M. Basseville and I. V. Nikiforov, Detection of Abrupt Changes, Theory and Application, vol.NJ, 1993.
URL : https://hal.archives-ouvertes.fr/hal-00008518

J. D. Scargle, Studies in astronomical time series analysis: V. Bayesian blocks, a new method to analyze structure in photon counting data, Astrophysical Journal, vol.504, pp.405-418, 1998.

J. Chen and A. K. Gupta, Parametric Statistical Change Point Analysis. Birkhäuser Basel, 2000.

J. Tourneret, M. Doisy, and M. Lavielle, Bayesian off-line detection of multiple change-points corrupted by multiplicative noise: application to SAR image edge detection, Signal Processing, vol.83, issue.9, pp.1871-1887, 2003.

E. L. Lehmann and G. Casella, Theory of Point Estimation, ser. Springer Texts in Statistics, 2003.

D. V. Hinkley, Inference about the change-point in a sequence of random variables, Biometrika, vol.57, issue.1, pp.1-18, 1970.

S. B. Fotopoulos, S. K. Jandhyala, and E. Khapalova, Exact asymptotic distribution of change-point MLE for change in the mean of Gaussian sequences, Annals of Applied Statistics, vol.4, issue.2, pp.1081-1104, 2010.

H. L. Trees, K. L. Bell, and Z. Thian, Detection Estimation and Modulation Theory, Part I: Detection, Estimation, and Filtering Theory, 2013.

E. W. Barankin, Locally best unbiased estimates, Annals of Mathematical Statistics, vol.20, issue.4, pp.477-501, 1949.

D. G. Chapman and H. Robbins, Minimum variance estimation without regularity assumptions, Annals of Mathematical Statistics, vol.22, issue.4, pp.581-586, 1951.

R. J. Mcaulay and E. M. Hofstetter, Barankin bounds on parameter estimation, IEEE Transactions on Information Theory, vol.17, issue.6, pp.669-676, 1971.

K. Todros and J. Tabrikian, General classes of performance lower bounds for parameter estimation -part I: non-Bayesian bounds for unbiased estimators, IEEE Transactions on Information Theory, vol.56, issue.10, pp.5045-5063, 2010.

E. Weinstein and A. J. Weiss, A general class of lower bounds in parameter estimation, IEEE Transactions on Information Theory, vol.34, issue.2, pp.338-342, 1988.

K. Todros and J. Tabrikian, General classes of performance lower bounds for parameter estimation -part II: Bayesian bounds, IEEE Transactions on Information Theory, vol.56, issue.10, pp.5064-5082, 2010.

A. Ferrari and J. Tourneret, Barankin lower bound for change points in independent sequences, Proc. IEEE Workshop Statist. Signal Process, pp.557-560, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00376422

P. S. Rosa, A. Renaux, A. Nehorai, and C. H. Muravchik, Barankin-type lower bound on multiple change-point estimation, IEEE Transactions on Signal Processing, vol.58, issue.11, pp.5534-5549, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00532893

L. Bacharach, A. Renaux, M. N. El-korso, and E. Chaumette, WeissWeinstein bound for change-point estimation, Proc. of IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp.477-480, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01234929

, Weiss-Weinstein bound on multiple change-points estimation, IEEE Transactions on Signal Processing, vol.65, issue.10, pp.2686-2700, 2017.

L. Bacharach, M. N. Korso, A. Renaux, and J. Tourneret, A Bayesian Lower Bound for Parameter Estimation of Poisson Data Including Multiple Changes, Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp.4486-4490, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01525499

, Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking, 2007.

A. Yeredor, The joint MAP-ML criterion and its relation to ML and to extended least-squares, IEEE Transactions on Signal Processing, vol.48, issue.12, pp.3484-3492, 2000.

Y. Yao, Estimating the number of change-points via Schwarz' criterion, Statistics and Probability Letters, vol.6, pp.181-189, 1988.

E. Lebarbier, Detecting multiple change-points in the mean of gaussian process by model selection, Signal Processing, vol.85, issue.4, pp.717-736, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00071847

M. Wax and T. Kailath, Detection of signals by information theoretic criteria, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.33, issue.2, pp.387-392, 1985.

P. Stoica and A. Nehorai, MUSIC, maximum likelihood and the Cramér-Rao bound, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.37, issue.5, pp.720-741, 1989.

S. Bay, B. Geller, A. Renaux, J. Barbot, and J. Brossier, On the hybrid Cramér-Rao bound and its application to dynamical phase estimation, IEEE Signal Processing Letters, vol.15, pp.453-456, 2008.

I. Reuven and H. Messer, A Barankin-type lower bound on the estimation error of a hybrid parameter vector, IEEE Transactions on Information Theory, vol.43, issue.3, pp.1084-1093, 1997.

K. Todros and J. Tabrikian, Hybrid lower bound via compression of the sampled CLR function, Proc. IEEE Workshop Statist. Signal Process, pp.602-605, 2009.

C. Ren, J. Galy, E. Chaumette, P. Larzabal, and A. Renaux, Hybrid Barankin-Weiss-Weinstein bounds, IEEE Signal Processing Letters, vol.22, issue.11, pp.2064-2068, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01234910

C. R. Rao, Information and accuracy attainable in the estimation of statistical parameters, Bulletin of the Calcutta Mathematical Society, vol.37, pp.81-91, 1945.

H. Cramér, Mathematical Methods of Statistics, ser. Princeton Mathematics, vol.9, 1946.

K. L. Bell and H. L. Van-trees, Combined Cramér-Rao/WeissWeinstein bound for tracking target bearing, Proc. IEEE Workshop Sensor Array Multi-channel Process, pp.273-277, 2006.

H. Messer, The hybrid Cramér-Rao lower bound -from practice to theory, Proc. of IEEE Workshop on Sensor Array and Multi-channel Processing (SAM), pp.304-307, 2006.

F. Gini, R. Reggiannini, and U. Mengali, The modified Cramér-Rao bound in vector parameter estimation, IEEE Transactions on Communications, vol.46, issue.1, pp.52-60, 1998.

W. Xu, Performance bounds on matched-field methods for source localization and estimation of ocean environmental parameters, 2001.

S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, 2008.

H. Chernoff and S. Zacks, Estimating the current mean of a normal distribution which is subjected to changes in time, Annals of Mathematical Statistics, vol.35, issue.3, pp.999-1018, 1964.

M. Lavielle and E. Lebarbier, An application of MCMC methods for the multiple change-points problem, Signal Processing, vol.81, pp.39-53, 2001.
URL : https://hal.archives-ouvertes.fr/hal-01588611

T. D. Johnson, R. M. Elashoff, and S. J. Harkema, A Bayesian changepoint analysis of electromyographic data: detecting muscle activation patterns and associated applications, Biostatistics, vol.4, pp.143-164, 2003.

A. E. Raftery and V. E. Akman, Bayesian analysis of a Poisson process with a change-point, Biometrika, vol.73, issue.1, pp.85-89, 1986.

B. Jackson, J. S. Scargle, D. Barnes, S. Arabhi, A. Alt et al., An algorithm for optimal partitioning of data on an interval, IEEE Signal Processing Letters, vol.12, pp.105-108, 2005.

N. Dobigeon, J. Tourneret, and J. D. Scargle, Joint segmentation of multivariate astronomical time series: Bayesian sampling with a hierarchical model, IEEE Transactions on Signal Processing, vol.55, issue.2, pp.414-423, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00475973

C. Choirat and R. Seri, Estimation in discrete parameter models, Statistical Science, vol.27, issue.2, pp.278-293, 2012.