Results 211 to 220 of about 75,653 (260)

Boosting Regression Estimators

Neural Computation, 1999
There is interest in extending the boosting algorithm (Schapire, 1990) to fit a wide range of regression problems. The threshold-based boosting algorithm for regression used an analogy between classification errors and big errors in regression. We focus on the practical aspects of this algorithm and compare it to other attempts to extend boosting to ...
Ran Avnimelech, Nathan Intrator
openaire   +3 more sources

Regression Estimation in Simulation

Journal of the Operational Research Society, 1980
In simulation an input variable like interarrival time is sampled, and hence its average deviates from its known expectation. This information can be used to improve the estimated simulation response: regression sampling or control variate technique. The usual crude estimator is shown to be biased.
Hopmans, Anton C. M., Kleijnen, Jack P. C.   +1 more
openaire   +2 more sources

Estimating Mixtures of Regressions

Journal of Computational and Graphical Statistics, 2003
This article shows how Bayesian inference for switching regression models and their generalizations can be achieved by the specification of loss functions which overcome the label switching problem common to all mixture models. We also derive an extension to models where the number of components in the mixture is unknown, based on the birthand-death ...
Hurn, Merrilee, Justel, Ana, Robert, Christian P.   +2 more
openaire   +3 more sources

An Improved Estimator of the Variance of the Regression Estimator

Biometrical Journal, 1999
Summary: 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)].
Singh, Sarjinder, Horn, Stephen
openaire   +2 more sources

A robust Liu regression estimator

Communications in Statistics - Simulation and Computation, 2017
The least-squares regression estimator can be very sensitive in the presence of multicollinearity and outliers in the data. We introduce a new robust estimator based on the MM estimator.
Peter Filzmoser, Fatma Sevinç Kurnaz
openaire   +3 more sources

On Estimating Regression

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.
openaire   +3 more sources

A Quick Estimate of the Regression Coefficient

Biometrika, 1958
1. In this note we investigate some properties of a 'quick' estimator, b', of the regression coefficient /? of a variable y on a variable x. This estimate has the advantage over the least squares estimate that (a) it is applicable to certain types of censored data, and (b) it provides a consistent estimator (under certain restrictions) of the slope ...
Barton, D. E., Casley, D. J.
openaire   +2 more sources

Estimation of Multivariate Regression

Theory of Probability & Its Applications, 2004
Summary: Let \((X,Y)\) be a random vector whose first component takes values in a measurable space \(({\mathfrak{X}},{\mathfrak{A}},\mu)\) with measure \(\mu\), and let \(Y\) be a real-valued random variable. Let \(f(x)={\mathbf E}\{Y\mid X=x\} \) be the regression function of \(Y\) on \(X\).
openaire   +2 more sources

Estimating Equivalence with Quantile Regression

Ecological Applications, 2010
Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero.
openaire   +2 more sources

The Estimation of a Lagged Regression Relation

Biometrika, 1967
SUMMARY The paper discusses a technique for estimating the matrices of coefficients, B(j), in a regression relation relating a vector time series, z(n), to lagged values, y(n -j), -p < j < q, of a second vector time series. The technique depends upon calculation of spectra and crossspectra.
openaire   +3 more sources