As the real estate market develops rapidly and is increasingly securitized, it has become an important investment asset in the portfolio design. Thus the measurement of its market risk exposure has attracted attentions from academics and industries due to its peculiar behavior and unique characteristics such as heteroscedasticity and multi scale heterogeneity in its risk and noise evolution etc...

In this paper a new least-squares (LS) approach is used to model the discrete-time fractional differintegrator. This approach is based on a mismatch error between the required response and the one obtained by the difference equation defining the auto-regressive, moving-average (ARMA) model. In minimizing the error power we obtain a set of suitable normal equations that allow us to obtain the AR...

In a recent publication Stadnitski (2012) presented an overview of methods to estimate fractal scaling in time series, outlined as an accessible tutorial1. The publication was set-up as a comparison between monofractal and ARFIMA methods, and promotes ARFIMA to distinguish between spurious and genuine 1/f noise, shedding light on “the problem that the log–log power spectrum of short-memory ARMA...

The classiication of High Range Resolution (HRR) radar signatures using multi-scale features is considered. We present a hierarchical autoregressive moving average (ARMA) model for modeling HRR radar signals at multiple scales, and use spectral features extracted from the model for classifying radar signatures. First, we show that the radar signal at a diierent scale follows an ARMA process if ...

In the recent years, the use of GARCH type (especially, ARMA-GARCH) models and computational-intelligence-based techniques—Support Vector Machine (SVM) and Relevance Vector Machine (RVM) have been successfully used for financial forecasting. This paper deals with the application of ARMA-GARCH, recurrent SVM (RSVM) and recurrent RVM (RRVM) in volatility forecasting. Based on RSVM and RRVM, two G...

The distribution of conjugative-plasmid-mediated 16S rRNA methylase genes among amikacin-resistant Enterobacteriaceae collected between 1995 and 1998 and between 2001 and 2006 at a university hospital in South Korea was examined, and conjugative plasmids carrying the 16S rRNA methylase genes were characterized by PCR-based replicon typing and by determination of their antimicrobial resistance p...

Time-series analysis, a relatively uncommon technique in ecological studies, has been applied to annual tree growth-ring series. In agreement with earlier North American work, ARMA(1,1) models were found to be the predominant form for expressing stochastic growth processes, occurring in 58% of the 36 Nothofagus menziesii and N. solandri time-series examined. The remaining 42% conformed to an AR...

The presence of outliers in time series can seriously affect the model specification and parameter estimation. To avoid these adverse effects, it is essential to detect these outliers and remove them from time series. By the Bayesian statistical theory, this article proposes a method for simultaneously detecting the additive outlier (AO) and innovative outlier (IO) in an autoregressive moving-a...

BACKGROUND Gene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time. METHODOLOGY We propose a statistical method for functional clustering of periodic gene expression by modeling the covariance matrix of serial mea...