We provide a rigorous proof that Jeffreys’ prior asymptotically maximizes Shannon’s mutual information between a sample of size n and the parameter. This was conjectured by Bernard0 (1979) and, despite the absence of a proof, forms the basis of the reference prior method in Bayesian statistical analysis. Our proof rests on an examination of large sample decision theoretic properties associated ...

Computational complexity of voting manipulation is one of the most actively studied topics in the area of computational social choice, starting with the groundbreaking work of [Bartholdi et al., 1989]. Most of the existing work in this area, including that of [Bartholdi et al., 1989], implicitly assumes that whenever several candidates receive the top score with respect to the given voting rule...

Previous studies on event-related functional magnetic resonance imaging experimental designs are primarily based on linear models, in which a known shape of the hemodynamic response function (HRF) is assumed. However, the HRF shape is usually uncertain at the design stage. To address this issue, we consider a nonlinear model to accommodate a wide spectrum of feasible HRF shapes, and propose eff...

The margin of victory of an election is a useful measure to capture the robustness of an election outcome. It also plays a crucial role in determining the sample size of various algorithms in post election audit, polling etc. In this work, we present efficient sampling based algorithms for estimating the margin of victory of elections. More formally, we introduce the (c, ε, δ)–MARGIN OF VICTORY...

We consider experiments for comparing treatments using units that are ordered linearly over time or space within blocks. In addition to the block effect, we assume that a trend effect influences the response. The latter is modeled as a smooth component plus a random term that captures departures from the smooth trend. The model is flexible enough to cover a variety of situations; for instance, ...

On numerous occasions and in many articles I have formally shown (proved) that Info-Gap’s robustness model (Ben-Haim 1999, 2001, 2006) is a Maximin model (Wald 1945, 1950) par excellence and that it is fundamentally flawed in the way it handles severe uncertainty. Furthermore, I have shown that the Info-Gap/Maximin connection is not strictly mathematical in nature. Indeed, the conceptual framew...

Previous studies on event-related functional magnetic resonance imaging experimental designs are primarily based on linear models, in which a known shape of the hemodynamic response function (HRF) is assumed. However, the HRF shape is usually uncertain at the design stage. To address this issue, we consider a nonlinear model to accommodate a wide spectrum of feasible HRF shapes, and propose eff...

We propose a new clustering algorithm based upon the maximin correlation analysis (MCA), a learning technique that can minimize the maximum misclassification risk. The proposed algorithm resembles conventional partition clustering algorithms such as k-means in that data objects are partitioned into k disjoint partitions. On the other hand, the proposed approach is unique in that an MCA-based ap...