Forecasting daily outflows from a bank account

This paper is concerned with forecasting the total dollar amounts of checks presented for payment against a company bank account. Checks are issued each day against the account and mailed to parties located in different parts of the country. The mail lag for each check is taken into account explicitly given that data on this variable are readily available from external sources. Based on the distribution of time until clearance of each check after its receipt, the probability distribution of the total amount presented for payment on each day between consecutive bank statements can be readily computed one day ahead. A procedure for implementation of this forecasting scheme is suggested.     

Alone,together: On the benefits of Bayesian borrowing in a meta-analytic setting

It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any $K\ge 1$ historical studies, naturally leads to empirical Bayes meta-analysis. In this paper we calculate the Bayesian BFPs and study the factors that drive them. We prove that simultaneous mean squared error reduction relative to an uninformed model is always achievable through application of this model. Power and sample size calculations for a future RCT, designed to be informed by multiple external RCTs, are also provided. Potential applications include inference on treatment efficacy from independent trials involving either heterogeneous patient populations or different therapies from a common class.