SOCIAL SCIENCE, STATISTICS AND POPULATION PROBLEMS

Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation

Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary” quantities such as goodness-of-fit statistics, predicted values, and residuals. Bayesian simulation treats th...

Social science. Computational social science.

Social Data and College Statistics

We correlate aspects of Twitter data with college statistics. We find that the amount of “buzz”about a college on twitter predicts the number of applicants to the college, even when controlling for the number of applicants in the previous year. We also explore various methods to classify the sentiment of tweets about a school. We find that the sentiment of insiders at a college predicts the fre...

Particle Statistics and Population Dynamics

We study a master equation system modelling a population dynamics problem in a lattice. The problem is the calculation of the minimum size of a refuge that can protect a population from hostile external conditions, the so called critical patch size problem. We analize both cases in which the particles are considered fermions and bosons and show using exact analitical methods that, while the Fer...

Inverse Problems as Statistics

What mathematicians, scientists, engineers, and statisticians mean by \inverse problem" di ers. For a statistician, an inverse problem is an inference or estimation problem. The data are nite in number and contain errors, as they do in classical estimation or inference problems, and the unknown typically is in nite-dimensional, as it is in nonparametric regression. The additional complication i...