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20242026
most citedChange Point Inference for Non-Euclidean Data Sequences using Distance Profiles

2 citations · 2 across the 4 of their papers we have counts for

collaborators

8 papers

stat.ME2026

Iterative Exploration-Driven Sparse SDP Clustering via Thompson Sampling

Jongmin Mun, Paromita Dubey, Yingying Fan

High-dimensional sparse clustering is a combinatorial NP-hard problem that arises from the coupling between cluster assignment and variable selection. We demonstrate that semidefin…

stat.ME2026

Inference for Fréchet Regression

Wookyeong Song, Paromita Dubey, Hans-Georg Müller +1

Linear regression is widely used to model relationships between responses and predictors. In modern applications, one encounters data where the responses are non-Euclidean random o…

stat.ME20262 cited

Change Point Inference for Non-Euclidean Data Sequences using Distance Profiles

Paromita Dubey, Minxing Zheng

We introduce a powerful scan statistic and the corresponding test for detecting the presence and pinpointing the location of a change point within the distribution of a data sequen…

stat.ML2025

LLmFPCA-detect: LLM-powered Multivariate Functional PCA for Anomaly Detection in Sparse Longitudinal Texts

Prasanjit Dubey, Aritra Guha, Zhengyi Zhou +3

Sparse longitudinal (SL) textual data arises when individuals generate text repeatedly over time (e.g., customer reviews, occasional social media posts, electronic medical records…

stat.ML2025

DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses

Kyum Kim, Yaqing Chen, Paromita Dubey

Regression with non-Euclidean responses -- e.g., probability distributions, networks, symmetric positive-definite matrices, and compositions -- has become increasingly important in…

stat.ME2025

DiPMInd: Distance Profile based Mutual Independence testing for random objects

Yaqing Chen, Paromita Dubey

This paper develops a novel unified framework for testing mutual independence among random objects residing in possibly different metric spaces. The framework generalizes existing…