arXiv subjects

Shoja'eddin Chenouri

Publications and source records attributed to Shoja'eddin Chenouri.

5 recordsLinked to original sources

arXiv2025

Differentially private multivariate medians

Statistical tools which satisfy rigorous privacy guarantees are necessary for modern data analysis. It is well-known that robustness against contamination is linked to differential privacy. Despite this fact, using multivariate medians for differentially private and robust multivariate location estimation has not been systematically studied. We develop novel finite-sample performance guarantees for differentially private multivariate depth-based medians, which are essentially sharp. Our results cover commonly used depth functions, such as the halfspace (or Tukey) depth, spatial depth, and the integrated dual depth. We show that under Cauchy marginals, the cost of heavy-tailed location estimation outweighs the cost of privacy. We demonstrate our results numerically using a Gaussian contamination model in dimensions up to $d = 100$, and compare them to a state-of-the-art private mean estimation algorithm. As a by-product of our investigation, we prove concentration inequalities for the output of the exponential mechanism about the maximizer of the population objective function. This bound applies to objective functions that satisfy a mild regularity condition.

math.ST↗

arXiv2024

Robust changepoint detection in the variability of multivariate functional data

We consider the problem of robustly detecting changepoints in the variability of a sequence of independent multivariate functions. We develop a novel changepoint procedure, called the functional Kruskal--Wallis for covariance (FKWC) changepoint procedure, based on rank statistics and multivariate functional data depth. The FKWC changepoint procedure allows the user to test for at most one changepoint (AMOC) or an epidemic period, or to estimate the number and locations of an unknown amount of changepoints in the data. We show that when the ``signal-to-noise'' ratio is bounded below, the changepoint estimates produced by the FKWC procedure attain the minimax localization rate for detecting general changes in distribution in the univariate setting (Theorem 1). We also provide the behavior of the proposed test statistics for the AMOC and epidemic setting under the null hypothesis (Theorem 2) and, as a simple consequence of our main result, these tests are consistent (Corollary 1). In simulation, we show that our method is particularly robust when compared to similar changepoint methods. We present an application of the FKWC procedure to intraday asset returns and f-MRI scans. As a by-product of Theorem 1, we provide a concentration result for integrated functional depth functions (Lemma 2), which may be of general interest.

stat.ME↗

arXiv2021

Robust multiple change-point detection for multivariate variability using data depth

In this paper, we introduce two robust, nonparametric methods for multiple change-point detection in the variability of a multivariate sequence of observations. We demonstrate that changes in ranks generated from data depth functions can be used to detect changes in the variability of a sequence of multivariate observations. In order to detect more than one change, the first algorithm uses methods similar to that of wild-binary segmentation. The second algorithm estimates change-points by maximizing a penalized version of the classical Kruskal Wallis ANOVA test statistic. We show that this objective function can be maximized via the well-known PELT algorithm. Under mild, nonparametric assumptions both of these algorithms are shown to be consistent for the correct number of change-points and the correct location(s) of the change-point(s). We demonstrate the efficacy of these methods with a simulation study, where we compare our new methods to several competing methods. We show our methods outperform existing methods in this problem setting, and our methods can estimate changes accurately when the data are heavy tailed or skewed.

stat.ME↗

arXiv2021

Statistical Monitoring of the Covariance Matrix in Multivariate Processes: A Literature Review

Monitoring several correlated quality characteristics of a process is common in modern manufacturing and service industries. Although a lot of attention has been paid to monitoring the multivariate process mean, not many control charts are available for monitoring the covariance matrix. This paper presents a comprehensive overview of the literature on control charts for monitoring the covariance matrix in a multivariate statistical process monitoring (MSPM) framework. It classifies the research that has previously appeared in the literature. We highlight the challenging areas for research and provide some directions for future research.

stat.ME↗

arXiv2021

Differentially private depth functions and their associated medians

In this paper, we investigate the differentially private estimation of data depth functions and their associated medians. We introduce several methods for privatizing depth values at a fixed point, and show that for some depth functions, when the depth is computed at an out of sample point, privacy can be gained for free when $n\rightarrow \infty$. We also present a method for privately estimating the vector of sample point depth values. Additionally, we introduce estimation methods for depth-based medians for both depth functions with low global sensitivity and depth functions with only highly probable, low local sensitivity. We provide a general result (Lemma 1) which can be used to prove consistency of an estimator produced by the exponential mechanism, provided the limiting cost function is sufficiently smooth at a unique minimizer. We also introduce a general algorithm to privately estimate a minimizer of a cost function which has, with high probability, low local sensitivity. This algorithm combines the propose-test-release algorithm with the exponential mechanism. An application of this algorithm to generate consistent estimates of the projection depth-based median is presented. Thus, for these private depth-based medians, we show that it is possible for privacy to be obtained for free when $n\rightarrow \infty$.

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