Journal of the American Statistical Association

最新文章

• The selection of prior distributions by formal rules AI翻译
  作者：Kass, RE; Wasserman, L
  摘要：Subjectivism has become the dominant philosophical foundation for Bayesian inference. Yet in practice, most Bayesian analyses are performed with so-called ''noninformative'' priors, that is, priors constructed by some formal rule. We review the plethora of techniques for constructing such priors and discuss some of the practical and philosophical issues that arise when they are used. We give special emphasis to Jeffreys's rules and discuss the evolution of his viewpoint about the interpretatio...
• Dual cones, dual norms, and simultaneous inference for partially ordered means AI翻译
  作者：Berk, R; Marcus, R
  作者单位：VOLCANI INSTITUTE OF AGRICULTURAL RESEARCH
  摘要：Exact simultaneous one-sided confidence intervals for contrasts in m normal means are discussed. The set K of Contrast vectors considered is of one of two forms: Either it is a cone whose coordinates are monotone for a partial ordering defined on the coordinate index set (1,...,m) or it is the polar of such a cone. It is shown that the intervals obtained are inverted from test statistics that may be used for testing the null hypothesis that the mean vector mu lies in the dual (or negative pola...
• Testing for serial correlation against an ARMA(1, 1) process AI翻译
  作者：Andrews, DWK; Ploberger, W
  作者单位：University of St Andrews
  摘要：This article is concerned with tests for serial correlation in time series and in the errors of regression models. In particular, the nonstandard problem of testing for white noise against autoregressive moving average model ARMA(1, 1) alternatives is considered. The likelihood ratio (LR), sup Lagrange multiplier (LM), and exponential average LM and LR tests are shown to be asymptotically admissible for ARMA(1, 1) alternatives. In addition, they are shown to be consistent against all (weakly s...
• Permutation distributions via generating functions with applications to sensitivity analysis of discrete data AI翻译
  作者：Baglivo, J; Pagano, M; Spino, C
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Generating functions provide a simple and elegant way to describe probability or frequency distributions of discrete statistics and, in particular, permutation distributions. They are also a computational tool. Many efficient algorithms, including those described as fast Fourier transform methods, network methods, and multiple shift methods, are different implementations of the recursions needed to evaluate generating functions efficiently. Our goals here are twofold. First, we make the relati...
• Predictive oscillation patterns: A synthesis of methods for spatial-temporal decomposition of random fields AI翻译
  作者：Kooperberg, C; OSullivan, F
  摘要：Spatial-temporal decompositions of climatologic fields have been obtained using a range of techniques, including principal component analysis (PCA) and principal oscillation patterns (POPS). PCA decompositions are forced to be correlated to the original held, but they may not capture interesting aspects of temporal variation. On the other hand, POPS decompositions focus on temporal variation but are not forced to correlate to the field. Here we introduce a hybrid of these methods that attempts...
• On variance estimation with imputed survey data - Comment AI翻译
  作者：Binder, DA
• Partial least squares regression on smooth factors AI翻译
  作者：Goutis, C; Fearn, T
  作者单位：University of London; University College London
  摘要：In this article we present a modification of partial least squares regression to account for inherent nonexchangeabilities of the columns of the design matrix. In chemometrics applications it is common to write the matrix as a bilinear form of latent variables and loadings. These loadings are often interpreted as sampled values of functions; hence they should exhibit a degree of smoothness. Our method forces the partial least squares factors to be smooth, by using a roughness penalty motivated...
• Density estimation with bivariate censored data AI翻译
  作者：Wells, MT; Yeo, KP
  作者单位：National University of Singapore
  摘要：In this article we construct a kernel estimate of the probability density function from bivariate data that have been randomly censored. We study the large-sample properties of the proposed estimator using a strong approximation result. We establish consistency and asymptotic normality and give a convenient representation of the kernel density estimator. Simulation studies show that the proposed procedure gives a good estimate of the true density function even when the sample size is moderate....
• Improved pivotal methods for constructing confidence regions with directional data AI翻译
  作者：Hall, P; Jing, BY; Wood, ATA
  作者单位：Australian National University; Hong Kong University of Science & Technology; University of Bath
  摘要：The importance of pivoting is well established in the context of nonparametric confidence regions. It ensures enhanced coverage accuracy. However, pivoting for directional data cannot be achieved simply by rescaling. A somewhat cumbersome pivotal method, which involves passing first into a space of higher dimension, has been developed by Fisher and Hall for samples of unit vectors. Although that method has some advantages over nonpivotal techniques, it does suffer from certain drawbacks-in par...
• Identification of outliers in multivariate data AI翻译
  作者：Rocke, DM; Woodruff, DL
  摘要：New insights are given into why the problem of detecting multivariate outliers can be difficult and why the difficulty increases with the dimension of the data. Significant improvements in methods for detecting outliers are described, and extensive simulation experiments demonstrate that a hybrid method extends the practical boundaries of outlier detection capabilities. Based on simulation results and examples from the literature, the question of what levels of contamination can be detected by...