mvt_mmf_task: Multivarite t distribution problem

In bhtang127/AccelBenchmark: Benchmark Various Accelerating Methods for Fixed Point Iterations

Multivarite t distribution problem

Description

A function that will generate the task list for problem of estimating parameters of a multivariate t distribution. We would simulate 1000 2-dimensional t distribution data with mean 0 and Σ = diag(0.1,1). And the base fixed point algorithm is MMF in Hasannasab (2021).

Usage

mvt_mmf_task(df)

Arguments

df	Degree of freedom for the t-distribution

Value

A list containing all components needed for benchmarking the problem

initfn	Parameter random initializing function
fixptfn	Updating function for the fixed point iteration problem
objfn	Function calculating the objective value for current parameter
...	Other arguments required in functions above

References

Hasannasab M, Hertrich J, Laus F, Steidl G (2021). Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student t distribution. Numerical Algorithms, 87(1): 77–118.

Examples

## Not run: 
set.seed(54321)
problem = mvt_mmf_task(df=25)
benchmark(
  problem,
  algorithm=c("raw", "squarem", "daarem", "pem", "qn", "nes"),
  ntimes = 200
)

## End(Not run)

bhtang127/AccelBenchmark documentation built on May 30, 2022, 2:21 a.m.