Arun G. Chandrasekhar

Arun G. Chandrasekhar
Are you Arun G. Chandrasekhar?

Claim your profile, edit publications, add additional information:

Contact Details

Name
Arun G. Chandrasekhar
Affiliation
Location

Pubs By Year

Pub Categories

 
Physics - Physics and Society (3)
 
Statistics - Theory (1)
 
Mathematics - Statistics (1)
 
Statistics - Methodology (1)

Publications Authored By Arun G. Chandrasekhar

Social and economic network data can be useful for both researchers and policymakers, but can often be impractical to collect. We propose collecting Aggregated Relational Data (ARD) using questions that are simple and easy to add to any survey. These question are of the form "how many of your friends in the village have trait k?" We show that by collecting ARD on even a small share of the population, researchers can recover the likely distribution of statistics from the underlying network. Read More

We develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g. Read More

Is it possible, simply by asking a few members of a community, to identify individuals who are best placed to diffuse information? A model of diffusion shows how members of a community can, just by tracking gossip about others, identify those who are most central in a network according to "diffusion centrality" -- a network centrality measure that predicts the diffusion of a piece of information seeded with a network member. Using rich network data we collected in Indian villages, we find that villagers accurately nominate those who are diffusion central -- not just those with many friends or in powerful positions. In a randomized field experiment designed to test this theory, and implemented in a new set of villages, we track the diffusion of a piece of information initially given to a small number of "seeds" in each community. Read More

This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to be any functions, including ones carrying an index, which can be estimated parametrically or non-parametrically. The identification region of the parameters of the best linear approximation is characterized via its support function, and limit theory is developed for the latter. Read More

We define a general class of network formation models, Statistical Exponential Random Graph Models (SERGMs), that nest standard exponential random graph models (ERGMs) as a special case. We provide the first general results on when these models' (including ERGMs) parameters estimated from the observation of a single network are consistent (i.e. Read More