eqevd: Estimate Quantiles of an Extreme Value (Gumbel) Distribution

Description

Estimate quantiles of an extreme value distribution.

Usage

eqevd(x, p = 0.5, method = "mle", pwme.method = "unbiased", 
    plot.pos.cons = c(a = 0.35, b = 0), digits = 0)

Arguments

a numeric vector of observations, or an object resulting from a call to an estimating function that assumes an extreme value distribution (e.g., eevd). If x is a numeric vector, missi

numeric vector of probabilities for which quantiles will be estimated. All values of p must be between 0 and 1. The default value is p=0.5.

method

character string specifying the method to use to estimate the location and scale parameters. Possible values are "mle" (maximum likelihood; the default), "mme" (methods of moments), "mmue" (method of mom

pwme.method

character string specifying what method to use to compute the probability-weighted moments when method="pwme". The possible values are "ubiased" (method based on the U-statistic; the default), or "plotting.posi

plot.pos.cons

numeric vector of length 2 specifying the constants used in the formula for the plotting positions when method="pwme" and pwme.method="plotting.position". The default value is plot.pos.cons=c(a=0.35, b=0)

digits

an integer indicating the number of decimal places to round to when printing out the value of 100*p. The default value is digits=0.

Value

If x is a numeric vector, eqevd returns a list of class "estimate" containing the estimated quantile(s) and other information. See estimate.object for details. If x is the result of calling an estimation function, eqevd returns a list whose class is the same as x. The list contains the same components as x, as well as components called quantiles and quantile.method.

Details

The function eqevd returns estimated quantiles as well as estimates of the location and scale parameters. Quantiles are estimated by 1) estimating the location and scale parameters by calling eevd, and then 2) calling the function qevd and using the estimated values for location and scale.

References

Castillo, E. (1988). Extreme Value Theory in Engineering. Academic Press, New York, pp.184--198. Downton, F. (1966). Linear Estimates of Parameters in the Extreme Value Distribution. Technometrics 8(1), 3--17. Forbes, C., M. Evans, N. Hastings, and B. Peacock. (2011). Statistical Distributions. Fourth Edition. John Wiley and Sons, Hoboken, NJ. Greenwood, J.A., J.M. Landwehr, N.C. Matalas, and J.R. Wallis. (1979). Probability Weighted Moments: Definition and Relation to Parameters of Several Distributions Expressible in Inverse Form. Water Resources Research 15(5), 1049--1054. Hosking, J.R.M., J.R. Wallis, and E.F. Wood. (1985). Estimation of the Generalized Extreme-Value Distribution by the Method of Probability-Weighted Moments. Technometrics 27(3), 251--261. Johnson, N. L., S. Kotz, and N. Balakrishnan. (1995). Continuous Univariate Distributions, Volume 2. Second Edition. John Wiley and Sons, New York. Landwehr, J.M., N.C. Matalas, and J.R. Wallis. (1979). Probability Weighted Moments Compared With Some Traditional Techniques in Estimating Gumbel Parameters and Quantiles. Water Resources Research 15(5), 1055--1064. Tiago de Oliveira, J. (1963). Decision Results for the Parameters of the Extreme Value (Gumbel) Distribution Based on the Mean and Standard Deviation. Trabajos de Estadistica 14, 61--81.

Examples

Run this code

# Generate 20 observations from an extreme value distribution with 
  # parameters location=2 and scale=1, then estimate the parameters 
  # and estimate the 90'th percentile.
  # (Note: the call to set.seed simply allows you to reproduce this example.)

  set.seed(250) 
  dat <- revd(20, location = 2) 
  eqevd(dat, p = 0.9) 

  #Results of Distribution Parameter Estimation
  #--------------------------------------------
  #
  #Assumed Distribution:            Extreme Value
  #
  #Estimated Parameter(s):          location = 1.9684093
  #                                 scale    = 0.7481955
  #
  #Estimation Method:               mle
  #
  #Estimated Quantile(s):           90'th %ile = 3.652124
  #
  #Quantile Estimation Method:      Quantile(s) Based on
  #                                 mle Estimators
  #
  #Data:                            dat
  #
  #Sample Size:                     20

  #----------

  # Clean up
  #---------
  rm(dat)

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