[1]   Billingsley, P. (1968) Convergence of Probability Measures, Wiley, New York. MR0233396

[2]   Borodin, A. N. and Ibragimov, I. A. (1994), Limit theorems for functionals of random walks. Trudy Mat. Inst. Steklov., 195. Transl. into English: Proc. Steklov Inst. Math.(1995), v 195, no.2. MR1368394

[3]   Bradley, R.C. (1997) On quantiles and the central limit theorem question for strongly mixing processes. Journal of Theoretical Probability 10, 507–555 MR1455156

[4]   Bradley, R.C. (1999) Two inequalities and some applications in connection with ρ^*-mixing, a survey. Advances in Stochastic Inequalities (Atlanta, GA, 1997) Contemporary Mathematics 234 , AMS, Providence, RI, 21–41, MR1694761

[5]   Bradley, R. C. (2002). Introduction to strong mixing conditions. Volume 1, Technical Report, Department of Mathematics, Indiana University, Bloomington. Custom Publishing of I.U., Bloomington. MR1979967

[6]   Bradley, R. C. (2003). Introduction to strong mixing conditions. Volume 2, Technical Report, Department of Mathematics, Indiana University, Bloomington. Custom Publishing of I.U., Bloomington.

[7]   Bradley, R. C. and Peligrad, M. (1987). Invariance principles under a two-part mixing assumption. Stochastic processes and their applications 22, 271–289. MR0860937

[8]   Bradley, R.C. and Utev, S. (1994) On second order properties of mixing random sequences and random fields, In.: Prob. Theory and Math. Stat., pp. 99-120, B. Grigelionis et al (Eds) VSP/TEV. MR1649574

[9]   Dedecker, J. and Doukhan, P. (2003). A new covariance inequality and applications, Stochastic processes and their applications 106, 63–80. MR1983043

[10]   Dedecker, J. and Merlevède, F. (2002). Necessary and sufficient conditions for the conditional central limit theorem.  The Annals of Probability 30, 1044–1081. MR1920101

[11]   Dedecker, J. and Merlevède, F. (2003). The conditional central limit theorem in Hilbert spaces. Stochastic processes and their applications 108, 229–262. MR2019054

[12]   Dedecker, J. and Rio, E. (2000). On the functional central limit theorem for stationary processes. Ann. Inst. H. Poincaré 34, 1–34. MR1743095

[13]   Dehling, H.; Denker, M. and Philipp, W. (1986). Central limit theorems for mixing sequences of random variables under minimal conditions. The Annals of Probability 14, no. 4, 1359–1370. MR0866356

[14]   Dehling, H. and Philipp, W. (2002). Empirical process techniques for dependent data. In.: 193–223, Empirical process techniques for dependent data, Birkhäuser Boston, Boston, MA, 3–113. MR1958777

[15]   Derriennic, Y., Lin, M. (2003). The central limit theorem for Markov chains started at a point. Probab. Theory Relat. Fields 125, 73–76. MR1952457

[16]   Doukhan, P. (1994). Mixing Properties and Examples, Lecture Notes in Statistics 85, Springer–Verlag. MR1312160

[17]   Doukhan, P.; Massart, P. and Rio, E. (1994). The functional central limit theorem for strongly mixing processes. Ann. Inst. H. Poincaré Probab. Statist. 30, 63–82. MR1262892

[18]   Esseen, C.G. and Janson, S. (1985). On moment conditions for normed sums of independent variables and martingale differences. Stochastic Process. Appl. 19, 173182. MR0780729

[19]   Garsia, A. M. (1965). A simple proof of E. Hopf’s maximal ergodic theorem. J. Math. and Mech. 14, 381–382. MR0209440

[20]   Gordin, M. I. (1969). The central limit theorem for stationary processes, Soviet. Math. Dokl. 10, n.5, 1174–1176. MR0251785

[21]   Gordin, M. I. (1973). The central limit theorem for stationary processes. In.: Abstracts of communications. International conference of Probability theory, Vilnius, T.1: A-K.

[22]   Hall, P. and Heyde, C.C. (1980). Martingale limit theory and its application. Academic Press. MR0624435

[23]   Hannan, E. J. (1979). The central limit theorem for time series regression. Stochastic Process. Appl. 9, 281–289. MR0562049

[24]   Herrndorf, N. (1983). The invariance principle for φ -mixing sequences. Z. Wahrscheinlichkeitstheoreie verw. Gebiete. 63, 97–108. MR0699789

[25]   Heyde, C. C. (1974). On the central limit theorem for stationary processes. Z. Wahrscheinlichkeitstheoreie verw. Gebiete. 30, 315–320. MR0372955

[26]   Heyde, C. C. (1975). On the central limit theorem and iterated logarithm law for stationary processes. Bull. Austral. Math. Soc. 12, 1–8. MR0372954

[27]   Ibragimov, I. A. (1962). Some limit theorems for stationary processes. Teor. Verojatnost. i Primenen. 7 361-392. MR0148125

[28]   Ibragimov, I. A. and Linnik, Yu. V. (1965). Nezavisimye stalionarno svyazannye velichiny. (in Russian). Izdat. ”Nauka”, Moscow. MR0202176

[29]   Ibragimov, I. A. and Linnik, Yu. V. (1971). Independent and stationary sequences of random variables. Wolters-Noordhoff Publishing, Groningen, 1971. With a supplementary chapter by I. A. Ibragimov and V. V. Petrov, Translation from the Russian edited by J. F. C. Kingman. MR0322926

[30]   Ibragimov, I. A. (1975). A note on the central limit theorem for dependent random variables. Theory Probability and its Applications 20, 135–141. MR0362448

[31]   Ibragimov, I. A. and Rozanov, Yu. A. (1978). Gaussian random processes. Springer verlag, New York. MR0543837

[32]   Isola, S. (1999). Renewal sequences and intermittency. Journal of Statistical Physics 24, 263–280. MR1733472

[33]   Kipnis, C. and Varadhan, S. R. S. (1986). Central limit theorem for additive functionals of reversible Markov processes. Comm. Math. Phys. 104 1-19. MR0834478

[34]   Liverani, C. (1996). Central limit theorem for deterministic systems. In.: International Conference on Dynamical systems (Montevideo, 1995), Pitman Res. Notes Math. Ser., v.362, Longman, Harlow, 56–75. MR1460797

[35]   Maxwell, M., Woodroofe, M. (2000). Central limit theorems for additive functionals of Markov chains, The Annals of Probability 28, 713–724. MR1782272

[36]   McLeish, D. L. (1974). Dependent central limit theorems and invariance principles, The Annals of Probability 2, 620–628. MR0358933

[37]   McLeish, D. L. (1975). A maximal inequality and dependent strong laws, The Annals of Probability 3, 829–839. MR0400382

[38]   McLeish, D. L. (1975). Invariance Principles for Dependent Variables, Z. Wahrscheinlichkeitstheoreie verw. Gebiete. 32 , 165-178. MR0388483

[39]   McLeish, D. L. (1977). On the invariance principle for nonstationary mixingales, The Annals of Probability 5, 616–621. MR0445583

[40]   Merlevède, F. (2003). On the central limit theorem and its weak invariance principle for strongly mixing sequences with values in a Hilbert space via martingale approximation. Journal of Theoretical Probability 16, 625–653. MR2009196

[41]   Merlevède, F. and Peligrad, M. (2000). The functional central limit theorem for strong mixing sequences of random variables. The Annals of Probability 28, 1336–1352. MR1797876

[42]   Merlevède, F. and Peligrad, M. (2004). On the weak invariance principle for stationary sequences under projective criteria. to appear in Journal of Theoretical Probability. http://www.geocities.com/irina8/proj.pdf.

[43]   Merlevède, F., Peligrad, M. and Utev, S. (1997). Sharp conditions for the CLT of linear processes in a Hilbert Space. Journal of Theoretical Probability 10, 681–693. MR1468399

[44]   Móricz, F.A. (1976). Moment inequalities and the strong laws of large numbers. Z. Wahrscheinlichkeitstheoreie verw. Gebiete. 35, 4, 299–314. MR0407950

[45]   Peligrad, M. (1981). An invariance principle for dependent random variables. Z. Wahrscheinlichkeitstheoreie verw. Gebiete. 57, 495–507. MR0631373

[46]   Peligrad, M. (1982). Invariance principles for mixing sequences of random variables. The Annals of Probability 10 , 968–981. MR0672297

[47]   Peligrad, M. (1986). Recent advances in the central limit theorem and its weak invariance principle for mixing sequences of random variables. In.: Dependence in probability and statistics (Oberwolfach, 1985), Progr. Probab. Statist., 11, Birkhäuser Boston, Boston, MA., 193–223. MR0899991

[48]   Peligrad, M. (1990). On Ibragimov–Iosifescu conjecture for φ–mixing sequences. Stochastic processes and their applications 35, 293–308. MR1067114

[49]   Peligrad, M. (1992). On the central limit theorem for weakly dependent sequences with a decomposed strong mixing coefficient. Stochastic processes and their applications 42, no. 2, 181–193. MR1176496

[50]   Peligrad, M. (1996) On the asymptotic normality of sequences of weak dependent random variables, Journal of Theoretical Probability 9, 703–715. MR1400595

[51]   Peligrad, M. (1998). Maximum of partial sums and an invariance principle for a class of weak dependent random variables, Proc. Amer. Math. Soc. 126, 4, 1181–1189. MR1425136

[52]   Peligrad, M. (1999). Convergence of stopped sums of weakly dependent random variables. Electronic Journal of Probability, 4, 1–13. MR1692676

[53]   Peligrad, M. and Gut, A. (1999). Almost sure results for a class of dependent random variables.  Journal of Theoretical Probability 12, 87–115. MR1674972

[54]   Peligrad, M. and Utev, S. (1997). Central limit theorem for stationary linear processes, The Annals of Probability 25, 443–456. MR1428516

[55]   Peligrad, M. and Utev, S. (2005). A new maximal inequality and invariance principle for stationary sequences. The Annals of Probability, 33, 798–815. MR2123210

[56]   Peligrad, M. and Utev, S. (2005). Central limit theorem for stationary linear processes. To appear in The Annals of Probability. http://arxiv.org/abs/math.PR/0509682

[57]   Peligrad, M.; Utev S and Wu W. B. (2005). A maximal L(p)- Inequality for stationary sequences and application To appear in Proc. AMS. http://www.geocities.com/irina8/lp.pdf

[58]   Philipp, W. (1986). Invariance principles for independent and weakly dependent random variables. In.: Dependence in probability and statistics (Oberwolfach, 1985), Progr. Probab. Statist., 11, Birkhäuser Boston, Boston, MA.,225–268. MR0899992

[59]   Phillips, P. C. B. and Solo, V. (1992). Asymptotics for linear processes. The Annals of Statistics 20, 971–1001. MR1165602

[60]   Rio, E. (1993). Almost sure invariance principles for mixing sequences of random variables. Stochastic processes and their applications 48, 319–334. MR1244549

[61]   Rio, E. (2000). Théorie asymptotique des processus aléatoires faiblement dépendants. Mathématiques et applications de la SMAI. 31, Springer–Verlag. MR2117923

[62]   Rosenblatt, M. (1956). A central limit theorem and a strong mixing condition. Proceeding National Academy of Sciences. U.S.A. 42, 43–47. MR0074711

[63]   Shao, Q. (1989). On the invariance principle for stationary ρ–mixing sequences of random variables. Chinese Ann.Math.  10B, 427–433. MR1038376

[64]   Utev, S.A. (1989). Summi sluchainih velichin s φ –peremeshivaniem. In.: Tr. IM. CO AH, T.1, 78–100. MR1037250

[65]   Utev, S.A. (1991). Sums of random variables with φ –mixing. Siberian Advances in Mathematics 1, 124–155. MR1128381

[66]   Utev, S. and Peligrad, M. (2003). Maximal inequalities and an invariance principle for a class of weakly dependent random variables. Journal of Theoretical Probability 16, 101–115. MR1956823

[67]   Volný, D (1993). Approximating martingales and the central limit theorem for strictly stationary processes. Stochastic processes and their applications 44, 41–74. MR1198662

[68]   Woodroofe, M. (1992), A central limit theorem for functions of a Markov chain with applications to shifts. Stochastic Process. Appl ., 41, 3344. MR1162717

[69]   Wu, W.B. (2002). Central limit theorems for functionals of linear processes and their applications. Statistica Sinica 12 , no. 2, 635–649. MR1902729

[70]   Wu, W.B. and Woodroofe, M. (2004). Martingale approximations for sums of stationary processes. The Annals of Probability 32, no. 2, 1674–1690. MR2060314