New Challenges in Statistical Analysis and Multivariate Data Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 471

Special Issue Editors


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Guest Editor
Resch School of Engineering, University of Wisconsin-Green Bay, Green Bay, WI, USA
Interests: circular statistics; multivariate analysis; bioinformatics; stochastic process
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Guest Editor
School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore City, Singapore
Interests: applied mathematics and statistics; mathematical finance; risk management; stochastic analysis

Special Issue Information

Dear Colleagues,

This Special Issue welcomes high-quality original research and review articles focused on recent developments and ongoing challenges in statistical and multivariate data analysis. We encourage submissions that present novel methodologies, cutting-edge computational techniques, and interdisciplinary applications across fields such as environmental science, public health, economics, and the social sciences.

Topics of interest include high-dimensional data analysis, robust and nonparametric methods, machine learning integration, and advanced multivariate modeling techniques.

The goal of this Issue is to provide a platform for researchers and practitioners to share innovative ideas, promote cross-disciplinary collaboration, and contribute to the advancement of modern data analysis.

Dr. Sungsu Kim
Dr. Chi Seng Pun
Guest Editors

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Keywords

  • multivariate data
  • machine learning
  • high-dimensional data

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Published Papers (1 paper)

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Research

39 pages, 507 KB  
Article
An LM-Type Unit Root Test for Functional Time Series
by Yichao Chen and Chi Seng Pun
Mathematics 2026, 14(5), 916; https://doi.org/10.3390/math14050916 - 8 Mar 2026
Viewed by 211
Abstract
In this paper, we propose a Lagrange multiplier (LM)-type unit root test for functional time series. The key novelty lies not in introducing a new LM principle but in establishing the asymptotic validity of such a test under the functional random walk null [...] Read more.
In this paper, we propose a Lagrange multiplier (LM)-type unit root test for functional time series. The key novelty lies not in introducing a new LM principle but in establishing the asymptotic validity of such a test under the functional random walk null hypothesis without relying on functional principal component analysis (FPCA) or finite-dimensional unit root subspace assumptions. We derive the limit distribution of our proposed test statistics under the null hypothesis of a random walk and its asymptotic behavior of alternative hypotheses of trend stationary, weakly dependent stationary, and autoregressive stationary models. Specifically, we establish the theoretical consistency of the test under all aforementioned alternative hypotheses. Simulation studies corroborate these theoretical findings and demonstrate the desirable finite-sample performance of the proposed functional unit root test. The proposed test is also applied to real data of intraday stock price curves, and the test results are plausible. Full article
(This article belongs to the Special Issue New Challenges in Statistical Analysis and Multivariate Data Analysis)
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