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作者：Bogdan, Malgorzata; Chakrabarti, Arijit; Frommlet, Florian; Ghosh, Jayanta K.
作者单位：Wroclaw University of Science & Technology; University of Vienna; Indian Statistical Institute; Indian Statistical Institute Kolkata; Purdue University System; Purdue University
摘要：Within a Bayesian decision theoretic framework we investigate some asymptotic optimality properties of a large class of multiple testing rules. A parametric setup is considered, in which observations come from a normal scale mixture model and the total loss is assumed to be the sum of losses for individual tests. Our model can be used for testing point null hypotheses, as well as to distinguish large signals from a multitude of very small effects. A rule is defined to be asymptotically Bayes o...
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作者：Cui, Xia; Haerdle, Wolfgang Karl; Zhu, Lixing
作者单位：Sun Yat Sen University; Humboldt University of Berlin; Hong Kong Baptist University; National Central University; Yunnan University of Finance & Economics
摘要：Single-index models are natural extensions of linear models and circumvent the so-called curse of dimensionality. They are becoming increasingly popular in many scientific fields including biostatistics, medicine, economics and financial econometrics. Estimating and testing the model index coefficients beta is one of the most important objectives in the statistical analysis. However, the commonly used assumption on the index coefficients, parallel to beta parallel to = 1, represents a nonregul...
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作者：Goldenshluger, Alexander; Lepski, Oleg
作者单位：University of Haifa; Aix-Marseille Universite
摘要：We address the problem of density estimation with L(s)(-)loss by selection of kernel estimators. We develop a selection procedure and derive corresponding L-s-risk oracle inequalities. It is shown that the proposed selection rule leads to the estimator being minimax adaptive over a scale of the anisotropic Nikol'skii classes. The main technical tools used in our derivations are uniform bounds on the L-s-norms of empirical processes developed recently by Goldenshluger and Lepski [Ann. Probab. (...
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作者：Kirch, Claudia; Politis, Dimitris N.
作者单位：Helmholtz Association; Karlsruhe Institute of Technology; University of California System; University of California San Diego
摘要：A new time series bootstrap scheme, the time frequency toggle (TFT)-bootstrap, is proposed. Its basic idea is to bootstrap the Fourier coefficients of the observed time series, and then to back-transform them to obtain a bootstrap sample in the time domain. Related previous proposals, such as the surrogate data approach, resampled only the phase of the Fourier coefficients and thus had only limited validity. By contrast, we show that the appropriate resampling of phase and magnitude, in additi...
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作者：Dick, Josef
作者单位：University of New South Wales Sydney
摘要：We study a random sampling technique to approximate integrals f[0,1](s) f (x) dx by averaging the function at some sampling points. We focus on cases where the integrand is smooth, which is a problem which occurs in statistics. The convergence rate of the approximation error depends on the smoothness of the function f and the sampling technique. For instance, Monte Carlo (MC) sampling yields a convergence of the root mean square error (RMSE) of order N(-1/2) (where N is the number of samples) ...
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作者：Ionides, Edward L.; Bhadra, Anindya; Atchade, Yves; King, Aaron
作者单位：University of Michigan System; University of Michigan; National Institutes of Health (NIH) - USA; NIH Fogarty International Center (FIC); University of Michigan System; University of Michigan
摘要：Inference for partially observed Markov process models has been a long-standing methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of filtering problems. We present new theoretical results pertaining to the convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This theory complements the growi...
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作者：Seijo, Emilio; Sen, Bodhisattva
作者单位：Columbia University
摘要：In this paper we study the consistency of different bootstrap procedures for constructing confidence intervals (CIs) for the unique jump discontinuity (change-point) in an otherwise smooth regression function in a stochastic design setting. This problem exhibits nonstandard asymptotics, and we argue that the standard bootstrap procedures in regression fail to provide valid confidence intervals for the change-point. We propose a version of smoothed bootstrap, illustrate its remarkable finite sa...
