1,721,003 research outputs found
Bayesian Experimental Design: A Review.
Full text link1 online resource (PDF, 70 pages)Chaloner, Kathryn; Verdinelli, Isabella. (1995). Bayesian Experimental Design: A Review. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/199630
Generalized Savage-Dickey Density Ratio
No full textEncyclopedia of Statistical Sciences, Update Volume 2, Wiley, New Yor
Minimax Manifold Estimation
No full textWe find the minimax rate of convergence in Hausdorff distance for estimating a manifold M of dimension d embedded in RD given a noisy sample from the manifold. Under certain conditions, we show that the optimal rate of convergence is n−2/(2+d). Thus, the minimax rate depends only on the dimension of the manifold, not on the dimension of the space in which M is embedded.CdF del 22 Luglio 200
Scanning D-dimensional spaces for finding clusters
No full textWe present a method that scans a random field for localized clusters while controlling the fraction of false discoveries. We use a kernel density estimator as the test statistic and adjust for the bias in this estimator by a method we introduce in this paper. We also show how to combine information across multiple bandwidths while maintaining false discovery control.CdF del 14 Settembre 200
Costruzione di Stimatori per dati in spazi multidimensionali
No full textThis research project is intended to continue an ongoing collaboration that has had a strong record of publications in the past few years and for which we see several directions for further
developments. This project proposes viable alternatives to the existing methods for estimating filaments and manifold in high dimensional spaces. The procedures available now are
characterized by strong theoretical properties, but they are not operationally accessible. We plan to develop 1. New methods for estimating hidden features in massive datasets, theoretically justified and of simple implementation. 2. New computational techniques to allow wide use of these estimators in diverse applied disciplines. 3. New statistical theory for exploring the features of the new estimators proposed.15 Luglio 201
Study of gradient fields
No full textWe consider the problem of reliably finding filaments in point clouds. Realistic data sets often have numerous filaments of various sizes and shapes. Statistical techniques exist for for finding one (or a few) filaments but these methods do not handle noisy data sets with many filaments. Other methods can be found in the astronomy literature but they do not have rigorous statistical guarantees. We propose the following method. Starting at each data point we construct the steepest ascent path along a kernel density estimator. We locate filaments by finding regions where these paths are highly concentrated. Formally, we define the density of these paths and we construct a consistent estimator of this path density.CdF del 19 Luglio 200
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