Estimates the two parameters of the slash distribution by maximum likelihood estimation.
slash(lmu = "identitylink", lsigma = "loglink",
imu = NULL, isigma = NULL, gprobs.y = ppoints(8), nsimEIM = 250,
zero = NULL, smallno = .Machine$double.eps*1000)Parameter link functions applied to the \(\mu\)
and \(\sigma\) parameters, respectively.
See Links for more choices.
Initial values.
A NULL means an initial value is chosen internally.
See CommonVGAMffArguments for more information.
Used to compute the initial values for mu.
This argument is fed into the probs argument of
quantile to construct a grid,
which is used to evaluate the log-likelihood.
This must have values between 0 and 1.
See CommonVGAMffArguments for information.
Small positive number, used to test for the singularity.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm,
and vgam.
The standard slash distribution is the distribution of the ratio of a standard normal variable to an independent standard uniform(0,1) variable. It is mainly of use in simulation studies. One of its properties is that it has heavy tails, similar to those of the Cauchy.
The general slash distribution can be obtained by replacing the univariate normal variable by a general normal \(N(\mu,\sigma)\) random variable. It has a density that can be written as $$f(y) = \left\{ \begin{array}{cl} 1/(2 \sigma \sqrt(2 \pi)) & if y=\mu, \\ 1-\exp(-(((y-\mu)/\sigma)^2)/2))/(\sqrt(2 pi) \sigma ((y-\mu)/\sigma)^2) & if y \ne \mu. \end{array} \right . $$ where \(\mu\) and \(\sigma\) are the mean and standard deviation of the univariate normal distribution respectively.
Johnson, N. L. and Kotz, S. and Balakrishnan, N. (1994). Continuous Univariate Distributions, 2nd edition, Volume 1, New York: Wiley.
Kafadar, K. (1982). A Biweight Approach to the One-Sample Problem Journal of the American Statistical Association, 77, 416--424.
# NOT RUN {
sdata <- data.frame(y = rslash(n = 1000, mu = 4, sigma = exp(2)))
fit <- vglm(y ~ 1, slash, data = sdata, trace = TRUE)
coef(fit, matrix = TRUE)
Coef(fit)
summary(fit)
# }
Run the code above in your browser using DataCamp Workspace