Composite hypothesis testing by optimally distinguishable distributions

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2008, Pages 3897-3900

  Razavi, Seyed Alireza ^a   Giurcǎneanu, Ciprian Doru ^a  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

Composite hypothesis testing; Generalized Likelihood ratio test; Kolmogorov structure function; Linear model; Optimally distinguishable distributions

Indexed keywords

ACOUSTICS; ARSENIC COMPOUNDS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; NUMERICAL ANALYSIS; SIGNAL PROCESSING; SPEECH;

COMPOSITE HYPOTHESIS TESTING; GENERALIZED LIKELIHOOD RATIO TEST; KOLMOGOROV STRUCTURE FUNCTION; LINEAR MODEL; OPTIMALLY DISTINGUISHABLE DISTRIBUTIONS;

VIDEODISKS;

EID: 51449105298     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2008.4518505     Document Type: Conference Paper

References (12)

1. J. Rissanen, "The structure function and distinguishable models of data," Computer Journal, vol. 49, no. 6, pp. 657-664, 2006.
2. J. Rissanen, Information and complexity in statistical modeling, Springer Verlag, 2007.
3. M. Li and P.M.B. Vitanyi, An introduction to Kolmogorov complexity and its applications, Springer Verlag, 1997.
4. A. Barron, J. Rissanen, and B. Yu, "The minimum description length principle in coding and modeling," IEEE Trans. Inf. Theory, vol. 44, pp. 2743-2760, Oct. 1998.
5. P. Stoica and Y. Selen, "A review of information criterion rules," IEEE Signal. Proces. Mag., vol. 21, no. 4, pp. 36-47, Jul. 2004.
6. V. Balasubramanian, "Statistical inference, Occam's razor, and statistical mechanics on the space of probability distributions," Neural Computation, vol. 9, no. 2, pp. 349-368, 1997.
7. J. Rissanen, "Optimally distinguishable distributions," Sep. 2007, (8 pages), personal communication.
8. S.M. Kay, Fundamentals of statistical signal processing: detection theory, Prentice Hall, 1998.
9. S.M. Kay, Fundamentals of statistical signal processing: estimation theory, Prentice Hall, 1993.
10. G.A.F. Seber and A.J. Lee, Linear regression analysis, Wiley-Interscience, 2003.
11. E.P. Liski, "Normalized ML and the MDL principle for variable selection in linear regression," in Festschrift for Tarmo Pukkila on his 60th birthday, E.P. Liski, J. Isotalo, J. Niemelä, S. Puntanen, and G.P.H. Styan, Eds., pp. 159-172. Univ. of Tampere, 2006.
12. J. Rissanen, "Hypothesis selection and testing by the MDL principle," Computer Journal, vol. 42, no. 4, pp. 260-269, 1999.