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作者：LU, HHS; WELLS, MT; TIWARI, RC
作者单位：Cornell University; University of North Carolina; University of North Carolina Charlotte
摘要：For two distribution functions, F and G, the shift function is defined by Delta(t) = G(-1) . F(t) - t. The shift function is the distance from the 45 degrees line and the quantity plotted in Q-Q plots. In the analysis of lifetime data, Delta represents the difference between two treatments. The shift function can also be used to find crossing points of two distribution functions. The large-sample distribution theory for estimates of Delta is studied for right-censored data. It turns out that t...
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作者：POLI, I; JONES, RD
作者单位：United States Department of Energy (DOE); Los Alamos National Laboratory
摘要：In this article we introduce a neural net designed for nonlinear statistical prediction. The net is based on a stochastic model featuring a multilayer feedforward architecture with random connections between units and noisy response functions. A Bayesian inferential procedure for this model, based on the Kalman filter, is derived. The resulting learning algorithm generalizes the so-called one-dimensional Newton method, an updating algorithm currently popular in the neural net literature. A num...
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作者：CROUX, C; ROUSSEEUW, PJ; HOSSJER, O
作者单位：University of Antwerp; Lund University
摘要：In this article we introduce a new type of positive-breakdown regression method, called a generalized S-estimator (or GS-estimator), based on the minimization of a generalized M-estimator of residual scale. We compare the class of GS-estimators with the usual S-estimators, including least median of squares. It turns out that GS-estimators attain a much higher efficiency than S-estimators, at the cost of a slightly increased worst-case bias. We investigate the breakdown point, the maxbias curve...
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作者：ONEILL, TJ
作者单位：Australian National University
摘要：The logistic regression classification method uses parameter estimates that are the solution of an estimating equation. This article derives a convenient expression for the bias of a vector estimator defined by estimating equations. The expression and the results of O'Neill are used to derive the bias and the error or misclassification rate of logistic regression classification in two examples where the assumed model for logistic regression does not hold. Logistic regression classification is ...
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作者：SU, JQ; LIU, JS
作者单位：Harvard University
摘要：The receiver operating characteristic (ROC) curve is a simple and meaningful measure to assess the usefulness of diagnostic markers. To use the information carried by multiple markers, we note that Fisher's linear discriminant function provides a linear combination of markers to maximize the sensitivity over the entire specificity range uniformly under the multivariate normal distribution model with proportional covariance matrices. With no restriction on covariance matrices, we also provide a...
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作者：STOKER, TM
摘要：This article discusses a generic feature of density estimation by local smoothing, namely that estimated derivatives and location score vectors will display a systematic downward (attenuation) bias. We study the behavior of kernel estimators, indicating how the derivative bias arises and showing a simple result. We then consider the estimation of score vectors (negative log-density derivatives), which are motivated by the problem of estimating average derivatives and the adaptive estimation of...
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作者：GU, C
摘要：Asa sequel to an earlier article by Gu and Qiu, this article describes and illustrates a dimensionless automatic algorithm for nonparametric probability density estimation using smoothing splines. The algorithm is designed to calculate an adaptive finite dimensional solution to the penalized likelihood problem, which was shown by Gu and Qiu to share the same asymptotic convergence rates as the nonadaptive infinite dimensional solution. The smoothing parameter is updated jointly with the estima...
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作者：CLEVELAND, WS; MALLOWS, CL; MCRAE, JE
摘要：ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the ''A'' in ATS) is carried out, then a variance-stabilizing transformation is applied (''T''), and finally the result is smoothed (''S'') using a nonparametric regression procedure. ATS methods are quite broad in terms of applications; in this article we show how they can be used for fitting a surface ...
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作者：MCCULLOCH, RE; TSAY, RS
摘要：This article is concerned with statistical inference and prediction of mean and variance changes in an autoregressive time series. We first extend the analysis of random mean-shift models to random variance-shift models. We then consider a method for predicting when a shift is about to occur. This involves appending to the autoregressive model a probit model for the probability that a shift occurs given a chosen set of explanatory variables. The basic computational tool we use in the proposed ...
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作者：BOWMAN, AW; FOSTER, PJ
作者单位：University of Manchester
摘要：Methods of adaptive smoothing of density estimates, where the amount of smoothing applied varies according to local features of the underlying density, are investigated. The difficulties of applying Taylor series arguments in this context are explored. Simple properties of the estimates are investigated by numerical integration and compared with the fixed kernel approach. Optimal smoothing strategies, based on the multivariate Normal distribution, are derived. As an application of these techni...
