4期

最新文章

• Complexities of convex combinations and bounding the generalization error in classification AI翻译
  作者：Koltchinskii, V; Panchenko, D
  作者单位：University of New Mexico; Massachusetts Institute of Technology (MIT)
  摘要：We introduce and study several measures of complexity of functions from the convex hull of a given base class. These complexity measures take into account the sparsity of the weights of a convex combination as well as certain clustering properties of the base functions involved in it. We prove new upper confidence bounds on the generalization error of ensemble (voting) classification algorithms that utilize the new complexity measures along with the empirical distributions of classification ma...
• Local Rademacher complexities AI翻译
  作者：Bartlett, PL; Bousquet, O; Mendelson, S
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Max Planck Society; Australian National University
  摘要：We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.
• Efficiency improvements in inference on stationary and nonstationary fractional time series AI翻译
  作者：Robinson, PM
  作者单位：University of London; London School Economics & Political Science
  摘要：We consider a time series model involving a fractional stochastic component, whose integration order can lie in the stationary/invertible or nonstationary regions and be unknown, and an additive deterministic component consisting of a generalized polynomial. The model can thus incorporate competing descriptions of trending behavior. The stationary input to the stochastic component has parametric autocorrelation, but innovation with distribution of unknown form. The model is thus semiparametric...
• Semiparametric estimation for stationary processes whose spectra have an unknown pole AI翻译
  作者：Hidalgo, J
  作者单位：University of London; London School Economics & Political Science
  摘要：We consider the estimation of the location of the pole and memory parameter, lambda(0) and alpha, respectively, of covariance stationary linear processes whose spectral density function f(lambda) satisfies f(lambda) similar to C vertical bar lambda - lambda(0)vertical bar(-alpha) in a neighborhood of lambda(0). We define a consistent estimator of lambda(0) and derive its limit distribution Z(lambda)0. As in related optimization problems, when the true parameter value can lie on the boundary of...
• On the Bahadur representation of sample quantiles for dependent sequences AI翻译
  作者：Wu, WB
  作者单位：University of Chicago
  摘要：We establish the Bahadur representation of sample quantiles for linear and some widely used nonlinear processes. Local fluctuations of empirical processes are discussed. Applications to the trimmed and Winsorized means are given. Our results extend previous ones by establishing sharper bounds under milder conditions and thus provide new insight into the theory of empirical processes for dependent random variables.