Sentence examples for use of robust regression from inspiring English sources

The robustness of the r-DFAn procedure is established by the systematic use of robust regression, piecewise linear regression, Analysis of Covariance (ANCOVA) and Multiple Comparison Procedure to determine statistically significant scaling exponents and optimum crossover locations.

In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration.

The current paper will illustrate the use of robust regression for SHM data analysis through experimental data acquired from the Z24 and Tamar Bridges, although the methods are general and not restricted to SHM or civil infrastructure.

The use of robust regression methods allows reduction of the contribution of extreme data points in a data set.

For summary statistics we recommend the use of robust regression on order statistics (ROS), which has been shown to produce reliable summary estimates of personal measurements with a substantive proportion of nondetects [ 30].

Neither method was found to be suitable for the analysis of very nonnormally distributed traits, although it is worth noting that the prospective QTDTM method could easily be extended to enable the analysis of nonnormal traits by use of robust regression, a generalised linear model (GLM), or by assuming a variance-mean relationship, according to the departure from normality.

Several sensitivity analyses, including the use of robust regressions and of nonparametric methods for fitting time trends and temperature effects in the data, supported these findings.

A second type of robust regression using a heteroscedastic autocorrelation consistent (HAC) estimator, which produces robust slope and variance estimates has been shown to perform well, with better Type I error control, but with large sample sizes (500 1000 subjects).

Two different types of robust regression procedures are utilized.

Although the mean is rather sensitive in the presence of outliers, the use of a robust regression function circumvents this limitation.

Ortiz et al. (2006) discussed some of the robust methods used for robust regression in analytical chemistry.