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asm  

Optimal Convex M-Estimation for Linear Regression via Antitonic Score Matching
View on CRAN: Click here


Download and install asm package within the R console
Install from CRAN:
install.packages("asm")

Install from Github:
library("remotes")
install_github("cran/asm")

Install by package version:
library("remotes")
install_version("asm", "0.2.1")



Attach the package and use:
library("asm")
Maintained by
Min Xu
[Scholar Profile | Author Map]
First Published: 2024-04-25
Latest Update: 2024-04-25
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>.
How to cite:
Min Xu (2024). asm: Optimal Convex M-Estimation for Linear Regression via Antitonic Score Matching. R package version 0.2.1, https://cran.r-project.org/web/packages/asm. Accessed 17 Feb. 2025.
Previous versions and publish date:
0.1.0 (2024-04-25 17:40), 0.2.0 (2024-05-11 04:23)
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