2016

最新文章

• Variable selection via additive conditional independence AI翻译
  作者：Lee, Kuang-Yao; Li, Bing; Zhao, Hongyu
  作者单位：Yale University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：We propose a non-parametric variable selection method which does not rely on any regression model or predictor distribution. The method is based on a new statistical relationship, called additive conditional independence, that has been introduced recently for graphical models. Unlike most existing variable selection methods, which target the mean of the response, the method proposed targets a set of attributes of the response, such as its mean, variance or entire distribution. In addition, the...
• Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting AI翻译
  作者：Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng
  作者单位：University of Washington; University of Washington Seattle; Chinese University of Hong Kong
  摘要：The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either function, we consider a wide class of calibration weights construct...
• Semiparametric estimation in the secondary analysis of case-control studies AI翻译
  作者：Ma, Yanyuan; Carroll, Raymond J.
  作者单位：University of South Carolina System; University of South Carolina Columbia; Texas A&M University System; Texas A&M University College Station
  摘要：We study the regression relationship between covariates in case-control data: an area known as the secondary analysis of case-control studies. The context is such that only the form of the regression mean is specified, so that we allow an arbitrary regression error distribution, which can depend on the covariates and thus can be heteroscedastic. Under mild regularity conditions we establish the theoretical identifiability of such models. Previous work in this context has either specified a ful...
• Model checking for parametric single-index models: a dimension reduction model-adaptive approach AI翻译
  作者：Guo, Xu; Wang, Tao; Zhu, Lixing
  作者单位：Nanjing University of Aeronautics & Astronautics; Hong Kong Baptist University; Yale University; Beijing Normal University
  摘要：Local smoothing testing based on multivariate non-parametric regression estimation is one of the main model checking methodologies in the literature. However, the relevant tests suffer from the typical curse of dimensionality, resulting in slow rates of convergence to their limits under the null hypothesis and less deviation from the null hypothesis under alternative hypotheses. This problem prevents tests from maintaining the level of significance well and makes tests less sensitive to altern...
• Methodology for non-parametric deconvolution when the error distribution is unknown AI翻译
  作者：Delaigle, Aurore; Hall, Peter
  作者单位：University of Melbourne
  摘要：In the non-parametric deconvolution problem, to estimate consistently a density or distribution from a sample of data contaminated by additive random noise, it is often assumed that the noise distribution is completely known or that an additional sample of replicated or validation data is available. Methods also have been suggested for estimating the scale of the error distribution, but they require somewhat restrictive smoothness assumptions on the signal distribution, which can be difficult ...
• Non-parametric inference for density modes AI翻译
  作者：Genovese, Christopher R.; Perone-Pacifico, Marco; Verdinelli, Isabella; Wasserman, Larry
  作者单位：Carnegie Mellon University; Sapienza University Rome
  摘要：We derive non-parametric confidence intervals for the eigenvalues of the Hessian at modes of a density estimate. This provides information about the strength and shape of modes and can also be used as a significance test. We use a data splitting approach in which potential modes are identified by using the first half of the data and inference is done with the second half of the data. To obtain valid confidence sets for the eigenvalues, we use a bootstrap based on an elementary symmetric polyno...
• Generalized additive and index models with shape constraints AI翻译
  作者：Chen, Yining; Samworth, Richard J.
  作者单位：University of Cambridge
  摘要：We study generalized additive models, with shape restrictions (e.g. monotonicity, convexity and concavity) imposed on each component of the additive prediction function. We show that this framework facilitates a non-parametric estimator of each additive component, obtained by maximizing the likelihood. The procedure is free of tuning parameters and under mild conditions is proved to be uniformly consistent on compact intervals. More generally, our methodology can be applied to generalized addi...
• A general framework for updating belief distributions AI翻译
  作者：Bissiri, P. G.; Holmes, C. C.; Walker, S. G.
  作者单位：University of Milano-Bicocca; University of Oxford; University of Texas System; University of Texas Austin
  摘要：We propose a framework for general Bayesian inference. We argue that a valid update of a prior belief distribution to a posterior can be made for parameters which are connected to observations through a loss function rather than the traditional likelihood function, which is recovered as a special case. Modern application areas make it increasingly challenging for Bayesians to attempt to model the true data-generating mechanism. For instance, when the object of interest is low dimensional, such...
• Bootstrapping the portmanteau tests in weak auto-regressive moving average models AI翻译
  作者：Zhu, Ke
  作者单位：Chinese Academy of Sciences
  摘要：The paper uses a random-weighting (RW) method to bootstrap the critical values for the Ljung-Box or Monti portmanteau tests and weighted Ljung-Box or Monti portmanteau tests in weak auto-regressive moving average models. Unlike the existing methods, no user-chosen parameter is needed to implement the RW method. As an application, these four tests are used to check the model adequacy in power generalized auto-regressive conditional heteroscedasticity models. Simulation evidence indicates that t...
• Estimation of a two-component mixture model with applications to multiple testing AI翻译
  作者：Patra, Rohit Kumar; Sen, Bodhisattva
  作者单位：Columbia University
  摘要：We consider a two-component mixture model with one known component. We develop methods for estimating the mixing proportion and the unknown distribution non-parametrically, given independent and identically distributed data from the mixture model, using ideas from shape-restricted function estimation. We establish the consistency of our estimators. We find the rate of convergence and asymptotic limit of the estimator for the mixing proportion. Completely automated distribution-free honest fini...