cmodes: Mode Mass Function

In extremis: Statistics of Extremes

Mode Mass Function

Description

This function computes the mode mass function.

Usage

cmodes(Y, thresholds = apply(Y[, -1], 2, quantile, probs =
                 0.95), nu = 100, ...)

Arguments

Y	data frame from which the estimate is to be computed; first column corresponds to time and the second to the variable of interest.
thresholds	values used to threshold the data y; by default threshold = quantile(y, 0.95).
nu	concentration parameter of beta kernel used to smooth mode mass function.
...	further arguments for density methods.

Details

The scedasis functions on which the mode mass function is based are computed using the default "nrd0" option for bandwidth.

Value

c	scedasis density estimators.
k	number of exceedances above the threshold.
w	standardized indices of exceedances.
Y	raw data.

The plot method depicts the smooth mode mass function along with the smooth scedasis densities.

Author(s)

Miguel de Carvalho

References

Rubio, R., de Carvalho, M., and Huser, R. (2018) Similarity-Based Clustering of Extreme Losses from the London Stock Exchange. Submitted.

Examples

data(lse)
attach(lse)
nlr <- -apply(log(lse[, -1]), 2, diff)
Y <- data.frame(DATE[-1], nlr)
T <- dim(Y)[1]
k <- floor((0.4258597) * T / (log(T)))
fit <- cmodes(Y, thresholds = as.numeric(apply(nlr, 2, sort)[T - k, ]),  
              kernel = "biweight", bw = 0.1 / sqrt(7), nu = 100)
plot(fit)

extremis documentation built on Dec. 9, 2022, 5:08 p.m.