A b s t r a c t

Since the beginning of Bayesian nonparametrics in early 1970s, there has been a wide interest constructing models for binary response data. Such data arise naturally problems dealing with bioassay, current status and sensitivity testing, are equivalent to left right censored observations if inputs one-dimensional. For based on Dirichlet process, inference is possible via Markov chain Monte Carl...

Social Desirability Bias (SDB) is the tendency in respondents to answer questions untruthfully in the hope of giving good impression to others. SDB occurs when the survey question is highly sensitive or personal, and responses cause sample statistics to systematically over-or underestimate corresponding population parameters. The Randomized Response Technique (RRT) is one of several methods to ...

The efficiency of an experimental design is ultimately measured in terms of time and resources needed for the experiment. This paper studies optimal sequential (multi-stage) designs in the situation where each stage involves a fixed cost. The problem is motivated by switching measurements on superconducting Josephson junctions. In this quantum mechanical experiment, the current is the controlle...

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Summary The objective of this paper is to introduce a flexible skewed link function for modeling binary data with covariates. The commonly used complementary log-log link is prone to link misspecification because of its...

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 a binary response regression model, classical residuals are diicult to deene and interpret due to the discrete nature of the response variable. In contrast, Bayesian residuals have continuous-valued posterior distributions which can be graphed to learn about outlying observations. Two deenitions of Bayesian residuals are proposed for binary regression data. Plots of the posterior distributio...