plot.BS | R Documentation |
Plot Kernel density by using Birnbaum-Saunders Kernel.
## S3 method for class 'BS' plot(x, ...)
x |
An object of class "BS" |
... |
Not presently used in this implementation |
Nothing
Javaria Ahmad Khan, Atif Akbar.
Jin, X.; Kawczak, J. 2003. Birnbaum-Saunders & Lognormal kernel estimators for modeling durations in high frequency financial data. Annals of Economics and Finance 4, 103-124.
For further kernels see plot.Beta
, plot.Erlang
, plot.Gamma
and plot.LogN
. For estimated values BS
and for MSE mse
.
alpha = 10 theta = 15 / 60 y <- rgamma(n = 10000, shape = alpha, scale = theta) h <- 1.5 xx <- seq(min(y) + 0.05, max(y), length = 200) den <- BS(x = xx, y = y, k = 200, h = h) plot(den, type = "l") ##other details can also be added y <- rgamma(n = 10000, shape = alpha, scale = theta) h <- 0.79 * IQR(y) * length(y) ^ (-1/5) #Normal Scale Rule Bandwidth gr <- BS(x = xx, y = y, k = 200, h = h) plot(gr, type = "s", ylab = "Density Function", lty = 1, xlab = "Time") ## To add true density along with estimated d1 <- density(y, bw = h) lines(d1, type = "p", col = "red") legend("topright", c("Real Density", "Density by Birnbaum-Saunders Kernel"), col=c("red", "black"), lty = c(1,2))
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