knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.align = "center", fig.cap = " ",dpi = 120,results = "hold")
In this vignette, we assume a simple parametric model of the form
$$ y_i = \mu_i + \epsilon_i, $$
where $\epsilon_i \sim N(0,\sigma_i^2)$, and both $\mu$ and $\sigma$ are unknowns. We use the "smash" procedure (SMoothing via Adaptive SHrinkage) to estimate both the mean and the variances. Here we present a brief demonstration of the method.
We first look at mean estimation, which is our primary focus. A sample mean function is presented, as well as a couple of different variance functions. Our method is compared against a few other simple methods.
We begin by loading the MASS, smashr, EbayesThresh and wavethresh packages.
library(MASS) library(smashr) library(EbayesThresh) library(wavethresh)
First, we define the mean function used to simulate the data.
spike.f <- function(x) (0.75 * exp(-500 * (x - 0.23)^2) + 1.5 * exp(-2000 * (x - 0.33)^2) + 3 * exp(-8000 * (x - 0.47)^2) + 2.25 * exp(-16000 * (x - 0.69)^2) + 0.5 * exp(-32000 * (x - 0.83)^2)) n <- 1024 t <- 1:n/n mu.s <- spike.f(t)
Define a few other functions which will be used in the code chunks below.
mse <- function(x, y) mean((x - y)^2) l2norm <- function(x) sum(x^2) mise <- function(x, y, r) 10000 * mean(apply(x - rep(1, r) %o% y, 1, l2norm)/l2norm(y)) sig.est.func <- function(x, n) sqrt(2/(3 * (n - 2)) * sum((1/2 * x[1:(n - 2)] - x[2:(n - 1)] + 1/2 * x[3:n])^2))
Define a function for the default wavelet thresholding method.
waveti.u <- function(x, filter.number = 10, family = "DaubLeAsymm", min.level = 3, noise.level) { TT = length(x) thresh = noise.level * sqrt(2 * log(TT)) x.w = wavethresh::wd(x, filter.number, family, type = "station") x.w.t = threshold(x.w, levels = (min.level):(x.w$nlevels - 1), policy = "manual", value = thresh, type = "hard") x.w.t.r = AvBasis(convert(x.w.t)) return(x.w.t.r) }
Define another function for the default EbayesThresh method.
waveti.ebayes <- function(x, filter.number = 10, family = "DaubLeAsymm", min.level = 3, noise.level) { n = length(x) J = log2(n) x.w = wd(x, filter.number, family, type = "station") for (j in min.level:(J - 1)) { x.pm = ebayesthresh(accessD(x.w, j), sdev = noise.level) x.w = putD(x.w, j, x.pm) } mu.est = AvBasis(convert(x.w)) return(mu.est) }
For the first demonstration, define the mean and variance functions, and set the signal to noise ratio.
mu.t <- (1 + mu.s)/5 rsnr <- sqrt(1) var1 <- rep(1, n) var2 <- (1e-04 + 4 * (exp(-550 * (t - 0.2)^2) + exp(-200 * (t - 0.5)^2) + exp(-950 * (t - 0.8)^2)))/1.35
We first look at the case of constant variance.
set.seed(327) sigma.ini <- sqrt(var1) sigma.t <- sigma.ini/mean(sigma.ini) * sd(mu.t)/rsnr^2 X.s <- matrix(rnorm(10 * n, mu.t, sigma.t), nrow = 10, byrow = TRUE) mu.est <- apply(X.s, 1, smash.gaus) mu.est.tivar.ash <- apply(X.s, 1, ti.thresh, method = "smash") mu.est.tivar.mad <- apply(X.s, 1, ti.thresh, method = "rmad") mu.est.ti <- matrix(0, 10, n) mu.est.ti.ebayes <- matrix(0, 10, n) for (i in 1:10) { sig.est = sig.est.func(X.s[i, ], n) mu.est.ti[i, ] = waveti.u(X.s[i, ], noise.level = sig.est) mu.est.ti.ebayes[i, ] = waveti.ebayes(X.s[i, ], noise.level = sig.est) }
Assess the accuracy of the results.
cat("SMASH:",mise(t(mu.est), mu.t, 10),"\n") cat("TI thresholding with variance estimated from smash:", mise(t(mu.est.tivar.ash), mu.t, 10),"\n") cat("TI thresholding with variance estimated from running MAD:", mise(t(mu.est.tivar.mad), mu.t, 10),"\n") cat("TI thresholding with constant variance (estimated):", mise(mu.est.ti, mu.t, 10),"\n") cat("EBayes with constant variance (estimated):", mise(mu.est.ti.ebayes, mu.t, 10),"\n")
Plot the estimated mean functions against the ground-truth function (in black).
plot(mu.t, xlab = "", ylab = "", type = "l") lines(mu.est[, 1], col = 2) lines(mu.est.tivar.mad[, 1], col = 3) lines(mu.est.ti[1, ], col = 4) lines(mu.est.ti.ebayes[1, ], col = 6) legend("topright", legend = c("smash", "ti_rmad", "ti_homo", "ebayes_homo"), fill = c(2, 3, 4, 6))
Generate the data for this example.
sigma.ini = sqrt(var2) sigma.t = sigma.ini/mean(sigma.ini) * sd(mu.t)/rsnr^2 set.seed(327) X.s = matrix(rnorm(10 * n, mu.t, sigma.t), nrow = 10, byrow = TRUE) mu.est = apply(X.s, 1, smash.gaus) mu.est.tivar.ash = apply(X.s, 1, ti.thresh, method = "smash") mu.est.tivar.mad = apply(X.s, 1, ti.thresh, method = "rmad") mu.est.ti = matrix(0, 10, n) mu.est.ti.ebayes = matrix(0, 10, n) for (i in 1:10) { sig.est = sig.est.func(X.s[i, ], n) mu.est.ti[i, ] = waveti.u(X.s[i, ], noise.level = sig.est) mu.est.ti.ebayes[i, ] = waveti.ebayes(X.s[i, ], noise.level = sig.est) }
Assess the accuracy of the results.
cat("SMASH:",mise(t(mu.est), mu.t, 10),"\n") cat("TI thresholding with variance estimated from smash:", mise(t(mu.est.tivar.ash), mu.t, 10),"\n") cat("TI thresholding with variance estimated from running MAD:", mise(t(mu.est.tivar.mad), mu.t, 10),"\n") cat("TI thresholding with constant variance (estimated):", mise(mu.est.ti, mu.t, 10),"\n") cat("EBayes with constant variance (estimated):", mise(mu.est.ti.ebayes, mu.t, 10) ,"\n")
Plot the estimated mean functions against the ground-truth function (in black).
plot(mu.t, xlab = "", ylab = "", type = "l") lines(mu.est[, 1], col = 2) lines(mu.est.tivar.mad[, 1], col = 3) lines(mu.est.ti[1, ], col = 4) lines(mu.est.ti.ebayes[1, ], col = 6) legend("topright", legend = c("smash", "ti_rmad", "ti_homo", "ebayes_homo"), fill = c(2, 3, 4, 6))
Next we look at variance estimation. Because variance estimation is much harder, we use a larger sample size. In this example, we set $n = 4096$.
n = 2^12 t = 1:n/n mu.s = spike.f(t)
Define the test functions and generate the data.
pos = c(0.1, 0.13, 0.15, 0.23, 0.25, 0.4, 0.44, 0.65, 0.76, 0.78, 0.81) hgt = c(4, 5, 3, 4, 5, 4.2, 2.1, 4.3, 3.1, 5.1, 4.2) wth = c(0.005, 0.005, 0.006, 0.01, 0.01, 0.03, 0.01, 0.01, 0.005, 0.008, 0.005) mu.b = rep(0, n) for (j in 1:length(pos)) { mu.b = mu.b + hgt[j]/((1 + (abs(t - pos[j])/wth[j]))^4) } dop.f = function(x) sqrt(x * (1 - x)) * sin((2 * pi * 1.05)/(x + 0.05)) mu.dop = dop.f(t) var1.ini = ((3 - 20 * t) * (t >= 0 & t < 0.1) + (20 * t - 1) * (t >= 0.1 & t < 0.25) + (4 + (1 - 4 * t) * 18/19) * (t >= 0.25 & t < 0.725) + (2.2 + 10 * (t - 0.725)) * (t >= 0.725 & t < 0.89) + (3.85 - 85 * (t - 0.89)/11) * (t >= 0.89 & t <= 1)) var1 = var1.ini/sqrt(var(var1.ini)) var2.ini = (1 + 4 * (exp(-550 * (t - 0.2)^2) + exp(-200 * (t - 0.5)^2) + exp(-950 * (t - 0.8)^2))) var2 = var2.ini/sqrt(var(var2.ini)) var3 = mu.b/sqrt(var(mu.b)) var4 = (mu.dop + 2)/sqrt(var(mu.dop)) sigma.t.1 = sqrt(var1) sigma.t.2 = sqrt(var2) sigma.t.3 = sqrt(var3) sigma.t.4 = sqrt(var4) set.seed(327) X.s.1 = rnorm(n, 0, sigma.t.1) set.seed(327) X.s.2 = rnorm(n, 0, sigma.t.2) set.seed(327) X.s.3 = rnorm(n, 0, sigma.t.3) set.seed(327) X.s.4 = rnorm(n, 0, sigma.t.4)
Fit the SMASH model to these data.
var.est.1 = smash.gaus(X.s.1, v.est = TRUE) var.est.2 = smash.gaus(X.s.2, v.est = TRUE) var.est.3 = smash.gaus(X.s.3, v.est = TRUE) var.est.4 = smash.gaus(X.s.4, v.est = TRUE)
Evaluate the fit of these models.
mse(var.est.1, sigma.t.1^2) mse(var.est.2, sigma.t.2^2) mse(var.est.3, sigma.t.3^2) mse(var.est.4, sigma.t.4^2)
These plots show the ground-truth variance (black) against the variance functions estimated by SMASH (red).
par(mfrow = c(2, 2)) plot(sigma.t.1^2, xlab = "", ylab = "", type = "l", main = "V1") lines(var.est.1, col = 2) plot(sigma.t.2^2, xlab = "", ylab = "", type = "l", main = "V2") lines(var.est.2, col = 2) plot(sigma.t.3^2, xlab = "", ylab = "", type = "l", main = "V3") lines(var.est.3, col = 2) plot(sigma.t.4^2, xlab = "", ylab = "", type = "l", main = "V4") lines(var.est.4, col = 2)
In this section, we examine how this wavelet-based approach handles unevenly spaced data. Wavelet methods are not optimized for unevenly spaced data, but still works. We first look at the case when the data are normally spaced.
n = 1024 t = sort(rnorm(1024, 0, 1)) t = (t - min(t))/(max(t) - min(t)) mu.s = spike.f(t)
Define mean function, signal-to-noise ratio, and the variance functions.
mu.t = (1 + mu.s)/5 rsnr = sqrt(1) var1 = rep(1, n) var2 = (1e-04 + 4 * (exp(-550 * (t - 0.2)^2) + exp(-200 * (t - 0.5)^2) + exp(-950 * (t - 0.8)^2)))/1.35
First, we look at the case of constant variance.
sigma.ini = sqrt(var1) sigma.t = sigma.ini/mean(sigma.ini) * sd(mu.t)/rsnr^2 set.seed(327) X.s = matrix(rnorm(10 * n, mu.t, sigma.t), nrow = 10, byrow = TRUE)
Apply the methods on the data.
mu.est = apply(X.s, 1, smash.gaus) mu.est.tivar.ash = apply(X.s, 1, ti.thresh, method = "smash") mu.est.tivar.mad = apply(X.s, 1, ti.thresh, method = "rmad") mu.est.ti = matrix(0, 10, n) mu.est.ti.ebayes = matrix(0, 10, n) for (i in 1:10) { sig.est = sig.est.func(X.s[i, ], n) mu.est.ti[i, ] = waveti.u(X.s[i, ], noise.level = sig.est) mu.est.ti.ebayes[i, ] = waveti.ebayes(X.s[i, ], noise.level = sig.est) }
Assess the accuracy of the estimates.
cat("SMASH:",mise(t(mu.est), mu.t, 10),"\n") cat("TI thresholding with variance estimated from smash:", mise(t(mu.est.tivar.ash), mu.t, 10),"\n") cat("TI thresholding with variance estimated from running MAD:", mise(t(mu.est.tivar.mad), mu.t, 10),"\n") cat("TI thresholding with constant variance (estimated):", mise(mu.est.ti, mu.t, 10),"\n") cat("EBayes with constant variance (estimated):", mise(mu.est.ti.ebayes, mu.t, 10),"\n")
Assess the accuracy over a grid.
xgrid = 1:n/n mu.est.inter = matrix(0, 10, n) mu.est.tivar.ash.inter = matrix(0, 10, n) mu.est.tivar.mad.inter = matrix(0, 10, n) mu.est.ti.inter = matrix(0, 10, n) mu.est.ti.ebayes.inter = matrix(0, 10, n) for (i in 1:10) { mu.est.inter[i, ] = approx(t, mu.est[, i], xgrid)$y mu.est.tivar.ash.inter[i, ] = approx(t, mu.est.tivar.ash[, i], xgrid)$y mu.est.tivar.mad.inter[i, ] = approx(t, mu.est.tivar.mad[, i], xgrid)$y mu.est.ti.inter[i, ] = approx(t, mu.est.ti[i, ], xgrid)$y mu.est.ti.ebayes.inter[i, ] = approx(t, mu.est.ti.ebayes[i, ], xgrid)$y } mu.t.inter = (spike.f(xgrid) + 1)/5 mise(mu.est.inter, mu.t.inter, 10) mise(mu.est.tivar.ash.inter, mu.t.inter, 10) mise(mu.est.tivar.mad.inter, mu.t.inter, 10) mise(mu.est.ti.inter, mu.t.inter, 10) mise(mu.est.ti.ebayes.inter, mu.t.inter, 10)
Plot the estimated mean functions against the ground-truth function (in black).
par(mfrow = c(1, 1)) plot(t, mu.t, type = "l") lines(t, mu.est[, 1], col = 2) lines(t, mu.est.tivar.mad[, 1], col = 3) lines(t, mu.est.ti[1, ], col = 4) lines(t, mu.est.ti.ebayes[1, ], col = 6) legend("topright", legend = c("smash", "ti_rmad", "ti_homo", "ebayes_homo"), fill = c(2, 3, 4, 6))
We then look at the case of non-constant variance:
sigma.ini = sqrt(var2) sigma.t = sigma.ini/mean(sigma.ini) * sd(mu.t)/rsnr^2 set.seed(327) X.s = matrix(rnorm(10 * n, mu.t, sigma.t), nrow = 10, byrow = TRUE)
mu.est = apply(X.s, 1, smash.gaus) mu.est.tivar.ash = apply(X.s, 1, ti.thresh, method = "smash") mu.est.tivar.mad = apply(X.s, 1, ti.thresh, method = "rmad") mu.est.ti = matrix(0, 10, n) mu.est.ti.ebayes = matrix(0, 10, n) for (i in 1:10) { sig.est = sig.est.func(X.s[i, ], n) mu.est.ti[i, ] = waveti.u(X.s[i, ], noise.level = sig.est) mu.est.ti.ebayes[i, ] = waveti.ebayes(X.s[i, ], noise.level = sig.est) }
Assess the accuracy of the estimates.
cat("SMASH:",mise(t(mu.est), mu.t, 10),"\n") cat("TI thresholding with variance estimated from smash:", mise(t(mu.est.tivar.ash), mu.t, 10),"\n") cat("TI thresholding with variance estimated from running MAD:", mise(t(mu.est.tivar.mad), mu.t, 10),"\n") cat("TI thresholding with constant variance (estimated):", mise(mu.est.ti, mu.t, 10),"\n") cat("EBayes with constant variance (estimated):", mise(mu.est.ti.ebayes, mu.t, 10),"\n")
Assess the accuracy over a grid.
xgrid = 1:n/n mu.est.inter = matrix(0, 10, n) mu.est.tivar.ash.inter = matrix(0, 10, n) mu.est.tivar.mad.inter = matrix(0, 10, n) mu.est.ti.inter = matrix(0, 10, n) mu.est.ti.ebayes.inter = matrix(0, 10, n) for (i in 1:10) { mu.est.inter[i, ] = approx(t, mu.est[, i], xgrid)$y mu.est.tivar.ash.inter[i, ] = approx(t, mu.est.tivar.ash[, i], xgrid)$y mu.est.tivar.mad.inter[i, ] = approx(t, mu.est.tivar.mad[, i], xgrid)$y mu.est.ti.inter[i, ] = approx(t, mu.est.ti[i, ], xgrid)$y mu.est.ti.ebayes.inter[i, ] = approx(t, mu.est.ti.ebayes[i, ], xgrid)$y } mu.t.inter = (spike.f(xgrid) + 1)/5 mise(mu.est.inter, mu.t.inter, 10) mise(mu.est.tivar.ash.inter, mu.t.inter, 10) mise(mu.est.tivar.mad.inter, mu.t.inter, 10) mise(mu.est.ti.inter, mu.t.inter, 10) mise(mu.est.ti.ebayes.inter, mu.t.inter, 10)
Plot the estimated mean functions against the ground-truth function (in black).
par(mfrow = c(1, 1)) plot(t, mu.t, xlab = "", ylab = "", type = "l") lines(t, mu.est[, 1], col = 2) lines(t, mu.est.tivar.mad[, 1], col = 3) lines(t, mu.est.ti[1, ], col = 4) lines(t, mu.est.ti.ebayes[1, ], col = 6) legend("topright", legend = c("smash", "ti_rmad", "ti_homo", "ebayes_homo"), fill = c(2, 3, 4, 6))
Now we consider the case in which the data are distributed according to a Poisson process.
n <- 1024 t <- c(0, rexp(n - 1, 1)) t <- cumsum(t) t <- (t - min(t))/(max(t) - min(t)) mu.s <- spike.f(t)
Next, define the mean and variance functions.
mu.t <- (1 + mu.s)/5 rsnr <- sqrt(1) var1 <- rep(1, n) var2 <- (1e-04 + 4 * (exp(-550 * (t - 0.2)^2) + exp(-200 * (t - 0.5)^2) + exp(-950 * (t - 0.8)^2)))/1.35
We first look at the case of constant variance.
sigma.ini = sqrt(var1) sigma.t = sigma.ini/mean(sigma.ini) * sd(mu.t)/rsnr^2 set.seed(327) X.s = matrix(rnorm(10 * n, mu.t, sigma.t), nrow = 10, byrow = TRUE) mu.est = apply(X.s, 1, smash.gaus) mu.est.tivar.ash = apply(X.s, 1, ti.thresh, method = "smash") mu.est.tivar.mad = apply(X.s, 1, ti.thresh, method = "rmad") mu.est.ti = matrix(0, 10, n) mu.est.ti.ebayes = matrix(0, 10, n) for (i in 1:10) { sig.est = sig.est.func(X.s[i, ], n) mu.est.ti[i, ] = waveti.u(X.s[i, ], noise.level = sig.est) mu.est.ti.ebayes[i, ] = waveti.ebayes(X.s[i, ], noise.level = sig.est) }
Assess accuracy of the results.
cat("SMASH:",mise(t(mu.est), mu.t, 10),"\n") cat("TI thresholding with variance estimated from smash:", mise(t(mu.est.tivar.ash), mu.t, 10),"\n") cat("TI thresholding with variance estimated from running MAD:", mise(t(mu.est.tivar.mad), mu.t, 10),"\n") cat("TI thresholding with constant variance (estimated):", mise(mu.est.ti, mu.t, 10),"\n") cat("EBayes with constant variance (estimated):", mise(mu.est.ti.ebayes, mu.t, 10),"\n")
Assess accuracy over a grid.
xgrid = 1:n/n mu.est.inter = matrix(0, 10, n) mu.est.tivar.ash.inter = matrix(0, 10, n) mu.est.tivar.mad.inter = matrix(0, 10, n) mu.est.ti.inter = matrix(0, 10, n) mu.est.ti.ebayes.inter = matrix(0, 10, n) for (i in 1:10) { mu.est.inter[i, ] = approx(t, mu.est[, i], xgrid)$y mu.est.tivar.ash.inter[i, ] = approx(t, mu.est.tivar.ash[, i], xgrid)$y mu.est.tivar.mad.inter[i, ] = approx(t, mu.est.tivar.mad[, i], xgrid)$y mu.est.ti.inter[i, ] = approx(t, mu.est.ti[i, ], xgrid)$y mu.est.ti.ebayes.inter[i, ] = approx(t, mu.est.ti.ebayes[i, ], xgrid)$y } mu.t.inter = (spike.f(xgrid) + 1)/5 mise(mu.est.inter, mu.t.inter, 10) mise(mu.est.tivar.ash.inter, mu.t.inter, 10) mise(mu.est.tivar.mad.inter, mu.t.inter, 10) mise(mu.est.ti.inter, mu.t.inter, 10) mise(mu.est.ti.ebayes.inter, mu.t.inter, 10)
Plot the estimated mean functions against the ground-truth function (in black).
par(mfrow = c(1, 1)) plot(t, mu.t, xlab = "", ylab = "", type = "l") lines(t, mu.est[, 1], col = 2) lines(t, mu.est.tivar.mad[, 1], col = 3) lines(t, mu.est.ti[1, ], col = 4) lines(t, mu.est.ti.ebayes[1, ], col = 6) legend("topright",legend = c("smash", "ti_rmad", "ti_homo", "ebayes_homo"), fill = c(2, 3, 4, 6))
We then look at the case of non-constant variance.
sigma.ini = sqrt(var2) sigma.t = sigma.ini/mean(sigma.ini) * sd(mu.t)/rsnr^2 set.seed(327) X.s = matrix(rnorm(10 * n, mu.t, sigma.t), nrow = 10, byrow = TRUE) mu.est = apply(X.s, 1, smash.gaus) mu.est.tivar.ash = apply(X.s, 1, ti.thresh, method = "smash") mu.est.tivar.mad = apply(X.s, 1, ti.thresh, method = "rmad") mu.est.ti = matrix(0, 10, n) mu.est.ti.ebayes = matrix(0, 10, n) for (i in 1:10) { sig.est = sig.est.func(X.s[i, ], n) mu.est.ti[i, ] = waveti.u(X.s[i, ], noise.level = sig.est) mu.est.ti.ebayes[i, ] = waveti.ebayes(X.s[i, ], noise.level = sig.est) }
Assess accuracy of the results.
cat("SMASH:",mise(t(mu.est), mu.t, 10),"\n") cat("TI thresholding with variance estimated from smash:", mise(t(mu.est.tivar.ash), mu.t, 10),"\n") cat("TI thresholding with variance estimated from running MAD:", mise(t(mu.est.tivar.mad), mu.t, 10),"\n") cat("TI thresholding with constant variance (estimated):", mise(mu.est.ti, mu.t, 10),"\n") cat("EBayes with constant variance (estimated):", mise(mu.est.ti.ebayes, mu.t, 10),"\n")
Assess accuracy over a grid.
xgrid = 1:n/n mu.est.inter = matrix(0, 10, n) mu.est.tivar.ash.inter = matrix(0, 10, n) mu.est.tivar.mad.inter = matrix(0, 10, n) mu.est.ti.inter = matrix(0, 10, n) mu.est.ti.ebayes.inter = matrix(0, 10, n) for (i in 1:10) { mu.est.inter[i, ] = approx(t, mu.est[, i], xgrid)$y mu.est.tivar.ash.inter[i, ] = approx(t, mu.est.tivar.ash[, i], xgrid)$y mu.est.tivar.mad.inter[i, ] = approx(t, mu.est.tivar.mad[, i], xgrid)$y mu.est.ti.inter[i, ] = approx(t, mu.est.ti[i, ], xgrid)$y mu.est.ti.ebayes.inter[i, ] = approx(t, mu.est.ti.ebayes[i, ], xgrid)$y } mu.t.inter = (spike.f(xgrid) + 1)/5 mise(mu.est.inter, mu.t.inter, 10) mise(mu.est.tivar.ash.inter, mu.t.inter, 10) mise(mu.est.tivar.mad.inter, mu.t.inter, 10) mise(mu.est.ti.inter, mu.t.inter, 10) mise(mu.est.ti.ebayes.inter, mu.t.inter, 10)
par(mfrow = c(1, 1)) plot(t, mu.t, xlab = "", ylab = "", type = "l") lines(t, mu.est[, 1], col = 2) lines(t, mu.est.tivar.mad[, 1], col = 3) lines(t, mu.est.ti[1, ], col = 4) lines(t, mu.est.ti.ebayes[1, ], col = 6) legend("topright", legend = c("smash", "ti_rmad", "ti_homo", "ebayes_homo"), fill = c(2, 3, 4, 6))
Now we look at some of the simulations in a couple of papers, as well as the datasets used in them. The first one is from Fan & Yao (1998). The simulation is as described in Example 2. To deal with the fact that the sample size is not a sample size of 2, we first reflect the right portion of the data about the right endpoint so that it is a power of 2. To make the data periodic, we then reflect the new data about the right endpoint again. Our results are shown below.
mu.mad = 0 var.mad = 0 cat("Running 400 simulations.\n") for (i in 1:400) { cat(i,"") x = sort(runif(200, -2, 2)) err = rnorm(200) sigma.t = 0.4 * exp(-2 * x^2) + 0.2 mu.t = x + 2 * exp(-16 * x^2) y = mu.t + sigma.t * err xgrid = seq(-1.8, 1.8, length.out = 101) mu.t.inter = xgrid + 2 * exp(-16 * xgrid^2) var.t.inter = (0.4 * exp(-2 * xgrid^2) + 0.2)^2 y.exp = c(y, y[200:145]) y.data = c(y.exp, y.exp[256:1]) mu.est = smash.gaus(y.data) mu.est = mu.est[1:200] var.est = smash.gaus(y.data, v.est = TRUE) var.est = var.est[1:200] mu.est.inter = approx(x, mu.est, xgrid, "linear")$y var.est.inter = approx(x, var.est, xgrid, "linear")$y mu.mad[i] = 1/101 * sum(abs(mu.est.inter - mu.t.inter)) var.mad[i] = 1/101 * sum(abs(var.est.inter - var.t.inter)) } cat("\n")
Show the model fitting results.
boxplot(var.mad) plot(x, mu.t, xlab = "", ylab = "", type = "l", main = "mean function") lines(x, mu.est[1:200], col = 2) plot(x, sigma.t^2, xlab = "", ylab = "", type = "l", main = "var function") lines(x, var.est[1:200], col = 2)
We also apply SMASH to the dataset in Example 1 of the Fan & Yao (1998) paper. To take into account replicates, we take the median of the response at the same point. The results are shown below.
data(treas) y = ar(treas,FALSE,5)$res y = y[!is.na(y)] x = sort(treas[5:(length(treas) - 1)]) y = y[order(treas[5:(length(treas) - 1)])]
Plot the Treasury bill data.
plot(x, y)
x.mod = unique(x) y.mod = 0 for (i in 1:length(x.mod)) { y.mod[i] = median(y[x == x.mod[i]]) } y.exp = c(y.mod, y.mod[length(y.mod):(2 * length(y.mod) - 2^10 + 1)]) y.final = c(y.exp, y.exp[length(y.exp):1]) y.est = smash.gaus(y.final) y.est.var = smash.gaus(y.final, v.est = TRUE)
More plots of the data.
par(mfrow = c(2, 2)) plot(treas, xlab = "year", ylab = "interest rate", type = "l") plot(x, y) lines(x.mod, y.est[1:(length(y.mod))], xlab = "X", ylab = "Y", col = 2) plot(x.mod, y.est[1:(length(y.mod))], xlab = "X", ylab = "Y", type = "l", ylim = c(-0.3, 0.3)) plot(x.mod, sqrt(y.est.var[1:(length(y.mod))]), xlab = "X", ylab = "volatility", type = "l") ```` ## Heavisine example from Delouille *et al* (2004) We now examine the example from Delouille et al. (2004). Specifically, we compare with the heavisine function used in section 6, as the actual function is readily available. The results for $n=200$ for both the homoskedastic and the heteroskedastic cases are given. ```r n = 200 mse.hsm.c = 0 mse.hsm.n = 0 for (i in 1:500) { xt = sort(rnorm(n, 0.5, 0.2)) xt = (xt - min(xt))/(max(xt) - min(xt)) mu.hsm = 4 * sin(4 * pi * xt) - 2 * sign(xt - 0.3) - 2 * sign(0.72 - xt) mu.t = mu.hsm var1 = rep(1, n) var2 = (xt <= 0.5) + 2 * (xt > 0.5) sigma.ini = sqrt(var1) rsnr = sqrt(4) sigma.t = sigma.ini/mean(sigma.ini) * sd(mu.t)/rsnr^2 y = rnorm(n, mu.t, sigma.t) y.exp = c(y, y[200:145]) y.final = c(y.exp, y.exp[256:1]) mu.est <- smash.gaus(y.final) mu.est = mu.est[1:n] mse.hsm.c[i] = mse(mu.est, mu.t) sigma.ini = sqrt(var2) rsnr = sqrt(4) sigma.t = sigma.ini/mean(sigma.ini) * sd(mu.t)/rsnr^2 y = rnorm(n, mu.t, sigma.t) y.exp = c(y, y[200:145]) y.final = c(y.exp, y.exp[256:1]) mu.est <- smash.gaus(y.final) mu.est = mu.est[1:n] mse.hsm.n[i] = mse(mu.est, mu.t) } sqrt(quantile(mse.hsm.c, seq(0.25, 0.75, 0.25))) sqrt(quantile(mse.hsm.n, seq(0.25, 0.75, 0.25)))
We also look at the motorcycle in the same paper. We plot the data points and fit, as well as the estimated variances.
data(mcycle) x.ini = sort(mcycle$times) y.ini = mcycle$accel[order(mcycle$times)] x = unique(x.ini) y = 0 for (i in 1:length(x)) { y[i] = median(y.ini[x.ini == x[i]]) } y.exp = c(y, y[length(y):(2 * length(y) - 128 + 1)]) y.final = c(y.exp, y.exp[length(y.exp):1]) y.est = smash.gaus(y.final) y.est = y.est[1:length(y)] y.var.est = smash.gaus(y.final, v.est = TRUE) y.var.est = y.var.est[1:length(y)]
This is the version of R and the packages that were used to generate the results shown above.
sessionInfo()
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