Fitting autoregressive models for prediction, Annals of the Institute of Statistical Mathematics, vol.28, issue.1, pp.243-247, 1969. ,
DOI : 10.1007/BF02532251
Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory (Tsahkadsor, pp.267-281, 1971. ,
Time series analysis and control through parametric models Applied Time Series Analysis, p.42, 1978. ,
Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting, Discovery Science, pp.22-36, 2012. ,
DOI : 10.1007/978-3-642-33492-4_5
URL : https://hal.archives-ouvertes.fr/hal-00671982
Model selection for weakly dependent time series forecasting, Bernoulli, vol.18, issue.3, pp.883-913, 2012. ,
DOI : 10.3150/11-BEJ359
URL : https://hal.archives-ouvertes.fr/inria-00386733
Shape Quantization and Recognition with Randomized Trees, Neural Computation, vol.1, issue.1, pp.1545-1588, 1997. ,
DOI : 10.1016/0031-3203(90)90098-6
Online learning for time series prediction, J. Mach. Learn. Res, vol.30, issue.84, pp.172-184, 2013. ,
An improved method for uniform simulation of stable minimum phase real ARMA (p,q) processes, IEEE Signal Processing Letters, vol.6, issue.6, pp.142-144, 1999. ,
DOI : 10.1109/97.763147
Sequential Adaptive Estimators in Nonparametric Autoregressive Models, Sequential Analysis, vol.9, issue.2, pp.229-247, 2011. ,
DOI : 10.1137/1135065
URL : https://hal.archives-ouvertes.fr/hal-00465587
An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift, Methodology and Computing in Applied Probability, vol.22, issue.2, pp.235-254, 2006. ,
DOI : 10.1007/s11009-006-8550-0
PAC-Bayesian Statistical Learning Theory, p.63, 2004. ,
Fast learning rates in statistical inference through aggregation, The Annals of Statistics, vol.37, issue.4, pp.1591-1646, 2009. ,
DOI : 10.1214/08-AOS623
URL : https://hal.archives-ouvertes.fr/hal-00139030
Robust linear regression through pac-bayesian truncation, p.40, 2010. ,
Robust linear least squares regression, The Annals of Statistics, vol.39, issue.5, pp.2766-2794, 2011. ,
DOI : 10.1214/11-AOS918SUPP
URL : https://hal.archives-ouvertes.fr/hal-00522534
Adaptive and Self-Confident On-Line Learning Algorithms, Journal of Computer and System Sciences, vol.64, issue.1, pp.48-75, 2000. ,
DOI : 10.1006/jcss.2001.1795
Analyse numérique, p.135, 1991. ,
Are Bayes Rules Consistent in Information?, Open Problems in Communication and Computation, pp.85-91, 1987. ,
DOI : 10.1007/978-1-4612-4808-8_22
Minimum complexity density estimation, IEEE Transactions on Information Theory, vol.37, issue.4, pp.1034-1054, 1991. ,
DOI : 10.1109/18.86996
Renewal theory and computable convergence rates for geometrically ergodic Markov chains, The Annals of Applied Probability, vol.15, issue.1B, pp.700-738, 2005. ,
DOI : 10.1214/105051604000000710
Uniform random parameter generation of stable minimum-phase real ARMA (p,q) processes, IEEE Signal Processing Letters, vol.4, issue.9, pp.259-261, 1999. ,
DOI : 10.1109/97.623043
Consistent Autoregressive Spectral Estimates, The Annals of Statistics, vol.2, issue.3, pp.489-502, 1974. ,
DOI : 10.1214/aos/1176342709
URL : http://projecteuclid.org/download/pdf_1/euclid.aos/1176342709
Linear Prediction by Autoregressive Model Fitting in the Time Domain, The Annals of Statistics, vol.6, issue.1, pp.224-231, 1978. ,
DOI : 10.1214/aos/1176344081
Approximation dans les espaces m???triques et th???orie de l'estimation, Zeitschrift f???r Wahrscheinlichkeitstheorie und Verwandte Gebiete, vol.3, issue.2, pp.181-237, 1983. ,
DOI : 10.1007/BF00532480
An Adaptive Compression Algorithm in Besov Spaces, Constructive Approximation, vol.16, issue.1, pp.1-36, 2000. ,
DOI : 10.1007/s003659910001
Online learning and stochastic approximations. On-line learning in neural networks, pp.142-186, 1998. ,
Large-scale machine learning with stochastic gradient descent, Statistical learning and data science, pp.17-25, 2012. ,
Bagging predictors Introduction to time series and forecasting. Springer Texts in Statistics, With 1 CD-ROM (Windows, pp.123-140, 1996. ,
Time Series, p.141, 1991. ,
DOI : 10.1007/978-3-642-04898-2_595
Inference in hidden Markov models Springer Series in Statistics With Randal Douc's contributions to Chapter 9 and Christian P. Robert's to Chapters 6, 7 and 13, With Chapter, Philippe Soulier and Moulines, and Chapter 15 by Stéphane Boucheron and Elisabeth Gassiat, p.48, 2005. ,
A mixture approach to universal model selection, pp.49-133, 1997. ,
Statistical learning theory and stochastic optimization Lecture notes from the 31st Summer School on Probability Theory held in Saint-Flour, Lecture Notes in Mathematics, vol.1851, issue.104, pp.64-76, 2001. ,
A bound for an unknown distribution density in terms of the observations, Dokl. Akad. Nauk SSSR, vol.147, issue.16, pp.45-48, 1962. ,
Analysis of two gradient-based algorithms for on-line regression, Proceedings of the tenth annual conference on Computational learning theory , COLT '97, pp.392-411, 1999. ,
DOI : 10.1145/267460.267492
Prediction, learning, and games, pp.88-89, 2006. ,
DOI : 10.1017/CBO9780511546921
Improved Second-Order Bounds for Prediction with Expert Advice, Learning theory, pp.217-232, 2005. ,
DOI : 10.1007/11503415_15
URL : https://hal.archives-ouvertes.fr/hal-00019799
Functions of one complex variable, Graduate Texts in Mathematics, vol.11, issue.146, 1973. ,
A triangular central limit theorem under a new weak dependence condition, Statistics & Probability Letters, vol.47, issue.1, pp.61-68, 2000. ,
DOI : 10.1016/S0167-7152(99)00138-8
Universal Portfolios, Mathematical Finance, vol.9, issue.1, pp.1-29, 1991. ,
DOI : 10.1016/0378-4266(79)90023-2
Local inference for locally stationary time series based on the empirical spectral measure, Journal of Econometrics, vol.151, issue.2, pp.101-112, 2009. ,
DOI : 10.1016/j.jeconom.2009.03.002
URL : https://hal.archives-ouvertes.fr/hal-00577962
Locally Stationary Processes, Time Series Analysis: Methods and Applications, pp.351-413, 2012. ,
DOI : 10.1016/B978-0-444-53858-1.00013-2
On the Optimal Segment Length for Parameter Estimates for Locally Stationary Time Series, Journal of Time Series Analysis, vol.19, issue.6, pp.629-655, 1998. ,
DOI : 10.1111/1467-9892.00114
Nonparametric quasi-maximum likelihood estimation for Gaussian locally stationary processes, The Annals of Statistics, vol.34, issue.6, pp.2790-2824, 2006. ,
DOI : 10.1214/009053606000000867
Empirical spectral processes for locally stationary time series, Bernoulli, vol.15, issue.1, pp.1-39, 2009. ,
DOI : 10.3150/08-BEJ137
Statistical inference for time-varying ARCH processes, The Annals of Statistics, vol.34, issue.3, pp.1075-1114, 2006. ,
DOI : 10.1214/009053606000000227
Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity, Machine Learning, pp.39-61, 2008. ,
DOI : 10.1007/s10994-008-5051-0
URL : https://hal.archives-ouvertes.fr/hal-00291504
Sparse regression learning by aggregation and Langevin Monte-Carlo, Journal of Computer and System Sciences, vol.78, issue.5, pp.1423-1443, 2012. ,
DOI : 10.1016/j.jcss.2011.12.023
URL : https://hal.archives-ouvertes.fr/hal-00362471
Weak dependence, Lecture Notes in Statistics, vol.190, issue.35, pp.34-64, 2007. ,
DOI : 10.1007/978-0-387-69952-3_2
URL : https://hal.archives-ouvertes.fr/hal-00686031
New dependence coefficients. Examples and applications to statistics. Probab. Theory Related Fields, pp.203-236, 2005. ,
Minimax estimation via wavelet shrinkage, The Annals of Statistics, vol.26, issue.3, pp.879-921, 1998. ,
DOI : 10.1214/aos/1024691081
Models, inequalities, and limit theorems for stationary sequences, Theory and applications of long-range dependence, pp.43-100, 2003. ,
A new weak dependence condition and applications to moment inequalities. Stochastic Process, Appl, vol.84, issue.33, pp.313-342, 1999. ,
Weakly dependent chains with infinite memory, Stochastic Processes and their Applications, vol.118, issue.11, pp.1997-2013, 2008. ,
DOI : 10.1016/j.spa.2007.12.004
Random iterative models, Translated from the 1990 French original by Stephen S. Wilson and revised by the author, 1997. ,
DOI : 10.1007/978-3-662-12880-0
A self-educating nonparametric filtration algorithm. Automation and Remote Control, pp.58-65, 1984. ,
On the Best Obtainable Asymptotic Rates of Convergence in Estimation of a Density Function at a Point, The Annals of Mathematical Statistics, vol.43, issue.1, pp.170-180, 1972. ,
DOI : 10.1214/aoms/1177692711
Batch means and spectral variance estimators in Markov chain Monte Carlo, The Annals of Statistics, vol.38, issue.2, pp.1034-1070, 2010. ,
DOI : 10.1214/09-AOS735
Prediction in the Worst Case, The Annals of Statistics, vol.19, issue.2, pp.1084-1090, 1991. ,
DOI : 10.1214/aos/1176348140
Boosting a Weak Learning Algorithm by Majority, Information and Computation, vol.121, issue.2, pp.256-285, 1995. ,
DOI : 10.1006/inco.1995.1136
Complex analysis. Undergraduate Texts in Mathematics, p.146, 2001. ,
Prediction of individual sequences and prediction in the statistical framework: some links around sparse regression and aggregation techniques, p.88, 2011. ,
URL : https://hal.archives-ouvertes.fr/tel-00653550
Sparsity regret bounds for individual sequences in online linear regression, J. Mach. Learn. Res, vol.14, issue.20, pp.729-769, 2013. ,
URL : https://hal.archives-ouvertes.fr/inria-00552267
Practical Markov Chain Monte Carlo, Statistical Science, vol.7, issue.4, p.49, 1992. ,
DOI : 10.1214/ss/1177011137
Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities, Ann. Statist, vol.29, issue.5, pp.1233-1263, 2001. ,
Applications of the van Trees Inequality: A Bayesian Cramer-Rao Bound, Bernoulli, vol.1, issue.1/2, pp.59-79, 1995. ,
DOI : 10.2307/3318681
Introduction to high-dimensional statistics, volume 139 of Monographs on Statistics and Applied Probability, p.49, 2015. ,
Spectral analysis of economic time series In association with M. Hatanaka. Princeton Studies in Mathematical Economics, No. I Toeplitz forms and their applications, N.J, vol.5, issue.36, p.147, 1964. ,
Time-dependent ARMA modeling of nonstationary signals, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.31, issue.4, pp.899-911, 1983. ,
DOI : 10.1109/TASSP.1983.1164152
Sequential prediction of individual sequences under general loss functions, IEEE Transactions on Information Theory, vol.44, issue.5, pp.1906-1925, 1998. ,
DOI : 10.1109/18.705569
An analysis of random design linear regression, Proc. COLT. Citeseer, p.40, 2011. ,
Regression and time series model selection in small samples, Biometrika, vol.76, issue.2, pp.297-307, 1989. ,
DOI : 10.1093/biomet/76.2.297
On the Markov chain central limit theorem, Probability Surveys, vol.1, issue.0, pp.299-320, 2004. ,
DOI : 10.1214/154957804100000051
Honest Exploration of Intractable Probability Distributions via
Markov Chain Monte Carlo, Statistical Science, vol.16, issue.4, pp.312-334, 2001. ,
DOI : 10.1214/ss/1015346317
Functional aggregation for nonparametric regression, Ann. Statist, vol.28, issue.49, pp.681-712, 2000. ,
Efficient algorithms for universal portfolios, Proceedings 41st Annual Symposium on Foundations of Computer Science, pp.423-440, 2002. ,
DOI : 10.1109/SFCS.2000.892136
A note on causal solutions for locally stationary ar-processes, p.98, 1995. ,
Nonasymptotic bounds on the estimation error of MCMC algorithms, Bernoulli, vol.19, issue.5A, pp.2033-2066, 2013. ,
DOI : 10.3150/12-BEJ442
Rigorous confidence bounds for MCMC under a geometric drift condition, Journal of Complexity, vol.27, issue.1, pp.23-38, 2011. ,
DOI : 10.1016/j.jco.2010.07.003
On a Problem of Adaptive Estimation in Gaussian White Noise, Theory of Probability & Its Applications, vol.35, issue.3, pp.459-470, 1990. ,
DOI : 10.1137/1135065
Asymptotically Minimax Adaptive Estimation. I: Upper Bounds. Optimally Adaptive Estimates, Theory of Probability & Its Applications, vol.36, issue.4, pp.645-659, 1991. ,
DOI : 10.1137/1136085
Information Theory and Mixing Least-Squares Regressions, IEEE Transactions on Information Theory, vol.52, issue.8, pp.3396-3410, 2006. ,
DOI : 10.1109/TIT.2006.878172
Prediction of multivariate time series by autoregressive model fitting, Journal of Multivariate Analysis, vol.16, issue.3, pp.393-411, 1985. ,
DOI : 10.1016/0047-259X(85)90027-2
The Weighted Majority Algorithm, Information and Computation, vol.108, issue.2, pp.212-261, 1994. ,
DOI : 10.1006/inco.1994.1009
Linear prediction: A tutorial review, Proceedings of the IEEE, pp.561-580, 1975. ,
DOI : 10.1109/PROC.1975.9792
Concentration inequalities and model selection, volume 1896 of Lecture Notes in Mathematics Lectures from the 33rd Summer School on Probability Theory held in Saint-Flour, p.120, 2003. ,
PAC-Bayesian model averaging, Proceedings of the twelfth annual conference on Computational learning theory , COLT '99, pp.164-170, 1999. ,
DOI : 10.1145/307400.307435
Rates of convergence of the Hastings and Metropolis algorithms, The Annals of Statistics, vol.24, issue.1, pp.101-121, 1996. ,
DOI : 10.1214/aos/1033066201
Markov chains and stochastic stability, p.49, 2009. ,
On recursive estimation for time varying autoregressive processes, The Annals of Statistics, vol.33, issue.6, pp.2610-2654, 2005. ,
DOI : 10.1214/009053605000000624
URL : https://hal.archives-ouvertes.fr/hal-00022067
Necessary conditions for efficient estimation of functionals of a nonparametric signal observed in white noise, Teor. Veroyatnost. i Primenen, vol.35, issue.16, pp.83-91, 1990. ,
Evolutionary spectra and non-stationary processes.(With discussion), J. Roy. Statist. Soc. Ser. B, vol.27, issue.36, pp.204-237, 1965. ,
Sparse Estimation by Exponential Weighting, Statistical Science, vol.27, issue.4, pp.558-575, 2012. ,
DOI : 10.1214/12-STS393
In??galit??s de Hoeffding pour les fonctions lipschitziennes de suites d??pendantes, Comptes Rendus de l'Acad??mie des Sciences - Series I - Mathematics, vol.330, issue.10, pp.905-908, 2000. ,
DOI : 10.1016/S0764-4442(00)00290-1
General state space Markov chains and MCMC algorithms, Probability Surveys, vol.1, issue.0, pp.20-71, 2004. ,
DOI : 10.1214/154957804100000024
A central limit theorem and a strong mixing condition Linear processes and bispectra, Proc. Nat. Acad. Sci, pp.43-47265, 1956. ,
RECURSIVE FORECAST COMBINATION FOR DEPENDENT HETEROGENEOUS DATA, Econometric Theory, vol.1, issue.02, pp.598-631, 2010. ,
DOI : 10.1111/j.1467-9965.1991.tb00002.x
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Time series analysis and its applications, p.146, 2011. ,
Contributions to the sequential prediction of arbitrary sequences: applications to the theory of repeated games and empirical studies of the performance of the aggregation of experts. Habilitation à diriger des recherches, pp.51-57, 2011. ,
On some nonstationary, nonlinear random processes and their stationary approximations, Adv. in Appl. Probab, vol.38, issue.6, pp.1155-1172, 2006. ,
Threshold Autoregression, Limit Cycles and Cyclical Data, Journal of the Royal Statistical Society, Series B, vol.42, issue.39, pp.245-292, 1980. ,
DOI : 10.1142/9789812836281_0002
Optimal Rates of Aggregation, Learning Theory and Kernel Machines, pp.303-313, 2003. ,
DOI : 10.1007/978-3-540-45167-9_23
URL : https://hal.archives-ouvertes.fr/hal-00104867
Introduction to nonparametric estimation Springer Series in Statistics Revised and extended from the 2004 French original, Translated by Vladimir Zaiats, pp.46-47, 2009. ,
A theory of the learnable, Communications of the ACM, vol.27, issue.11, pp.1134-1142, 1984. ,
DOI : 10.1145/1968.1972
Some Limit Theorems for Random Functions. I, Theory of Probability & Its Applications, vol.4, issue.2, pp.178-197, 1959. ,
DOI : 10.1137/1104015
A game of prediction with expert advice, J. Comput. System Sci. Eighth Annual Workshop on Computational Learning Theory (COLT), vol.56, issue.2, pp.153-173, 1995. ,
On-Line Regression Competitive with Reproducing Kernel Hilbert Spaces, Lecture Notes in Comput. Sci, vol.3959, issue.20, pp.452-463, 2006. ,
DOI : 10.1007/11750321_43
AGGREGATING STRATEGIES, Proc. Third Workshop on Computational Learning Theory, pp.371-383, 1990. ,
DOI : 10.1016/B978-1-55860-146-8.50032-1
On the fitting of multivariate autoregressions, and the approximate canonical factorization of a spectral density matrix, Biometrika, vol.50, issue.1-2, pp.129-134, 1963. ,
DOI : 10.1093/biomet/50.1-2.129
Combining Different Procedures for Adaptive Regression, Journal of Multivariate Analysis, vol.74, issue.1, pp.135-161, 2000. ,
DOI : 10.1006/jmva.1999.1884
Mixing strategies for density estimation, The Annals of Statistics, vol.28, issue.1, pp.75-87, 2000. ,
DOI : 10.1214/aos/1016120365
COMBINING FORECASTING PROCEDURES: SOME THEORETICAL RESULTS, Econometric Theory, vol.137, issue.01, 2004. ,
DOI : 10.1017/S0266466604201086
Information-theoretic determination of minimax rates of convergence, Ann. Statist, vol.27, issue.16, pp.1564-1599, 1999. ,