Convergence guarantees for the Good-Turing estimator
A Painsky - Journal of Machine Learning Research, 2022 - jmlr.org
Consider a finite sample from an unknown distribution over a countable alphabet. The
occupancy probability (OP) refers to the total probability of symbols that appear exactly k …
occupancy probability (OP) refers to the total probability of symbols that appear exactly k …
Generalized Good-Turing improves missing mass estimation
A Painsky - Journal of the American Statistical Association, 2023 - Taylor & Francis
Consider a finite sample from an unknown distribution over a countable alphabet. The
missing mass refers to the probability of symbols that do not appear in the sample …
missing mass refers to the probability of symbols that do not appear in the sample …
Confidence Intervals for Parameters of Unobserved Events
A Painsky - Journal of the American Statistical Association, 2024 - Taylor & Francis
Consider a finite sample from an unknown distribution over a countable alphabet.
Unobserved events are alphabet symbols which do not appear in the sample. Estimating the …
Unobserved events are alphabet symbols which do not appear in the sample. Estimating the …
Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence
We study the problem of estimating the stationary mass---also called the unigram mass---
that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem …
that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem …
Near-optimal estimation of the unseen under regularly varying tail populations
Near-optimal estimation of the unseen under regularly varying tail populations Page 1
Bernoulli 29(4), 2023, 3423–3442 https://doi.org/10.3150/23-BEJ1589 Near-optimal …
Bernoulli 29(4), 2023, 3423–3442 https://doi.org/10.3150/23-BEJ1589 Near-optimal …
A data-driven missing mass estimation framework
A Painsky - 2022 IEEE International Symposium on Information …, 2022 - ieeexplore.ieee.org
Consider a finite sample from an unknown distribution over a countable alphabet. The
missing mass refers to the probability of symbols that do not appear in the sample. Missing …
missing mass refers to the probability of symbols that do not appear in the sample. Missing …
Confidence intervals for unobserved events
A Painsky - arXiv preprint arXiv:2211.03052, 2022 - arxiv.org
Consider a finite sample from an unknown distribution over a countable alphabet.
Unobserved events are alphabet symbols which do not appear in the sample. Estimating the …
Unobserved events are alphabet symbols which do not appear in the sample. Estimating the …
Necessary and sufficient conditions for the asymptotic normality of higher order Turing estimators
J Chang, M Grabchak - Bernoulli, 2023 - projecteuclid.org
Necessary and sufficient conditions for the asymptotic normality of higher order Turing
estimators Page 1 Bernoulli 29(4), 2023, 3369–3395 https://doi.org/10.3150/23-BEJ1587 …
estimators Page 1 Bernoulli 29(4), 2023, 3369–3395 https://doi.org/10.3150/23-BEJ1587 …
Just Wing It: Optimal Estimation of Missing Mass in a Markovian Sequence
We study the problem of estimating the stationary mass--also called the unigram mass--that
is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has …
is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has …
Bayesian nonparametric estimation of coverage probabilities and distinct counts from sketched data
The estimation of coverage probabilities, and in particular of the missing mass, is a classical
statistical problem with applications in numerous scientific fields. In this paper, we study this …
statistical problem with applications in numerous scientific fields. In this paper, we study this …