u.felmKL: Find the envelope dimensions in the functional envelope...

In Renvlp: Computing Envelope Estimators

Find the envelope dimensions in the functional envelope linear model

Description

Fit the dimensions of the response and predictor envelopes in function-on-function linear regression, under Karhunen-Loeve expansion based estimation.

Usage

  u.felmKL(X, Y, t1, t2, knots = c(0, 0.25, 0.5, 0.75, 1))

Arguments

X	Predictor function. An n by T1 matrix, T1 is number of observed time points, which is the length of t1. Here we assume that each function is observed at the same time points.
Y	Response function. An n by T2 matrix, T2 is number of observed time points, which is the length of t2. Here we assume that each function is observed at the same time points.
t1	The observed time points for the predictor functions.
t2	The observed time points for the response functions.
knots	The location of knots of the cubic splines used for estimation. Locations should be positive. The default location of the knots are 0, 0.25, 0.5, 0.75, 1.

Details

This function finds the dimension of the predictor and response envelope model by Bayesian information criterion (BIC) performed on the Karhunen-Lo'eve expansion based estimation. To be more specific, consider the envelope model to the function-on-function linear regression,

Y = \alpha + B X + \epsilon,

where X and Y are random functions in Hilbert spaces H_X and H_Y, \alpha is a fixed member in H_Y, \epsilon is a random member of H_Y, and B: H_X -> H_Y is a linear operator. We use cubic splines as the basis for both H_X and H_Y in the estimation of the eigenfunctions of Sigma_X and Sigma_\epsilon. The coefficients [X] and [Y] with respect to the estimated eigenfunctions are computed. The predictor and response envelope model is fitted on the linear regression model of [Y] on [X], and the dimensions of the predictor and response envelopes are calculated using BIC. The details are included in Section 7 of Su et al. (2022).

Value

The output is a list that contains the following components:

ux	The estimated dimension of the predictor envelope.
uy	The estimated dimension of the response envelope.
beta	The envelope estimator of the regression coefficients in the regression of [Y] on [X], when the dimensions of envelopes are taken at their estimated values.
betafull	The standard estimator, i.e., the OLS estimator of the regression coefficients in the regression of [Y] on [X].
alpha	The envelope estimator of the intercept in the regression of [Y] on [X], when the dimensions of envelopes are taken at their estimated values.
alphafull	The standard estimator of the intercept in the regression of [Y] on [X].

References

Su, Z., Li, B. and Cook, R. D. (2022+) Envelope model for function-on-function linear regression.

Examples

  data(NJdata)
  dataX <- matrix(NJdata[,6], nrow = 21)
  X <- as.matrix(dataX[, 32:61])
  dataY <- matrix(NJdata[,3], nrow = 21)
  Y <- as.matrix(dataY[, 32:61])
  t1 <- 0:29
  t2 <- t1
  
  ## Not run: m <- u.felmKL(X, Y, t1, t2)
  ## Not run: m$ux
  ## Not run: m$uy

Renvlp documentation built on Oct. 11, 2023, 1:06 a.m.