In this work, a powerful parametric spectral estimation technique, 2D-auto regressive moving average modeling (ARMA), has been applied to contrast transfer function (CTF) detection in electron microscopy. Parametric techniques such as auto regressive (AR) and ARMA models allow a more exact determination of the CTF than traditional methods based only on the Fourier transform of the complete imag...

In recent work, ARMARKOV representations have been proposed as an extension of ARMA representations of nite-dimensional linear time-invariant systems. ARMARKOV representations have the same form as ARMA representations, but explicitly involve Markov parameters. This paper generalizes ARMA least-squares time-domain identi cation to ARMARKOV representations. The ARMARKOV/least-squares identi cati...

Under 5 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.5 5 to 9 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.8 10 to 14 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.7 15 to 19 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.0 20 to 24 years . . . . . . . . . . . . . . . . . . . . . . ....

7. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 8. Supplemental Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 8.1. Description of Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 8.2. Assumption V1 for Vector β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 ...

A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and ...

We consider two relations between Fisher's information matrix of a stationary ARMA (autoregressive moving average) process and Sylvester's resultant matrix. One is based on the Wald test statistic for testing common roots of the AR and MA polynomials of an ARMA process, and the other one is established by using the structure of Fisher's information matrix. It turns out that the latter is also a...

ARFIMA is a time series forecasting model, which is an improve d ARMA model, the ARFIMA model proposed in this article is d emonstrated and deduced in detail. combined with network traffi c of CERNET backbone and the ARFIMA model,the result sho ws that,compare to the ARMA model, the prediction efficiency a nd accuracy has increased significantly, and not susceptible to sa mpling.

Abstract Background Patient Reported Outcomes Measure (PROM) are commonly used in research and essential to understand the patient experience when receiving treatment. Arm Activity (ArmA) is a valid reliable self-report questionnaire for assessing passive (section A) active B) real-life arm function patients with disabling spasticity. The original English version of ArmA has been psychometrical...

In this paper an approach to on-line ARMA parameter estimation based on a pseudo-linear regression and a QR matrix decomposition is developed. The algorithm has proven to be stable and has fast convergence properties if the unknown ARMA model satisses the strictly positive real condition. The derivation of the algorithm is straightforward and the computational complexity is O(N 2), however, fas...

There have been a lot of works relating to time series analysis. In this paper, the Bayesian analysis method for ARMA model is discussed and an application example is given. Firstly, the Bayesian theoretic results about AR model and the determination approach for model order are obtained. Then, the approach are presented for Bayesian analysis of MA and ARMA models. As its application, the forec...