Based on autoregression(AR) model in phase domain, this paper proposes a novel impulsive interference(IMI) detection algorithm for over-the-horizon radar. This is achieved by regarding IMI phase spectrum as complex sinusoid signal and modeling it by AR model. Then we can take the full advantage of the sinusoid signal estimation algorithm. After getting zeros of AR model transfer function, the a...

A spatially autocorrelated effect exists in precipitation of a mountainous basin. This study examines the relationship between maximum annual rainfall and elevation in the Kaoping River Basin of southern Taiwan using spatial regression models (i.e. geographically weighted regression (GWR), simultaneous autoregression (SAR), and conditional autoregression (CAR)). Results show that the GWR, SAR, ...

Reversible data hiding embeds information in a host media in a visually plausible way such that both the embedded message and the original host media can be exactly recovered. In this paper we present a new reversible data hiding framework based on prediction error histogram modification. This framework is general and flexible that includes some of the state-of-the-art methods as special cases....

where (εi)i=1,...,n are i.i.d. random variables. The unknown autoregression function f is then the target of statistical inference and the development of efficient estimators is a natural task for theoretically oriented statisticians. On the one hand, it has been recognized for a long time that commonly used estimators in model (1) have the same asymptotic behavior as corresponding estimators i...

The time-inhomogeneous autoregressive model AR(1) is studied, which the process of form ${X_{n+1}}={\alpha _{n}}{X_{n}}+{\varepsilon _{n}}$, where ${\alpha _{n}}$ are constants, and ${\varepsilon independent random variables. Conditions on distributions established that guarantee geometric recurrence process. This result applied to estimate stability n-steps transition probabilities for two pro...

We develop a variational Bayesian (VB) approach for estimating large-scale dynamic network models in the autoregression framework. The VB allows automatic identification of structure such model and obtains direct approximation posterior density. Compared to Markov chain Monte Carlo (MCMC)-based sampling approaches, achieves enhanced computational efficiency without sacrificing estimation accura...