2010

最新文章

• A multiresolution approach to time warping achieved by a Bayesian prior-posterior transfer fitting strategy AI翻译
  作者：Claeskens, Gerda; Silverman, Bernard W.; Slaets, Leen
  作者单位：KU Leuven; KU Leuven; University of Oxford
  摘要：Warping is an approach to the reduction and analysis of phase variability in functional observations, by applying a smooth bijection to the function argument. We propose a natural representation of warping functions in terms of a new type of elementary functions named 'warping component functions', or 'warplets', which are combined into the warping function by composition. The inverse warping function is trivial and explicit to obtain. A sequential Bayesian estimation strategy is introduced wh...
• Minimally informative prior distributions for non-parametric Bayesian analysis AI翻译
  作者：Bush, Christopher A.; Lee, Juhee; MacEachern, Steven N.
  作者单位：University System of Ohio; Ohio State University; Novartis; Novartis USA
  摘要：We address the problem of how to conduct a minimally informative, non-parametric Bayesian analysis. The central question is how to devise a model so that the posterior distribution satisfies a few basic properties. The concept of 'local mass' provides the key to the development of the limiting Dirichlet process model. This model is then used to provide an engine for inference in the compound decision problem and for multiple-comparisons inference in a one-way analysis-of-variance setting. Our ...
• Sufficient dimension reduction for spatial point processes directed by Gaussian random fields AI翻译
  作者：Guan, Yongtao; Wang, Hansheng
  作者单位：Yale University
  摘要：We develop a sufficient dimension reduction paradigm for inhomogeneous spatial point processes driven by Gaussian random fields. Specifically, we introduce the notion of the kth-order central intensity subspace. We show that a central subspace can be defined as the combination of all central intensity subspaces. For many commonly used spatial point process models, we find that the central subspace is equivalent to the first-order central intensity subspace. To estimate the latter, we propose a...
• Semiparametric marginal regression analysis for dependent competing risks under an assumed copula AI翻译
  作者：Chen, Yi-Hau
  作者单位：Academia Sinica - Taiwan
  摘要：Competing risks problems arise in many fields of science, where two or more types of event may occur on a subject, but only the event occurring first is observed together with its occurrence time, and other events are censored. The marginal and joint distributions of event times for competing risks cannot be identified from the observed data without assuming the relationship between events. The commonly adopted independent censoring assumption may be easily violated. An alternative is to assum...
• Local additive estimation AI翻译
  作者：Park, Juhyun; Seifert, Burkhardt
  作者单位：Lancaster University; University of Zurich
  摘要：Additive models are popular in high dimensional regression problems owing to their flexibility in model building and optimality in additive function estimation. Moreover, they do not suffer from the so-called curse of dimensionality generally arising in non-parametric regression settings. Less known is the model bias that is incurred from the restriction to the additive class of models. We introduce a new class of estimators that reduces additive model bias, yet preserves some stability of the...
• Criteria for surrogate end points based on causal distributions AI翻译
  作者：Ju, Chuan; Geng, Zhi
  作者单位：Peking University
  摘要：When a treatment has a positive average causal effect (ACE) on an intermediate variable or surrogate end point which in turn has a positive ACE on a true end point, the treatment may have a negative ACE on the true end point due to the presence of unobserved confounders, which is called the surrogate paradox. A criterion for surrogate end points based on ACEs has recently been proposed to avoid the surrogate paradox. For a continuous or ordinal discrete end point, the distributional causal eff...
• Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression AI翻译
  作者：Kai, Bo; Li, Runze; Zou, Hui
  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：Local polynomial regression is a useful non-parametric regression tool to explore fine data structures and has been widely used in practice. We propose a new non-parametric regression technique called local composite quantile regression smoothing to improve local polynomial regression further. Sampling properties of the estimation procedure proposed are studied. We derive the asymptotic bias, variance and normality of the estimate proposed. The asymptotic relative efficiency of the estimate wi...
• Simultaneous estimation and reduction of nonconformity in interlaboratory studies AI翻译
  作者：Strawderman, William E.; Rukhin, Andrew L.
  作者单位：Rutgers University System; Rutgers University New Brunswick; National Institute of Standards & Technology (NIST) - USA
  摘要：Several procedures that are designed to reduce nonconformity in interlaboratory studies by shrinking data towards a consensus weighted mean are suggested. Some of them are shown to have a smaller quadratic risk than the vector sample means. Shrinkage towards a weighted mean in a random-effects model and a statistic appearing in models which allow for systematic errors are also considered. The results are illustrated by two examples of collaborative studies.
• Likelihood for statistically equivalent models AI翻译
  作者：Copas, John; Eguchi, Shinto
  作者单位：University of Warwick; Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan
  摘要：In likelihood inference we usually assume that the model is fixed and then base inference on the corresponding likelihood function. Often, however, the choice of model is rather arbitrary, and there may be other models which fit the data equally well. We study robustness of likelihood inference over such 'statistically equivalent' models and suggest a simple 'envelope likelihood' to capture this aspect of model uncertainty. Robustness depends critically on how we specify the parameter of inter...
• Combining probability forecasts AI翻译
  作者：Ranjan, Roopesh; Gneiting, Tilmann
  作者单位：Ruprecht Karls University Heidelberg; University of Washington; University of Washington Seattle
  摘要：Linear pooling is by far the most popular method for combining probability forecasts. However, any non-trivial weighted average of two or more distinct, calibrated probability forecasts is necessarily uncalibrated and lacks sharpness. In view of this, linear pooling requires recalibration, even in the ideal case in which the individual forecasts are calibrated. Towards this end, we propose a beta-transformed linear opinion pool for the aggregation of probability forecasts from distinct, calibr...