-
作者：Fan, Jianqing; Wu, Yichao; Feng, Yang
作者单位：Princeton University; North Carolina State University
摘要：Generalized linear models and the quasi-likelihood method extend the ordinary regression models to accommodate more general conditional distributions of the response. Nonparametric methods need no explicit parametric specification, and the resulting model is completely determined by the data themselves. However, nonparametric estimation schemes generally have a slower convergence rate such as the local polynomial smoothing estimation of nonparametric generalized linear models studied in Fan, H...
-
作者：Park, Yonil; Sheetlin, Sergey; Spouge, John L.
作者单位：National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM)
摘要：The gapped local alignment score of two random sequences follows a Gumbel distribution. If computers could estimate the parameters of the Gumbel distribution within one second, the use of arbitrary alignment scoring schemes could increase the sensitivity of searching biological sequence databases over the web. Accordingly, this article gives a novel equation for the scale parameter of the relevant Gumbel distribution. We speculate that the equation is exact, although present numerical evidence...
-
作者：Mueller, Ursula U.
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：We consider regression models with parametric (linear or nonlinear) regression function and allow responses to be missing at random. We assume that the errors have mean zero and are independent of the covariates. In order to estimate expectations of functions of covariate and response we use a fully imputed estimator, namely all empirical estimator based oil estimators of conditional expectations given the covariate. We exploit the independence of covariates and errors by writing the condition...
-
作者：Gorfine, Malka; Zucker, David M.; Hsu, Li
作者单位：Technion Israel Institute of Technology; Hebrew University of Jerusalem; Fred Hutchinson Cancer Center
摘要：In this work we deal with correlated failure time (age at onset) data arising from population-based, case-control studies, where case and control probands are selected by population-based sampling and all array of risk factor measures is collected for both cases and controls and their relatives. Parameters of interest are effects of risk factors on the failure time hazard function and within-family dependencies among failure times after adjusting for the risk factors. Due to the retrospective ...
-
作者：Vovk, Vladimir; Nouretdinov, Ilia; Gammerman, Alex
作者单位：University of London; Royal Holloway University London
摘要：We consider the on-line predictive version of the standard problem of linear regression; the goal is to predict each consecutive response given the corresponding explanatory variables and all the previous observations. The standard treatment of prediction in linear regression analysis has two drawbacks: (1) the classical prediction intervals guarantee that the probability of error is equal to the nominal significance level epsilon, but this property per se does not imply that the long-run freq...
-
作者：Benko, Michal; Haerdle, Wolfgang; Kneip, Alois
作者单位：Humboldt University of Berlin; University of Bonn
摘要：Functional principal component analysis (FPCA) based on the Karhunen-Loeve decomposition has been successfully applied in many applications, mainly for one sample problems. In this paper we consider common functional principal components for two sample problems. Our research is motivated not only by the theoretical challenge of this data situation, but also by the actual question of dynamics of implied volatility (IV) functions. For different maturities the log-returns of IVs are samples of (s...
-
作者：Boysen, Leif; Kempe, Angela; Liebscher, Volkmar; Munk, Axel; Wittich, Olaf
作者单位：University of Gottingen; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Universitat Greifswald; Eindhoven University of Technology
摘要：We study the asymptotics for jump-penalized least squares regression aiming at approximating a regression function by piecewise constant functions. Besides conventional consistency and convergence rates of the estimates in L-2([0, 1)) our results cover other metrics like Skorokhod metric on the space of cadlag functions and uniform metrics on C([0, 1]). We will show that these estimators are in an adaptive sense rate optimal over certain classes of approximation spaces. Special cases are the c...
-
作者：Cai, T. Tony; Zhou, Harrison H.
作者单位：University of Pennsylvania; Yale University
摘要：Asymptotic equivalence theory developed in the literature so far are only for bounded loss functions. This limits the potential applications of the theory because many commonly used loss functions in statistical inference are unbounded. In this paper we develop asymptotic equivalence results for robust nonparametric regression with unbounded loss functions. The results imply that all the Gaussian nonparametric regression procedures can be robustified in a unified way. A key step in our equival...
-
作者：Hu, Feifang; Zhang, Li-Xin; He, Xuming
作者单位：University of Virginia; Zhejiang University; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Response-adaptive randomization hits recently attracted a lot of attention in the literature. In this paper, we propose a new and simple family of response-adaptive randomization procedures that attain the cramer-Rao lower bounds on the allocation variances for any allocation proportions including optimal allocation proportions. The allocation Probability functions of proposed procedures are discontinuous. The existing large sample theory for adaptive designs relies on Taylor expansions of the...
-
作者：Audibert, Jean-Yves
作者单位：Universite Gustave-Eiffel; Institut Polytechnique de Paris; Ecole Nationale des Ponts et Chaussees; Universite PSL; Ecole Normale Superieure (ENS); Centre National de la Recherche Scientifique (CNRS); Inria
摘要：We develop minimax optimal risk bounds for the general learning task consisting in predicting as well as the best function in a reference set g up to the smallest possible additive term, called the convergence rate. When the reference set is finite and when n denotes the size of the training data, we provide minimax convergence rates of the form C(log|g|/n)(nu) with tight evaluation of the positive constant C and with exact 0 < nu <= 1, the latter value depending on the convexity of the loss f...
