hellinger.disc: Estimate a Discrete Mixture's Complexity Based on Hellinger...

In anjaweigel/mixComp_package: Estimation of the Order of Finite Mixture Distributions

Description

Estimation of a discrete mixture's complexity as well as its component weights and parameters by minimizing the squared Hellinger distance to the empirical probability mass function.

Usage

hellinger.disc(obj, j.max = 10, threshold = "SBC", control = c(trace = 0))

hellinger.boot.disc(obj, j.max = 10, B = 100, ql = 0.025, qu = 0.975,
                    control = c(trace = 0), ...)

Arguments

obj	object of class datMix.
j.max	integer stating the maximal number of components to be considered.
threshold	function or character string in c("AIC", "SBC") specifying which threshold should be used to compare two mixture estimates of complexities j and j+1. If the difference in minimized squared distances is smaller than the relevant threshold, j will be returned as complexity estimate.
B	integer specifying the number of bootstrap replicates.
ql	numeric between 0 and 1 specifying the lower quantile to which the observed difference in minimized squared distances will be compared.
qu	numeric between 0 and 1 specifying the upper quantile to which the observed difference in minimized squared distances will be compared.
control	control list of optimization parameters, see solnp.
...	further arguments passed to the boot function.

Details

Define the complexity of a finite discrete mixture F as the smallest integer p, such that its probability mass function (pmf) f can be written as

f(x) = w_1*g(x;θ_1) + … + w_p*g(x;θ_p).

Further, let g, f be two probability mass functions. The squared Hellinger distance between g and f is given by

H^2(g,f) = ∑ (√{g(x)}-√{f(x)})^2

where √{g(x)}, respectively √{f(x)} denotes the square root of the probability mass functions at point x.

To estimate p, hellinger.disc iteratively increases the assumed complexity j and finds the “best” estimate for both, the pmf of a mixture with j and j+1 components, by calculating the parameters that minimize the squared Hellinger distances to the empirical probability mass function. Once these parameters have been obtained, the difference in squared distances is compared to a predefined threshold. If this difference is smaller than the threshold, the algorithm terminates and the true complexity is estimated as j, otherwise j is increased by 1 and the procedure is started over. The predefined thresholds are the "AIC" given by (d+1)/n and the "SBC" given by ((d+1)log(n))/(2n), n being the sample size and d the number of component parameters, i.e. θ \subseteq R^d. Note that, if a customized function is to be used, it may only take the arguments j and n, so if the user wants to include the number of component parameters d, it has to be entered explicitly.

hellinger.boot.disc works similarly to hellinger.disc with the exception that the difference in squared distances is not compared to a predefined threshold but a value generated by a bootstrap procedure. At every iteration (of j), the function sequentially tests p = j versus p = j+1 for j = 1,2, …, using a parametric bootstrap to generate B samples of size n from a j-component mixture given the previously calculated “best” parameter values. For each of the bootstrap samples, again the “best” estimates corresponding to pmfs with j and j+1 components are calculated, as well as their difference in squared Hellinger distances from the empirical probability mass function. The null hypothesis H_0: p = j is rejected and j increased by 1 if the original difference in squared distances lies outside of the interval [ql, qu], specified by the ql and qu empirical quantiles of the bootstrapped differences. Otherwise, j is returned as the complexity estimate.

To calculate the minimum of the Hellinger distance (and the corresponding parameter values), the solver solnp is used. The initial values supplied to the solver are calculated as follows: the data is clustered into j groups by the function clara and the data corresponding to each group is given to MLE.function (if supplied to the datMix object obj, otherwise numerical optimization is used here as well). The size of the groups is taken as initial component weights and the MLE's are taken as initial component parameter estimates.

Value

Object of class paramEst with the following attributes:

dat	data based on which the complexity is estimated.
dist	character string stating the (abbreviated) name of the component distribution, such that the function ddist evaluates its density function and rdist generates random numbers.
ndistparams	integer specifying the number of parameters identifying the component distribution, i.e. if θ \subseteq R^d then ndistparams = d.
formals.dist	string vector specifying the names of the formal arguments identifying the distribution dist and used in ddist and rdist, e.g. for a gaussian mixture (dist = norm) amounts to mean and sd, as these are the formal arguments used by dnorm and rnorm.
discrete	logical indicating whether the underlying mixture distribution is discrete. Will always be TRUE in this case.
mle.fct	attribute MLE.function of obj.
pars	Say the complexity estimate is equal to some j. Then pars is a numeric vector of size (d+1)*j-1 specifying the component weight and parameter estimates, given as (w_1, ... w_{j-1}, θ 1_1, ... θ 1_j, θ 2_1, ... θ d_j).
values	numeric vector of function values gone through during optimization at iteration j, the last entry being the value at the optimum.
convergence	indicates whether the solver has converged (0) or not (1 or 2) at iteration j.

References

M.-J. Woo and T. Sriram, "Robust estimation of mixture complexity for count data", Computational Statistics and Data Analysis 51, 4379-4392, 2007.

See Also

L2.disc for the same estimation method using the L2 distance, hellinger.cont for the same estimation method for continuous mixtures, solnp for the solver, datMix for the creation of the datMix object.

Examples

## create 'Mix' object
poisMix <- Mix("pois", w = c(0.45, 0.45, 0.1), lambda = c(1, 5, 10))


## create random data based on 'Mix' object (gives back 'rMix' object)
set.seed(0)
poisRMix <- rMix(1000, obj = poisMix)


## create 'datMix' object for estimation

# generate list of parameter bounds
poisList <- vector(mode = "list", length = 1)
names(poisList) <- "lambda"
poisList$lambda <- c(0, Inf)

# generate MLE function
MLE.pois <- function(dat){
  mean(dat)
}

# generating 'datMix' object
pois.dM <- RtoDat(poisRMix, theta.bound.list = poisList, MLE.function = MLE.pois)


## complexity and parameter estimation 
set.seed(0)
res <- hellinger.disc(pois.dM)
plot(res)

anjaweigel/mixComp_package documentation built on Sept. 2, 2020, 3:55 p.m.