Journal of the Royal Statistical Society: Series B

最新文章

• Statistics of heteroscedastic extremes AI翻译
  作者：Einmahl, John H. J.; de Haan, Laurens; Zhou, Chen
  作者单位：Tilburg University; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam; Universidade de Lisboa; European Central Bank; De Nederlandsche Bank NV
  摘要：We extend classical extreme value theory to non-identically distributed observations. When the tails of the distribution are proportional much of extreme value statistics remains valid. The proportionality function for the tails can be estimated non-parametrically along with the (common) extreme value index. For a positive extreme value index, joint asymptotic normality of both estimators is shown; they are asymptotically independent. We also establish asymptotic normality of a forecasted high...
• Using post-outcome measurement information in censoring-by-death problems AI翻译
  作者：Yang, Fan; Small, Dylan S.
  作者单位：University of Chicago; University of Pennsylvania
  摘要：Many clinical studies on non-mortality outcomes such as quality of life suffer from the problem that the non-mortality outcome can be censored by death, i.e. the non-mortality outcome cannot be measured if the subject dies before the time of measurement. To address the problem that this censoring by death is informative, it is of interest to consider the average effect of the treatment on the non-mortality outcome among subjects whose measurement would not be censored under either treatment or...
• Robust inference in sample selection models AI翻译
  作者：Zhelonkin, Mikhail; Genton, Marc G.; Ronchetti, Elvezio
  作者单位：University of Lausanne; King Abdullah University of Science & Technology; University of Geneva
  摘要：The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function...
• Double one-sided cross-validation of local linear hazards AI翻译
  作者：Luz Gamiz, Maria; Mammen, Enno; Martinez Miranda, Maria Dolores; Nielsen, Jens Perch
  作者单位：University of Granada; Ruprecht Karls University Heidelberg; HSE University (National Research University Higher School of Economics); City St Georges, University of London
  摘要：The paper brings together the theory and practice of local linear kernel hazard estimation. Bandwidth selection is fully analysed, including double one-sided cross-validation that is shown to have good practical and theoretical properties. Insight is provided into the choice of the weighting function in the local linear minimization and it is pointed out that classical weighting sometimes lacks stability. A new semiparametric hazard estimator transforming the survival data before smoothing is ...
• Estimating multivariate volatility models equation by equation 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：The paper investigates the estimation of a wide class of multivariate volatility models. Instead of estimating an m-multivariate volatility model, a much simpler and numerically efficient method consists in estimating m univariate generalized auto-regressive conditional heteroscedasticity type models equation by equation in the first step, and a correlation matrix in the second step. Strong consistency and asymptotic normality of the equation-by-equation estimator are established in a very gen...
• Lasso regression: estimation and shrinkage via the limit of Gibbs sampling AI翻译
  作者：Rajaratnam, Bala; Roberts, Steven; Sparks, Doug; Dalal, Onkar
  作者单位：Stanford University; Australian National University
  摘要：The application of the lasso is espoused in high dimensional settings where only a small number of the regression coefficients are believed to be non-zero (i.e. the solution is sparse). Moreover, statistical properties of high dimensional lasso estimators are often proved under the assumption that the correlation between the predictors is bounded. In this vein, co-ordinatewise methods, which are the most common means of computing the lasso solution, naturally work well in the presence of low-t...
• Inference for multiple change points in time series via likelihood ratio scan statistics AI翻译
  作者：Yau, Chun Yip; Zhao, Zifeng
  作者单位：Chinese University of Hong Kong; University of Wisconsin System; University of Wisconsin Madison
  摘要：We propose a likelihood ratio scan method for estimating multiple change points in piecewise stationary processes. Using scan statistics reduces the computationally infeasible global multiple-change-point estimation problem to a number of single-change-point detection problems in various local windows. The computation can be efficiently performed with order O{nptlog(n)}. Consistency for the estimated numbers and locations of the change points are established. Moreover, a procedure is developed...
• High dimensional ordinary least squares projection for screening variables AI翻译
  作者：Wang, Xiangyu; Leng, Chenlei
  作者单位：Duke University; University of Warwick
  摘要：Variable selection is a challenging issue in statistical applications when the number of predictors p far exceeds the number of observations n. In this ultrahigh dimensional setting, the sure independence screening procedure was introduced to reduce the dimensionality significantly by preserving the true model with overwhelming probability, before a refined second-stage analysis. However, the aforementioned sure screening property strongly relies on the assumption that the important variables ...
• A tilting approach to ranking influence AI翻译
  作者：Genton, Marc G.; Hall, Peter
  作者单位：King Abdullah University of Science & Technology; University of Melbourne; University of California System; University of California Davis
  摘要：We suggest a new approach, which is applicable for general statistics computed from random samples of univariate or vector-valued or functional data, to assessing the influence that individual data have on the value of a statistic, and to ranking the data in terms of that influence. Our method is based on, first, perturbing the value of the statistic by 'tilting', or reweighting, each data value, where the total amount of tilt is constrained to be the least possible, subject to achieving a giv...
• Truncated linear models for functional data AI翻译
  作者：Hall, Peter; Hooker, Giles
  作者单位：University of Melbourne; Cornell University
  摘要：A conventional linear model for functional data involves expressing a response variable Y in terms of the explanatory function X(t), via the model Y=a+integral(I)b(t) X(t)dt + error, where a is a scalar, b is an unknown function and I = [0, alpha] is a compact interval. However, in some problems the support of b or X, I-1 say, is a proper and unknown subset of I, and is a quantity of particular practical interest. Motivated by a real data example involving particulate emissions, we develop met...