Results 341 to 350 of about 7,875,205 (365)
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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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Simple Baselines for Human Pose Estimation and Tracking
European Conference on Computer Vision, 2018There has been significant progress on pose estimation and increasing interests on pose tracking in recent years. At the same time, the overall algorithm and system complexity increases as well, making the algorithm analysis and comparison more difficult.
Bin Xiao, Haiping Wu, Yichen Wei
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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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Maximum Likelihood Estimation of Misspecified Models
, 1982This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be ...
H. White
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Multivariate Density Estimation, Theory, Practice and Visualization
, 1992Representation and Geometry of Multivariate Data. Nonparametric Estimation Criteria. Histograms: Theory and Practice. Frequency Polygons. Averaged Shifted Histograms. Kernel Density Estimators.
D. W. Scott
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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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Theory of Probability & Its Applications, 1964
A study is made of certain properties of an approximation to the regression line on the basis of sampling data when the sample size increases unboundedly.
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A study is made of certain properties of an approximation to the regression line on the basis of sampling data when the sample size increases unboundedly.
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