The concept of forecasting asthma using humans as animal sentinels is uncommon. This study explores the plausibility of predicting future asthma daily admissions using retrospective data in London (2005-2006). Negative binomial regressions were used in modeling; allowing the non-contiguous autoregressive components. Selected lags were based on partial autocorrelation function (PACF) plot with a...

We extend the functional coefficient autoregressive (FCAR) model to the multivariate nonlinear time series framework. We show how to estimate parameters of the model using kernel regression techniques, discuss properties of the estimators, and provide a bootstrap test for determining the presence of nonlinearity in a vector time series. The power of the test is examined through extensive simula...

Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normaliz...

In this paper a new approach to factor vector autoregressive estimation, based on Stock and Watson (Implications of dynamic factor models for VAR analysis, NBER Working Paper, no. 11467, 2005), is introduced. In addition to sharing all the relevant features of the Stock–Watson approach, in its static formulation, the proposed method has the advantage of allowing for a more clear-cut interpretat...

inflation has always been an economic problem and different solutions have been proposed to control it. although it is said that “higher output lowers inflation rate” but it is true when other factors are constant. this study searches the answer to the following question: “what is the effect of inflation rate and output in a case that inflation rate and output growth has a volatility trend?” to...

A system identification method for nonlinear systems with unknown structure by means of short input-output data is proposed. This method introduces more general model structure for nonlinear systems. Moreover, based on gray-box idea and its salient feature with expanding NARMAX (Nonlinear Autoregressive, Moving Average eXogenous) modeling, this method integrates different system information. Th...

Abstract: Detection of moving targets in nature is an important problem in neuroscience. In freshwater turtles, it is believed that the visual cortex performs this job. In this paper, a set of ‘moving fishes’ are considered and the visual data is compressed using a set of spatial basis functions. Input to the model cortex is this compressed visual data, and the associated cortical response is g...

The third instar larvae of Gepus invisus and Solter liber are comparatively described and illustrated for the first time with a particular emphasis on genus level characters. Larval morphology confirms a close relationship between these genera as they differ only in minor characters.

Žydelis et al. (2009 [this issue]) is a direct critique of 2 recent papers we published on the use of biodiversity offsets in managing fisheries bycatch (Wilcox & Donlan 2007; Donlan & Wilcox 2008). The authors misrepresent the use of biodiversity offsets as we presented them, misunderstand the policy objectives of fisheries (and bycatch) management, present a flawed analysis of the appropriate...

A kernel method has been developed to model finite degree, finite memory length and infinite degree, finite memory length Volterra series using polynomial and exponential kernels, respectively. Here, the kernel method is extended to identify NARX (Nonlinear AutoRegressive with eXogenous inputs) models. To verify its effectiveness, the proposed approach is used in modeling friction dynamics, whi...