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Detection Boundary in Sparse Regression


  • Yu I. Ingster


  • Alexandre B. Tsybakov


  • N. Verzelzn



We study the problem of detection of a p-dimensional sparse vector ofparameters in the linear regression model with Gaussian noise. We establishthe detection boundary, i.e., the necessary and sufficient conditions for thepossibility of successful detection as both the sample size n and the dimensionp tend to the infinity. Testing procedures that achieve this boundary arealso exhibited. Our results encompass the high-dimensional setting (p » n).The main message is that, under some conditions, the detection boundaryphenomenon that has been proved for the Gaussian sequence model, extendsto high-dimensional linear regression. Finally, we establish the detectionboundaries when the variance of the noise is unknown. Interestingly, thedetection boundaries sometimes depend on the knowledge of the variance ina high-dimensional setting.

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  • Yu I. Ingster & Alexandre B. Tsybakov & N. Verzelzn, 2010. "Detection Boundary in Sparse Regression," Working Papers 2010-28, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2010-28

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    Cited by:

    1. Ery Arias-Castro & Meng Wang, 2017. "Distribution-free tests for sparse heterogeneous mixtures," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 71-94, March.
    2. repec:spr:testjl:v:26:y:2017:i:4:d:10.1007_s11749-017-0558-y is not listed on IDEAS

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