fsim.kernel.fit: Functional single-index model fit using kernel estimation and...

In fsemipar: Estimation, Variable Selection and Prediction for Functional Semiparametric Models

Functional single-index model fit using kernel estimation and joint LOOCV minimisation

Description

This function fits a functional single-index model (FSIM) between a functional covariate and a scalar response. It employs kernel estimation with Nadaraya-Watson weights and uses B-spline expansions to represent curves and eligible functional indexes.

The function also utilises the leave-one-out cross-validation (LOOCV) criterion to select the bandwidth (h.opt) and the coefficients of the functional index in the spline basis (theta.est). It performs a joint minimisation of the LOOCV objective function in both the bandwidth and the functional index.

Usage

fsim.kernel.fit(x, y, seed.coeff = c(-1, 0, 1), order.Bspline = 3, 
nknot.theta = 3,  min.q.h = 0.05, max.q.h = 0.5, h.seq = NULL, num.h = 10, 
kind.of.kernel = "quad", range.grid = NULL, nknot = NULL, n.core = NULL)

Arguments

x	Matrix containing the observations of the functional covariate (i.e. curves) collected by row.
y	Vector containing the scalar response.
seed.coeff	Vector of initial values used to build the set \Theta_n (see section Details). The coefficients for the B-spline representation of each eligible functional index \theta \in \Theta_n are obtained from seed.coeff. The default is c(-1,0,1).
order.Bspline	Positive integer giving the order of the B-spline basis functions. This is the number of coefficients in each piecewise polynomial segment. The default is 3
nknot.theta	Positive integer indicating the number of regularly spaced interior knots in the B-spline expansion of \theta_0. The default is 3.
min.q.h	Minimum quantile order of the distances between curves, which are computed using the projection semi-metric. This value determines the lower endpoint of the range from which the bandwidth is selected. The default is 0.05.
max.q.h	Maximum quantile order of the distances between curves, which are computed using the projection semi-metric. This value determines the upper endpoint of the range from which the bandwidth is selected. The default is 0.5.
h.seq	Vector containing the sequence of bandwidths. The default is a sequence of num.h equispaced bandwidths in the range constructed using min.q.h and max.q.h.
num.h	Positive integer indicating the number of bandwidths in the grid. The default is 10.
kind.of.kernel	The type of kernel function used. Currently, only Epanechnikov kernel ("quad") is available.
range.grid	Vector of length 2 containing the endpoints of the grid at which the observations of the functional covariate x are evaluated (i.e. the range of the discretisation). If range.grid=NULL, then range.grid=c(1,p) is considered, where p is the discretisation size of x (i.e. ncol(x)).
nknot	Positive integer indicating the number of interior knots for the B-spline expansion of the functional covariate. The default value is (p - order.Bspline - 1)%/%2.
n.core	Number of CPU cores designated for parallel execution.The default is n.core<-availableCores(omit=1).

Details

The functional single-index model (FSIM) is given by the expression:

Y_i=r(\langle\theta_0,X_i\rangle)+\varepsilon_i, \quad i=1,\dots,n,

where Y_i denotes a scalar response, X_i is a functional covariate valued in a separable Hilbert space \mathcal{H} with an inner product \langle \cdot, \cdot\rangle. The term \varepsilon denotes the random error, \theta_0 \in \mathcal{H} is the unknown functional index and r(\cdot) denotes the unknown smooth link function.

The FSIM is fitted using the kernel estimator

\widehat{r}_{h,\hat{\theta}}(x)=\sum_{i=1}^nw_{n,h,\hat{\theta}}(x,X_i)Y_i, \quad \forall x\in\mathcal{H},

with Nadaraya-Watson weights

w_{n,h,\hat{\theta}}(x,X_i)=\frac{K\left(h^{-1}d_{\hat{\theta}}\left(X_i,x\right)\right)}{\sum_{i=1}^nK\left(h^{-1}d_{\hat{\theta}}\left(X_i,x\right)\right)},

where

• the real positive number h is the bandwidth.

• K is a kernel function (see the argument kind.of.kernel).

• d_{\hat{\theta}}(x_1,x_2)=|\langle\hat{\theta},x_1-x_2\rangle| is the projection semi-metric, and \hat{\theta} is an estimate of \theta_0.

The procedure requires the estimation of the function-parameter \theta_0. Therefore, we use B-spline expansions to represent curves (dimension nknot+order.Bspline) and eligible functional indexes (dimension nknot.theta+order.Bspline). Then, we build a set \Theta_n of eligible functional indexes by calibrating (to ensure the identifiability of the model) the set of initial coefficients given in seed.coeff. The larger this set is, the greater the size of \Theta_n. Since our approach requires intensive computation, a trade-off between the size of \Theta_n and the performance of the estimator is necessary. For that, Ait-Saidi et al. (2008) suggested considering order.Bspline=3 and seed.coeff=c(-1,0,1). For details on the construction of \Theta_n, see Novo et al. (2019).

We obtain the estimated coefficients of \theta_0 in the spline basis (theta.est) and the selected bandwidth (h.opt) by minimising the LOOCV criterion. This function performs a joint minimisation in both parameters, the bandwidth and the functional index, and supports parallel computation. To avoid parallel computation, we can set n.core=1.

Value

call	The matched call.
fitted.values	Estimated scalar response.
residuals	Differences between y and the fitted.values.
theta.est	Coefficients of \hat{\theta} in the B-spline basis: a vector of length(order.Bspline+nknot.theta).
h.opt	Selected bandwidth.
r.squared	Coefficient of determination.
var.res	Redidual variance.
df	Residual degrees of freedom.
yhat.cv	Predicted values for the scalar response using leave-one-out samples.
CV.opt	Minimum value of the CV function, i.e. the value of CV for theta.est and h.opt.
CV.values	Vector containing CV values for each functional index in \Theta_n and the value of h that minimises the CV for such index (i.e. CV.values[j] contains the value of the CV function corresponding to theta.seq.norm[j,] and the best value of the h.seq for this functional index according to the CV criterion).
H	Hat matrix.
m.opt	Index of \hat{\theta} in the set \Theta_n.
theta.seq.norm	The vector theta.seq.norm[j,] contains the coefficientes in the B-spline basis of the jth functional index in \Theta_n.
h.seq	Sequence of eligible values for h.
...

Author(s)

German Aneiros Perez [e-mail]

Silvia Novo Diaz [e-mail]

References

Ait-Saidi, A., Ferraty, F., Kassa, R., and Vieu, P. (2008) Cross-validated estimations in the single-functional index model. Statistics, 42(6), 475–494, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1080/02331880801980377")}.

Novo S., Aneiros, G., and Vieu, P., (2019) Automatic and location-adaptive estimation in functional single–index regression. Journal of Nonparametric Statistics, 31(2), 364–392, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1080/10485252.2019.1567726")}.

See Also

See also fsim.kernel.test, predict.fsim.kernel, plot.fsim.kernel.

Alternative procedure fsim.kNN.fit.

Examples

data(Tecator)
y<-Tecator$fat
X<-Tecator$absor.spectra2

#FSIM fit.
ptm<-proc.time()
fit<-fsim.kernel.fit(y[1:160],x=X[1:160,],max.q.h=0.35, nknot=20,
range.grid=c(850,1050),nknot.theta=4)
proc.time()-ptm
fit
names(fit)

fsemipar documentation built on May 29, 2024, 1:31 a.m.