The Annals of Statistics

最新文章

• WEAK CONVERGENCE OF A PSEUDO MAXIMUM LIKELIHOOD ESTIMATOR FOR THE EXTREMAL INDEX AI翻译
  作者：Berghaus, Betina; Buecher, Axel
  作者单位：Ruhr University Bochum
  摘要：The extremes of a stationary time series typically occur in clusters. A primary measure for this phenomenon is the extremal index, representing the reciprocal of the expected cluster size. Both disjoint and sliding blocks estimator for the extremal index are analyzed in detail. In contrast to many competitors, the estimators only depend on the choice of one parameter sequence. We derive an asymptotic expansion, prove asymptotic normality and show consistency of an estimator for the asymptotic ...
• EMPIRICAL BEST PREDICTION UNDER A NESTED ERROR MODEL WITH LOG TRANSFORMATION AI翻译
  作者：Molina, Isabel; Martin, Nirian
  作者单位：Universidad Carlos III de Madrid; Complutense University of Madrid
  摘要：In regression models involving economic variables such as income, log transformation is typically taken to achieve approximate normality and stabilize the variance. However, often the interest is predicting individual values or means of the variable in the original scale. Under a nested error model for the log transformation of the target variable, we show that the usual approach of back transforming the predicted values may introduce a substantial bias. We obtain the optimal (or best) predict...
• HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES AI翻译
  作者：Shi, Chengchun; Fan, Ailin; Song, Rui; Lu, Wenbin
  作者单位：North Carolina State University
  摘要：Precision medicine is a medical paradigm that focuses on finding the most effective treatment decision based on individual patient information. For many complex diseases, such as cancer, treatment decisions need to be tailored over time according to patients' responses to previous treatments. Such an adaptive strategy is referred as a dynamic treatment regime. A major challenge in deriving an optimal dynamic treatment regime arises when an extraordinary large number of prognostic factors, such...
• ESTIMATION OF A MONOTONE DENSITY IN s-SAMPLE BIASED SAMPLING MODELS AI翻译
  作者：Chan, Kwun Chuen Gary; Ling, Hok Kan; Sit, Tony; Yam, Sheung Chi Phillip
  作者单位：University of Washington; University of Washington Seattle; Columbia University; Chinese University of Hong Kong
  摘要：We study the nonparametric estimation of a decreasing density function go in a general s-sample biased sampling model with weight (or bias) functions w(i )for i = 1, ...,s. The determination of the monotone maximum likelihood estimator (g) over cap (n) and its asymptotic distribution, except for the case when s = 1, has been long missing in the literature due to certain nonstandard structures of the likelihood function, such as nonseparability and a lack of strictly positive second order deriv...
• COMMUNITY DETECTION IN DEGREE-CORRECTED BLOCK MODELS AI翻译
  作者：Gao, Chao; Ma, Zongming; Zhang, Anderson Y.; Zhou, Harrison H.
  作者单位：University of Chicago; University of Pennsylvania; Yale University
  摘要：Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of clusters, which could often help reveal interesting structures. The present paper studies community detection in Degree-Corrected Block Models (DCBMs). We first derive asymptotic minimax risks of the problem for a misclassification proportion loss under appropriate conditions. The minimax risks are shown to depend on degree...
• EFFICIENT AND ADAPTIVE LINEAR REGRESSION IN SEMI-SUPERVISED SETTINGS AI翻译
  作者：Chakrabortty, Abhishek; Cai, Tianxi
  作者单位：University of Pennsylvania; Harvard University
  摘要：We consider the linear regression problem under semi-supervised settings wherein the available data typically consists of: (i) a small or moderate sized labeled data, and (ii) a much larger sized unlabeled data. Such data arises naturally from settings where the outcome, unlike the covariates, is expensive to obtain, a frequent scenario in modern studies involving large databases like electronic medical records (EMR). Supervised estimators like the ordinary least squares (OLS) estimator utiliz...
• MODERATE DEVIATIONS AND NONPARAMETRIC INFERENCE FOR MONOTONE FUNCTIONS AI翻译
  作者：Gao, Fuqing; Xiong, Jie; Zhao, Xingqiu
  作者单位：Wuhan University; University of Macau; Hong Kong Polytechnic University
  摘要：This paper considers self-normalized limits and moderate deviations of nonparametric maximum likelihood estimators for monotone functions. We obtain their self-normalized Cramer-type moderate deviations and limit distribution theorems for the nonparametric maximum likelihood estimator in the current status model and the Grenander-type estimator. As applications of the results, we present a new procedure to construct asymptotical confidence intervals and asymptotical rejection regions of hypoth...
• GRADIENT-BASED STRUCTURAL CHANGE DETECTION FOR NONSTATIONARY TIME SERIES M-ESTIMATION AI翻译
  作者：Wu, Weichi; Zhou, Zhou
  作者单位：University of London; University College London; University of Toronto
  摘要：We consider structural change testing for a wide class of time series M-estimation with nonstationary predictors and errors. Flexible predictor-error relationships, including exogenous, state-heteroscedastic and autoregressive regressions and their mixtures, are allowed. New uniform Bahadur representations are established with nearly optimal approximation rates. A CUSUMtype test statistic based on the gradient vectors of the regression is considered. In this paper, a simple bootstrap method is...
• ON BAYESIAN INDEX POLICIES FOR SEQUENTIAL RESOURCE ALLOCATION AI翻译
  作者：Kaufmann, Emilie
  作者单位：Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Information Sciences & Technologies (INS2I); Universite de Lille; Centrale Lille
  摘要：This paper is about index policies for minimizing (frequentist) regret in a stochastic multi-armed bandit model, inspired by a Bayesian view on the problem. Our main contribution is to prove that the Bayes-UCB algorithm, which relies on quantiles of posterior distributions, is asymptotically optimal when the reward distributions belong to a one-dimensional exponential family, for a large class of prior distributions. We also show that the Bayesian literature gives new insight on what kind of e...
• HIGH-DIMENSIONAL ASYMPTOTICS OF PREDICTION: RIDGE REGRESSION AND CLASSIFICATION AI翻译
  作者：Dobriban, Edgar; Wager, Stefan
  作者单位：University of Pennsylvania; Stanford University
  摘要：We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asymptotic regime where p, n -> infinity and p/n -> gamma > 0, and allow for arbitrary covariance among the features. For both methods, we provide an explicit and efficiently computable expression for the limiting predictive risk, which depends only on the spectrum of the feature-covariance matrix, the signal strength and ...