Information in a statistical experiment is often measured through the determinant of its information matrix. Under first order normal linear models, the determinant of the information matrix of a two-level factorial experiment neither depends on where the experiment is centered, nor on how it is oriented, and balanced allocations are more informative than unbalanced ones with the same number of...

In this paper we introduce a binary search algorithm that efficiently finds initial maximum likelihood estimates for sequential experiments where a binary response is modeled by a continuous factor. The problem is motivated by switching measurements on superconducting Josephson junctions. In this quantum mechanical experiment, the current is the factor controlled by the experimenter and a binar...

Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of 1/0, with 1 generally indicating a success and 0 a failure. However, the actual values that 1 and 0 can take vary widely, depending on the purpose of the study. For example, for a study of the odds of failure in a school setting, 1 may have the value of f...

In this paper, minimal conditions under which a semi-parametric binary response model is identified in a Bayesian framework are presented and compared to the conditions usually required in a sampling theory framework. Running headline: Semi-parametric Binary Response Models.