Results 221 to 230 of about 75,653 (260)

Estimation of Variance of the Regression Estimator

Journal of the American Statistical Association, 1987
Abstract The regression estimator and the ratio estimator are commonly used in survey practice. In the past more attention has been given to the ratio estimator because of its computational ease and applicability for general sampling designs. The ratio estimator is appropriate for populations whose regression line passes close to the origin.
Lih-Yuan Deng, C. F. J. Wu
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Variance estimation of survey estimates calibrated on estimated control totals—An application to the extended regression estimator and the regression composite estimator

Computational Statistics & Data Analysis, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yves G. Berger, Juan Francisco Muñoz, Eric Rancourt   +2 more
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Reporting the Fragility of Regression Estimates

The Review of Economics and Statistics, 1983
E MPIRICAL results reported in economics journals are selected from a large set of estimated models. Journals, through their editorial policies, engage in some selection, which in turn stimulates extensive model searching and prescreening by prospective authors.
Leamer, Edward E, Leonard, Herman B
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Estimation of Partial Regression Coefficient

Biometrical Journal, 1993
AbstractA sampling scheme providing unbiased partial regression coefficient has been proposed. The proposed sampling scheme is not only unbiased but also superior to simple random sampling and that due to Singh and Bathla (1990) for estimation of partial regression coefficient.
Singh, Padam, Talwar, Harsh Kumari
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ESTIMATION AND INFERENCE IN PREDICTIVE REGRESSIONS [PDF]

Hitotsubashi Journal of Economics, 2011
This paper proposes new point estimates for predictive regressions. Our estimates are easily obtained by the least squares and the instrumental variable methods. Our estimates, called the plug-in estimates, have nice asymptotic properties such as median unbiasedness and the approximated normality of the associated t-statistics. In addition, the plug-in
KUROZUMI, EIJI, AONO, KOHEI
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Extended Regression on Manifolds Estimation

2016
Let fX be unknown smooth function which maps p-dimensional manifold-valued inputs X, whose values lie on unknown Input manifold M of lower dimensionality qi¾?
Alexander P. Kuleshov, Alexander V. Bernstein   +1 more
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Shrinkage Estimators for Uplift Regression

2020
Uplift modeling is an approach to machine learning which allows for predicting the net effect of an action (with respect to not taking the action). To achieve this, the training population is divided into two parts: the treatment group, which is subjected to the action, and the control group, on which the action is not taken. Our task is to construct a
Krzysztof Rudas, Szymon Jaroszewicz
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The regression estimator in presence of not at home

Statistica & Applicazioni : VI, 1, 2008, 2008
Being temporarily not at home can often cause the impossibility to give out a questionnaire to each selected unit of a sample; moreover, a high percentage of nonrespondents can strongly affect the quality of estimates. In order to solve this problem the "not at home" are usually called back until they become available; however, this methodology highly ...
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Asymmetry of estimators in nonlinear regression

Biometrika, 1987
The distributions of estimators \({\hat \vartheta}\) of the parameter \(\vartheta\) of intrinsically nonlinear regression functions f(x,\(\vartheta)\) are unknown but even with normally distributed errors they are known to be skew. A measure of asymmetry \(\lambda\) (i) of the distribution of the i-th component \({\hat \vartheta}\)(i) of \({\hat ...
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An Implementation for Regression Quantile Estimation

2002
Of the many methods that have been developed for quantile regression (Wright and Royston, 1997) the LMS method (Cole and Green, 1992) can be easily understood, is flexible and based on splines. The basic idea is that, for a fixed value of the covariate, a Box-Cox transformation of the response is applied to obtain standard normality.
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