decode {rDecode} | R Documentation |
Descent-based Calibrated Optimal Direct Estimation
Description
Implement DECODE
for sigma
and beta
to estimate \Sigma^{-1}\beta
where sigma
is an estimator of \Sigma
and beta
is an estimator of \beta
.
Usage
decode(sigma, beta, lambda0, decode.tol = 1e-06, decode.maxit = 100,
trace = FALSE, solver = c("apg", "homotopy"), solver.tol = 1e-08,
solver.maxit = 10000, return.sigma = FALSE, return.beta = FALSE,
return.param = FALSE)
Arguments
sigma |
|
beta |
|
lambda0 |
number between 0 and 1. |
decode.tol |
error tolerance for |
decode.maxit |
maximum iterations for |
trace |
logical. If |
solver |
solver for |
solver.tol |
tolerance for solver. |
solver.maxit |
maximum iterations for solver (only for APG). |
return.sigma |
logical. If |
return.beta |
logical. If |
return.param |
logical. If |
Value
An object of class decode
containing:
eta |
|
theta |
final |
lambda |
final |
sigma.mult |
multiplier applied on |
total.iter |
number of iterations until convergence. |
call |
the matched call. |
method |
the solver used, if requested. |
lambda0 |
the |
decode.tol |
the |
decode.maxit |
the |
trace |
the |
solver.tol |
the |
solver.maxit |
the |
eta.trace |
matrix of |
theta.trace |
vector of |
lambda.trace |
vector of |
References
Pun, C. S. (2018). A Sparse Learning Approach to Relative-Volatility-Managed Portfolio Selection. Hadimaja, M. Z., & Pun, C. S. (2018). A Self-Calibrated Regularized Direct Estimation for Graphical Selection and Discriminant Analysis.
Examples
# estimate A^(-1) b with a certain lambda0
X <- matrix(rnorm(100), 10, 10)
A <- t(X) %*% X
b <- rnorm(10)
object <- decode(A, b, lambda0 = 0.8)
object
summary(object)
coef(object)