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作者：Mu, Yunming; He, Xuming
作者单位：Texas A&M University System; Texas A&M University College Station; University of Illinois System; University of Illinois Urbana-Champaign
摘要：In this article we consider the linear quantile regression model with a power transformation on the dependent variable. Like the classical Box-Cox transformation approach, it extends the applicability of linear models without resorting to nonparametric smoothing, but transformations on the quantile models are more natural due to the equivariance property of the quantiles under monotone transformations. We propose an estimation procedure and establish its consistency and asymptotic normality un...
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作者：Gomes, M. Ivette; Pestana, Dinis
作者单位：Universidade de Lisboa
摘要：The main objective of statistics of extremes lies in the estimation of quantities related to extreme events. In many areas of application, such as statistical quality control, insurance, and finance, a typical requirement is to estimate a high quantile, that is, the value at risk at a level p (VaR(p)), high enough so that the chance of an exceedance of that value is equal to p, small. In this article we deal with the semiparametric estimation of VaRp for heavy tails. The classical semiparametr...
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作者：Li, Youjuan; Liu, Yufeng; Zhu, Ji
作者单位：University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
摘要：In this article we consider quantile regression in reproducing kernel Hilbert spaces, which we call kernel quantile regression (KQR). We make three contributions: (1) we propose an efficient algorithm that computes the entire solution path of the KQR, with essentially the same computational cost as fitting one KQR model; (2) we derive a simple formula for the effective dimension of the KQR model, which allows convenient selection of the regularization parameter; and (3) we develop an asymptoti...
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作者：Liang, Faming; Liu, Chuanhai; Carroll, Raymond J.
作者单位：Texas A&M University System; Texas A&M University College Station; Purdue University System; Purdue University
摘要：The Wang-Landau (WL) algorithm is an adaptive Markov chain Monte Carlo algorithm used to calculate the spectral density for a physical system. A remarkable feature of the WL algorithm is that it is not trapped by local energy minima, which is very important for systems with rugged energy landscapes. This feature has led to many successful applications of the algorithm in statistical physics and biophysics; however, there does not exist rigorous theory to support its convergence, and the estima...
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作者：Scheuren, Fritz
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作者：Rosenbaum, Paul R.; Ross, Richard N.; Silber, Jeffrey H.
作者单位：University of Pennsylvania; University of Pennsylvania; Pennsylvania Medicine; Childrens Hospital of Philadelphia; University of Pennsylvania
摘要：In observational studies of treatment effects, matched samples have traditionally been constructed using two tools, namely close matches on one or two key covariates and close matches on the propensity score to stochastically balance large numbers of covariates. Here we propose a third tool, fine balance, obtained using the assignment algorithm in a new way. We use all three tools to construct a matched sample for an ongoing study of provider specialty in the treatment of ovarian cancer. Fine ...
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作者：Rosenbaum, Paul R.
作者单位：University of Pennsylvania
摘要：In a randomized experiment comparing two treatments, there is interference between units if applying the treatment to one unit may affect other units. Interference implies that treatment effects are not comparisons of two potential responses that a unit may exhibit, one under treatment and the other under control, but instead are inherently more complex. Interference is common in social settings where people communicate, compete, or spread disease; in studies that treat one part of an organism...
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作者：Cheng, Ming-Yen; Peng, Liang
作者单位：National Taiwan University; University System of Georgia; Georgia Institute of Technology
摘要：Local likelihood modeling is a unified and effective approach to establishing the dependence of a response variable, which can be of various types, on independent variables. Therefore, these models have become popular in a wide range of applications. There is an increasing interest in employing multiparameter local likelihood models to investigate trends of sample extremes in environmental statistics. When sample maxima are modeled by a generalized extreme value distribution, the sample size i...
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作者：Hinich, Melvin J.; Serletis, Apostolos
作者单位：University of Texas System; University of Texas Austin; University of Calgary
摘要：This article uses daily observations for the Canadian dollar-U.S. dollar nominal exchange rate over the recent flexible exchange rate period and a new statistical technique, recently developed by Hinich, to detect major political and economic events that have affected the exchange rate.
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作者：Tymofyeyev, Yevgen; Rosenberger, William F.; Hu, Feifang
作者单位：Merck & Company; Merck & Company USA; George Mason University; University of Virginia; University of Virginia
摘要：For sequential experiments with K treatments, we establish two formal optimization criteria to find optimal allocation strategies. Both criteria involve the sample sizes on each treatment and a concave noncentrality parameter from a multivariate test. We show that these two criteria are equivalent. We apply this result to specific questions: (1) How do we maximize power of a multivariate test of homogeneity with binary response?, and (2) for fixed power, how do we minimize expected treatment f...
