Lifetime prevalence estimates of major depression: An indirect estimation method and a quantification of recall bias

The measurement of lifetime prevalence of depression in cross-sectional surveys is biased by recall problems. We estimated it indirectly for two countries using modelling, and quantified the underestimation in the empirical estimate for one. A microsimulation model was used to generate population-based epidemiological measures of depression. We fitted the model to 1-and 12-month prevalence data from the Netherlands Mental Health Survey and Incidence Study (NEMESIS) and the Australian Adult Mental Health and Wellbeing Survey. The lowest proportion of cases ever having an episode in their life is 30% of men and 40% of women, for both countries. This corresponds to a lifetime prevalence of 20 and 30%, respectively, in a cross-sectional setting (aged 15-65). The NEMESIS data were 38% lower than these estimates. We conclude that modelling enabled us to estimate lifetime prevalence of depression indirectly. This method is useful in the absence of direct measurement, but also showed that direct estimates are underestimated by recall bias and by the cross-sectional setting.

Improved detection in CDMA for biased sources

We consider the detection of biased information sources in the ubiquitous code-division multiple-access (CDMA) scheme. We propose a simple modification to both the popular single-user matched-filter detector and a recently introduced near-optimal message-passing-based multiuser detector. This modification allows for detecting modulated biased sources directly with no need for source coding. Analytical results and simulations with excellent agreement are provided, demonstrating substantial improvement in bit error rate in comparison with the unmodified detectors and the alternative of source compression. The robustness of error-performance improvement is shown under practical model settings, including bias estimation mismatch and finite-length spreading codes. © 2007 IOP Publishing Ltd.

Semi-parametric geolocation estimation in NLOS environments

The position of a stationary target can be determined using triangulation in combination with time of arrival measurements at several sensors. In urban environments, none-line-of-sight (NLOS) propagation leads to biased time estimation and thus to inaccurate position estimates. Here, a semi-parametric approach is proposed to mitigate the effects of NLOS propagation. The degree of contamination by NLOS components in the observations, which result in asymmetric noise statistics, is determined and incorporated into the estimator. The proposed method is adequate for environments where the NLOS error plays a dominant role and outperforms previous approaches that assume a symmetric noise statistic.