*Research partially supported bv ONR Contract N00014-80-C0741.

be that tuple ranks alone are not enough to reveal the private attribute values, our investigation shows that this is not the case in reality. Specifically, we define a novel problem of rank based inference, and introduce a taxonomy of the problem space according to two dimensions, (1) the type of query

In recent years, there has been much research in the adoption of Ranked Retrieval model (in addition to the Boolean retrieval model) in structured databases, especially those in a client-server environ-ment (e.g., web databases). With this model, a search query re-turns top-k tuples according

in the context of R-estimation. Although they are not strictly affine-equivariant, they are shown to be equivariant in a weak asymptotic sense. Simulations confirm their feasibility and excellent finite-sample performances. 1. Introduction. 1.1. Rank-based inference for elliptical families. An elliptical density

A class of R-estimators, based on the concepts of multivariate signed ranks and the optimal rank-based tests developed in Hallin and Paindaveine (2006a), is proposed for the estimation of the shape ma-trix of an elliptical distribution. These R-estimators are root-n con-sistent under any radial

orthography without computing depth as an intermediate step. An image stream can be represented by the 2FxP measurement matrix of the image coordinates of P points tracked through F frames. We show that under orthographic projection this matrix is of rank 3. Based on this observation, the factorization method

inference in the proportional hazards model for interval censored data

and Jaeckel proposed rank estimation for linear models. Since that time, several authors have developed inference and diagnostic methods for these estimators. These rank-based estimators and their associated inference are highly efficient and are robust to outliers in response space. The methods

Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter updates can be made. SampleRank is a rankbased learning framework that alleviates this problem by updating the parameters during inference. Most semi-supervised learning

to uniform marginals. We numerically study the finite sample properties of these tests when the data are transformed to uniform as well as normal marginals. The performance of the tests is compared with that of the BDS test as well as with a parametric rank-based test against ARCH alternatives.