Sparsity-promoting bootstrap method for large-scale data

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)


Performing statistical inference on massive data sets may not be computationally feasible using the conventional statistical inference methodology. In particular, there is a need for methods that are scalable to large volume and variability of data. Moreover, veracity of the inference is crucial. Hence, there is a need to produce quantitative information on the statistical correctness of parameter estimates or decisions. In this paper, we propose a scalable nonparametric bootstrap method that operates with smaller number of distinct data points on multiple disjoint subsets of data. The sampling approach stems from the Bag of Little Bootstraps method and is compatible with distributed storage systems and distributed and parallel processing architectures. Iterative reweighted l1 method is used for each bootstrap replica to find a sparse solution in the face of high-dimensional signal model. The proposed method finds reliable estimates even if the problem is not overdetermined for full data or distinct data subsets by exploiting sparseness. The performance of the proposed method in identifying sparseness in parameter vector and finding confidence intervals and parameter estimates is studied in simulation.

OtsikkoConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
ToimittajatMichael B. Matthews
ISBN (elektroninen)9781538639542
DOI - pysyväislinkit
TilaJulkaistu - 1 maalisk. 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaAsilomar Conference on Signals, Systems & Computers - Pasific Grove, Yhdysvallat
Kesto: 6 marrask. 20169 marrask. 2016
Konferenssinumero: 50


NimiAsilomar Conference on Signals, Systems, and Computers proceedings
ISSN (elektroninen)1058-6393


ConferenceAsilomar Conference on Signals, Systems & Computers
KaupunkiPasific Grove


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