Description Usage Arguments Details Value Warning Author(s) References See Also Examples

This function defines the Skew Power exponential (SEP) distribution, a four parameter distribution,
for a `gamlss.family`

object to be used for a
GAMLSS fitting using the function `gamlss()`

. The functions `dSEP`

,
`pSEP`

, `qSEP`

and `rSEP`

define the density,
distribution function, quantile function and random
generation for the Skew Power exponential (SEP) distribution.

1 2 3 4 5 6 7 8 9 | ```
SEP(mu.link = "identity", sigma.link = "log", nu.link = "identity",
tau.link = "log")
dSEP(x, mu = 0, sigma = 1, nu = 0, tau = 2, log = FALSE)
pSEP(q, mu = 0, sigma = 1, nu = 0, tau = 2, lower.tail = TRUE,
log.p = FALSE)
qSEP(p, mu = 0, sigma = 1, nu = 0, tau = 2, lower.tail = TRUE,
log.p = FALSE, lower.limit = mu - 5 * sigma,
upper.limit = mu + 5 * sigma)
rSEP(n, mu = 0, sigma = 1, nu = 0, tau = 2)
``` |

`mu.link` |
Defines the |

`sigma.link` |
Defines the |

`nu.link` |
Defines the |

`tau.link` |
Defines the |

`x,q` |
vector of quantiles |

`mu` |
vector of location parameter values |

`sigma` |
vector of scale parameter values |

`nu` |
vector of skewness |

`tau` |
vector of kurtosis |

`log, log.p` |
logical; if TRUE, probabilities p are given as log(p). |

`lower.tail` |
logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x] |

`p` |
vector of probabilities. |

`n` |
number of observations. If |

`lower.limit` |
lower limit for the golden search to find quantiles from probabilities |

`upper.limit` |
upper limit for the golden search to find quantiles from probabilities |

The probability density function of the Skew Power exponential distribution, (`SEP`

), is defined as

*f(y|mu,sigma,nu,tau)=(z/sigma)*pnorm(w)*dPE(z,0,1,tau)*

for *0<y<0*,
*mu=(-Inf,+Inf)*,
*sigma>0*,
*nu=(-Inf,+Inf)* and
*tau>0*. where *z=(y-mu)/(sigma)*, *w=sign(z)|z|^(t/2)
*nu*sqrt(2/tau)* and *dPE(z,0,1,tau)* is
the pdf of an Exponential Power distribution.

`SEP()`

returns a `gamlss.family`

object which can be used to fit the SEP distribution in the `gamlss()`

function.
`dSEP()`

gives the density, `pSEP()`

gives the distribution
function, `qSEP()`

gives the quantile function, and `rSEP()`

generates random deviates.

The qSEP and rSEP are slow since they are relying on golden section for finding the quantiles

Bob Rigby and Mikis Stasinopoulos [email protected]

Diciccio, T. J. and Mondi A. C. (2004). Inferential Aspects of the Skew Exponential Power distribution.,
*JASA*, **99**, 439-450.

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
*Appl. Statist.*, **54**, part 3, pp 507-554.

Stasinopoulos D. M. Rigby R. A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
*Journal of Statistical Software*, Vol. **23**, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017)
*Flexible Regression and Smoothing: Using GAMLSS in R*, Chapman and Hall/CRC.

1 2 3 4 5 6 7 8 | ```
SEP() #
plot(function(x)dSEP(x, mu=0,sigma=1, nu=1, tau=2), -5, 5,
main = "The SEP density mu=0,sigma=1,nu=1, tau=2")
plot(function(x) pSEP(x, mu=0,sigma=1,nu=1, tau=2), -5, 5,
main = "The BCPE cdf mu=0, sigma=1, nu=1, tau=2")
dat <- rSEP(100,mu=10,sigma=1,nu=-1,tau=1.5)
# library(gamlss)
# gamlss(dat~1,family=SEP, control=gamlss.control(n.cyc=30))
``` |

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