Exponential Autoregressive Parameters Estimation

Iterative Estimation Algorithm of Autoregressive Parameters

This paper presents an iterative autoregressive system parameter estimation algorithm in the presence of white observation noise. The algorithm is based on the parameter estimation bias correction approach. We use high order Yule–Walker equations, sequentially estimate the noise variance, and exploit these estimated variances for the bias correction. The improved performance of the proposed alg...

Efficient On-Line Estimation of Autoregressive Parameters

New procedures for estimating autoregressive parameters in AR(m) models are proposed. The proposed method allows for incorporation of auxiliary information into the estimation process and, under certain regularity conditions are consistent and asymptotically efficient. Also, these procedures are naturally on-line and do not require storing all the data. Theoretical results are presented in the ...

Efficient estimation of parameters for non-Gaussian autoregressive processes

Regularized Autoregressive Multiple Frequency Estimation

The paper addresses a problem of tracking multiple number of frequencies using Regularized Autoregressive (RAR) approximation. The RAR procedure allows to decrease approximation bias, comparing to other AR-based frequency detection methods, while still providing competitive variance of sample estimates. We show that the RAR estimates of multiple periodicities are consistent in probabilit...

Neural Autoregressive Distribution Estimation

We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density esitmation. They leverage the probability product rule and a weight sharing scheme inspired from restricted Boltzmann machines, to yield an estimator that is both tractable and has good generalization performance. We discu...