Approximating Cross-validatory Predictive P-values with Integrated IS for Disease Mapping Models

An important statistical task in disease mapping problems is to identify out- lier/divergent regions with unusually high or low residual risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is a gold standard for computing predictive p-value that can flag such outliers. However, actual LOOCV is time-consuming because one needs to re-simulate a Markov chain for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called iIS, for approximating LOOCV with only Markov chain samples simulated from a posterior based on a full data set. iIS is based on importance sampling (IS). iIS integrates the p-value and the likelihood of the test observation with respect to the distribution of the latent variable without reference to the actual observation. The predictive p-values computed with iIS can be proved to be equivalent to the LOOCV predictive p-values, following the general theory for IS. We com- pare iIS and other three existing methods in the literature with a lip cancer dataset collected in Scotland. Our empirical results show that iIS provides predictive p-values that are al- most identical to the actual LOOCV predictive p-values and outperforms the existing three methods, including the recently proposed ghosting method by Marshall and Spiegelhalter (2007).Comment: 21 page

Weak Measurement of Qubit Oscillations with Strong Response Detectors: Violation of the Fundamental Bound Imposed on Linear Detectors

We investigate the continuous weak measurement of a solid-state qubit by single electron transistors in nonlinear response regime. It is found that the signal-to-noise ratio can violate the universal upper bound imposed quantum mechanically to any linear response detectors. We understand the violation by means of the cross-correlation of the detector currents.Comment: 4 pages, 4 figure