About

Vladas Pipiras is a Professor in the Department of Statistics and Operations Research at the University of North Carolina, Chapel Hill. His academic work is centered in the fields of Statistics and Probability, with a broad interest in various applications within these disciplines. He maintains a comprehensive curriculum vitae that details his professional information and contributions. The website serves to expand on other professional aspects of his career, and he encourages contact for any questions related to his work.

Research topics

• Computer Science
• Mathematics
• Machine Learning
• Econometrics
• Statistics
• Artificial Intelligence
• Data Mining
• Classical mechanics
• Geology
• Statistical physics
• Physics
• Telecommunications
• Engineering
• Quantum mechanics
• Mathematical analysis
• Marine engineering

Selected publications

• Multivariate integer-valued time series with flexible autocovariances and their application to major hurricane counts

  UNC Libraries · 2026-04-07

  articleOpen access1st authorCorresponding

  This paper examines a bivariate count time series with some curious statistical features: Saffir–Simpson Category 3 and stronger annual hurricane counts in the North Atlantic and eastern Pacific Ocean Basins. As land and ocean temperatures on our planet warm, an intense climatological debate has arisen over whether hurricanes are becoming more numerous, or whether the strengths of the individual storms are increasing. Recent literature concludes that an increase in hurricane counts occurred in the Atlantic Basin circa 1994. This increase persisted through 2012; moreover, the 1994–2012 period was one of relative inactivity in the eastern Pacific Basin. When Atlantic activity eased in 2013, heavy activity in the eastern Pacific Basin commenced. When examined statistically, a Poisson white noise model for the annual severe hurricane counts is difficult to resoundingly reject. Yet, decadal cycles (longer term dependence) in the hurricane counts is plausible. This paper takes a statistical look at the issue, developing a stationary multivariate count time series model with Poisson marginal distributions and a flexible autocovariance structure. Our auto- and cross-correlations can be negative and have long-range dependence; features that most previous count models cannot achieve in tandem. Our model is new in the literature and is based on categorizing and super-positioning multivariate Gaussian time series. We derive the autocovariance function of the model and propose a method to estimate model parameters. In the end, we conclude that severe hurricane counts are indeed negatively correlated across the two ocean basins. Some evidence for long-range dependence is also presented; however, with only a 49-year record, this issue cannot be definitively judged without additional data.

  PublisherDOI

• Consistency of Lloyd’s algorithm under perturbations

  Electronic Journal of Statistics · 2026-01-01

  articleOpen access
  PublisherDOI

• Penalized Subgrouping of Heterogeneous Time Series

  Multivariate Behavioral Research · 2026-02-20

  article

  Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterogeneous dynamics in multiple-subject time series through structured penalization. In the original multi-VAR proposal, individual-level transition matrices are decomposed into common and unique dynamics, allowing for generalizable and person-specific features. The current project extends this framework to allow additionally for the identification and penalized estimation of subgroup-specific dynamics; that is, patterns of dynamics that are shared across subsets of individuals. The performance of the proposed subgrouping extension is evaluated in the context of both a simulation study and empirical application, and results are compared to alternative methods for subgrouping multiple-subject, multivariate time series.

  PublisherDOI

• Penalized Subgrouping of Heterogeneous Time Series

  Figshare · 2026-01-01

  articleOpen access

  Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterogeneous dynamics in multiple-subject time series through structured penalization. In the original multi-VAR proposal, individual-level transition matrices are decomposed into common and unique dynamics, allowing for generalizable and person-specific features. The current project extends this framework to allow additionally for the identification and penalized estimation of subgroup-specific dynamics; that is, patterns of dynamics that are shared across subsets of individuals. The performance of the proposed subgrouping extension is evaluated in the context of both a simulation study and empirical application, and results are compared to alternative methods for subgrouping multiple-subject, multivariate time series.

  PublisherDOI

• Parametric Multi-Fidelity Monte Carlo Estimation With Applications to Extremes

  Technometrics · 2026-05-21

  preprintOpen accessSenior authorCorresponding

  In a multi-fidelity setting, data are available from two sources, high- and low-fidelity. Low-fidelity data has larger size and can be leveraged to make more efficient inference about quantities of interest, e.g. the mean, for high-fidelity variables. In this work, such multi-fidelity setting is studied when the goal is to fit more efficiently a parametric model to high-fidelity data. Three multi-fidelity parameter estimation methods are considered, joint maximum likelihood, (multi-fidelity) moment estimation and (multi-fidelity) marginal maximum likelihood, and are illustrated on several parametric models, with the focus on parametric families used in extreme value analysis. An application is also provided concerning quantification of occurrences of extreme ship motions generated by two computer codes of varying fidelity.

  PublisherOA PDFDOI

• Attribute network models, stochastic approximation and network sampling

  The Annals of Applied Probability · 2026-02-01

  articleSenior author

  Motivated by the central role of social networks in the diffusion of information, the study of network valued data where nodes and/or edges have attributes, which modulate the dynamics of both network evolution, and information flow on the network itself, has witnessed significant research interest across multiple disciplines. A key ingredient of this general area comprises probabilistic network models that incorporate (a) heterogeneity in edge creation across different attribute groups; (b) temporal network evolution and (c) popularity bias. Such models are then used to understand a host of domain specific questions, including bias in network sampling, PageRank and degree centrality scores and their impact in network ranking and recommendation algorithms. Despite significant interest, for these network models, the main network functional amenable to analysis has so far been degree distribution asymptotics. In this paper, we analyze dynamic random network models where younger vertices connect to older ones with probabilities proportional to their degrees as well as a propensity kernel governed by their attribute types. Using stochastic approximation techniques we show that, in the large network limit, such networks converge in the local weak sense to limiting infinite random trees with an explicit description in terms of randomly stopped multi-type branching processes. This allows for the derivation of asymptotics for a wide class of network functionals implying, for example, that while degree distribution tail exponents depend on the attribute type (already derived by (Electron. J. Probab. 18 (2013) 8)), PageRank centrality scores have the same tail exponent across attributes. The limit results also give explicit formulae for the performance of various network sampling mechanisms. One surprising consequence is the efficacy of PageRank and walk based network sampling schemes for directed networks in the setting of rare minorities.

  PublisherDOI

• Penalized Subgrouping of Heterogeneous Time Series

  Figshare · 2026-01-01

  articleOpen access

  Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterogeneous dynamics in multiple-subject time series through structured penalization. In the original multi-VAR proposal, individual-level transition matrices are decomposed into common and unique dynamics, allowing for generalizable and person-specific features. The current project extends this framework to allow additionally for the identification and penalized estimation of subgroup-specific dynamics; that is, patterns of dynamics that are shared across subsets of individuals. The performance of the proposed subgrouping extension is evaluated in the context of both a simulation study and empirical application, and results are compared to alternative methods for subgrouping multiple-subject, multivariate time series.

  PublisherDOI

• On Physics-Informed Statistical Reduced-Order Model for Response and Peaks of Ship Vertical Bending Moment in Irregular Waves

  2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails

  SIAM/ASA Journal on Uncertainty Quantification · 2025-01-03 · 3 citations

  article
  PublisherDOI

• Non-Gaussianity, Discretization and Their Consequences for Uncertainty Quantification in Longuet-Higgins Model of Random Waves

  2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

Recent grants

• Statistical Models, Inference, and Computation for Multidimensional Time Series Data

  NSF · $200k · 2017–2020

• Network Time Series: From Dynamics to Coevolution

  NSF · $220k · 2021–2025

• Random Processes and Fields: Discrete Approximations, Special Wavelet-Based Decompositions and Simulation

  NSF · $105k · 2005–2010

• Collaborative Research: Heavy Traffic Limit Models and Control Analysis for Wireless Queuing Systems - incorporating Long-Range Dependence and Heavy Tails

  NSF · $43k · 2006–2010

Frequent coauthors

• Murad S. Taqqu

  Boston University

  50 shared

• Nelson Antunes

  University of Algarve

  32 shared

• Stefanos Kechagias

  Athens University of Economics and Business

  30 shared

• Changryong Baek

  27 shared

• Patrice Abry

  Centre National de la Recherche Scientifique

  27 shared

• Robert Lund

  University of California, Santa Cruz

  23 shared

• Gustavo Didier

  23 shared

• James Livsey

  United States Census Bureau

  21 shared

Education

• PhD, Mathematics and Statistics

  Boston University

  2002

• DEA, Laboratoire de Probabilités, Statistique et Modélisation

  Universite Paris-Sorbonne

  1997

• BS, Mathematics and Informatics

  Vilnius University

  1996