Binary response experiments are very common in scientific studies. However, the study of optimal designs in this area is in a very underdeveloped stage. Sitter and Torsney (1995a) studied optimal designs for binary response experiments with two design variables. In this paper, we consider a general situation with multiple design variables. A novel approach is proposed to identify optimal design...

This paper adapts sparse factor models for exploring covariation in multivariate binary data, with an application to measuring latent factors in U.S. Congressional roll call voting patterns. This straightforward modification provides two advantages over traditional factor analysis of binary data. First, a sparsity prior can be used to assess the evidence that a given factor loading may be exact...

This paper presents the design of a program to handle the specific estimation problems asso ciated with time series-cross section data. In order to minimize the costs of dealing with this kind of data, the program design relies on the forming of the appropriate moment matrices by summing over one or the other dimension, weighting or not, as desired. It is shown how this method may be used to es...

A binary probit model based on random utility theory is employed to obtain the implicit determinants of alcohol treatment effectiveness revealed by decisions made by programme evaluators of behavioural changes by patients. A scale of equivalences for the behavioural variables is constructed which uses reductions in alcohol drinking as the unit of account. Because one of the behavioural variable...

A fast and simple method is proposed that produces approximate multivariate local D-optimal designs of high e¢ ciency for models with binary response. The method assumes availability of a D-optimal design for a parallel normal response linear problem that has the same linear predictor, with an assumption of homogenous variance; the change required to transform the standard design into an e¢ cie...

In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of the conventional probit regression algorithm. In the second part we discuss auxiliary variable methods for...

This paper provides a uni ed simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Monte Carlo version of the E-M algorithm. Computation of Bayes factors from the simulation output is also considered. The met...

We propose a nonparametric approach for estimating single-index, binarychoice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without specifying a parametric probability function a priori; second, we estimate the unknown probability function using kerne...

In this abstract I present the problem of learning from pairwise preference judgements as a special case of binary classification. I discuss why kernel classifiers using traditional kernels based on the distance between items cannot be used to address the problem effectively: the preference prediction problem has inherent symmetry properties that these kernels cannot model. I will review a hier...

For the binary response model, we determine optimal designs based on the D-optimal criterion which are robust with respect to misspecifications of the unknown parameters. We propose a maximin approach and provide a numerical method to identify the best two point designs for the commonly applied link functions. This method is broadly applicable and can be extended to designs with a given number ...