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current statistical practice combines the Fisherian and Neyman-Pearson perspectives
deep density estimation
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small worlds vs large worlds
splines
squared error
statistical decision theory
statistical inference
stopping-time
strong approximations
sub-exponential distributions
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sub-psi process
sufficiency and the likelihood
sufficient statistic
supermartingale
supervised learning
survey sampling
t-test
techniques for multivariate concentration
test-martingale
testing by betting—composite vs composite
testing by betting—simple vs composite
testing by betting—simple vs simple
testing by betting—two-sample testing
testing exchangeability
testing forecasters by betting
testing group invariance
time-uniform
total variation distance
two-sample testing
u-statistics
uncertainty quantification
uniformly most powerful test
universal inference
v-statistics
variational approach to concentration
variational autoencoders
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Wasserstein Distance
wavelets
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zero sum game
PRDS
Last modified
Sep 02, 2024
1 min read
PRDS is a generalization of independence.
todo
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BH procedure
e-BH procedure
Explore
active statistical inference
anisotropic distribution
anytime-valid
anytime-valid p-values
asymptotic confidence sequences
average calibration error
Banach space
basic inequalities
basic matrix inequalities
Bayes factors
Bayesian interpretation of probability
Bayesian nonparametrics
Bayesian parametrics
Bayesian statistics
Bernstein von-Mises theorem
Berry-Esseen bounds
betting strategies
BH procedure
bounded difference inequalities
calibration
Catoni-Giulini M-estimator
causal inference
cdf concentration
cdf estimation
central limit theorems
chaining
characteristic function
Chernoff bounds specify e-values
Chernoff method
chi-squared divergence
CLTs in Banach spaces
coarsened filtrations can increase power
comparing forecasters by betting
concentration in Banach spaces
concentration inequalities
concentration of functions
concentration of measure
concentration of self-bounding functions
concentration via covering
conditional independence testing
confidence intervals
confidence sequences
confidence sequences for quantiles
confidence sequences via conjugate mixtures
confidence sequences via predictable plug-ins
conformal prediction
conjugate transpose
contextual bandit
covering and packing
credible intervals
current statistical practice combines the Fisherian and Neyman-Pearson perspectives
deep density estimation
density estimation
differential privacy
Dirichlet process
distributional distance
Doob decomposition
doubly robust estimator
duality between hypothesis tests and CIs
Dudley chaining
Dudley's entropy bound
e-BH procedure
e-process
e-value
e-values enable post-hoc hypothesis testing
Efron-Stein inequality
empirical Bernstein bounds
empirical process theory
ensemble learning
entropy number
estimating means by betting
evidence against the null
evidence is quantifiable in small-worlds
exchangeable distribution
exponential families
exponential inequalities
external randomization
f-divergence
FDR control
Fisher information
Fisher information distance
Fisher's paradigm
fixed-time
fork-convex
foundations of statistics
frequentist interpretation of probability
frequentist statistics
from boundedness to variance adaptivity
game theory
game-theoretic concentration inequalities
game-theoretic convergence of opinions
game-theoretic expectations
game-theoretic hypothesis testing
game-theoretic LLN
game-theoretic probabilities
game-theoretic probability
game-theoretic statistics
Gaussian complexity
Gaussian process regression
Gaussian sequence model
generic chaining
Glivenko-Cantelli classes
GRO e-variable
GROW e-variable
growth rate conditions in sequential testing
heavy-tailed scalar concentration
Hellinger distance
Hermitian matrix
hidden Markov model
hilbert space
histograms
Hölder space
hypothesis testing
ideal metrics
infinitely divisible distribution
information processing inequality
information theory
instrumentalist theory of probability
inverse problems
irregular problems in hypothesis testing
isotropic distributions
issues with p-values
Jeffreys prior
Jeffreys' paradigm of hypothesis testing
Karlin-Rubin theorem
Kelly betting
kernel density estimation
kernel regression
kernel trick
KL divergence
knn
KS distance
lady tasting tea
law of likelihood
light-tailed maximal inequalities
light-tailed scalar concentration
likelihood principle
likelihood-ratio test
Lindeberg-Feller CLT
Lindeberg-Levy CLT
Linear Regression
linear regression diagnostics
linear smoothers
local differential privacy
local polynomial regression
Loewner order
log-concave distribution
Lp norm
Lyapunov CLT
M-estimation
marginal consistency
Markovian alternatives
martingale CLT
martingale concentration
matrix inequalities
maximal inequalities
maximizing log-wealth
Mayo's error statistics
median-of-means
Mercer kernel
method of moments for concentration
method of moments for estimation
metric entropy
metric space
MGF
minimal sufficiency
MLE
model selection
model-X assumption
monotone likelihood ratio
multi-group calibration
multi-group consistency
multiarmed bandit
multiple testing
multivariate concentration
multivariate heavy-tailed concentration
multivariate light-tailed concentration
Nash equilibrium
Neyman-Pearson lemma
Neyman-Pearson lemma for discrete distributions
Neyman-Pearson paradigm
Neyman-Pearson paradigm with losses
nonparametric classification
nonparametric density estimation
nonparametric regression
numeraire e-variable
online calibration
online gradient descent
online marginal estimation
Online Newton Step
operator norm
operator norm inequalities
optimal transport
optimization perspective on Markov's inequality
optional continuation
optional stopping
Orlicz norm
p-hacking
p-value
PAC-Bayes
parametric density estimation
parametric versus nonparametric statistics
partitions and trees
permutation test
permutation testing by betting
Petrov's CLT template
pinball loss
Pinelis approach to concentration
portfolio optimization
post-hoc confidence sequences via e-processes
post-hoc hypothesis testing
post-hoc hypothesis testing with losses
post-hoc valid confidence sequences
PRDS
prediction-powered inference
quantile estimation
quantitative CLT template with ideal metrics
Rademacher complexity
randomized inequalities
Rao-Blackwell theorem
REGROW e-variable
reinforcement learning
representer theorem
reverse information projection (RIPr)
RKHS
rkhs regression
Royall's three questions
safe, anytime-valid inference (SAVI)
score function
sequential hypothesis testing
sequential statistics
Simpson's paradox
small worlds vs large worlds
splines
squared error
statistical decision theory
statistical inference
stopping-time
strong approximations
sub-exponential distributions
sub-Gaussian distributions
sub-Gaussian process
sub-psi process
sufficiency and the likelihood
sufficient statistic
supermartingale
supervised learning
survey sampling
t-test
techniques for multivariate concentration
test-martingale
testing by betting—composite vs composite
testing by betting—simple vs composite
testing by betting—simple vs simple
testing by betting—two-sample testing
testing exchangeability
testing forecasters by betting
testing group invariance
time-uniform
total variation distance
two-sample testing
u-statistics
uncertainty quantification
uniformly most powerful test
universal inference
v-statistics
variational approach to concentration
variational autoencoders
variational inference
Ville's inequality
Wald interval
Wald test
Warner's randomized response
Wasserstein Distance
wavelets
weighted least squares
zero sum game