Robustness Analysis in Sequential Statistical Decisions

Statistical Decisions under Ambiguity: An Axiomatic Analysis

Consider a decision maker who faces a number of possible models of the world. Every model generates objective probabilities, but no probabilities of models are given. Applications of this framework include statistical decision making with model uncertainty, e.g. due to concerns for misspecification, or Robust Bayesian decision analysis. I characterize a number of decision rules including Bayesi...

Sequential decisions with tests

We consider a principal-agent problem where the principal wishes to be endorsed by a sequence of agents, but cannot truthfully reveal type. In the standard "herding" model, the agents learn from each other’s decisions, which can lead to cascades on a given decision when later agents’ private information is swamped. We augment the standard model to allow the principal to subject herself to a tes...

Identifying Optimal Sequential Decisions

We consider conditions that allow us to find an optimal strategy for sequential decisions from a given data situation. For the case where all interventions are unconditional (atomic), identifiability has been discussed by Pearl & Robins (1995). We argue here that an optimal strategy must be conditional, i.e. take the information available at each decision point into account. We show that the id...

tape... Marcus HutterOPTIMAL SEQUENTIAL DECISIONS

A Statistical Method for Sequential Images – Based Process Monitoring

Today, with the growth of technology, monitoring processes by the use of video and satellite sensors have been more expanded, due to their rich and valuable information. Recently, some researchers have used sequential images for image defect detection because a single image is not sufficient for process monitoring. In this paper, by adding the time dimension to the image-based process monitorin...