Description Usage Arguments Value Examples
Calculates the maximum likelihood estimator of the parameter vector alpha for a a piecewise Pareto distribution with given vector t and (if applicable) a known truncation
1 2 3 4 5 6 7 8 9 10 11 |
losses |
Numeric vector. Losses that are used for the ML estimation. |
t |
Numeric vector. Thresholds of the piecewise Pareto distribution. |
truncation |
Numeric. If |
truncation_type |
Character. If |
reporting_thresholds |
Numeric vector. Allows to enter loss specific reporting thresholds. If |
is.censored |
Logical vector. |
weights |
Numeric vector. Weights for the losses. For instance |
alpha_min |
Numeric. Lower bound for the estimated alphas (only used in truncated case). |
alpha_max |
Numeric. Upper bound for the estimated alphas (only used in truncated case). |
Maximum likelihood estimator for the parameter alpha
of a Pareto distribution with threshold t
given the observations losses
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | losses <- rPiecewisePareto(10000, t = c(100,200,300), alpha = c(1,2,3))
PiecewisePareto_ML_Estimator_Alpha(losses, c(100,200,300))
losses <- rPiecewisePareto(10000, t = c(100,200,300), alpha = c(1,2,3),
truncation = 500, truncation_type = "wd")
PiecewisePareto_ML_Estimator_Alpha(losses, c(100,200,300))
PiecewisePareto_ML_Estimator_Alpha(losses, c(100,200,300),
truncation = 500, truncation_type = "wd")
reporting_thresholds <- rPareto(10000, 100, 3)
index <- losses > reporting_thresholds
losses <- losses[index]
reporting_thresholds <- reporting_thresholds[index]
PiecewisePareto_ML_Estimator_Alpha(losses, c(100,200,300),
truncation = 500, truncation_type = "wd")
PiecewisePareto_ML_Estimator_Alpha(losses, c(100,200,300),
truncation = 500, truncation_type = "wd",
reporting_thresholds = reporting_thresholds)
losses <- c(140, 240, 490, 200, 110, 710, 120, 190, 210, 310)
w <- rep(1, length(losses))
w[1] <- 2
losses2 <- c(losses, losses[1])
PiecewisePareto_ML_Estimator_Alpha(losses, c(100,200,300), weights = w)
PiecewisePareto_ML_Estimator_Alpha(losses2, c(100,200,300))
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