Explicit bivariate rate functions for large deviations in AR(1)and MA(1)processes with Gaussian innovations

We investigate the large deviations properties for centered stationary AR(1)and MA(1)processes with independent Gaussian innovations,by giving the explicit bivariate rate functions for the sequence of two-dimensional random vectors.Via the Contraction Principle,we provide the explicit rate functions for the sample mean and the sample second moment.In the AR(1)case,we also give the explicit rate function for the sequence of two-dimensional random vectors(W_(n))n≥2=(n^(-1(∑_(k=1)^(n)X_(k),∑_(k=1)^(n)X_(k)^(2))))_(n∈N)n≥2,but we obtain an analytic rate function that gives different values for the upper and lower bounds,depending on the evaluated set and its intersection with the respective set of exposed points.A careful analysis of the properties of a certain family of Toeplitz matrices is necessary.The large deviations properties of three particular sequences of one-dimensional random variables will follow after we show how to apply a weaker version of the Contraction Principle for our setting,providing new proofs for two already known results on the explicit deviation function for the sample second moment and Yule-Walker estimators.We exhibit the properties of the large deviations of the first-order empirical autocovariance,its explicit deviation function and this is also a new result.

Parameter estimation in Manneville–Pomeau processes

In this paper,we study a class of stochastic processes{X_(t)}t∈N,where X_(t)=φ■T_(s)^(t)(X_(0))is obtained from the iterations of the transformation T_(s),invariant for an ergodic probabilityμ_(s)on[0,1]and a certain constant by partial functionφ:[0,1]→R.We consider here the family of transformations T_(s):[0,1]→[0,1]indexed by a parameters>0,known as the Manneville–Pomeau family of transformations.The autocorrelation function of the resulting process decays hyperbolically(or polynomially)and we obtain efficient methods to estimate the parameter s from a finite time series.As a consequence,we also estimate the rate of convergence of the autocorrelation decay of these processes.We compare different estimation methods based on the periodogram function,the smoothed periodogram function,the variance of the partial sum,and the wavelet theory.To obtain our results we analyzed the properties of the spectral density function and the associated Fourier series.