Vladas Pipiras

Biographic Data

ID1980541
NAMEVladas Pipiras
GIVEN NAMESVladas
FAMILY NAMEPipiras
SIGNATUREPIPIRAS V
AFFILIATIONSUniversity of North Carolina at Chapel Hill
ORCID0000-0001-6838-6294
VERIFIEDYes
TOTAL WORKS6
TOTAL CITATIONS1
WORKS AS AUTHOR6
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2011
LATEST PUBLICATION YEAR2026
H-INDEX1

  • Penalized Subgrouping of Heterogeneous Time Series

    Christopher M Crawford, Jonathan Park et al.ARTICLEMultivariate Behavioral Research2026

    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 heterog…

  • Structured Estimation of Heterogeneous Time Series

    Zachary F Fisher, Young-Hoon Kim et al.ARTICLEMultivariate Behavioral Research2024

    How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. introduced the multi-VAR approach for simultaneously estimating multiple-subject multivariate time series characterized by common and individualizing features using penalized estimation. This approach differs from many popular modeling approaches for multiple-subject time series in that qualitati…

  • Representation, ranking and bias of minorities in sampling attributed networks

    Open AccessNelson Antunes, Sayan Banerjee et al.ARTICLESocial Network Analysis and Mining2024

  • A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters

    Zachary F Fisher, Sy‐miin Chow et al.ARTICLEMultivariate Behavioral Research2022

    Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, …

  • Sampling methods and estimation of triangle count distributions in large networks

    Open AccessNelson Antunes, Tianjian Guo et al.ARTICLENetwork Science2021Cited by: 1

    This paper investigates the distributions of triangle counts per vertex and edge, as a means for network description, analysis, model building, and other tasks. The main interest is in estimating these distributions through sampling, especially for large networks. A novel sampling method tailored for the estimation analysis is proposed, with three sampling designs motivated by several network access scenarios. An estimation method based on invers…

  • Local and Global Rank Tests for Multivariate Varying-Coefficient Models

    Stephen G Donald, Natércia Fortuna et al.ARTICLEJournal of Business and Economic…2011

    In a multivariate varying-coefficient model, the response vectors Y are regressed on known functions v(X) of some explanatory variables X and the coefficients in an unknown regression matrix θ(Z) depend on another set of explanatory variables Z. We provide statistical tests, called local and global rank tests, which allow one to estimate the rank of an unknown regression coefficient matrix θ(Z) locally at a fixed level of the variable Z or global…