|
adaptive splitting |
1 |
|
anomaly detection |
1 |
|
approximate empirical bayes |
1 |
|
asymmetric error |
1 |
|
asymmetric error control |
1 |
|
asymptotic distribution |
1 |
|
asymptotic expansions |
1 |
|
bayesian learning |
1 |
|
binary classification |
1 |
|
chance constrained optimization |
1 |
|
citation network of statistics publications |
1 |
|
citation trends |
1 |
|
classical and neyman-pearson paradigms |
1 |
|
classical classification (cc) paradigm |
1 |
|
classiffication |
1 |
|
classification |
1 |
|
clustering |
1 |
|
conductance |
1 |
|
cost-sensitive (cs) learning paradigm |
1 |
|
density estimation |
1 |
|
dependence |
1 |
|
dsml 1: concept: basic principles of a new data science output observed and reported |
1 |
|
dsml3: development/pre-production: data science output has been rolled out/validated across multiple domains/problems |
1 |
|
eigen selection |
1 |
|
eigenvalues |
1 |
|
eigenvectors |
1 |
|
empirical constraint |
1 |
|
empirical risk minimization |
1 |
|
external impact of statistics publications |
1 |
|
feature augmentation |
1 |
|
feature selection |
1 |
|
finite population learning |
1 |
|
fisher discriminant |
1 |
|
genomic applications |
1 |
|
high dimension |
1 |
|
high dimensional classification |
1 |
|
high dimensionality |
1 |
|
high-dimension |
1 |
|
high-dimensional model |
1 |
|
high-dimensional space |
1 |
|
imbalance ratio |
1 |
|
imbalanced data |
1 |
|
independence rule |
1 |
|
label noise |
1 |
|
large scale multiple testing |
1 |
|
learning rates |
1 |
|
linear discriminant analysis |
1 |
|
linear discriminant analysis (lda) |
1 |
|
local clustering |
1 |
|
low-rank models |
1 |
|
marginal feature ranking |
1 |
|
methodology |
1 |
|
minimum sample size requirement |
1 |
|
mixture model |
1 |
|
mixture of linear dependences |
1 |
|
model selection consistency |
1 |
|
model-free |
1 |
|
multi-class classification |
1 |
|
mutual fund |
1 |
|
naive bayes |
1 |
|
neyman-pearson |
1 |
|
neyman-pearson (np) paradigm |
1 |
|
neyman-pearson paradigm |
1 |
|
neyman–pearson (np) paradigm |
1 |
|
nonlinear decision boundary |
1 |
|
nonparametric statistics |
1 |
|
np oracle inequalities |
1 |
|
np oracle inequality |
1 |
|
np umbrella algorithm |
1 |
|
oracle inequality |
1 |
|
oracle property |
1 |
|
parallel computing |
1 |
|
penalized least squares |
1 |
|
perfect learning |
1 |
|
personalized pagerank |
1 |
|
plug-in approach |
1 |
|
plug-in methods |
1 |
|
regularized optimal affine discriminant |
1 |
|
resampling methods |
1 |
|
sampling bias |
1 |
|
screening |
1 |
|
scrna-seq data featurization |
1 |
|
specified and unspecified generalized pearson correlation squares |
1 |
|
statistical learning |
1 |
|
type i error |
1 |
|
umbrella algorithm |
1 |
|
weakly dependent |
1 |
