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最新文章

• Empirical process of residuals for high-dimensional linear models AI翻译
  作者：Mammen, E
  摘要：We give a stochastic expansion for the empirical distribution function (F) over cap(n)$ Of residuals in a p-dimensional linear model. This expansion holds for p increasing with n. It shows that, for high-dimensional linear models, (F) over cap(n)$ strongly depends on the chosen estimator <(theta)over cap> of the parameter theta of the linear model. In particular, if one uses an ML-estimator <(theta)over cap (ML)> which is motivated by a wrongly specified error distribution function G, then (F)...
• Convergence properties of the Gibbs sampler for perturbations of Gaussians AI翻译
  作者：Amit, Y
  摘要：The exact second eigenvalue of the Markov operator of the Gibbs sampler with random sweep strategy for Gaussian densities is calculated, A comparison lemma yields an upper hound on the second eigenvalue for bounded perturbations of Gaussians which is a significant improvement over previous bounds. For two-block Gibbs sampler algorithms with a perturbation of the form chi(g(1)(x((1))) + g(2)(x((2)))) the derivative of the second eigenvalue of the algorithm is calculated exactly at chi = 0, in t...
• Asymptotics of some estimators and sequential residual empiricals in nonlinear time series AI翻译
  作者：Koul, HL
  摘要：This paper establishes the asymptotic uniform linearity of M- and R-scores in a family of nonlinear time series and regression models. It also gives an asymptotic expansion of the standardized sequential residual empirical process in these models. These results are, in turn, used to obtain the asymptotic normality of certain classes of M-, R- and minimum distance estimators of the underlying parameters. The classes of estimators considered include analogs of Hodges-Lehmann, Huber and LAD (leas...
• Quasi-likelihood models and optimal inference AI翻译
  作者：Wefelmeyer, W
  摘要：Consider an ergodic Markov chain on the real line, with parametric models for the conditional mean and variance of the transition distribution. Such a setting is an instance of a quasi-likelihood model. The customary estimator for the parameter is the maximum quasi-likelihood estimator. It is not efficient, but as good as the best estimator that ignores the parametric model for the conditional variance. We construct two efficient estimators. One is a convex combination of solutions of two esti...
• Optimal rates of convergence of empirical Bayes tests for the continuous one-parameter exponential family AI翻译
  作者：Karunamuni, RJ
  摘要：The empirical Bayes linear loss two-action problem in the continuous one-parameter exponential family is studied, Previous results on this problem construct empirical Bayes tests via kernel density estimates, They also obtain upper bounds for the unconditional regret at some prior distribution. In this paper, we discuss the general question of how difficult the above empirical Bayes problem is, and why empirical Bayes rules based on kernel density estimates are useful. Asymptotic minimax-type ...