Reassign low sample states to close states
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.
remove_small_sample_states(weight.matrix, min)
weight.matrix |
N \times K weight matrix |
min |
minimum effective sample size to stay in the weight matrix |
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)
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