We provide √n-consistency results regarding estimation of the spectral representation of covariance operators of Hilbertian time series, in a setting with imperfect measurements. This is a generalization of the method developed in Bathia et al. (2010). The generalization relies on an important property of centered random elements in a separable Hilbert space, namely, that they lie almost surely in the closed linear span of the associated covariance operator. We provide a straightforward proof to this fact. This result is, to our knowledge, overlooked in the literature. It incidentally gives a rigorous formulation of Principal Component Analysis in Hilbert spaces.

We study the dispersion relation for low frequency waves in the whistler mode propagating along
the ambient magnetic field, considering ions and electrons with product-bi-kappa (PBK) velocity
distributions and taking into account the presence of a population of dust particles. The results
obtained by numerical analysis of the dispersion relation show that the decrease in the j indexes in
the ion PBK distribution contributes to the increase in magnitude of the growth rates of the ion
firehose instability and the size of the region in wave number space where the instability occurs. It
is also shown that the decrease in the j indexes in the electron PBK distribution contribute to
decrease in the growth rates of instability, despite the fact that the instability occurs due to the
anisotropy in the ion distribution function. For most of the interval of j values which has been
investigated, the ability of the non-thermal ions to increase the instability overcomes the tendency
of decrease due to the non-thermal electron distribution, but for very small values of the kappa
indexes the deleterious effect of the non-thermal electrons tends to overcome the effect due to the
non-thermal ion distribution.

Bayesian inference for fractionally integrated exponential generalized autoregressive conditional heteroscedastic (FIEGARCH) models using Markov chain Monte Carlo (MCMC) methods is described. A simulation study is presented to assess the performance of the procedure, under the presence of long-memory in the volatility. Samples from FIEGARCH processes are obtained upon considering the generalized error distribution (GED) for the innovation process. Different values for the tail-thickness parameter ν are considered covering both scenarios, innovation processes with lighter (ν > 2) and heavier (ν < 2) tails than the Gaussian distribution (ν = 2). A comparison between the performance of quasi-maximum likelihood (QML) and MCMC procedures is also discussed. An application of the MCMC procedure to estimate the parameters of a FIEGARCH model for the daily log-returns of the S&P500 U.S. stock market index is provided.