cmodes | R Documentation |

This function computes the mode mass function.

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

`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 |

`nu` |
concentration parameter of beta kernel used to smooth mode mass function. |

`...` |
further arguments for |

The scedasis functions on which the mode mass function is based are
computed using the default `"nrd0"`

option for bandwidth.

`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.

Miguel de Carvalho

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

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)

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