condmixt.nll {condmixt} | R Documentation |

Computes negative log-likelihood and gradient for given neural network parameters, numbers of hidden units and of components, the type of components and the presence of a discrete dirac component on a given data set.

```
condhparetomixt.nll(theta, h, m, x, y)
condhparetomixt.nll.tailpen(theta,h,m,x,y,lambda=0,w=0.2,beta=50,mu=0.2,sigma=0.2)
condhparetomixt.dirac.nll.tailpen(theta,h,m,x,y,lambda=0,w=0.2,beta=50,mu=0.2,sigma=0.2)
condhparetomixt.dirac.nll(theta,h,m,x,y)
condgaussmixt.nll(theta,h,m,x,y)
condgaussmixt.dirac.nll(theta,h,m,x,y)
condlognormixt.nll(theta,h,m,x,y)
condlognormixt.dirac.nll(theta,h,m,x,y)
condbergamixt.nll(theta,h,x,y)
```

`theta` |
Vector of neural network parameters |

`h` |
Number of hidden units |

`m` |
Number of components |

`x` |
Matrix of explanatory (independent) variables of dimension d x n, d is the number of variables and n is the number of examples (patterns) |

`y` |
Vector of n dependent variables |

`lambda` |
penalty parameter which controls the trade-off between the penalty and the negative log-likelihood, takes on positive values. If zero, no penalty |

`w` |
penalty parameter in [0,1] which is the proportion of
components with light tails, 1- |

`beta` |
positive penalty parameter which indicates the importance of the light tail components (it is the parameter of an exponential which represents the prior over the light tail components) |

`mu` |
penalty parameter in (0,1) which represents the a priori value for the heavy tail index of the underlying distribution |

`sigma` |
positive penalty parameter which controls the spread around the a priori value for the heavy tail index of the underlying distribution |

`condhparetomixt`

indicates a mixture with hybrid Pareto
components,
`condgaussmixt`

for Gaussian components,
`condlognormixt`

for Log-Normal components,
`condbergam`

for a Bernoulli-Gamma two component mixture,
`tailpen`

indicates that a penalty is added to the log-likelihood
to guide the tail index parameter estimation,
`dirac`

indicates that a discrete dirac component at zero is included in
the mixture

In order to drive the tail index estimation, a penalty is introduced in the log-likelihood. The goal of the penalty is to include a priori information which in our case is that only a few mixture components have a heavy tail index which should approximate the tail of the underlying distribution while most other mixture components have a light tail and aim at modelling the central part of the underlying distribution.

The penalty term is given by the logarithm of the following two-component mixture, as a function of a tail index parameter xi :

w beta exp(-beta xi) + (1-w) exp(-(xi-mu)^2/(2 sigma^2))/(sqrt(2 pi) sigma)

where the first term is the prior on the light tail component and the second term is the prior on the heavy tail component.

Returns a single value (the negative log-likelihood for given parameters and sample) and a vector, the gradient, which is passed as an attribute.

Julie Carreau

Bishop, C. (1995), Neural Networks for Pattern Recognition, Oxford

Carreau, J. and Bengio, Y. (2009), A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distributions, 20, IEEE Transactions on Neural Networks

`hparetomixt.negloglike`

,
`hparetomixt.negloglike.tailpen`

, `condmixt.init`

```
n <- 200
x <- runif(n) # x is a random uniform variate
# y depends on x through the parameters of the Frechet distribution
y <- rfrechet(n,loc = 3*x+1,scale = 0.5*x+0.001,shape=x+1)
plot(x,y,pch=22)
# 0.99 quantile of the generative distribution
qgen <- qfrechet(0.99,loc = 3*x+1,scale = 0.5*x+0.001,shape=x+1)
points(x,qgen,pch="*",col="orange")
h <- 2 # number of hidden units
m <- 4 # number of components
# initialize a conditional mixture with hybrid Pareto components
thetainit <- condhparetomixt.init(1,h,m,y)
# computes negative log-likelihood and gradient for initial neural network parameters
condhparetomixt.nll(thetainit,h,m,t(x),y)
```

[Package *condmixt* version 1.1 Index]