Universal Hypothesis Testing and Universal Decoding

N. Merhav, M. Gutman, and J. Ziv,
``On the estimation of the order of
a Markov chain and universal data compression,''
IEEE Trans. Inform. Theory
vol. 35, no. 5, pp. 10141019, September 1989.

N. Merhav,
``On the estimation of the model order in exponential families,''
IEEE Trans. Inform. Theory,
vol. IT35, no. 5, pp. 11091114, September 1989.

J. Ziv and N. Merhav,
``Estimating the number of states of a finitestate
source,''
IEEE Trans. Inform. Theory,
vol. 38, no. 1, pp. 6165, January 1992.

O. Zeitouni, J. Ziv, and N. Merhav,
``When is the generalized likelihood
ratio test optimal?''
IEEE Trans. Inform. Theory,
vol. 38, no. 5, pp. 15971602, September 1992.

N. Merhav,
``Universal decoding for memoryless Gaussian
channels with a deterministic interference,''
IEEE Trans. Inform. Theory,
vol. 39, no. 4, pp. 12611269, July 1993

J. Ziv and N. Merhav,
``A measure of relative entropy between individual
sequences with application to universal classification,''
IEEE Trans. Inform. Theory,
vol. 39, no. 4, pp. 12701279, July 1993.

N. Merhav,
``Universal detection of messages via finitestate channels,''
IEEE Trans. Inform. Theory,
vol. 46, no. 6, pp. 22422246, September 2000.

M. Feder and N. Merhav,
``Universal composite hypothesis
testing: A competitive minimax approach,'' (invited paper)
IEEE Trans. Inform. Theory,
special issue in memory of Aaron D. Wyner,
vol. 48, no. 6, pp. 15041517, June 2002.

E. Levitan and N. Merhav,
``A competitive NeymanPearson approach to
universal hypothesis testing with applications,''
IEEE Trans. Inform. Theory,
vol. 48, no. 8, pp. 22152229, August 2002.

N. Merhav,
``An informationtheoretic view of watermark embeddingdetection and
geometric attacks,'' presented at WaCha `05 ,
Barcelona, Spain, June 2005.

N. Merhav and E. Sabbag,
``Optimal watermark embedding and detection
strategies under limited detection resources,''
IEEE Trans. Inform. Theory,
vol. 54, no. 1, pp. 255274, January 2008.

N. Merhav and M. Feder,
``Minimax universal decoding with an erasure option,''
IEEE Trans. Inform. Theory, vol. 53, no. 5, pp. 16641675, May 2007.

Y. Akirav and N. Merhav,
``Competitive minimax universal decoding for several ensembles of
random codes,''
IEEE Trans. Inform. Theory, vol. 55, no. 4, pp. 14501459, April 2009.

N. Merhav,
``Universal decoding for arbitrary channels relative to a given class of
decoding metrics,'' IEEE Trans. Inform. Theory,
vol. 59, no. 9, pp. 55665576, September 2013.

N. Merhav,
``Asymptotically optimal decision rules for joint detection and source
coding,''
IEEE Trans. Inform. Theory,
vol. 60, no. 11, pp. 67876795, November 2014.

W. Huleihel and N. Merhav,
``Universal decoding for Gaussian intersymbol inteference channels,''
IEEE Trans. Inform. Theory, vol. 61, no. 4, pp. 16061618,
April 2015.

W. Huleihel, N. Weinberger and N. Merhav,
``Erasure/list random coding error exponents are not universally achievable,''
IEEE Trans. Inform. Theory, vol. 62, no. 10,
pp. 54035421, October 2016.

N. Merhav,
``Universal decoding for sourcechannel coding with side information,''
Communications in Information and Systems,
vol. 16, no. 1, pp. 1758, 2016.

N. Weinberger and N. Merhav,
``Channel detection in coded communication,''
submitted to IEEE Trans. Inform. Theory, September 2015.

B. Tondi, M. Barni, and N. Merhav,
``Detection games with a fully active
attacker,'' in Proc. 7th IEEE Innternational Workshop on Information
Forensics and Security (WIFS 2015), Rome, Italy, November 1619, 2015.

N. Merhav,
``Universal decoding using a noisy codebook,''
submitted to IEEE Trans. Inform. Theory, September 2016.

N. Merhav,
``Reliability of universal decoding based on vectorquantized codewords,''
IEEE Trans. Inform. Theory,
vol. 63, no. 5,
pp. 26962709, May 2017.

R. Averbuch and N. Merhav,
``Exact ramdom coding exponents and universal decoders for the asymmetric
broadcast channel,''
submitted to IEEE Trans. Inform. Theory, February 2017.

B. Tondi, N. Merhav and M. Barni,
``Detection games under fully active
adversaries,'' submitted to IEEE Trans. Inform. Theory, February 2018.