ERLS: Exponentially Weighted Recursive Least Squares with Missing...

In DLMRMV: Distributed Linear Regression Models with Response Missing Variables

Exponentially Weighted Recursive Least Squares with Missing Value Imputation

Description

Exponentially Weighted Recursive Least Squares with Missing Value Imputation

Usage

ERLS(data, rho = 0.01, lambda = 0.95, nb = 100, niter = 1)

Arguments

data	Linear regression dataset (1st column as Y, others as X)
rho	Regularization parameter
lambda	Forgetting factor
nb	Maximum iterations
niter	Initial iteration count (typically 1)

Value

List containing:

Yhat	Imputed response vector
betahat	Estimated coefficients

Examples

set.seed(123)
data <- data.frame(
  y = c(rnorm(50), rep(NA, 10)),
  x1 = rnorm(60),
  x2 = rnorm(60)
)
result <- ERLS(data, rho = 0.01, lambda = 0.95, nb = 100, niter = 1)
head(result$Yhat)

DLMRMV documentation built on Aug. 8, 2025, 6:27 p.m.