DERLS_InfoFilter: Distributed Exponentially Weighted Recursive Least Squares...

In DLMRMV: Distributed Linear Regression Models with Response Missing Variables

Distributed Exponentially Weighted Recursive Least Squares (DERLS) using Information Filter

Description

Impute missing values in the response variable Y using a distributed Exponentially Weighted Recursive Least Squares (DERLS) method that employs an Information Filter. Multiple independent runs are performed to stabilize coefficient estimates, and missing values are imputed recursively and refined over multiple iterations.

Usage

DERLS_InfoFilter(data, rho, lambda, R, nb)

Arguments

data	A data frame whose first column is the response variable Y (which may contain NAs), and the remaining columns are predictor variables X.
rho	Regularization parameter.
lambda	Forgetting factor.
R	Number of independent runs to stabilize estimates.
nb	Number of iterations per run.

Value

A list with two components:

Yhat	A numeric vector of length n equal to the number of rows in data. Missing values in the original Y have been imputed.
betahat	Numeric vector of final averaged regression coefficient estimates (length p, where p is the number of predictors).

Examples

set.seed(123)
n <- 60
data <- data.frame(
  Y = c(rnorm(n - 10), rep(NA, 10)),
  X1 = rnorm(n),
  X2 = rnorm(n)
)
result <- DERLS_InfoFilter(data, rho = 0.01, lambda = 0.95, R = 3, nb = 50)
head(result$Yhat)  # inspect imputed Y
result$betahat     # inspect estimated coefficients

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