Aurora
Aurora.AuroraKNN
— TypeAuroraKNN(;kKNN=1000, loocv=true, tree=KDTree,
pca=false, pca_dimension=false)
Aurora with k
-Nearest neighbors. If looc=false
, then k
is chosen equal to kKNN
, while if loocv=true
, then k
is selected for each held-out replicate by Leave-One-Out Cross-validation among the choices 1,...,kKNN
.
tree
describes the nearest neighbor computation strategy. The following options are available: :auto
, as well as , :kdtree
, :balltree
and :brutetree
from the NearestNeighbors.jl
package.
If pca=true
, a dimension reduction strategy is employed to find nearest neighbors using PCA (principal component analysis). For each held-out replicate, the order statistics are projected into the principal component subspace of dimension pca_dimension
. Note that in this case, the resulting nearest neighbors may only be interpreted as approximate nearest neighbors.
Aurora.Auroral
— TypeAuroral()
Aurora with linear regression.
Aurora.ReplicatedSample
— TypeReplicatedSample(Z::AbstractVector)
This type represents K
iid samples $Z_{i1},\dotsc, Z_{iK}$ drawn from the same distribution $F_i$. In the setting, for which Aurora was developed, the distribution $F_i$is parameterized by its mean
\mu_i
`, i.e.,
\[\mu_i = \mathbb E_{F_i}[ Z_{ij}],\]
as well as a nuisance parameter $�lpha_i$, so that $F_i = F(\cdot \mid \mu_i, \alpha_i)$ and
\[Z_{i1},\dotsc, Z_{iK} \mid \mid \mu_i, \alpha_i \; \sim \; F(\cdot \mid \mu_i, \alpha_i).\]