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...

A major beneet of object oriented software development is the support for reuse provided by object oriented and object based languages. Yet, measures and measurement tools that quantify such language supported reuse have been lacking. Comprehensive reuse measures, particularly for reuse with modiications, are necessary to evaluate the status of reuse in an organization and to monitor improvemen...

The analysis of non-Gaussian time series has been studied extensively and has many applications. Many successful models can be viewed as special cases or variations of the generalized autoregressive moving average (GARMA) models of Benjamin et al. (2003), where a link function similar to that used in generalized linear models is introduced and the conditional mean, under the link function, assu...

The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Recent research activities in forecasting with art...

It has been shown earlier that the problem of multichannel autoregressive moving average (ARMA) parameter estimation can be tackled in a computationally efficient way by converting the given process into an equivalent scalar, periodic ARMA process. This correspondence presents methods to compute the Cramer-Rao bound associated with the identification of the scalar ARMA equivalent of a given mul...

A time-series autoregressive moving average (ARMA) approach was used to develop stochastic models of tree crown profiles for five conifer species of the Sierran mixed conifer habitat type. Models consisted of three components: (1) a polynomial trend; (2) an ARMA model; and (3) random error. A Bayesian information criterion was used to evaluate alternative models. It was found that 70% of the cr...

Long intermediate AR models are used in Durbin's algorithms for ARMA estimation. The order of that long AR model is infinite in the asymptotical theory, but very high AR orders are known to give inaccurate ARMA models in practice. A theoretical derivation is given for two different finite AR orders, as a function of the sample size. The first is the AR order optimal for prediction with a purely...