MM_optim {ADLP}

Minorization-Maximisation Algorithm performed to fit the ADLPs

Description

The Minorization-Maximization algorithm aims to optimize a surrogate objective function that approximates the Log Score. This approach typically results in fast and stable convergence, while ensuring that combination weights adhere to the constraints of being non-negative and summing to one. For detailed description of the algorithm, one might refer to: Conflitti, De Mol, and Giannone (2015)

Usage

MM_optim(w_init, dat, niter = 500)

Arguments

w_init

initial weights for each ADLP

dat

matrix of densities for each ADLP

niter

maximum number of iterations. Defaults to 500

Value

An object of class mm_optim. mm_optim is a list that stores the results of the MM algorithm performed, including the final parameters, the final loss and numer of iterations.

References

Conflitti, Cristina, Christine De Mol, and Domenico Giannone. "Optimal combination of survey forecasts." International Journal of Forecasting 31.4 (2015): 1096-1103.

Examples

w_init <- rep(1/3, 3)
set.seed(1)
density_data <- matrix(runif(9), nrow = 3, ncol = 3)
MM_optim(w_init, density_data, niter = 500)