RRRR: Robust Reduced-Rank Regression using...

In RRRR: Online Robust Reduced-Rank Regression Estimation

Robust Reduced-Rank Regression using Majorisation-Minimisation

Description

Majorisation-Minimisation based Estimation for Reduced-Rank Regression with a Cauchy Distribution Assumption. This method is robust in the sense that it assumes a heavy-tailed Cauchy distribution for the innovations. This method is an iterative optimization algorithm. See References for a similar setting.

Usage

RRRR(
  y,
  x,
  z = NULL,
  mu = TRUE,
  r = 1,
  itr = 100,
  earlystop = 1e-04,
  initial_A = matrix(rnorm(P * r), ncol = r),
  initial_B = matrix(rnorm(Q * r), ncol = r),
  initial_D = matrix(rnorm(P * R), ncol = R),
  initial_mu = matrix(rnorm(P)),
  initial_Sigma = diag(P),
  return_data = TRUE
)

Arguments

y	Matrix of dimension N*P. The matrix for the response variables. See Detail.
x	Matrix of dimension N*Q. The matrix for the explanatory variables to be projected. See Detail.
z	Matrix of dimension N*R. The matrix for the explanatory variables not to be projected. See Detail.
mu	Logical. Indicating if a constant term is included.
r	Integer. The rank for the reduced-rank matrix AB'. See Detail.
itr	Integer. The maximum number of iteration.
earlystop	Scalar. The criteria to stop the algorithm early. The algorithm will stop if the improvement on objective function is small than earlystop * objective_from_last_iteration.
initial_A	Matrix of dimension P*r. The initial value for matrix A. See Detail.
initial_B	Matrix of dimension Q*r. The initial value for matrix B. See Detail.
initial_D	Matrix of dimension P*R. The initial value for matrix D. See Detail.
initial_mu	Matrix of dimension P*1. The initial value for the constant mu. See Detail.
initial_Sigma	Matrix of dimension P*P. The initial value for matrix Sigma. See Detail.
return_data	Logical. Indicating if the data used is return in the output. If set to TRUE, update.RRRR can update the model by simply provide new data. Set to FALSE to save output size.

Details

The formulation of the reduced-rank regression is as follow:

y = μ +AB' x + D z+innov,

where for each realization y is a vector of dimension P for the P response variables, x is a vector of dimension Q for the Q explanatory variables that will be projected to reduce the rank, z is a vector of dimension R for the R explanatory variables that will not be projected, μ is the constant vector of dimension P, innov is the innovation vector of dimension P, D is a coefficient matrix for z with dimension P*R, A is the so called exposure matrix with dimension P*r, and B is the so called factor matrix with dimension Q*r. The matrix resulted from AB' will be a reduced rank coefficient matrix with rank of r. The function estimates parameters μ, A, B, D, and Sigma, the covariance matrix of the innovation's distribution, assuming the innovation has a Cauchy distribution.

Value

A list of the estimated parameters of class RRRR.

spec

The input specifications. N is the sample size.

history

The path of all the parameters during optimization and the path of the objective value.

mu

The estimated constant vector. Can be NULL.

A

The estimated exposure matrix.

B

The estimated factor matrix.

D

The estimated coefficient matrix of z.

Sigma

The estimated covariance matrix of the innovation distribution.

obj

The final objective value.

data

The data used in estimation if return_data is set to TRUE. NULL otherwise.

Author(s)

Yangzhuoran Yang

References

Z. Zhao and D. P. Palomar, "Robust maximum likelihood estimation of sparse vector error correction model," in2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 913–917,IEEE, 2017.

Examples

set.seed(2222)
data <- RRR_sim()
res <- RRRR(y=data$y, x=data$x, z = data$z)
res

RRRR documentation built on March 7, 2023, 8:02 p.m.