|
adaptive functional thresholding |
1 |
|
asymptotic identifiability |
1 |
|
autocovariance |
1 |
|
b-spline |
1 |
|
binary segmentation |
1 |
|
binning |
1 |
|
block coordinate descent algorithm |
1 |
|
block regularized minimum distance estimation |
1 |
|
block sparse precision matrix |
1 |
|
constrained á1-minimization |
1 |
|
cross-spectral stability measure |
1 |
|
dimension reduction |
1 |
|
eigenanalysis |
1 |
|
error-in-variables |
1 |
|
errors-in-predictors |
1 |
|
functional connectivity |
1 |
|
functional data |
1 |
|
functional factor model |
1 |
|
functional linear regression |
1 |
|
functional precision matrix |
1 |
|
functional principal component |
1 |
|
functional principal component analysis |
1 |
|
functional regression |
1 |
|
functional sparsity |
1 |
|
functional stability measure |
1 |
|
functional thresholding |
1 |
|
functional time series |
1 |
|
generalized method-of-moments |
1 |
|
graphical model |
1 |
|
graphical models |
1 |
|
high-dimensional data |
1 |
|
high-dimensional functional data |
1 |
|
high-dimensional functional time series |
1 |
|
hilbert–schmidt norm |
1 |
|
homogeneity pursuit |
1 |
|
index model |
1 |
|
local linear smoothing |
1 |
|
model selection |
1 |
|
non-asymptotics |
1 |
|
nonlinear regression |
1 |
|
partially observed functional data |
1 |
|
permutation |
1 |
|
segmentation transformation |
1 |
|
single index models |
1 |
|
sparse principal component analysis |
1 |
|
sparsely sampled functional data |
1 |
|
sparsity |
1 |
|
sub-gaussian functional linear process |
1 |
|
vector functional autoregression |
1 |
