Autoregressive model Research Papers - Academia.edu (original) (raw)

In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an Integer Autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data and metereological data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that an appropriate statistical model should be used to account for the existing autocorrelation in the data.

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- Psychology, Time Series, Climate, Risk assessment

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- by Daniel Massicotte
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- Computer Hardware, Parallel Processing, Kalman Filter, Integration

Using annual data spanning two centuries for dollar-sterling and franc-sterling real exchange rates, we find strong evidence of meanreverting real exchange rate behavior. Using simple, stationary, autoregressive models estimated on prefloat data, we easily outperform nonstationary real exchange rate models in dynamic forecasting exercises over the recent float. Such stationary univariate equations explain 60-80 percent of the in-sample variation in real exchange rates, although the degree of short-run persistence may be high. The econometric estimates imply a half-life of shocks to the real exchange rate of about 6 years for dollar-sterling and a little under 3 years for franc-sterling.

- by James Lothian
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- Economics, Political Economy, Real Exchange Rate, Autoregressive model

Interchangeability between fast Fourier transform (FFT) and autoregressive (AR) analysis was assessed on series of 256 R-R intervals recorded in 56 seated subjects and in 15 men performing an orthostatic test. Low-(LF) and high-frequency (HF) components were calculated and expressed both in absolute (square milliseconds) and normalized units (NU). During orthostatic stress, the same upward trend for LF square milliseconds and LF/HF ratio and downward trend for HF square milliseconds and HF NU were observed with FFT and AR analysis. However, the values for HF square milliseconds were significantly greater with FFT, as compared with AR analysis in standing position ( P b .05). Moreover, Bland & Altman method highlighted a large discrepancy between the results of FFT and AR analysis for all heart rate variability indices in the 3 conditions. Therefore, parametric and nonparametric spectral analyses could not be considered as interchangeable at rest in healthy subjects even if they give same qualitative results. D

- by Aurélien Pichon
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- Adolescent, Arterial hypertension, Low Frequency, Electrocardiography

The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural networks (RNN) models are not only important for the forecasting of time series but also generally for the control of the dynamical system. A RNN with a sufficiently large number of neurons is a nonlinear autoregressive and moving average (NARMA) model, with "moving average" referring to the inputs. The prediction can be assimilated to identification of dynamic process. An architectural approach of RNN with embedded memory, "Nonlinear Autoregressive model process with eXogenous input" (NARX), showing promising qualities for dynamic system applications, is analyzed in this paper. The performances of the NARX model are verified for several types of chaotic or fractal time series applied as input for neural network, in relation with the number of neurons, the training algorithms and the dimensions of his embedded memory. In addition, this work has attempted to identify a way to use the classic statistical methodologies (R/S Rescaled Range analysis and Hurst exponent) to obtain new methods of improving the process efficiency of the prediction chaotic time series with NARX.

- by Eugen Diaconescu
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- Time Series, Moving average, Chaotic Time Series, Dynamic System
- by Fernando S Schlindwein
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- Ultrasound, Doppler Ultrasound, Ultrasonography, Regression Analysis
- by Cassandra James
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- Water quality, Tropical stream, Biological Sciences, Environmental Sciences

This paper presents the possibility of early detection and localization of epileptogenic focus in the iEEG (intracranial Electroencephalography) signal using a method based on multidimensional autoregressive models. The work provides the first results of the method in the iEEG signal, and discusses technical aspects in terms of the suitability of the sampling frequency, AR model order and segmentation step.

- by Petr Jezdik
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- Quality of life, Electroencephalography, Early Detection, DR-AR Model

We propose an asynchronous, low cost, and accurate mobility estimation scheme for wireless mobile networks. This scheme considers the round-trip time (RTT) of the signal from the mobile station to the base station as observation and estimates position and speed of the mobile user in two dimensions. Our scheme uses an earlier proposed autoregressive mobility model,

- by Asad Faraz
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- Kalman Filter, Wireless Network, Mobile Network, Two Dimensions

Total disc replacement emerged as an alternative to fusion for the treatment of degenerative disc disease. Optimization of the bearing surfaces is critical to mitigate wear-related biological reaction. The purpose of this study was to characterize the wear of the A-MAV TM metal-on-metal total disc replacement using a spine wear simulator, per the ASTM F2423-05 standard guide. Six specimens were tested under flexion-extension (FE) conditions for ten million cycles (MC), followed by lateral bending (LB) combined with axial rotation (AR) for an additional ten MC. A run-in wear period was observed during the first 0.5 MC for both testing conditions, followed by a steady-state wear rate of 0.33 ± 0.12 mm 3 /MC in FE and 0.43 ± 0.06 mm 3 /MC in combined motion. Phasing between LB and AR led to a crossing-path motion as observed on explanted devices. This study suggests that clinically-realistic surface morphology may be achieved by carefully selecting the wear test parameters specified in the ASTM standard guide. Furthermore, the use of metal-on-metal bearings in spinal arthroplasty may be viable in view of the low wear exhibited by this material combination.

- by Megan Powell and +1
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- Materials Engineering, Mechanical Engineering, Orthopedic Surgery, Morphology

Sexual satisfaction, marital quality, and marital instability have been studied over the life course of couples in many previous studies, but less in relation to each other. On the basis of the longitudinal data from 283 married couples, the authors used autoregressive models in this study to examine the causal sequences among these 3 constructs for husbands and wives separately. Results of cross-lagged models, for both husbands and wives, provided support for the causal sequences that proceed from sexual satisfaction to marital quality, from sexual satisfaction to marital instability, and from marital quality to marital instability. Initially higher levels of sexual satisfaction resulted in an increase in marital quality, which in turn led to a decrease in marital instability over time. Effects of sexual satisfaction on marital instability appear to have been mediated through marital quality.

- by Glen Elder
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- Psychology, Family, Life course, Family Psychology

In this paper long run structural relationship for freight transport demand is derived for railways in India using annual time series data for 1960±1995. Some of the recent developments in multivariate dynamic econometric time series modelling have been employed such as estimation of long-run structural cointegrating relationship, short-run dynamics and measurement of the eects of shocks and their persistence during the evolution of dynamic freight transport demand system. The models are estimated using a cointegrating vector autoregressive (VAR) modelling framework, which allows for endogeneity of regressors. Results indicate high GDP elasticity and low price elasticity, with real freight rate, i.e. the price variable behaving exogenously with respect to the system. Any disequilibrium in the short-run is likely to be corrected in the long run via adjustments in freight transport demand and GDP. Further, the demand system seems to be stable in the long run and converges to equilibrium in a period of around 3 years after a typical system-wide shock. Ó

- by Mukul Kulshrestha
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- Time Series, Forecasting, Urban And Regional Planning, Price Elasticity

We present a content-based image retrieval system that supports decision making in clinical pathology. The image-guided decision support system locates, retrieves, and displays cases which exhibit morphological profiles consistent to the case in question. It uses an image database containing 261 digitized specimens which belong to three classes of lymphoproliferative disorders and a class of healthy leukocytes. The reliability of the central module, the fast color segmenter, makes possible unsupervised on-line analysis of the query image and extraction of the features of interest: shape, area, and texture of the nucleus. The nuclear shape is characterized through similarity invariant Fourier descriptors, while the texture analysis is based on a multiresolution simultaneous autoregressive model. The system performance was assessed through tenfold cross-validated classification and compared with that of a human expert. To facilitate a natural man-machine interface, speech recognition and voice feedback are integrated. Client-server communication is multithreaded, Internet-based, and provides access to supporting clinical records and video databases.

- by David Foran
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- Cognitive Science, Decision Making, Clinical Pathology, Decision support system

In recent years, artificial intelligence methods have demonstrated to be appropriated for treatment of environmental problems. This paper presents a novel work for assessment and prediction of the water quality in shrimp aquaculture based on environmental pattern processing. Water quality studies are based on analyzing negative concentrations of compounds in shrimp ponds that inhibit the good growing and reproduction of organism. The physical-chemical variables are classified basing on the negative ecological impact using the Gamma (Γ) classifier, which calculates the frequency and the deviation of the measurements from a specific level. A fuzzy inference system processes the level classifications using a reasoning process that determines when a specific concentration is good or harmful for the organism, and providing a water quality index, which describe the condition of the ecosystem: excellent, good, regular and poor. An autoregressive model (AR) predicts a section of an environmental signal using historical information, the set of predicted variables are assessed in order to estimate future water quality conditions in the system. This methodology emerges as a suitable and alternative tool to be used in the developing effective water management plans.

- by LUIS PASTOR SANCHEZ FERNANDEZ
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- Signal Processing, Water quality, Aquaculture, Water Management

In this paper the multi-model partitioning theory is used for simultaneous order and parameter estimation of multivariate autoregressive models. Simulation experiments show that the proposed method successfully selects the correct model order and estimates the parameters accurately, in very few steps, even with a small sample size. They also show that the proposed method performs equally well when the complexity of the model is increased. The results are compared to those obtained using well-established order selection criteria. Finally, it is shown that the method is also successful in tracking model order changes, in real time.

An overview of model building with periodic autoregression (PAR) models is given emphasizing the three stages of model development: identification, estimation and diagnostic checking. New results on the distribution of residual autocorrelations and suitable diagnostic checks are derived. The validity of these checks is demonstrated by simulation. The methodology discussed is illustrated with an application. It is pointed out that the PAR approach to model development offers some important advantages over the more general approach using periodic autoregressive moving-average (PARMA) models.

- by Ian Mcleod
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- Econometrics, Statistics, Time series analysis, Model development

A modification of directed transfer function */direct DTF */is proposed for the analysis of direct information transfer among brain structures on the basis of local field potentials (LFP). Comparison of results obtained by the analysis of simulated and experimental data with a new dDTF and DTF method is shown. A new measure to estimate direct causal relations between signals is defined. The present results demonstrate the effectiveness of the new dDTF method and indicate that the dDTF method can be used to obtain the reliable patterns of connections between various brain structures. #

- by Anna Korzeniewska and +2
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- Neuroscience, Cognitive Science, Mathematics, Electrophysiology

A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patient's EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. ...

- by Mohammad Ghavami
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- Mathematics, Economics, International Relations, Biomedical Engineering

This paper introduces a new frequencydomain approach to describe the relationships (direction of information¯ow) between multivariate time series based on the decomposition of multivariate partial coherences computed from multivariate autoregressive models. We discuss its application and compare its performance to other approaches to the problem of determining neural structure relations from the simultaneous measurement of neural electrophysiological signals. The new concept is shown to re¯ect a frequency-domain representation of the concept of Granger causality.

- by Luiz Baccalá
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- Cognitive Science, Electroencephalography, Multivariate Analysis, Information Flow

Despite powerful advances in yield curve modeling in the last twenty years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach, which focuses on accurately fitting the cross section of interest rates at any given time but neglects time-series dynamics, nor

- by Dimitris Korobilis
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- Mathematics, Economics, Time Series, Portfolio Management

This paper presents a modified k-nearest neighbor approach for streamflow generation. In this model, first, a local polynomial (a nonparametricfunction) is fitted to estimate the mean of the conditional probability density function. The simulation at any time point ‘t+1’ given the value at the current time ‘t’ then involves two steps (i) obtaining the conditional mean from the local

- by edith zagona
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- Civil Engineering, Time Series, Nearest Neighbor, Colorado River

In this paper we try to provide additional insight into the problem of how to discriminate between the two most common spatial processes: the autoregressive and the moving average. This problem, whose analogous time series is apparently simple, acquires a certain complexity when it is considered in an irregular system of spatial units, mainly because there are few tools to carry out this discussion. Nevertheless, even with this lack, we believe that it is possible to make some progress using the methods available at present. In this paper we discuss the advantages and inconveniences of the different techniques that can help us to discriminate between both processes. We finish off the examination with a Monte Carlo exercise, and an application to the European regional income, which has enabled us to better understand the performance of several proposals such as the Lagrange Multipliers, the so-called Variance criterion and the tests of Vuong and Clarke.

- by Jesus Mur
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- Economics, Time Series, Monte Carlo, Spanish Economic History
- by Kirk Shelley
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- Algorithms, Biomedical Engineering, Signal Processing, Comparative Study
- by Ian Mcleod
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- Econometrics, Statistics, Time series analysis, Autoregressive model
- by Adnan Kavak
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- Computer Science, Algorithms, Smart Antenna, Computer Aided Design

Александр Цыплаков † Новосибирский государственный университет, Новосибирск, Россия В настоящем эссе обсуждаются базовые понятия прогнозирования временных рядов и излагаются традиционные подходы к прогнозированию в классических моделях... more

Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species' distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing

- by Roger S Bivand and +2
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- Ecology, Spatial Statistics, Parameter estimation, Spatial autocorrelation

- by Hsiu-chen Yeh
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- Psychology, Family, Life course, Family Psychology

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

This study examines the long-run relationship between carbon emissions and energy consumption, income and foreign trade in the case of China by employing time series data of 1975-2005. In particular the study aims at testing whether environmental Kuznets curve (EKC) relationship between CO 2 emissions and per capita real GDP holds in the long run or not. Auto regressive distributed lag (ARDL) methodology is employed for empirical analysis. A quadratic relationship between income and CO 2 emission has been found for the sample period, supporting EKC relationship. The results of Granger causality tests indicate one way causality runs through economic growth to CO 2 emissions. The results of this study also indicate that the carbon emissions are mainly determined by income and energy consumption in the long run. Trade has a positive but statistically insignificant impact on CO 2 emissions.

- by abdul jalil
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- International Trade, Economic Growth, Energy Policy, Energy Consumption

This correspondence discusses the application of random autoregressive (AR) models to signal processing problems: specifically, to adaptive line enhancement (ALE). The advantage of this approach is that random AR models may reflect more accurately the uncertainty in the stochastic process that generates the received signal. It is shown, through Monte Carlo simulations, that by using random AR models one obtains better results than by using the conventional deterministic AR models under the same conditions.

- by Ahmed Abutaleb
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- Stochastic Process, Computer Science, Image Processing, Signal Processing

We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observations which are required to be neither independent nor identically distributed. We give general results on the rate of convergence of the posterior measure relative to distances derived from a testing criterion. We then specialize our results to independent, nonidentically distributed observations, Markov processes, stationary Gaussian time series and the white noise model. We apply our general results to several examples of infinite-dimensional statistical models including nonparametric regression with normal errors, binary regression, Poisson regression, an interval censoring model, Whittle estimation of the spectral density of a time series and a nonlinear autoregressive model. POSTERIOR CONVERGENCE RATES 193 dressed by Amewou-Atisso, Ghosal, Ghosh and Ramamoorthi [1] and Choudhuri, Ghosal and Roy . The main purpose of the present paper is to obtain a theorem on rates of convergence of posterior distributions in a general framework not restricted to the setup of i.i.d. observations. We specialize this theorem to several classes of non-i.i.d. models including i.n.i.d. observations, Gaussian time series, Markov processes and the white noise model. The theorem applies in every situation where it is possible to test the true parameter versus balls of alternatives with exponential error probabilities and it is not restricted to any particular structure on the joint distribution. The existence of such tests has been proven in many special cases by Le Cam and Birgé , who used them to construct estimators with optimal rates of convergence, determined by the (local) metric entropy or "Le Cam dimension" of the model. Our main theorem uses the same metric entropy measure of the complexity of the model and combines this with a measure of prior concentration around the true parameter to obtain a bound on the posterior rate of convergence, generalizing the corresponding result of Ghosal, Ghosh and van der Vaart . We apply these results to obtain posterior convergence rates for linear regression, nonparametric regression, binary regression, Poisson regression, interval censoring, spectral density estimation and nonlinear autoregression. van der Meulen, van der Vaart and van Zanten have extended the approach of this paper to several types of diffusion models.

- by rk choudhury
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- Stochastic Process, Econometrics, Statistics, Time Series

Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the timevarying spectrum of multichannel EEG recordings-in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.

In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms digital transforms, textural edgeness, structural element, gray tone 'cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture ~ased on more complex primitives than gray tone. We conclude With some structural-statistical generalizations which apply the statistical techniques to the structural primitives.

- by robert haralick
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- Biomedical Engineering, Image Processing, Remote Sensing, Data Mining

Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper we propose a hybridization of intelligent techniques such as ANNs, fuzzy systems and evolutionary algorithms, so that the final hybrid ARIMA-ANN model could outperform the prediction accuracy of those models when used separately. More specifically, we propose the use of fuzzy rules to elicit the order of the ARMA or ARIMA model, without the intervention of a human expert, and the use of a hybrid ARIMA-ANN model that combines the advantages of the easy-to-use and relatively easy-to-tune ARIMA models, and the computational power of ANNs. Fig. 15. Lorenz time series: (a) histogram, (b) phase diagram, (c) original and predicted Lorenz time series using the proposed methodology (which are indistinguishable) and (d) prediction error.

- by Alberto Guillen
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- Engineering, Time Series, Fuzzy Sets, Neural Networks

AbstractThe electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (eg, late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct... more

This paper presents a new robust method to estimate the parameters of ARMA models. This method makes use of the autocorrelations estimates based on the ratio of medians together with a robust filter cleaner able to reject a large fraction of outliers, and a Gaussian maximum likelihood estimation which handles missing values. The main advantages of the procedure are its easiness, robustness and fast execution. Its effectiveness is demonstrated on an example of the forecasting of the French daily electricity consumptions.

- by Yacine Chakhchoukh and +1
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- Statistics, Signal Processing, Time Series, Modeling

We propose a new and intuitive risk-neutral valuation model for real estate derivatives which are linked to autocorrelated indices. We model the observed index with an autoregressive model which can be estimated using standard econometric techniques. The resulting index behavior can easily be analyzed and closed-form pricing solutions are derived for forwards, swaps and European put and call options. We demonstrate the application of the model by valuing a put option on a house price index. Autocorrelation in the index returns appears to have a large impact on the option value. We also study the effect of an over-or undervalued real estate market. The observed effects are significant and as expected.

- by A. Pelsser
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- Real Estate, Seasonality, Price Discovery, Stochastic Volatility

The effect of nonparsimonious time series models is studied by deriving the approximate variance of the one-step-ahead forecast error. Also, in a simulation experiment we show the loss in forecast accuracy that can result when a... more

- by Bovas Abraham and +1
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- Statistics, First-Order Logic, Time Series Forecasting, Moving average

The paper studies the long-run relation and short-run dynamics between real oil prices and real exchange rates in a sample of 13 oil-exporting countries. The purpose of the study is to examine the possibility of Dutch disease in these countries. Tests of cointegration using threshold and momentum-threshold autoregressive (TAR and M-TAR) models suggest the possibility of the disease in 3-out-of 13 countries-Bolivia, Mexico and Norway. For these countries, we also find that (a) oil prices have a long-run effect on the exchange rates; and (b) exchange rates adjust faster to positive deviations from the equilibrium; and (c) there is no evidence of short-run causality between real exchange rates and real oil prices in either direction. Over all, these findings suggest a weak link between oil prices and real exchange rates and thus limited evidence in favor of the Dutch disease.

- by Hassan Mohammadi
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- Applied Economics, Dutch Disease, Cointegration, Error Correction

Asymmetric Laplace distributions have received much attention in recent years. It can be used in modeling currency exchange rate, interest rate, stock price changes, etc. But no time-series models with asymmetric Laplace marginal are yet developed. Present work aims at developing autoregressive models with asymmetric Laplace marginal distribution. r

- by A.p. Kuttykrishnan
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- Applied Mathematics, Econometrics, Statistics, Exchange rate

Multivariate autoregressive modelling provides a method to analyse the dynamic interactions between heart rate (HR), blood pressure (BP) and respiration (RESP) by means of noise source contributions (NSCs). The conventional approach presumes the modelled noise sources are mutually independent. This presumption is, in general, not satisfied and causes an error in the results. In the present study, the effect of this error is analysed. A method is presented to remove the error by making the noise sources independent. The method is based on the inclusion of immediate transfer paths in the model. To quantify the strength of the interactions, a measure called NSC ratio (NSCR); is calculated; this states the amount of variability of the signal arising from other signals. The method is demonstrated by studying the inter-relationships between HR, BP and RESP in a healthy male subject in supine and standing positions. It is found that the error is marked and that the presented method provides corrected estimates for spectral decomposition and NSC analysis. The results show it is necessary to include the immediate transfer mechanisms in the model, while analysing the cardiopulmonary dynamics by means of HR and BP variability.

- by Ilkka Korhonen
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- Biomedical Engineering, Hemodynamics, Blood Pressure, Heart rate

In the process of economic development Small and Medium Enterprises (SMEs) play a pivotal role in poverty alleviation and rapid industrialization of the developing countries like Bangladesh. In this paper we tried to appraise the Problems and Prospects of SMEs in Bangladesh. We observed from the research that non availability of adequate credit, complex loan granting procedure, inadequate infrastructure facilities, problems of collateral requirements, paucity of working capital, non availability of skilled work force, poor salary structure, lack of coordination among SME related organizations, lack of appropriate marketing strategies etc. are the major hindrances to the development of the SMEs in Bangladesh. In order to overcome the problems researchers have tried to provide some recommendations for the developments of SMEs in Bangladesh based on sound reasoning.

- by Shafiqul Islam
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- Environmental Engineering, Time Series, Nonlinear dynamics, Atmospheric sciences

This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction along with a support vector machine (SVM) for binary classification between preictal/ictal and interictal states. This choice is characterized by low computational requirements compatible with a real-time implementation of the overall system. Moreover, experimental results on the Freiburg dataset exhibited correct prediction of all seizures (100% sensitivity) and, due to a novel regularization of the SVM classifier based on the Kalman filter, also a low false alarm rate.

- by Luigi Chisci
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- Algorithms, Artificial Intelligence, Biomedical Engineering, Time Series

Estimation of reference growth curves for children's height and weight has traditionally relied on normal theory to construct families of quantile curves based on samples from the reference population. Age-specific parametric transformation has been used to significantly broaden the applicability of these normal theory methods. Nonparametric quantile regression methods offer a complementary strategy for estimating conditional quantile functions. We compare estimated reference curves for height using the penalized likelihood approach of Cole and Green (1992) with quantile regression curves based on data used for modern Finnish reference charts. An advantage of the quantile regression approach is that it is relatively easy to incorporate prior growth and other covariates into the analysis of longitudinal growth data. Quantile specific autoregressive models for unequally spaced measurements are introduced and their application to diagnostic screening is illustrated.

- by Xuming He
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- Mathematics, Child Development, Biometry, Adolescent

El presente articulo describe el desarrollo de un metodo experimental empleado para construir las curvas de Corriente de Soldadura Vs. Velocidad de Alimentacion de Alambre en proceso GMAW, con dos extensiones de electrodo en un modo de transferencia por corto circuito, utilizando un electrodo ER70S-6 y un gas 98%Ar-2%CO2. Se encontro que, manteniendo el voltaje constante, la corriente de soldadura se incremento de manera proporcional (aproximadamente lineal) a la velocidad de alimentacion de alambre e inversamente proporcional a la extension del electrodo. Se compararon las curvas construidas con las disponibles en la literatura y se encontro un desfase de aproximadamente 20 A respecto a la curva resultante para una distancia tubo de contacto-trabajo de 15 mm. Se determino indirectamente la extension del electrodo a partir de la estimacion de las longitudes de arco fotografiadas durante los ensayos.

- by Santiago Aristizábal
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- Modeling, Regression Analysis, GMAW, Weld

In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose an estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices.

- by André Santos
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- Radial Basis Function, Neural Network, Genetic Algorithm, Electric Power Systems

Comparative analysis of economic structure and forecasts generated from simultaneous equation, VAR and autoregressive models based on quarterly series from 1966:1 to 2007:3 of UK to those from the stochastic general equilibrium models has provided insights into objective and subjective evaluation of macro economic impacts of public policies. Econometric estimates are used in formulation of stochastic dynamic general equilibrium models to generate time series of macro variables from stochastic general equilibrium models. Calibraing to ratios, variances, covariance and correlations econometric analyses and general equilibrium models are integrated and shown to be complementary not competitive techniques.

- by Keshab Bhattarai
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- Marketing, Time Series, Economic Growth, Forecasting