickernell, FJ; Govaerts, B; Haines, LM; Schwabe, R; Settimi, R; Titterington, DM; Atkinson, AC; Bates, RA; Buck, RJ; Riccomagno, E; Wynn, HP
  作者单位：City St Georges, University of London; University of Southampton; University of Udine; University of Pavia; University of Glasgow; University of Reading; University of Oxford; University of London; London School Economics & Political Science; University of Hong Kong; Poznan University of Life Sciences; University of Wisconsin System; University of Wisconsin Madison; Hong Kong Baptist University; Solvay SA; Universite Catholique Louvain; University of Kwazulu Natal; Free University of Berlin; University of Perugia
• Vector generalized additive models AI翻译
  作者：Yee, TW; Wild, CJ
  摘要：Vector smoothing is used to extend the class of generalized additive models in a very natural way to include a class of multivariate regression models. The resulting models are called 'vector generalized additive models'. The class of models for which the methodology gives generalized additive extensions includes the multiple logistic regression model for nominal responses, the continuation ratio model and the proportional and nonproportional odds models for ordinal responses, and the bivariat...
• Reducing the use of laboratory animals in toxicological research and testing by better experimental design AI翻译
  作者：Festing, MFW; Lovell, DP
  摘要：More than 50 million animals are used in biomedical research in the world each year. It is highly desirable that this number is reduced both for ethical and for economic reasons. Better experimental design could lead to the use of fewer animals and improve the repeatability of animal experiments so that alternative methods would be easier to validate. Screening experiments aimed at identifying rodent carcinogens would be more powerful if more than one strain of mice and/or rats were used. Atte...
• On the choice of smoothing parameter, threshold and truncation in nonparametric regression by non-linear wavelet methods AI翻译
  作者：Hall, P; Patil, P
  作者单位：Australian National University; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
  摘要：Concise asymptotic theory is developed for non-linear wavelet estimators of regression means, in the context of general error distributions, general designs, general normalizations in the case of stochastic design, and non-structural assumptions about the mean. The influence of the tail weight of the error distribution is addressed in the setting of choosing threshold and truncation parameters. Mainly, the tail weight is described in an extremely simple way, by a moment condition; previous wor...