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Economic Data Science Seminar Series - Mingli Chen

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The Alan Turing Institute

96 Euston Road

London

NW1 2DB

United Kingdom

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Seminar talk by Mingli Chen, on Analysis of Networks via the Sparse β-Model

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Registration - 15:30 - 16:00

Seminar talk by Mingli Chen - 16:00 - 17:00

The Alan Turing Institute, the Data Science Campus of the Office for National Statistics (ONS) and the Financial Conduct Authority (FCA) are collaborating for the economic data science seminar series. The intended audience for these seminars includes both policymakers and academics, and the seminars will be overseen by a group of seminar partners made up of representatives of key stakeholder organisations.

The economic data science series has four objectives:

to promote economic data science as a discipline

to link researchers across institutions and disciplines and bring together the academic and policy communities

to demonstrate cutting-edge applications of data science to economic questions to an audience of economists, data scientists and economic data scientists

to provide a forum for addressing challenges in current work.

This seminar speaker will be Mingli Chen -On analysis of Networks via the Sparse β-Model

Abstract

We propose the Sparse Beta Model, a novel network model that interpolates the celebrated Erdos-Renyi model and the Beta Model. We show our Sparse Beta Model is a tractable model for modelling both ”local” and ”global” sparseness of a network, along with the core-periphery network or the leaders-followers network. Core-periphery networks appear frequently in financial networks, e.g. conventional OTC markets.

We derive the asymptotic distribution of the maximum likelihood estimator of the SβM when the support of the parameter vector is known. When the sparsity is unknown, we formulate a penalized likelihood approach with the l0 penalty. We overcome the seemingly combinatorial computational problem due to the l0 penalty by utilising the sufficient statistics of the network.

We apply the proposed model and estimation procedure the famous microfinance take-up example, a.k.a. Banerjee et al. 2013, and find our beta-centrality (from our sparse beta model) is significantly related to eventual microfinance participation -- which shows our work can provide insights on the role of social importance (node position/attribution in a network), through providing new network statistics such as beta-centrality, on program participation.

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Location

The Alan Turing Institute

96 Euston Road

London

NW1 2DB

United Kingdom

View Map

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