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A distributed one-step estimator
Mathematical programming, 2015Distributed statistical inference has recently attracted enormous attention. Many existing work focuses on the averaging estimator, e.g., Zhang and Duchi (J Mach Learn Res 14:3321–3363, 2013) together with many others.
Cheng Huang, X. Huo
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2007
This chapter describes the four main methods of estimating and their approximate levels of accuracy. These methods are: subjective (±20%–40%), parametric (±10%–20%), comparative (±10%) and analytical (±5%). The need to always allow a contingency is stressed and a diagram shows how the accuracy percentage improves as the project moves from the concept ...
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This chapter describes the four main methods of estimating and their approximate levels of accuracy. These methods are: subjective (±20%–40%), parametric (±10%–20%), comparative (±10%) and analytical (±5%). The need to always allow a contingency is stressed and a diagram shows how the accuracy percentage improves as the project moves from the concept ...
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2003
We know from our basic knowledge of statistics that one of the objectives in statistics is to better understand and model the underlying process which generates data. This is known as statistical inference: we infer from information contained in sample properties of the population from which the observations are taken.
Léopold Simar, Wolfgang Karl Härdle
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We know from our basic knowledge of statistics that one of the objectives in statistics is to better understand and model the underlying process which generates data. This is known as statistical inference: we infer from information contained in sample properties of the population from which the observations are taken.
Léopold Simar, Wolfgang Karl Härdle
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, 1991 This Chapter 6 and the next Chapter 7 are devoted to the proof of Theorem 1.2. In this chapter we study the operator Ap, and prove a priori estimates for the operator Ap − λI (Theorem 6.3) which will play a fundamental role in the next chapter. In the proof we make good use of Agmon’s method (Proposition 6.4). This is a technique of treating a spectral
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An Improved Estimator of the Variance of the Regression Estimator
Biometrical Journal, 1999Summary: The problem of estimation of variance of the general linear regression estimator has been considered. It has been shown that the first order calibration approach is a special case of the class of estimators proposed by \textit{L.-Y. Deng} and \textit{C. F. J. Wu} [J. Am. Stat. Assoc. 82, 568-576 (1987; Zbl 0629.62016)].
Stephen Horn, Sarjinder Singh
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, 2005
The purpose of this review is to clarify the concepts of bias, precision and accuracy as they are commonly defined in the biostatistical literature, with our focus on the use of these concepts in quantitatively testing the performance of point estimators
B. Walther, Joslin L. Moore
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The purpose of this review is to clarify the concepts of bias, precision and accuracy as they are commonly defined in the biostatistical literature, with our focus on the use of these concepts in quantitatively testing the performance of point estimators
B. Walther, Joslin L. Moore
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
, 1989An unbiased stochastic estimator of tr(I-A), where A is the influence matrix associated with the calculation of Laplacian smoothing splines, is described.
M. Hutchinson
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Estimating the Loss of Estimators of a Binomial Parameter
Biometrika, 1988A loss function is introduced, which combines the estimation error of a statistical procedure with a measure of its accuracy. The properties of this loss function are illustrated by the estimator of a binomial parameter. The choice of a conjugate prior distribution is discussed from this point of view.
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