Výsledky vyhledávání - "Lecci, Fabrizio"

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High-Dimensional Longitudinal Classification with the Multinomial Fused Lasso

Autor: Adhikari, Samrachana, Lecci, Fabrizio, Becker, James T., Junker, Brian W., Kuller, Lewis H., Lopez, Oscar L., Tibshirani, Ryan J.

We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem

Externí odkaz: http://arxiv.org/abs/1501.07518

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Robust Topological Inference: Distance To a Measure and Kernel Distance

Autor: Chazal, Frédéric, Fasy, Brittany T., Lecci, Fabrizio, Michel, Bertrand, Rinaldo, Alessandro, Wasserman, Larry

Let P be a distribution with support S. The salient features of S can be quantified with persistent homology, which summarizes topological features of the sublevel sets of the distance function (the distance of any point x to S). Given a sample from

Externí odkaz: http://arxiv.org/abs/1412.7197

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Introduction to the R package TDA

Autor: Fasy, Brittany Terese, Kim, Jisu, Lecci, Fabrizio, Maria, Clément

We present a short tutorial and introduction to using the R package TDA, which provides some tools for Topological Data Analysis. In particular, it includes implementations of functions that, given some data, provide topological information about the

Externí odkaz: http://arxiv.org/abs/1411.1830

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Subsampling Methods for Persistent Homology

Autor: Chazal, Frédéric, Fasy, Brittany Terese, Lecci, Fabrizio, Michel, Bertrand, Rinaldo, Alessandro, Wasserman, Larry

Persistent homology is a multiscale method for analyzing the shape of sets and functions from point cloud data arising from an unknown distribution supported on those sets. When the size of the sample is large, direct computation of the persistent ho

Externí odkaz: http://arxiv.org/abs/1406.1901

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Stochastic Convergence of Persistence Landscapes and Silhouettes

Autor: Chazal, Frédéric, Fasy, Brittany Terese, Lecci, Fabrizio, Rinaldo, Alessandro, Wasserman, Larry

Persistent homology is a widely used tool in Topological Data Analysis that encodes multiscale topological information as a multi-set of points in the plane called a persistence diagram. It is difficult to apply statistical theory directly to a rando

Externí odkaz: http://arxiv.org/abs/1312.0308

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On the Bootstrap for Persistence Diagrams and Landscapes

Autor: Chazal, Frédéric, Fasy, Brittany Terese, Lecci, Fabrizio, Rinaldo, Alessandro, Singh, Aarti, Wasserman, Larry

Publikováno v: Modeling and Analysis of Information Systems, 20(6), 96-105

Persistent homology probes topological properties from point clouds and functions. By looking at multiple scales simultaneously, one can record the births and deaths of topological features as the scale varies. In this paper we use a statistical tech

Externí odkaz: http://arxiv.org/abs/1311.0376

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Statistical Analysis of Metric Graph Reconstruction

Autor: Lecci, Fabrizio, Rinaldo, Alessandro, Wasserman, Larry

A metric graph is a 1-dimensional stratified metric space consisting of vertices and edges or loops glued together. Metric graphs can be naturally used to represent and model data that take the form of noisy filamentary structures, such as street map

Externí odkaz: http://arxiv.org/abs/1305.1212

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Confidence sets for persistence diagrams

Autor: Fasy, Brittany Terese, Lecci, Fabrizio, Rinaldo, Alessandro, Wasserman, Larry, Balakrishnan, Sivaraman, Singh, Aarti

Publikováno v: Annals of Statistics 2014, Vol. 42, No. 6, 2301-2339

Persistent homology is a method for probing topological properties of point clouds and functions. The method involves tracking the birth and death of topological features (2000) as one varies a tuning parameter. Features with short lifetimes are info

Externí odkaz: http://arxiv.org/abs/1303.7117

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