Springer Science & Business Media, Apr 20, 2000 - Business & Economics - 177 pages
Adaptive Regression is intended for researchers and students of statistics who are interested in adaptive estimation and testing and their mathematical properties in the context of linear regression. There have been a large number of estimation methods pr
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
a-trimmed LS absolutely continuous adaptive combination adaptive procedures assume asymptotic distribution asymptotic representation asymptotic variance asymptotically normally distributed based on regression Boscovich Brownian bridge calculate Chapter choice combination of LAD component computed consistent estimator convex combination corresponding data set decision procedure defined denote density f design matrix diagonal elements Dodge and Jurecková equations errors F-test finite fixed Gutenbrunner hence Koenker and Bassett kurtosis L1-norm LAD and LS LAD estimator LAD+TLS least absolute deviations least squares estimator leverage linear model linear regression model Ln(a location model LS-TLS M-test mean squared error median-type test minimization normal distribution obtain off-diagonal elements Op(n optimal order statistics parameter problem proposed regression coefficients regression quantile regression rank scores residuals resulting estimator S-PLUS sample satisfying Sn(Y solution studentized M-estimators studentized residuals symmetric TABLE Theorem trimmed least squares trimmed LS estimator trimmed mean trimming proportion vector
Page 162 - Dodge, Y. (1984). Robust estimation of regression coefficients by minimizing a convex combination of least squares and least absolute deviations. Computational Statistics Quarterly, vol. 1, pp. 139-153. Field, CA, and EM Ronchetti (1991). An overview of small sample asymptotic«.