Small area estimation based on area level models typically assumes that sampling error variances for the direct survey small area estimates are known. In practice we use estimates of the sampling error variances, and these can contain substantial error. This suggests modeling the sampling variances to improve them and to quantify e , dects of their estimation error on small area inferences. We ...

Measuring the size of the Internet via Monte Carlo sampling requires probing a large portion of the Internet protocol (IP) address space to obtain an accurate estimate. However, the distribution of information servers on the Internet is highly nonuniform over the IP address space. This allows us to design probing strategies based on importance sampling for measuring the prevalence of an informa...

This paper presents a new procedure for fitting multiple geometric structures without having a priori knowledge of scale. Our method leverages on Consensus Clustering, a single-term model selection strategy relying on the principle of stability, thereby avoiding the explicit tradeoff between data fidelity (i.e., modeling error) and model complexity. In particular we tailored this model selectio...

In the absence of long-term monitoring data, inferences about extinctions of species and populations are generally based on past observations about the presence of a particular species at specified places and times (sightings). Several methods have been developed to estimate the probability and timing of extinctions from records of such sightings, but they differ in their computational complexi...

We establish a framework for assessing the validity of a given model using Monte Carlo simulations and inferences based on sampling distributions. Using this framework, we show that geometric brownian motion alone cannot generate a majority of the patterns in the distribution of stock returns and wealth creation. Our paper represents an often overlooked departure from the traditional way of val...