| Name : R-asm
| |
| Version : 0.2.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.2
| Date : 2024-06-14 11:25:53
|
| Group : Development/Libraries/Other
| Source RPM : R-asm-0.2.0-lp153.2.2.src.rpm
|
| Size : 0.06 MB
| |
| Packager : (none)
| |
| Summary : Optimal Convex M-Estimation for Linear Regression via Antitonic Score Matching
|
Description :
Performs linear regression with respect to a data-driven convex loss
function that is chosen to minimize the asymptotic covariance of the
resulting M-estimator. The convex loss function is estimated in 5
steps: (1) form an initial OLS (ordinary least squares) or LAD (least
absolute deviation) estimate of the regression coefficients; (2) use
the resulting residuals to obtain a kernel estimator of the error
density; (3) estimate the score function of the errors by
differentiating the logarithm of the kernel density estimate; (4)
compute the L2 projection of the estimated score function onto the set
of decreasing functions; (5) take a negative antiderivative of the
projected score function estimate. Newton\'s method (with Hessian
modification) is then used to minimize the convex empirical risk
function. Further details of the method are given in Feng et al. (2024)
< doi:10.48550/arXiv.2403.16688>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
