asm: Linear regression via antitonic score matching

In asm: Optimal Convex M-Estimation for Linear Regression via Antitonic Score Matching

Linear regression via antitonic score matching

Description

Performs linear regression with a data-driven convex loss function

Usage

asm(formula, data = NULL, ...)

Arguments

formula	regression formula
data	input data frame
...	additional arguments for asm.fit

Value

asm class object containing the following components:

betahat:

vector of estimated coefficients

std_errs:

vector of standard errors of the estimated coefficients

fitted.values:

fitted values

residuals:

residuals

zvals:

z-values

sig_vals:

p-values

info_asm:

antitonic information

I_mat:

estimated antitonic information matrix

Cov_mat:

covariance matrix of the estimated coefficients

psi:

estimated antitonic score function

Examples

asm(mpg ~ cyl + hp + disp, data=mtcars)

asm(mpg ~ cyl + hp + disp, data=mtcars, symmetric=FALSE)

n <- 1000 ; d <- 2
X <- matrix(rnorm(n * d), n, d)
Y <- X %*% c(2, 3) + 1 + rnorm(n)
asm(Y ~ X - 1)

Y <- X %*% c(2, 3) + rchisq(n, 6) - qchisq(0.4, 6)
asm(Y ~ X, symmetric=FALSE, intercept.selection="quantile", error_quantile=0.4)

Y <- X %*% c(2, 3) + rcauchy(n)
asm(Y ~ X, symmetric=FALSE, intercept.selection="median")

asm documentation built on June 8, 2025, 1:52 p.m.