Mixture models for partially unclassified data: a case study of renal venous renin in hypertension

In many applications of discriminant analysis in medicine, data of known origin which can be reasonably assumed to be a random sample from the entire class may not be available for each of the possible classes. In this paper we note how such situations can be handled by using finite mixture models to formulate the estimation problem. This approach is adopted to model the distribution of the renal venous renin ratio (RVRR) between left and right kidneys in patients with hypertension. This distribution is used in the formation of a probabilistic allocation rule as an aid in the diagnosis of renal artery stenosis, which is potentially curable by surgery.