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Learning Human Activity Patterns Using Clustered Point Processes With Active and Inactive States

Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and long periods of inactivity. Such heterogeneity in event patterns poses challenges for existing point process models. In this article, we propose a new class of clustered point processes that alternate between active and inactive states. The proposed model is flexible, highly interpretable, and can provide useful insights into event patterns. A composite likelihood approach and a composite EM estimation procedure are developed for efficient and numerically stable parameter estimation. We study both the computational and statistical properties of the estimator including convergence, consistency, and asymptotic normality. The proposed method is applied to Donald Trump’s Twitter data to investigate if and how his behaviors evolved before, during, and after the presidential campaign. Additionally, we analyze large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF BUSINESS & ECONOMIC STATISTICS; Vol. 41, No. 2, April 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 388-398

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

SOCIAL MEDIA
COMPOSITE LIKELIHOOD
CLUSTERED POINT PROCESSES
COMPOSITE EM ALGORITHM
NONOVERLAPPING CLUSTERS

Info Detail Spesifik

https://doi.org/10.1080/07350015.2021.2025065

Pernyataan Tanggungjawab

Jingfei Zhanga, Biao Cai, Xuening Zhu, Hansheng Wang, Ganggang Xu, Yongtao Guan

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