An algorithmic and a geometric characterization of Coarsening At Random

Details An algorithmic and a geometric characterization of Coarsening At Random An algorithmic and a geometric characterization of Coarsening At Random

2005-10-13

arxiv.org/abs/math/0510276v3

Mathematics

Statistics Theory

16 pages; accepted in this form for publication by Annals of Statistics

Scientific paper

We show that the class of conditional distributions satisfying the coarsening at Random (CAR) property for discrete data has a simple and robust algorithmic description based on randomized uniform multicovers: combinatorial objects generalizing the notion of partition of a set. However, the complexity of a given CAR mechanism can be large: the maximal "height" of the needed multicovers can be exponential in the number of points in the sample space. The results stem from a geometric interpretation of the set of CAR distributions as a convex polytope and a characterization of its extreme points. The hierarchy of CAR models defined in this way could be useful in parsimonious statistical modelling of CAR mechanisms, though the results also raise doubts in applied work as to the meaningfulness of the CAR assumption in its full generality.

Affiliated with

Also associated with

No associations

Rating

An algorithmic and a geometric characterization of Coarsening At Random does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with An algorithmic and a geometric characterization of Coarsening At Random, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and An algorithmic and a geometric characterization of Coarsening At Random will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-470281