quapdq3: Quantile Function of the Polynomial Density-Quantile3...

In lmomco: L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions

Quantile Function of the Polynomial Density-Quantile3 Distribution

Description

This function computes the quantiles of the Polynomial Density-Quantile3 distribution (PDQ3) given parameters (\xi, \alpha, and \kappa) computed by parpdq3. The quantile function is

x(F) = \xi + \alpha \biggl[\log\biggl(\frac{F}{1-F}\biggr) + \kappa \log\bigg(\frac{[1-\kappa(2F-1)]^2}{4F(1-F)}\biggr)\biggr]\mbox{,}

where x(F) is the quantile for nonexceedance probability F, \xi is a location parameter, \alpha is a scale parameter, and \kappa is a shape parameter. The range of the distribution is -\infty < x < \infty. This formulation of logistic distribution generalization is unique in the literature.

Usage

quapdq3(f, para, paracheck=TRUE)

Arguments

f	Nonexceedance probability (0 \le F \le 1).
para	The parameters from parpdq3 or vec2par.
paracheck	A logical controlling whether the parameters are checked for validity. Overriding of this check might be extremely important and needed for use of the quantile function in the context of TL-moments with nonzero trimming.

Details

The PDQ3 was proposed by Hosking (2007) with the core justification of maximizing entropy and that “maximizing entropy subject to a set of constraints can be regarded as deriving a distribution that is consistent with the information specified in the constraints while making minimal assumptions about the form of the distribution other than those embodied in the constraints.” The PDQ3 is that family constrained to the \lambda_1, \lambda_2, and \tau_3 values of the L-moments. (See also the Polynomial Density-Quantile4 function for constraint on \lambda_1, \lambda_2, and \tau_4 values of the L-moments, quapdq4.)

The PDQ3 has maximum entropy conditional on having specified values for the L-moments of \lambda_1, \lambda_2, and \lambda_3 = \tau_3\lambda_2. The tails of the PDQ3 are exponentially decreasing and the distribution could be useful in distributional analysis with data showing similar tail characteristics. The attainable L-kurtosis range is \tau_4 = (5\tau_3/\kappa) - 1.

Value

Quantile value for nonexceedance probability F.

Author(s)

W.H. Asquith

References

Hosking, J.R.M., 2007, Distributions with maximum entropy subject to constraints on their L-moments or expected order statistics: Journal of Statistical Planning and Inference, v. 137, no. 9, pp. 2870–2891, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jspi.2006.10.010")}.

See Also

cdfpdq3, pdfpdq3, lmompdq3, parpdq3

Examples

lmr <- lmoms(c(123, 34, 4, 654, 37, 78))
quapdq3(0.5, parpdq3(lmr)) # [1] 51.22802

## Not run: 
  FF <- seq(0.002475, 1 - 0.002475, by=0.001)
  para <- list(para=c(0.6933, 1.5495, 0.5488), type="pdq3")
  plot(log(FF/(1-FF)), quapdq3(FF, para), type="l", col=grey(0.8), lwd=4,
       xlab="Logistic variate, log(f/(1-f))", ylab="Quantile, Q(f)")
  lines(log(FF/(1-FF)), log(qf(FF, df1=7, df2=1)), lty=2)
  legend("topleft", c("log F(7,1) distribution with same L-moments",
                      "PDQ3 distribution with same L-moments as the log F(7,1)"),
         lwd=c(1, 4), lty=c(2, 1), col=c(1, grey(0.8)), cex=0.8)
  mtext("Mimic Hosking (2007, fig. 2 [right])") # 
## End(Not run)

lmomco documentation built on May 29, 2024, 10:06 a.m.