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作者：Holst, M; Irle, A
作者单位：University of Kiel
摘要：The asymptotic classification risk for nearest neighbor procedures is well understood in the case of i.i.d. training sequences. In this article, we generalize these results to a class of dependent models including hidden Markov models. In the case where the observed patterns have Lebesgue densities, the asymptotic risk takes the same expression as in the i.i.d. case. For discrete distributions, we show that the asymptotic risk depends on the rule used for breaking ties of equal distances.
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作者：Chen, GM; Lockhart, RA
作者单位：University of Manitoba; Simon Fraser University
摘要：When fitting, by least squares, a linear model (with an intercept term) with p parameters to n data points, the asymptotic behavior of the residual empirical process is shown to be the same as in the single sample problem provided p(3) log(2) (p)/n --> 0 for any error density having finite variance and a bounded first derivative. No further conditions are imposed on the sequence of design matrices. The result is extended to more general estimates with the property that the average error and av...
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作者：Cheng, CS; Mukerjee, R
作者单位：University of California System; University of California Berkeley; Indian Institute of Management (IIM System); Indian Institute of Management Calcutta
摘要：In this paper, the problem of constructing optimal blocked regular fractional factorial designs is considered. The concept of minimum aberration due to Fries and Hunter is a well-accepted criterion for selecting good unblocked fractional factorial designs. Cheng, Steinberg and Sun showed that a minimum aberration design of resolution three or higher maximizes the number of two-factor interactions which are not aliases of main effects and also tends to distribute these interactions over the ali...
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作者：Friedman, JH
作者单位：Stanford University
摘要：Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion. Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likel...
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作者：Yuan, A; Clarke, B
作者单位：Howard University; University of British Columbia
摘要：Work due to Junker and more recently due to Junker and Ellis characterized desired latent properties of an educational testing procedure in terms of a collection of other manifest properties. This is important because one can only propose tests for manifest quantities, not latent ones. Here, we complete the conversion of a pair of latent properties to equivalent conditions in terms of four manifest quantities and identify a general method for producing tests for manifest properties.
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作者：Berrendero, JR; Zamar, RH
作者单位：Autonomous University of Madrid; University of British Columbia
摘要：Maximum bias curves for some regression estimates were previously derived assuming that (i) the intercept term is known and/or (ii) the regressors have an elliptical distribution. We present a single method to obtain the maximum bias curves for a large class of regression estimates. Our results are derived under very mild conditions and, in particular, do not require the restrictive assumptions (i) and (ii) above. Using these results it is shown that the maximum bias curves heavily depend on t...
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作者：Carolan, C; Dykstra, R
作者单位：University of North Carolina; East Carolina University; University of Iowa
摘要：A clean, closed form, joint density is derived for Brownian motion, its least concave majorant, and its derivative, all at the same fixed point. Some remarkable conditional and marginal distributions follow from this,joint density, For example, it is shown that the height of the least concave majorant of Brownian motion at a fixed time point has the same distribution as the distance from the Brownian motion path to its least concave majorant at the same fixed time point. Also, it is shown that...
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作者：Delgado, MA; Manteiga, WG
作者单位：Universidad Carlos III de Madrid; Universidade de Santiago de Compostela
摘要：This paper proposes a test for selecting explanatory variables in nonparametric regression. The test does not need to estimate the conditional expectation function given all the variables, but only those which are significant under the null hypothesis. This feature is computationally convenient and solves, in part, the problem of the curse of dimensionality when selecting regressors in a nonparametric context. The proposed test statistic is based on functionals of a U-process. Contiguous alter...
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作者：Imhof, LA; Studden, WJ
作者单位：RWTH Aachen University; Purdue University System; Purdue University
摘要：E-optimal and standardized-E-optimal designs for various types of rational regression models are determined. In most cases, optimal designs are found for every parameter subsystem. The design points and weights are given explicitly in terms of Bernstein-Szego polynomials, The analysis is based on a general theorem on E-optimal designs for Chebyshev systems.
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作者：Horváth, L; Kokoszka, P; Teyssière, G
作者单位：Utah System of Higher Education; University of Utah; European Commission Joint Research Centre; EC JRC ISPRA Site; University of Liverpool
摘要：We derive the asymptotic distribution of the sequential empirical process of the squared residuals of an ARCH(p) sequence. Unlike the residuals of an ARMA process, these residuals do not behave in this context like asymptotically independent random variables, and the asymptotic distribution involves a term depending on the parameters of the model. We show that in certain applications, including the detection of changes in the distribution of the unobservable innovations, our result leads to as...
