2期

最新文章

• Independent screening for single-index hazard rate models with ultrahigh dimensional features AI翻译
  作者：Gorst-Rasmussen, Anders; Scheike, Thomas
  作者单位：Aalborg University; University of Copenhagen
  摘要：. In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that, in the case of generalized linear models, independent screening may suffice to capture all relevant features with high probability, even in ultrahigh dimension. It is unclear whether this formal sure screening property is attainable when the response is a right-censored survival...
• Non-parametric survival analysis of infectious disease data AI翻译
  作者：Kenah, Eben
  作者单位：State University System of Florida; University of Florida
  摘要：. The paper develops non-parametric methods based on contact intervals for the analysis of infectious disease data. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. The hazard function of the contact interval distribution equals the hazard of infectious contact from i to j, so it provides a summary of the evolution...
• Inference for linear models with dependent errors AI翻译
  作者：Zhou, Zhou; Shao, Xiaofeng
  作者单位：University of Toronto; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：. The paper is concerned with inference for linear models with fixed regressors and weakly dependent stationary time series errors. Theoretically, we obtain asymptotic normality for the M-estimator of the regression parameter under mild conditions and establish a uniform Bahadur representation for recursive M-estimators. Methodologically, we extend the recently proposed self-normalized approach of Shao from stationary time series to the regression set-up, where the sequence of response variabl...
• Doubly robust and efficient estimators for heteroscedastic partially linear single-index models allowing high dimensional covariates AI翻译
  作者：Ma, Yanyuan; Zhu, Liping
  作者单位：Texas A&M University System; Texas A&M University College Station; Shanghai University of Finance & Economics
  摘要：. We study the heteroscedastic partially linear single-index model with an unspecified error variance function, which allows for high dimensional covariates in both the linear and the single-index components of the mean function. We propose a class of consistent estimators of the parameters by using a proper weighting strategy. An interesting finding is that the linearity condition which is widely assumed in the dimension reduction literature is not necessary for methodological or theoretical ...
• Optimal predictions of powers of conditionally heteroscedastic processes AI翻译
  作者：Francq, Christian; Zakoian, Jean-Michel
  作者单位：Institut Polytechnique de Paris; ENSAE Paris; Universite de Lille
  摘要：. In conditionally heteroscedastic models, the optimal prediction of powers, or logarithms, of the absolute value has a simple expression in terms of the volatility and an expectation involving the independent process. A natural procedure for estimating this prediction is to estimate the volatility in the first step, for instance by Gaussian quasi-maximum-likelihood or by least absolute deviations, and to use empirical means based on rescaled innovations to estimate the expectation in the seco...
• A return to an old paper: Tests of separate families of hypotheses' AI翻译
  作者：Cox, D. R.
  作者单位：University of Oxford
  摘要：Two papers of 50 years ago concerning the comparison of non-nested models are reviewed and some subsequent developments and problems are sketched.