condmixt.train: Training of conditional mixtures

In condmixt: Conditional Density Estimation with Neural Network Conditional Mixtures

Description

Training involves, for given numbers of hidden units and components and a given mixture specification, minimizing the negative log-likelihood from initial parameter values. The minimization is re-started several times from various initial parameter values and the best minimum is kept. This helps avoiding local minima.

Usage

condhparetomixt.train(h, m, x, y, nstart = 1, ...)
condhparetomixt.train.tailpen(h,m,x,y,lambda=0,w=0.2,beta=50,
                              mu=0.2,sigma=0.2, nstart = 1, ...)
condhparetomixt.dirac.train.tailpen(h,m,x,y,lambda=0,w=0.2,beta=50,
                                   mu=0.2,sigma=0.2, nstart = 1, ...)
condgaussmixt.train(h,m,x,y, nstart = 1, ...)
condgaussmixt.dirac.train(h,m,x,y, nstart = 1, ...)
condlognormixt.train(h,m,x,y, nstart = 1, ...)
condlognormixt.dirac.train(h,m,x,y, nstart = 1, ...)
condbergamixt.train(h,x,y, nstart = 1, ...)

Arguments

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
nstart	Number of minimization re-starts, default is one.
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-w being the proportion of components with heavy tails
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
...	optional arguments for the optimizer nlm

Details

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.

Value

Returns a vector of neural network parameters corresponding to the best minimum among nstart optimizations.

Author(s)

Julie Carreau

References

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

See Also

condmixt.train,condmixt.nll, condmixt.init

Examples

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

# train a mixture with hybrid Pareto components
condhparetomixt.train(h,m,t(x),y,nstart=2,iterlim=100)

condmixt documentation built on July 1, 2020, 6:04 p.m.