LICORS (version 0.2.0)

remove_small_sample_states: Reassign low sample states to close states

Description

This function removes small sample states by reassigning points in those state to nearby states.

This can become necessary when in an iterative algorithm (like mixed_LICORS) the weights start moving away from e.g. state \(j\). At some point the effective sample size of state \(j\) (sum of column \(\mathbf{W}_j\)) is so small that state-conditional estimates (mean, variance, kernel density estimate, etc.) can not be obtained accurately anymore. Then it is good to remove state \(j\) and reassign its samples to other (close) states.

Usage

remove_small_sample_states(weight.matrix, min)

Arguments

weight.matrix

\(N \times K\) weight matrix

min

minimum effective sample size to stay in the weight matrix

Examples

# NOT RUN {
set.seed(10)
WW <- matrix(c(rexp(1000, 1/10), runif(1000)), ncol = 5, byrow = FALSE)
WW <- normalize(WW)
colSums(WW)
remove_small_sample_states(WW, 20)
# }