We develop an estimation procedure of multiple regression function and its derivatives (formarginal e¤ects) based on multivariate maximum entropy (ME) density estimation. The relevantmoments and cross moments conditions are selected to be inlcuded in the inferred distribution.Finite sample and...

Background: Identifying effective treatments and policies early in a pandemic is challenging because only limited and noisy data are available, and biological processes are unknown or uncertain. Consequently, classical statistical procedures may not work or require strong structural assumptions....

The possibility of reoccurring waves of the novel coronavirus that triggered the 2020 pandemic makes it critical to identify underlying policy-relevant factors that could be leveraged to decrease future COVID-19 death rates. We examined variation in a number of underlying, policy-relevant,...

In this note we present a way of solving linear inverse problems with convex constraints and noisy data. Combining the methods of maximum entropy in the mean and the generalized maximum entropy, we are able to treat both the noiseless and the noisy cases as conceptually the same problem. This...

Although in principle prior information can significantly improve inference, incorporating incorrect prior information will bias the estimates of any inferential analysis. This fact deters many scientists from incorporating prior information into their inferential analyses. In the natural...

The Navy’s promotion-retention process involves two successive decisions: The Navy decides whether an individual is selected for promotion, and then, conditional on the Navy’s decision, the sailor decides whether to reenlist or leave the Navy. Rates of promotion and retention...