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Full-Text Articles in Life Sciences
Propensity Score Modelling In Observational Studies Using Dimension Reduction Methods, Debashis Ghosh
Propensity Score Modelling In Observational Studies Using Dimension Reduction Methods, Debashis Ghosh
Debashis Ghosh
Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in this article. The concept of covariate sufficiency plays an important role, and we provide theoretical justication when dimension reduction and partial least squares methods will allow for valid causal inference to be performed. The methods are illustrated with application to a medical study and to simulated data.
Clustering With Exclusion Zones: Genomic Applications, Mark Segal, Yuanyuan Xiao, Fred Huffer
Clustering With Exclusion Zones: Genomic Applications, Mark Segal, Yuanyuan Xiao, Fred Huffer
Mark R Segal
Methods for formally evaluating the clustering of events in space or time, notably the scan statistic, have been richly developed and widely applied. In order to utilize the scan statistic and related approaches, it is necessary to know the extent of the spatial or temporal domains wherein the events arise. Implicit in their usage is that these domains have no “holes”—hereafter “exclusion zones”—regions in which events a priori cannot occur. However, in many contexts, this requirement is not met. When the exclusion zones are known, it is straightforward to correct the scan statistic for their occurrence by simply adjusting the …