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pro vyhledávání: '"Berrett, Thomas B"'
We study the effects of missingness on the estimation of population parameters. Moving beyond restrictive missing completely at random (MCAR) assumptions, we first formulate a missing data analogue of Huber's arbitrary $\epsilon$-contamination model.
Externí odkaz:
http://arxiv.org/abs/2410.10704
Autor:
Berrett, Thomas B.
We study the efficient estimation of a class of mean functionals in settings where a complete multivariate dataset is complemented by additional datasets recording subsets of the variables of interest. These datasets are allowed to have a general, in
Externí odkaz:
http://arxiv.org/abs/2409.05729
Most of the literature on differential privacy considers the item-level case where each user has a single observation, but a growing field of interest is that of user-level privacy where each of the $n$ users holds $T$ observations and wishes to main
Externí odkaz:
http://arxiv.org/abs/2405.11923
Autor:
Bordino, Alberto, Berrett, Thomas B.
We study the problem of testing whether the missing values of a potentially high-dimensional dataset are Missing Completely at Random (MCAR). We relax the problem of testing MCAR to the problem of testing the compatibility of a collection of covarian
Externí odkaz:
http://arxiv.org/abs/2401.05256
We introduce a new nonparametric framework for classification problems in the presence of missing data. The key aspect of our framework is that the regression function decomposes into an anova-type sum of orthogonal functions, of which some (or even
Externí odkaz:
http://arxiv.org/abs/2305.11672
Autor:
Berrett, Thomas B, Samworth, Richard J
Given a set of incomplete observations, we study the nonparametric problem of testing whether data are Missing Completely At Random (MCAR). Our first contribution is to characterise precisely the set of alternatives that can be distinguished from the
Externí odkaz:
http://arxiv.org/abs/2205.08627
Network data are ubiquitous in our daily life, containing rich but often sensitive information. In this paper, we expand the current static analysis of privatised networks to a dynamic framework by considering a sequence of networks with potential ch
Externí odkaz:
http://arxiv.org/abs/2205.07144
Autor:
Berrett, Thomas B
Invited discussion for Biometrika of 'Multivariate Fisher's independence test for multivariate dependence' by Gorsky and Ma (2022).
Comment: 4 pages
Comment: 4 pages
Externí odkaz:
http://arxiv.org/abs/2205.00992
It is of soaring demand to develop statistical analysis tools that are robust against contamination as well as preserving individual data owners' privacy. In spite of the fact that both topics host a rich body of literature, to the best of our knowle
Externí odkaz:
http://arxiv.org/abs/2201.00751
We present the $U$-Statistic Permutation (USP) test of independence in the context of discrete data displayed in a contingency table. Either Pearson's chi-squared test of independence, or the $G$-test, are typically used for this task, but we argue t
Externí odkaz:
http://arxiv.org/abs/2101.10880