|
bert |
2 |
|
bias |
2 |
|
bootstrap |
2 |
|
bootstrap iteration |
2 |
|
importance sampling |
2 |
|
m-estimator |
2 |
|
mle |
2 |
|
monte carlo |
2 |
|
mvue |
2 |
|
over-smoothing |
2 |
|
saddlepoint |
2 |
|
tail probability |
2 |
|
transformer |
2 |
|
adaptive |
1 |
|
alternative hypothesis |
1 |
|
ancillary |
1 |
|
asymptotic |
1 |
|
asymptotic distribution |
1 |
|
asymptotic normality |
1 |
|
asymptotics |
1 |
|
backwards percentile |
1 |
|
bandwidth |
1 |
|
bayes rule |
1 |
|
bias reduction |
1 |
|
block bootstrap |
1 |
|
boostrap iteration |
1 |
|
bootstrap consistency |
1 |
|
bootstrap-t |
1 |
|
calibration |
1 |
|
centre exponent |
1 |
|
centre-outward ordering |
1 |
|
chernoff's modal estimator |
1 |
|
classification |
1 |
|
computer engineering |
1 |
|
computers |
1 |
|
conditional inference |
1 |
|
confidence bound |
1 |
|
confidence interval |
1 |
|
confidence limit |
1 |
|
confidence region |
1 |
|
confidence set |
1 |
|
configural polysampling |
1 |
|
confrontation |
1 |
|
consistency |
1 |
|
constrained bootstrap |
1 |
|
constrained maximum likelihood estimator |
1 |
|
convergence rate |
1 |
|
cornish-fisher expansion |
1 |
|
coverage |
1 |
|
coverage calibration |
1 |
|
coverage error |
1 |
|
data depth |
1 |
|
density function |
1 |
|
depth function |
1 |
|
double block bootstrap |
1 |
|
double bootstrap |
1 |
|
edgeworth |
1 |
|
edgeworth expansion |
1 |
|
empirical likelihood |
1 |
|
equi-tailed |
1 |
|
estimation of distribution |
1 |
|
extreme percentile |
1 |
|
gaussian process |
1 |
|
global maximum likelihood estimator |
1 |
|
goodness-of-fit tests |
1 |
|
hard thresholding |
1 |
|
high dimensions |
1 |
|
hybrid confidence region |
1 |
|
hybrid percentile |
1 |
|
interpoint distance |
1 |
|
interpolation |
1 |
|
iterated bootstrap |
1 |
|
james–stein estimator |
1 |
|
kernel |
1 |
|
kernel density estimate |
1 |
|
kernel density estimator |
1 |
|
kernel function |
1 |
|
l(p) estimator |
1 |
|
lasso-type estimator |
1 |
|
least favourable family |
1 |
|
least squares estimator |
1 |
|
likelihood |
1 |
|
linear approximation |
1 |
|
linear regression |
1 |
|
local linear regression |
1 |
|
local parametric fit |
1 |
|
lp estimator |
1 |
|
m -estimation |
1 |
|
m out of n bootstrap |
1 |
|
m out of n parametric bootstrap |
1 |
|
m-estimation |
1 |
|
m/n bootstrap |
1 |
|
markov chain |
1 |
|
maximum likelihood |
1 |
|
minimax |
1 |
|
missing data |
1 |
|
mixture estimator |
1 |
|
monte carlo approximation |
1 |
|
monte carlo simulation |
1 |
|
moving-parameter |
1 |
|
multimodality |
1 |
|
nadaraya quantile estimator |
1 |
|
nadaraya–watson estimator |
1 |
|
nearest neighbour |
1 |
|
negativity correction |
1 |
|
noncoverage |
1 |
|
nonparametric likelihood |
1 |
|
nonparametric regression |
1 |
|
nonsmooth functional |
1 |
|
nuisance parameter |
1 |
|
null hypothesis |
1 |
|
omnidirectional data perturbations |
1 |
|
oracle property |
1 |
|
order-calibration |
1 |
|
p-value |
1 |
|
parametric bootstrap |
1 |
|
partially linear model |
1 |
|
percentile method |
1 |
|
pivot |
1 |
|
plug-in |
1 |
|
post-model-selection |
1 |
|
power |
1 |
|
prepivoting |
1 |
|
quantile |
1 |
|
r-value plot |
1 |
|
ratewise efficient |
1 |
|
recentred bootstrap |
1 |
|
regression |
1 |
|
regression. |
1 |
|
rejection sampling |
1 |
|
representativeness |
1 |
|
resample |
1 |
|
resampling |
1 |
|
ridge regression |
1 |
|
robustness |
1 |
|
sample quantile |
1 |
|
sampling error |
1 |
|
scad |
1 |
|
second-order accurate |
1 |
|
semiparametric estimation |
1 |
|
sequential bagging |
1 |
|
shrinkage-type |
1 |
|
simulation |
1 |
|
size |
1 |
|
smooth function model |
1 |
|
smoothed bootstrap |
1 |
|
standardized statistic |
1 |
|
stein estimator |
1 |
|
stochastically optimal |
1 |
|
strong mixing |
1 |
|
studentization |
1 |
|
studentized sample quantile |
1 |
|
subsampling |
1 |
|
undersmoothing |
1 |
|
uniform consistency |
1 |
|
uniformly correct |
1 |
|
variable selection |
1 |
|
weak sparsity |
1 |
|
weakly dependent |
1 |
|
weighted bootstrap |
1 |
|
weighted bootstrap iteration |
1 |
