BACKGROUND Selection bias is common in clinic-based HIV surveillance. Clinics located in HIV hotspots are often the first to be chosen and monitored, while clinics in less prevalent areas are added to the surveillance system later on. Consequently, the estimated HIV prevalence based on clinic data is substantially distorted, with markedly higher HIV prevalence in the earlier periods and trends ...

In an attempt to determine if selection bias could be a reason that religious attendance and depression are related, the predictive value of elevated depressive symptoms for a decrease in future attendance at religious services was examined in a longitudinal panel of 1,673 Dutch adults. Religious attendance was assessed yearly over five years using the single question, "how often do you attend ...

OBJECTIVES Population-based HIV testing surveys have become central to deriving estimates of national HIV prevalence in sub-Saharan Africa. However, limited participation in these surveys can lead to selection bias. We control for selection bias in national HIV prevalence estimates using a novel approach, which unlike conventional imputation can account for selection on unobserved factors. ME...

We analyze the effects of redistricting as revealed in the votes received by the Democratic and Republican candidates for state legislature. We develop measures of partisan bias and the responsiveness of the composition of the legislature to changes in statewide votes. Our statistical model incorporates a mixed hierarchical Bayesian and non-Bayesian estimation, requiring simulation along the li...

Instructors and researchers of peer review would benefit from a consistent way of characterizing peer review among students. One factor that can affect peer review is reviewer bias. For example, students may give biased assessments if some reviewers are lenient and others stringent. Accordingly, statistical models of peer review should account for reviewer bias. We present work in progress comp...

OBJECTIVE One of the main goals of health examination surveys is to provide unbiased estimates of health indicators at the population level. We demonstrate how multiple imputation methods may help to reduce the selection bias if partial data on some nonparticipants are collected. STUDY DESIGN AND SETTING In the FINRISK 2007 study, a population-based health study conducted in Finland, a random...

Are there relationships between consciousness and the material world? Empirical evidence for such a connection was reported in several meta-analyses of mind-matter experiments designed to address this question. In this paper we consider such meta-analyses from a statistical modeling perspective, emphasizing strategies to validate the models and the associated statistical procedures. In particul...

It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However, the improvements to the statistical properties of the data are ...

Statistical analysis methods are generally derived under the assumption that forecast errors are strictly random and zero in the mean If the short term forecast used as the background eld in the statistical analysis equation is in fact biased so will the resulting analysis be biased The only way to properly account for bias in a statistical analysis is to do so explicitly by estimating the fore...

Statistical analysis methods are generally derived under the assumption that forecast errors are strictly random and zero in the mean. If the short-term forecast, used as the background eld in the statistical analysis equation, is in fact biased, so will the resulting analysis be biased. The only way to properly account for bias in a statistical analysis is to do so explicitly, by estimating th...