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Estimation of the Parameters in a Cl...

North Carolina State University.

Estimation of the Parameters in a Class of Dynamic Network Models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Estimation of the Parameters in a Class of Dynamic Network Models.
作者:	Zhao, Wei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2019
面頁冊數:	111 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
附註:	Advisor: Pelletier, Denis;Song, Rui;Lu, Wenbin;Lahiri, Soumendra.
Contained By:	Dissertations Abstracts International81-03B.
標題:	Statistics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27528204
ISBN:	9781085644624

Estimation of the Parameters in a Class of Dynamic Network Models.
Zhao, Wei.

Estimation of the Parameters in a Class of Dynamic Network Models. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 111 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2019.

This item must not be sold to any third party vendors.

Models have been proposed for static networks, such as stochastic block model, exponential network models. However, under many situations, the networks are time-varying. In this paper, we introduce three kinds of dynamic network models. For each model, we give the estimators of parameters based on the Maximum Likelihood method. Then, the asymptotic distributions are derived for the estimators using Martingale’s Central Limit Theorem. The first model introduced isMarkov Dynamic Network base model, in which edges are placed independently with the same probabilities conditioned on their former status, and the network size is kept unchanged. The second model is called Growing Size Markov Dynamic Network, where the first model is generalized by allowing fixed/random number of nodes becoming active/inactive at each time. The last model introduced is Multi-Classes Markov Dynamic Network. In this model, nodes are assigned to multiple classes in considering both the former status of the network and the nodes covariates. Simulations are conducted for each model to analyze the properties and behaviors of the estimators. The MathOverflow network data set and MovieLens Ratings network data set are introduced as applications of the second and third model.

ISBN: 9781085644624Subjects--Topical Terms:

182057
Statistics.

Estimation of the Parameters in a Class of Dynamic Network Models.
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520 $a Models have been proposed for static networks, such as stochastic block model, exponential network models. However, under many situations, the networks are time-varying. In this paper, we introduce three kinds of dynamic network models. For each model, we give the estimators of parameters based on the Maximum Likelihood method. Then, the asymptotic distributions are derived for the estimators using Martingale’s Central Limit Theorem. The first model introduced isMarkov Dynamic Network base model, in which edges are placed independently with the same probabilities conditioned on their former status, and the network size is kept unchanged. The second model is called Growing Size Markov Dynamic Network, where the first model is generalized by allowing fixed/random number of nodes becoming active/inactive at each time. The last model introduced is Multi-Classes Markov Dynamic Network. In this model, nodes are assigned to multiple classes in considering both the former status of the network and the nodes covariates. Simulations are conducted for each model to analyze the properties and behaviors of the estimators. The MathOverflow network data set and MovieLens Ratings network data set are introduced as applications of the second and third model.
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