The Annals of Statistics

最新文章

• Statistical properties of the method of regularization with periodic Gaussian reproducing kernel AI翻译
  作者：Lin, Y; Brown, LD
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Pennsylvania
  摘要：The method of regularization with the Gaussian reproducing kernel is popular in the machine learning literature and successful in many practical applications. In this paper we consider the periodic version of the Gaussian kernel regularization. We show in the white noise model setting, that in function spaces of very smooth functions, such as the infinite-order Sobolev space and the space of analytic functions, the method under consideration is asymptotically minimax; in finite-order Sobolev s...
• Density estimation for biased data AI翻译
  作者：Efromovich, S
  作者单位：University of New Mexico
  摘要：The concept of biased data is well known and its practical applications range front social sciences and biology to economics and quality control. These observations arise when a sampling procedure chooses an observation with probability that depends on the value of the observation. This is an interesting sampling procedure because it favors some observations and neglects others. It is known that biasing does not change rates of nonparametric density estimation, but no results are available abo...
• The golden chain - Rejoinder AI翻译
  作者：Jiang, WX
  作者单位：Northwestern University
• Influence function and maximum bias of projection depth based estimators AI翻译
  作者：Zuo, YJ; Cui, HJ; Young, D
  作者单位：Michigan State University; Beijing Normal University; Arizona State University; Arizona State University-Tempe
  摘要：Location estimators induced from depth functions increasingly have been pursued and studied in the literature. Among them are those induced from projection depth functions. These projection depth based estimators have favorable properties among their competitors. In particular, they possess the best possible finite sample breakdown point robustness. However, robustness of estimators cannot be revealed by the finite sample breakdown point alone. The influence function, gross error sensitivity, ...
• Bayesian-motivated tests of function fit and their asymptotic frequentist properties AI翻译
  作者：Aerts, M; Claeskens, G; Hart, JD
  作者单位：Hasselt University; KU Leuven; Texas A&M University System; Texas A&M University College Station
  摘要：We propose and analyze nonparametric tests of the null hypothesis that a function belongs to a specified parametric family. The tests are based on BIC approximations, pi(BIC), to the posterior probability of the null model, and may be carried out in either Bayesian or frequentist fashion. We obtain results on the asymptotic distribution Of pi(BIC) under both the null hypothesis and local alternatives. One version Of pi(BIC), call it pi*(BIC), uses a class of models that are orthogonal to each ...
• Estimation of nonlinear models with Berkson measurement errors AI翻译
  作者：Wang, LQ
  作者单位：University of Manitoba
  摘要：This paper is concerned with general nonlinear regression models where the predictor variables are subject to Berkson-type measurement errors. The measurement errors are assumed to have a general parametric distribution, which is not necessarily normal. In addition, the distribution of the random error in the regression equation is nonparametric. A minimum distance estimator is proposed, which is based on the first two conditional moments of the response variable given the observed predictor v...
• Generalization bounds for averaged classifiers AI翻译
  作者：Freund, Y; Mansour, Y; Schapire, RE
  作者单位：Columbia University; Tel Aviv University; Princeton University
  摘要：We study a simple learning algorithm for binary classification. Instead of predicting with the best hypothesis in the hypothesis class, that is, the hypothesis that minimizes the training error, our algorithm predicts with a weighted average of all hypotheses, weighted exponentially with respect to their training error. We show that the prediction of this algorithm is much more stable than the prediction of an algorithm that predicts with the best hypothesis. By allowing the algorithm to absta...
• Statistical inference for time-inhomogeneous volatility models AI翻译
  作者：Mercurio, D; Spokoiny, V
  作者单位：Humboldt University of Berlin; Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
  摘要：This paper offers a new approach for estimating and forecasting the volatility of financial time series. No assumption is made about the parametric form of the processes. On the contrary, we only suppose that the volatility can be approximated by a constant over some interval. In such a framework, the main problem consists of filtering this interval of time homogeneity; then the estimate of the volatility can be simply obtained by local averaging. We construct a locally adaptive volatility, es...
• Statistical behavior and consistency of classification methods based on convex risk minimization - Discussion AI翻译
  作者：Bartlett, PL; Jordan, MI; McAuliffe, JD
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
• Approximating a sequence of observations by a simple process AI翻译
  作者：Rosenberg, D; Solan, E; Vieille, N
  作者单位：Universite Paris 13; Northwestern University; Tel Aviv University
  摘要：Given an arbitrary long but finite sequence of observations from a finite set, we construct a simple process that approximates the sequence, in the sense that with high probability the empirical frequency, as well as the empirical one-step transitions along a realization from the approximating process, are close to that of the given sequence. We generalize the result to the case where the one-step transitions are required to be in given polyhedra.