2002

最新文章

• Single observation unbiased priors AI翻译
  作者：Meng, XL; Zaslavsky, AM
  作者单位：Harvard University; Harvard University; Harvard Medical School
  摘要：This paper studies a class of default priors, which we call single observation unbiased priors (SOUP). A prior for a parameter is a SOUP if the corresponding posterior mean of the parameter based on a single observation is an unbiased estimator of the parameter. We prove that, under mild regularity conditions, a default prior for a convolution parameter is noninformative in the sense of yielding a posterior inference invariant under amalgamation only if it is a SOUP. Therefore, when amalgamati...
• What is a statistical model? Discussion AI翻译
  作者：Tjur, T
  作者单位：Copenhagen Business School
• Marginal models for categorical data AI翻译
  作者：Bergsma, WP; Rudas, T
  作者单位：Tilburg University; HUN-REN; HUN-REN Centre for Social Sciences; Institute for Sociology - HAS; Eotvos Lorand University; Central European University
  摘要：Statistical models defined by imposing restrictions on marginal distributions of contingency tables have received considerable attention recently. This paper introduces a general definition of marginal log-linear parameters and describes conditions for a marginal log-linear parameter to be a smooth parameterization of the distribution and to be variation independent. Statistical models defined by imposing affine restrictions on the marginal log-linear parameters are investigated. These models ...
• On weak base hypotheses and their implications for boosting regression and classification AI翻译
  作者：Jiang, WX
  作者单位：Northwestern University
  摘要：When studying the training error and the prediction error for boosting, it is often assumed that the hypotheses returned by the base learner are weakly accurate, or are able to beat a random guesser by a certain amount of difference. It has been an open question how much this difference can be. whether it will eventually disappear in the boosting process or be bounded by a positive amount. This question is crucial for the behavior of both the training error and the prediction error. In this pa...
• The partitioning principle: A powerful tool in multiple decision theory AI翻译
  作者：Finner, H; Strassburger, K
  作者单位：Heinrich Heine University Dusseldorf
  摘要：A first general principle and nowadays state of the art for the construction of powerful multiple test procedure, controlling a multiple level alpha is the so-called closure principle. In this article we introduce another powerful tool for the construction of multiple decision procedures. especially for the construction of multiple test procedure, and,election procedure. This tool is based on a partition of the parameter space and will be called partitioning principle (PP). In the first part o...
• Recovering edges in ill-posed inverse problems:: Optimality of curvelet frames AI翻译
  作者：Candès, EJ; Donoho, DL
  作者单位：Stanford University
  摘要：We consider a model problem of recovering a function f (x(1), x(2)) from noisy Radon data. The function f to be recovered is assumed smooth apart from a discontinuity along a C-2 curve, that is, an edge. We use the continuum white-noise model, with noise level epsilon. Traditional linear methods for solving such inverse problems behave poorly in the presence of edges. Qualitatively, the reconstructions are blurred near the edges; quantitatively, they give in our model mean squared errors (MSEs...
• Dimension reduction for conditional mean in regression AI翻译
  作者：Cook, RD; Bing, L
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：In many situations regression analysis is mostly concerned with inferring about the conditional mean of the response given the predictors, and less concerned with the other aspects of the conditional distribution. In this paper we develop dimension reduction methods that incorporate this consideration. We introduce the notion of the Central Mean Subspace (CMS), a natural inferential object for dimension reduction when the mean function is of interest. We study properties of the CMS, and develo...
• Spectral analysis for harmonizable processes AI翻译
  作者：Lii, KS; Rosenblatt, M
  作者单位：University of California System; University of California Riverside; University of California System; University of California San Diego
  摘要：Spectral estimation of nonstationary but harmonizable processes is considered. Given a single realization of the process, periodogram-like and consistent estimators are proposed for spectral mass estimation when the spectral support of the process consists of lines. Such a process can arise in signals of a moving source from array data or multipath signals with Doppler stretch from a single receiver. Such processes also include periodically correlated (or cyclostationary) and almost periodical...
• New methods for bias correction at endpoints and boundaries AI翻译
  作者：Hall, P; Park, BU
  作者单位：Australian National University; Seoul National University (SNU)
  摘要：We suggest two new, translation-based methods for estimating and correcting for bias when estimating the edge of a distribution. The first uses an empirical translation applied to the argument of the kernel, in order to remove the main effects of the asymmetries that are inherent when constructing estimators at boundaries. Placing the translation inside the kernel is in marked contrast to traditional approaches, such as the use of high-order kernels, which are related to the jackknife and, in ...
• What is a statistical model? Discussion AI翻译
  作者：Kalman, R
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich