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Author (up) Araya, H.; Bahamonde, N.; Fermin, L.; Roa, T.; Torres, S. doi  openurl
  Title ON THE CONSISTENCY OF LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE JITTERED CASE Type
  Year 2023 Publication Statistica Sinica Abbreviated Journal Stat. Sin.  
  Volume 33 Issue 1 Pages 331-351  
  Keywords Least squares estimator; long-memory noise; random times; regression model  
  Abstract In numerous applications, data are observed at random times. Our main purpose is to study a model observed at random times that incorporates a longmemory noise process with a fractional Brownian Hurst exponent H. We propose a least squares estimator in a linear regression model with long-memory noise and a random sampling time called “jittered sampling”. Specifically, there is a fixed sampling rate 1/N, contaminated by an additive noise (the jitter) and governed by a probability density function supported in [0, 1/N]. The strong consistency of the estimator is established, with a convergence rate depending on N and the Hurst exponent. A Monte Carlo analysis supports the relevance of the theory and produces additional insights, with several levels of long-range dependence (varying the Hurst index) and two different jitter densities.  
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  ISSN 1017-0405 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001021364800009 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1840  
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