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SUMMARY:The Bootstrap Paradigm in Signal Processing
DTSTART:20110614T100000
DTSTAMP:20260407T210931Z
UID:a0cc0071010c62ff9f724280ee80af44e9a6bae6ec94134a023295c2
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Abdelhak M. Zoubir\, Technische Universität Darmstadt \
 nThe use of more accurate models in signal processing applications such as
  communications\, radar\, sonar\, biomedical engineering\, speech and imag
 e processing and machine learning has become a fundamental requirement. Wi
 th an improved accuracy the models have become more complex and inferentia
 l statistical signal processing required in parameter estimation and signa
 l detection and classification\, for example\, has become intractable. The
  signal processing practitioner requires a simple but accurate method for 
 assessing errors of estimâtes and answering inferential questions. Asympt
 otic approximations are useful only when enough data is available\, which 
 is not always possible due to time constraints\, the nature of the signal 
 or the measurement setting. In place of the formulae and tables of paramet
 ric and non-parametric procedures based on complicated mathematics and asy
 mptotic approximations\, tools such as the bootstrap are powerful for solv
 ing complex engineering problems. It is the method of an engineer's choice
  for solving inferential signal processing problems\, such as signal detec
 tion\, confidence limits estimation and model selection\, to mention a few
 . In this talk\, we first give a brief overview of the the basic principle
  underlying the bootstrap methodology. We then discuss bootstrap technique
 s for independent as well as dependent data. Bootstrap methods for signal 
 detection and model selection are presented along with frequency domain bo
 otstrap methods for spectral analysis. Real-data examples are given throug
 hout the talk.
LOCATION:ELA 2
STATUS:CONFIRMED
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