Exact maximum likelihood estimators for drift fractional Brownian motions

This paper deals with the problems of consistence and strong consistence of the maximum likelihood estimators of the mean and variance of the drift fractional Brownian motions observed at discrete time instants. A central limit theorem for these estimators is also obtained by using the Malliavin calculus.

[1]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[2]  J. Azéma,et al.  Seminaire de Probabilites XXXIV , 2000 .

[3]  Vern Paxson,et al.  Fast, approximate synthesis of fractional Gaussian noise for generating self-similar network traffic , 1997, CCRV.

[4]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[5]  W. Palma Long-Memory Time Series: Theory and Methods , 2007 .

[6]  D. Nualart The Malliavin Calculus and Related Topics , 1995 .

[7]  E. Hannan The asymptotic theory of linear time-series models , 1973, Journal of Applied Probability.

[8]  A unified approach to several inequalities for gaussian and diffusion measures , 2000 .

[9]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[10]  Gene H. Golub,et al.  Matrix computations , 1983 .

[11]  David Nualart,et al.  Central limit theorems for multiple stochastic integrals and Malliavin calculus , 2007 .

[12]  B. Øksendal,et al.  Stochastic Calculus for Fractional Brownian Motion and Applications , 2008 .

[13]  Nicolas Privault,et al.  Stein estimation for the drift of Gaussian processes using the Malliavin calculus , 2008, 0811.1153.

[14]  David Nualart,et al.  Parameter estimation for fractional Ornstein–Uhlenbeck processes , 2009, 0901.4925.

[15]  M. Taqqu,et al.  Large-Sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series , 1986 .

[16]  G. Peccati,et al.  Stein’s method and exact Berry–Esseen asymptotics for functionals of Gaussian fields , 2008, 0803.0458.

[17]  Z. Weiping,et al.  The superiority of empirical bayes estimation of parameters in partitioned normal linear model , 2008 .