Efficient resampling methods for nonsmooth estimating functions
نویسندگان
چکیده
منابع مشابه
Resampling methods in Microsoft Excel® for estimating reference intervals
Computer-intensive resampling/bootstrap methods are feasible when calculating reference intervals from non-Gaussian or small reference samples. Microsoft Excel® in version 2010 or later includes natural functions, which lend themselves well to this purpose including recommended interpolation procedures for estimating 2.5 and 97.5 percentiles. The purpose of this paper is to introduce the reade...
متن کاملResampling Methods for the Extrema of Certain Conditional Sample Functions
In this paper some resampling methods for the extrema of certain conditional functions, including delta method along with jackkniing and bootstrapping, are examined, and the properties of the resulted statistics are discussed.
متن کاملFingerprint resampling: A generic method for efficient resampling
In resampling methods, such as bootstrapping or cross validation, a very similar computational problem (usually an optimization procedure) is solved over and over again for a set of very similar data sets. If it is computationally burdensome to solve this computational problem once, the whole resampling method can become unfeasible. However, because the computational problems and data sets are ...
متن کاملResampling Methods for Sample Surveys
Application of resampling methods in sample survey settings presents considerable practical and conceptual difficulties. Various potential solutions have recently been proffered in the statistical literature. This paper provides a brief critical review of these methods. Our main conclusion is that, while resampling methods may be useful in some problems, there is little evidence of their useful...
متن کاملSmooth minimization of nonsmooth functions with parallel coordinate descent methods
We study the performance of a family of randomized parallel coordinate descent methods for minimizing the sum of a nonsmooth and separable convex functions. The problem class includes as a special case L1-regularized L1 regression and the minimization of the exponential loss (“AdaBoost problem”). We assume the input data defining the loss function is contained in a sparse m× n matrix A with at ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biostatistics
سال: 2007
ISSN: 1468-4357,1465-4644
DOI: 10.1093/biostatistics/kxm034