-
作者：HWANG, JTG; DASPEDDADA, S
作者单位：University of Virginia
摘要：This article deals with the construction of confidence intervals when the components of the location parameter mu of the random variable X, which is elliptically symmetrically distributed, are subject to order restrictions. Several domination results are proved by studying the derivative of the coverage probability of the confidence intervals centered at the improved point estimators. Consequently, we strengthen the previously known results regarding the simple ordering and obtain several new ...
-
作者：DONOHO, DL
作者单位：University of California System; University of California Berkeley
摘要：New formulas are given for the minimax linear risk in estimating a linear functional of an unknown object from indirect data contaminated with random Gaussian noise. The formulas cover a variety of loss functions and do not require the symmetry of the convex a priori class. It is shown that affine minimax rules are within a few percent of minimax even among nonlinear rules, for a variety of loss functions. It is also shown that difficulty of estimation is measured by the modulus of continuity ...
-
作者：KOUL, HL; OSSIANDER, M
作者单位：Oregon State University
摘要：This paper establishes the uniform closeness of a randomly weighted residual empirical process to its natural estimator via weak convergence techniques. The weights need not be independent, bounded or even square integrable. This result is used to yield the asymptotic uniform linearity of a class of rank statistics in pth-order autoregression models. The latter result, in turn, yields the asymptotic distributions of a class of robust and Jaeckel-type rank estimators. The main result is also us...
-
作者：BUJA, A
-
作者：HASTIE, T
-
作者：MCKEAGUE, IW; ZHANG, MJ
作者单位：Medical College of Wisconsin
摘要：A new approach to the problem of identifying a nonlinear time series model is considered, we argue that cumulative lagged conditional mean and variance functions are the appropriate 'signatures' of a nonlinear time series for the purpose of model identification, being analogous to cumulative distribution functions or cumulative hazard functions in iid models. We introduce estimators of the cumulative lagged conditional mean and variance functions and study their asymptotic properties. A goodne...
-
作者：MENG, XL
摘要：The fundamental result on the rate of convergence of the EM algorithm has proven to be theoretically valuable and practically useful. Here, this result is generalized to the ECM algorithm, a more flexible and applicable iterative algorithm proposed recently by Meng and Rubin. Results on the rate of convergence of variations of ECM are also presented. An example is given to show that intuitions accurate for complete-data iterative algorithms may not be trustworthy in the presence of missing data.
-
作者：MYKLAND, PA
摘要：Bartlett type identities are shown to exist for martingales. As applications, we give a cumulant-based proof of the martingale central limit theorem, and we give an algorithm for calculating approximate cumulants of the least squares estimator in the AR(1) process.
-
作者：CHEN, H; SHIAU, JJH
作者单位：National Yang Ming Chiao Tung University
摘要：Chen and Shiau showed that a two-stage spline smoothing method and the partial regression method lead to efficient estimators for the parametric component of a partially linear model when the smoothing parameter is a deterministic sequence tending to zero at an appropriate rate. This paper is concerned with the large-sample behavior of these estimators when the smoothing parameter is chosen by the generalized cross validation (GCV) method or Mallows' C(L). Under mild conditions, the estimated ...
-
作者：RUPPERT, D; CLINE, DBH
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：A modification of kernel density estimation is proposed. The first step is ordinary kernel estimation of the density and its cdf. In the second step the data are transformed, using this estimated cdf, to an approximate uniform (or normal or other target) distribution. The density and cdf of the transformed data are then estimated by the kernel method and, by change of variable, converted to new estimates of the density and the cdf of the original data. This process is repeated for a total of k...
