4期

最新文章

• 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...
• 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...
• Mixtures, envelopes and hierarchical duality AI翻译
  作者：Polson, Nicholas G.; Scott, James G.
  作者单位：University of Chicago; University of Texas System; University of Texas Austin
  摘要：We develop a connection between mixture and envelope representations of objective functions that arise frequently in statistics. We refer to this connection by using the term hierarchical duality'. Our results suggest an interesting and previously underexploited relationship between marginalization and profiling, or equivalently between the Fenchel-Moreau theorem for convex functions and the Bernstein-Widder theorem for Laplace transforms. We give several different sets of conditions under whi...
• Making the cut: improved ranking and selection for large-scale inference AI翻译
  作者：Henderson, Nicholas C.; Newton, Michael A.
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood approaches tend to favour units with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized...
• Distance shrinkage and Euclidean embedding via regularized kernel estimation AI翻译
  作者：Zhang, Luwan; Wahba, Grace; Yuan, Ming
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：Although recovering a Euclidean distance matrix from noisy observations is a common problem in practice, how well this could be done remains largely unknown. To fill in this void, we study a simple distance matrix estimate based on the so-called regularized kernel estimate. We show that such an estimate can be characterized as simply applying a constant amount of shrinkage to all observed pairwise distances. This fact allows us to establish risk bounds for the estimate, implying that the true ...
• Extensible grids: uniform sampling on a space filling curve AI翻译
  作者：He, Zhijian; Owen, Art B.
  作者单位：Tsinghua University; Stanford University
  摘要：We study the properties of points in [0,1]d generated by applying Hilbert's space filling curve to uniformly distributed points in [0,1]. For deterministic sampling we obtain a discrepancy of O(n-1/d) for d2. For random stratified sampling, and scrambled van der Corput points, we derive a mean-squared error of O(n-1-2/d) for integration of Lipschitz continuous integrands, when d3. These rates are the same as those obtained by sampling on d-dimensional grids and they show a deterioration with i...
• Principal causal effect identification and surrogate end point evaluation by multiple trials AI翻译
  作者：Jiang, Zhichao; Ding, Peng; Geng, Zhi
  作者单位：Peking University; University of California System; University of California Berkeley
  摘要：Principal stratification is a causal framework to analyse randomized experiments with a post-treatment variable between the treatment and end point variables. Because the principal strata defined by the potential outcomes of the post-treatment variable are not observable, we generally cannot identify the causal effects within principal strata. Motivated by a real data set of phase III adjuvant colon cancer clinical trials, we propose approaches to identifying and estimating the principal causa...
• Bayesian regularization of the length of memory in reversible sequences AI翻译
  作者：Bacallado, Sergio; Pande, Vijay; Favaro, Stefano; Trippa, Lorenzo
  作者单位：Stanford University; University of Turin; Collegio Carlo Alberto; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
  摘要：Variable order Markov chains have been used to model discrete sequential data in a variety of fields. A host of methods exist to estimate the history-dependent lengths of memory which characterize these models and to predict new sequences. In several applications, the data-generating mechanism is known to be reversible, but combining this information with the procedures mentioned is far from trivial. We introduce a Bayesian analysis for reversible dynamics, which takes into account uncertainty...
• Robust inference in sample selection models AI翻译
  作者：Zhelonkin, Mikhail; Genton, Marc G.; Ronchetti, Elvezio
  作者单位：University of Lausanne; King Abdullah University of Science & Technology; University of Geneva
  摘要：The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function...
• Double one-sided cross-validation of local linear hazards AI翻译
  作者：Luz Gamiz, Maria; Mammen, Enno; Martinez Miranda, Maria Dolores; Nielsen, Jens Perch
  作者单位：University of Granada; Ruprecht Karls University Heidelberg; HSE University (National Research University Higher School of Economics); City St Georges, University of London
  摘要：The paper brings together the theory and practice of local linear kernel hazard estimation. Bandwidth selection is fully analysed, including double one-sided cross-validation that is shown to have good practical and theoretical properties. Insight is provided into the choice of the weighting function in the local linear minimization and it is pointed out that classical weighting sometimes lacks stability. A new semiparametric hazard estimator transforming the survival data before smoothing is ...