Empirical Vector Autoregressive Modeling
M. Ooms (Marius)
1993-01-01
textabstractChapter 2 introduces the baseline version of the VAR model, with its basic statistical assumptions that we examine in the sequel. We first check whether the variables in the VAR can be transformed to meet these assumptions. We analyze the univariate characteristics of the series.
Model reduction methods for vector autoregressive processes
Brüggemann, Ralf
2004-01-01
1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant research tools in the analysis of macroeconomic time series during the last two decades. The great success of this modeling class started with Sims' (1980) critique of the traditional simultaneous equation models (SEM). Sims criticized the use of 'too many incredible restrictions' based on 'supposed a priori knowledge' in large scale macroeconometric models which were popular at that time. Therefore, he advo cated largely unrestricted reduced form multivariate time series models, unrestricted VAR models in particular. Ever since his influential paper these models have been employed extensively to characterize the underlying dynamics in systems of time series. In particular, tools to summarize the dynamic interaction between the system variables, such as impulse response analysis or forecast error variance decompo sitions, have been developed over the years. The econometrics of VAR models and related quantities i...
Modeling non-Gaussian time-varying vector autoregressive process
National Aeronautics and Space Administration — We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical...
Vector bilinear autoregressive time series model and its superiority ...
African Journals Online (AJOL)
In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.
A Bayesian Infinite Hidden Markov Vector Autoregressive Model
D. Nibbering (Didier); R. Paap (Richard); M. van der Wel (Michel)
2016-01-01
textabstractWe propose a Bayesian infinite hidden Markov model to estimate time-varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture
Elbourne, A.; de Haan, J.
2009-01-01
Using the vector autoregressive methodology, we present estimates of monetary transmission for five new EU member countries in Central and Eastern Europe with more or less flexible exchange rates. We select sample periods to estimate over the longest possible period that can be considered as a
Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model based on the conditional Gaussian likelihood. The model allows the process X(t) to be fractional of order d and cofractional of order d-b; that is, there exist vectors ß for which ß...... the asymptotic distribution of the likelihood ratio test for cointegration rank, which is a functional of fractional Brownian motion of type II....
Likelihood inference for a fractionally cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model based on the conditional Gaussian likelihood. The model allows the process X_{t} to be fractional of order d and cofractional of order d-b; that is, there exist vectors β for which β...... also find the asymptotic distribution of the likelihood ratio test for cointegration rank, which is a functional of fractional Brownian motion of type II....
Testing exact rational expectations in cointegrated vector autoregressive models
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
1999-01-01
This paper considers the testing of restrictions implied by rational expectations hypotheses in a cointegrated vector autoregressive model for I(1) variables. If the rational expectations involve one-step-ahead observations only and the coefficients are known, an explicit parameterization...
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA
2009-09-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
Likelihood inference for a fractionally cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Ørregård Nielsen, Morten
2012-01-01
We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model with a restricted constant term, ¿, based on the Gaussian likelihood conditional on initial values. The model nests the I(d) VAR model. We give conditions on the parameters such that the......We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model with a restricted constant term, ¿, based on the Gaussian likelihood conditional on initial values. The model nests the I(d) VAR model. We give conditions on the parameters...... such that the process X_{t} is fractional of order d and cofractional of order d-b; that is, there exist vectors ß for which ß'X_{t} is fractional of order d-b, and no other fractionality order is possible. We define the statistical model by 0... process in the parameters when errors are i.i.d. with suitable moment conditions and initial values are bounded. When the limit is deterministic this implies uniform convergence in probability of the conditional likelihood function. If the true value b0>1/2, we prove that the limit distribution of (ß...
Optimal hedging with the cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Gatarek, Lukasz; Johansen, Søren
We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated with the......We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated...... with the hedged asset and among themselves. We nd that the minimum variance hedge for assets driven by the CVAR, depends strongly on the portfolio holding period. The hedge is dened as a function of correlation and cointegration parameters. For short holding periods the correlation impact is predominant. For long...... horizons, the hedge ratio should overweight the cointegration parameters rather then short-run correlation information. In the innite horizon, the hedge ratios shall be equal to the cointegrating vector. The hedge ratios for any intermediate portfolio holding period should be based on the weighted average...
Temporal aggregation in first order cointegrated vector autoregressive models
DEFF Research Database (Denmark)
La Cour, Lisbeth Funding; Milhøj, Anders
We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline...
The cointegrated vector autoregressive model with general deterministic terms
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X(t)= Z(t) + Y(t), where Z(t) belongs to a large class...... of deterministic regressors and Y(t) is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended...... model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are khi squared distributed....
Some Identification Problems in the Cointegrated Vector Autoregressive Model
DEFF Research Database (Denmark)
Johansen, Søren
2010-01-01
The paper analyses some identification problems in the cointegrated vector autoregressive model. A criteria for identification by linear restrictions on individual relations is given. The asymptotic distribution of the estimators of a and ß is derived when they are identified by linear restrictions...... on ß , and when they are identified by linear restrictions on a . It it shown that, in the latter case, a component of is asymptotically Gaussian. Finally we discuss identification of shocks by introducing the contemporaneous and permanent effect of a shock and the distinction between permanent...... and transitory shocks, which allows one to identify permanent shocks from the long-run variance and transitory shocks from the short-run variance....
Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets
DEFF Research Database (Denmark)
Dias, Gustavo Fruet; Kapetanios, George
We address the issue of modelling and forecasting macroeconomic variables using rich datasets, by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares...
Temporal Aggregation in First Order Cointegrated Vector Autoregressive models
DEFF Research Database (Denmark)
Milhøj, Anders; la Cour, Lisbeth Funding
2011-01-01
with the frequency of the data. We also introduce a graphical representation that will prove useful as an additional informational tool for deciding the appropriate cointegration rank of a model. In two examples based on models of time series of different grades of gasoline, we demonstrate the usefulness of our...
On vector autoregressive modeling in space and time
di Giacinto, Valter
2010-06-01
Despite the fact that it provides a potentially useful analytical tool, allowing for the joint modeling of dynamic interdependencies within a group of connected areas, until lately the VAR approach had received little attention in regional science and spatial economic analysis. This paper aims to contribute in this field by dealing with the issues of parameter identification and estimation and of structural impulse response analysis. In particular, there is a discussion of the adaptation of the recursive identification scheme (which represents one of the more common approaches in the time series VAR literature) to a space-time environment. Parameter estimation is subsequently based on the Full Information Maximum Likelihood (FIML) method, a standard approach in structural VAR analysis. As a convenient tool to summarize the information conveyed by regional dynamic multipliers with a specific emphasis on the scope of spatial spillover effects, a synthetic space-time impulse response function (STIR) is introduced, portraying average effects as a function of displacement in time and space. Asymptotic confidence bands for the STIR estimates are also derived from bootstrap estimates of the standard errors. Finally, to provide a basic illustration of the methodology, the paper presents an application of a simple bivariate fiscal model fitted to data for Italian NUTS 2 regions.
Regime-dependent impulse response functions in a Markov-switching vector autoregression model
Ehrmann, Michael; Ellison, Martin; Valla, Natacha
2001-01-01
In this paper we introduce identifying restrictions into a Markov-switching vector autoregression model. We define a separate set of impulse responses for each Markov regime to show how fundamental disturbances affect the variables in the model dependent on the regime. We go to illustrate the use of these regime-dependent impulse response functions in a model of the U.S. economy. The regimes we identify come close to the “old” and “new economy” regimes found in recent research. We provide evi...
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
predictive power for the future distribution of inflation over and above the own history of inflation, but not vice versa. This may be interpreted as evidence against the new Keynesian model that implies Granger causality from inflation to GDP growth, provided GDP growth is a reasonable proxy of the marginal...... as a by-product. We apply the methods to postwar quarterly U.S. inflation and GDP growth series. The noncausal VAR model turns out to be superior in terms of both in-sample fit and out-of-sample forecasting performance over its conventional causal counterpart. In addition, we find GDP growth to have...
Oracle Inequalities for High Dimensional Vector Autoregressions
DEFF Research Database (Denmark)
Callot, Laurent; Kock, Anders Bredahl
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order of mag...
Application of vector autoregressive model for rainfall and groundwater level analysis
Keng, Chai Yoke; Shan, Fam Pei; Shimizu, Kunio; Imoto, Tomoaki; Lateh, Habibah; Peng, Koay Swee
2017-08-01
Groundwater is a crucial water supply for industrial, agricultural and residential use, hence it is important to understand groundwater system. Groundwater is a dynamic natural resource and can be recharged. The amount of recharge depends on the rate and duration of rainfall, as rainfall comprises an important component of the water cycle and is the prime source of groundwater recharge. This study applies Vector Autoregressive (VAR) model in the analysis of rainfall and groundwater level. The study area that is focused in the study is along the East-West Highway, Gerik-Jeli, Malaysia. The VAR model with optimum lag length 8, VAR(8) is selected to model the rainfall and groundwater level in the study area. Result of Granger causality test shows significant influence of rainfall to groundwater level. Impulse Response Function reveals that changes in rainfall significantly affect changes in groundwater level after some time lags. Moreover, Variance Decomposition reported that rainfall contributed to the forecast of the groundwater level. The VAR(8) model is validated by comparing the actual value with the in-sample forecasted value and the result is satisfied with all forecasted groundwater level values lies inside the confidence interval which indicate that the model is reliable. Furthermore, the closeness of both actual and forecasted groundwater level time series plots implies the high degree of accurateness of the estimated model.
A graphical vector autoregressive modelling approach to the analysis of electronic diary data
Directory of Open Access Journals (Sweden)
Zipfel Stephan
2010-04-01
Full Text Available Abstract Background In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied. Methods We propose to analyse the temporal interrelationships among the variables by a structural modelling approach based on graphical vector autoregressive (VAR models. We give a comprehensive description of the underlying concepts and explain how the dependence structure can be recovered from electronic diary data by a search over suitable constrained (graphical VAR models. Results The graphical VAR approach is applied to the electronic diary data of 35 obese patients with and without binge eating disorder (BED. The dynamic relationships for the two subgroups between eating behaviour, depression, anxiety and eating control are visualized in two path diagrams. Results show that the two subgroups of obese patients with and without BED are distinguishable by the temporal patterns which influence their respective eating behaviours. Conclusion The use of the graphical VAR approach for the analysis of electronic diary data leads to a deeper insight into patient's dynamics and dependence structures. An increasing use of this modelling approach could lead to a better understanding of complex psychological and physiological mechanisms in different areas of medical care and research.
Suharsono, Agus; Aziza, Auliya; Pramesti, Wara
2017-12-01
Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship
Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model
DEFF Research Database (Denmark)
Møller, Niels Framroze
, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers...
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
The Integration Order of Vector Autoregressive Processes
DEFF Research Database (Denmark)
Franchi, Massimo
We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2...
DEFF Research Database (Denmark)
Zhao, Yongning; Ye, Lin; Pinson, Pierre
2018-01-01
The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependenciesbetweentensorhundredsofspatiallydistributedwind farms, e.g., over a region. In this paper, a Sparsity......-Controlled Vector Autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained Mixed Integer Non-Linear Programming (MINLP) problem. It allows controlling the sparsity of the coefﬁcient...... and forecasting, the original SC-VAR is modiﬁed and a Correlation-Constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set...
Directory of Open Access Journals (Sweden)
Yunan Helmi Mahendra
2017-01-01
Full Text Available Tidur merupakan kebutuhan dasar manusia. Salah satu gangguan tidur yang cukup berbahaya adalah narkolepsi, yaitu gangguan tidur kronis yang ditandai dengan rasa kantuk yang luar biasa di siang hari dan serangan tidur yang terjadi secara tiba-tiba. Salah satu metode dokter untuk mendiagnosis penyakit narkolepsi adalah dengan melihat aktivitas gelombang otak (melalui sinyal EEG pasien. Penelitian ini bertujuan untuk mengembangkan perangkat lunak yang dapat mengklasifikasikan keadaan tidur dan bangun melalui sinyal EEG secara otomatis. Dataset EEG yang digunakan tersedia di Physionet. Pertama-tama data EEG yang menjadi masukan dilakukan normalisasi dan filtering. Proses filtering dilakukan untuk membagi data menjadi 3 subband yaitu theta, alpha, dan beta. Setelah itu pada masing-masing subband dilakukan tahap ekstraksi fitur menggunakan Autoregressive Model. Hasil estimasi koefisien AR model digunakan sebagai fitur. Metode yang digunakan untuk mengestimasi koefisien AR model yaitu metode Yule-Walker dan metode Burg. Dataset dibagi menjadi data latih dan data uji menggunakan 10-fold cross validation. Data training digunakan untuk membuat SVM Model. SVM Model digunakan untuk mengklasifikasikan data testing sehingga menghasilkan keluaran label 1 untuk tidur dan label 0 untuk bangun. Untuk menentukan kelas final dilakukan majority vote dari hasil klasifikasi masing-masing subband. Performa sistem diperoleh dengan menghitung akurasi, presisi, dan sensitivitas pada setiap skenario uji coba. Skenario uji coba yang dilakukan antara lain dengan memvariasikan order AR, fungsi kernel, dan parameter C pada SVM. Dari hasil uji coba yang dilakukan, metode Yule-Walker menghasilkan rata-rata akurasi 80.60%, presisi 78.19%, dan sensitivitas 77.56%. Metode Burg menghasilkan akurasi 94.01%, presisi 95.70%, dan sensitivitas 93.39%. Hasil tersebut menunjukkan metode Burg memiliki performa lebih baik dibandingan dengan metode Yule-Walker.
Automating Vector Autoregression on Electronic Patient Diary Data
Emerencia, Ando Celino; van der Krieke, Lian; Bos, Elisabeth H.; de Jonge, Peter; Petkov, Nicolai; Aiello, Marco
Finding the best vector autoregression model for any dataset, medical or otherwise, is a process that, to this day, is frequently performed manually in an iterative manner requiring a statistical expertize and time. Very few software solutions for automating this process exist, and they still
CSIR Research Space (South Africa)
Das, Sonali
2010-01-01
Full Text Available This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, the authors forecast house price inflation for five...
Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus
2016-07-01
Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.
Unsupervised parsing of gaze data with a beta-process vector auto-regressive hidden Markov model.
Houpt, Joseph W; Frame, Mary E; Blaha, Leslie M
2017-10-26
The first stage of analyzing eye-tracking data is commonly to code the data into sequences of fixations and saccades. This process is usually automated using simple, predetermined rules for classifying ranges of the time series into events, such as "if the dispersion of gaze samples is lower than a particular threshold, then code as a fixation; otherwise code as a saccade." More recent approaches incorporate additional eye-movement categories in automated parsing algorithms by using time-varying, data-driven thresholds. We describe an alternative approach using the beta-process vector auto-regressive hidden Markov model (BP-AR-HMM). The BP-AR-HMM offers two main advantages over existing frameworks. First, it provides a statistical model for eye-movement classification rather than a single estimate. Second, the BP-AR-HMM uses a latent process to model the number and nature of the types of eye movements and hence is not constrained to predetermined categories. We applied the BP-AR-HMM both to high-sampling rate gaze data from Andersson et al. (Behavior Research Methods 49(2), 1-22 2016) and to low-sampling rate data from the DIEM project (Mital et al., Cognitive Computation 3(1), 5-24 2011). Driven by the data properties, the BP-AR-HMM identified over five categories of movements, some which clearly mapped on to fixations and saccades, and others potentially captured post-saccadic oscillations, smooth pursuit, and various recording errors. The BP-AR-HMM serves as an effective algorithm for data-driven event parsing alone or as an initial step in exploring the characteristics of gaze data sets.
DEFF Research Database (Denmark)
Teräsvirta, Timo; Yang, Yukai
is illustrated by two applications. In the first one, the dynamic relationship between the US gasoline price and consumption is studied and possible asymmetries in it considered. The second application consists of modelling two well known Icelandic riverflow series, previously considered by many hydrologists...
Temporal aggregation in first order cointegrated vector autoregressive
DEFF Research Database (Denmark)
la Cour, Lisbeth Funding; Milhøj, Anders
2006-01-01
We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline....
Aydin, Alev Dilek; Caliskan Cavdar, Seyma
2015-01-01
The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method. PMID:26550010
Unit Root Vector Autoregression with volatility Induced Stationarity
DEFF Research Database (Denmark)
Rahbek, Anders; Nielsen, Heino Bohn
We propose a discrete-time multivariate model where lagged levels of the process enter both the conditional mean and the conditional variance. This way we allow for the empirically observed persistence in time series such as interest rates, often implying unit-roots, while at the same time maintain...... stationarity despite such unit-roots. Specifically, the model bridges vector autoregressions and multivariate ARCH models in which residuals are replaced by levels lagged. An empirical illustration using recent US term structure data is given in which the individual interest rates have unit roots, have...... no finite first-order moments, but remain strictly stationary and ergodic, while they co-move in the sense that their spread has no unit root. The model thus allows for volatility induced stationarity, and the paper shows conditions under which the multivariate process is strictly stationary...
R.W. Strachan (Rodney); H.K. van Dijk (Herman)
2007-01-01
textabstractA Bayesian model averaging procedure is presented within the class of vector autoregressive (VAR) processes and applied to two empirical issues. First, stability of the "Great Ratios" in U.S. macro-economic time series is investigated, together with the presence and e¤ects of permanent
Directory of Open Access Journals (Sweden)
VIAN RISKA AYUNING TYAS
2014-08-01
Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%
Directory of Open Access Journals (Sweden)
VIAN RISKA AYUNING TYAS
2014-01-01
Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%
A Vector AutoRegressive (VAR) Approach to the Credit Channel for ...
African Journals Online (AJOL)
This paper is an attempt to determine the presence and empirical significance of monetary policy and the bank lending view of the credit channel for Mauritius, which is particularly relevant at these times. A vector autoregressive (VAR) model of order three is used to examine the monetary transmission mechanism using ...
Malesevic, Nebojsa; Markovic, Dimitrije; Kanitz, Gunter; Controzzi, Marco; Cipriani, Christian; Antfolk, Christian
2017-07-01
In this paper we present a novel method for predicting individual fingers movements from surface electromyography (EMG). The method is intended for real-time dexterous control of a multifunctional prosthetic hand device. The EMG data was recorded using 16 single-ended channels positioned on the forearm of healthy participants. Synchronously with the EMG recording, the subjects performed consecutive finger movements based on the visual cues. Our algorithm could be described in following steps: extracting mean average value (MAV) of the EMG to be used as the feature for classification, piece-wise linear modeling of EMG feature dynamics, implementation of hierarchical hidden Markov models (HHMM) to capture transitions between linear models, and implementation of Bayesian inference as the classifier. The performance of our classifier was evaluated against commonly used real-time classifiers. The results show that the current algorithm setup classifies EMG data similarly to the best among tested classifiers but with equal or less computational complexity.
Todd E. Clark; Francesco Ravazzolo
2012-01-01
This paper compares alternative models of time-varying macroeconomic volatility on the basis of the accuracy of point and density forecasts of macroeconomic variables. In this analysis, we consider both Bayesian autoregressive and Bayesian vector autoregressive models that incorporate some form of time-varying volatility, precisely stochastic volatility (both with constant and time-varying autoregressive coeffi cients), stochastic volatility following a stationary AR process, stochastic volat...
Interval Forecast for Smooth Transition Autoregressive Model ...
African Journals Online (AJOL)
In this paper, we propose a simple method for constructing interval forecast for smooth transition autoregressive (STAR) model. This interval forecast is based on bootstrapping the residual error of the estimated STAR model for each forecast horizon and computing various Akaike information criterion (AIC) function. This new ...
THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...
African Journals Online (AJOL)
The application of an allometric-autoregressive model in the quantification of growth and efficiency of feed uti- lization for purposes of selection for efficiency and growth has been discussed by Scholtz & Roux (1980). This model is based mainly on the following 2 equa- tions. (2). The equation for cumulative feed intake is ...
Very-short-term wind power probabilistic forecasts by sparse vector autoregression
DEFF Research Database (Denmark)
Dowell, Jethro; Pinson, Pierre
2016-01-01
A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...
New interval forecast for stationary autoregressive models ...
African Journals Online (AJOL)
In this paper, we proposed a new forecasting interval for stationary Autoregressive, AR(p) models using the Akaike information criterion (AIC) function. Ordinarily, the AIC function is used to determine the order of an AR(p) process. In this study however, AIC forecast interval compared favorably with the theoretical forecast ...
Spatial Autocorrelation And Autoregressive Models In Ecology
Jeremy W. Lichstein; Theodore R. Simons; Susan A. Shriner; Kathleen E. Franzreb
2003-01-01
Abstract. Recognition and analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available...
Kumaraswamy autoregressive moving average models for double bounded environmental data
Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme
2017-12-01
In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.
Testing for rational bubbles in a co-explosive vector autoregression
DEFF Research Database (Denmark)
Engsted, Tom; Nielsen, Bent
We derive the parameter restrictions that a standard equity market model implies for a bivariate vector autoregression for stock prices and dividends, and we show how to test these restrictions using likelihood ratio tests. The restrictions, which imply that stock returns are unpredictable......, are derived both for a model without bubbles and for a model with a rational bubble. In both cases we show how the restrictions can be tested through standard chi-squared inference. The analysis for the no-bubble case is done within the traditional Johansen model for I(1) variables, while the bubble model...
Modeling Touch and Palpation Using Autoregressive Models.
Laufer, Shlomi; Pugh, Carla; Van Veen, Barry D
2017-05-05
The human haptic system uses a set of reproducible and subconscious hand maneuvers to identify objects. Similar subconscious maneuvers are used during medical palpation for screening and diagnosis. The goal of this work was to develop a mathematical model that can be used to describe medical palpation techniques. Palpation data were measured using a two-dimensional array of force sensors. A novel algorithm for estimating the hand position from force data was developed. The hand position data were then modeled using multi-variate autoregressive models. Analysis of these models provided palpation direction and frequency as well as palpation type. The models were tested and validated using three different data sets: simulated data, a simplified experiment in which participant followed a known pattern and breast simulator palpation data. Simulated data showed that minimal error in estimating palpation direction and frequency is achieved when the sampling frequency is five to ten times the palpation frequency. The classification accuracy was 99% for the simplified experiment and 73% for the breast simulator data. Proper palpation is one of the vital components of many hands-on clinical examinations. In this study an algorithm for characterizing medical palpation was developed. The algorithm measured palpation frequency and direction for the first time and provided classification of palpation type. These newly developed models can be used for quantifying and assessing clinical technique and consequently lead to improved performance in palpation-based exams. Furthermore, they provide a general tool for the study of human haptics.
DEFF Research Database (Denmark)
Callot, Laurent; Kristensen, Johannes Tang
This paper studies vector autoregressive models with parsimoniously time-varying parameters. The parameters are assumed to follow parsimonious random walks, where parsimony stems from the assumption that increments to the parameters have a non-zero probability of being exactly equal to zero......, or parameters varying randomly.We characterize the finite sample properties of the Lasso by deriving upper bounds on the estimation and prediction errors that are valid with high probability, and provide asymptotic conditions under which these bounds tend to zero with probability tending to one.We also provide...
Modeling of non-stationary autoregressive alpha-stable processe
National Aeronautics and Space Administration — In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models...
Directory of Open Access Journals (Sweden)
Senbet Dawit
2016-05-01
Full Text Available There is more consensus on the effects of monetary policy than its transmission mechanism. Two channels of transmission mechanisms are the conventional interest rate channel and the credit channel. I investigate the channels of monetary policy transmission in the U.S. using the factor-augmented vector autoregressive (FAVAR models developed by Bernanke, Boivin & Eliasz (2005. The newly developed FAVAR approach allows the researcher to include all relevant macroeconomic variables in the model and analyze them. Therefore, the FAVAR models span a larger information set and generate better estimates of impulse response functions than the commonly used vector autoregressive (VAR models that utilize only 4–8 variables. I include 154 monthly U.S. time series variables for the period 1970–2014. The findings support the existence of the credit channel in the U.S. The conclusion remains the same when the non-borrowed reserve operating regime (October 1979–October 1982 is removed from the sample period.
Cardiac arrhythmia classification using autoregressive modeling
Directory of Open Access Journals (Sweden)
Srinivasan Narayanan
2002-11-01
Full Text Available Abstract Background Computer-assisted arrhythmia recognition is critical for the management of cardiac disorders. Various techniques have been utilized to classify arrhythmias. Generally, these techniques classify two or three arrhythmias or have significantly large processing times. A simpler autoregressive modeling (AR technique is proposed to classify normal sinus rhythm (NSR and various cardiac arrhythmias including atrial premature contraction (APC, premature ventricular contraction (PVC, superventricular tachycardia (SVT, ventricular tachycardia (VT and ventricular fibrillation (VF. Methods AR Modeling was performed on ECG data from normal sinus rhythm as well as various arrhythmias. The AR coefficients were computed using Burg's algorithm. The AR coefficients were classified using a generalized linear model (GLM based algorithm in various stages. Results AR modeling results showed that an order of four was sufficient for modeling the ECG signals. The accuracy of detecting NSR, APC, PVC, SVT, VT and VF were 93.2% to 100% using the GLM based classification algorithm. Conclusion The results show that AR modeling is useful for the classification of cardiac arrhythmias, with reasonably high accuracies. Further validation of the proposed technique will yield acceptable results for clinical implementation.
Testing for co-integration in vector autoregressions with non-stationary volatility
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, Robert M.
2010-01-01
Many key macroeconomic and financial variables are characterized by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...... cases. We show that the conventional rank statistics computed as in (Johansen, 1988) and (Johansen, 1991) are potentially unreliable. In particular, their large sample distributions depend on the integrated covariation of the underlying multivariate volatility process which impacts on both the size...... and power of the associated co-integration tests, as we demonstrate numerically. A solution to the identified inference problem is provided by considering wild bootstrap-based implementations of the rank tests. These do not require the practitioner to specify a parametric model for volatility, or to assume...
Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility
DEFF Research Database (Denmark)
Cavaliere, Guiseppe; Rahbæk, Anders; Taylor, A.M. Robert
Many key macro-economic and financial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...... cases. We show that the conventional rank statistics computed as in Johansen (1988,1991) are potentially unreliable. In particular, their large sample distributions depend on the integrated covariation of the underlying multivariate volatility process which impacts on both the size and power...... of the associated co-integration tests, as we demonstrate numerically. A solution to the identified inference problem is provided by considering wild bootstrap-based implementations of the rank tests. These do not require the practitioner to specify a parametric model for volatility, nor to assume that the pattern...
Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
Sign-identified structural vector autoregressive (SVAR) models have recently become popular. However, the conventional approach to sign restrictions only yields set identification, and implicitly assumes an informative prior distribution of the impulse responses whose influence does not vanish...... a genuinely noninformative prior and thus learning from the data about the impulse responses. While the shocks are statistically identified, they carry no economic meaning as such, and we propose a procedure for labeling them by their probabilities of satisfying each of the given sign restrictions....... The impulse responses of the identified economic shocks can subsequently be computed in a straightforward manner. Our approach is quite flexible in that it facilitates labeling only a subset of the sign-restricted shocks, and also concluding that none of the sign restrictions is plausible. We illustrate...
DEFF Research Database (Denmark)
Lassen Kaspersen, Line; Føyn, Tullik Helene Ystanes
This paper investigates price transmission for agricultural commodities between world markets and the Ugandan market in an attempt to determine the impact of world market prices on the Ugandan market. Based on the realization that price formation is not a static concept, a dynamic vector...... autoregressive (VAR) model is presented. The prices of Robusta coffee and sorghum are examined, as both of these crops are important for the domestic economy of Uganda – Robusta as a cash crop, mainly traded internationally, and sorghum for consumption at household level. The analysis focuses on the spatial...... price relations, i.e. the price variations between geographically separated markets in Uganda and the world markets. Our analysis indicates that food markets in Uganda, based on our study of sorghum price transmission, are not integrated into world markets, and that oil prices are a very determining...
Modeling of uncertain spectra through stochastic autoregressive systems
Wang, Yiwei; Wang, X. Q.; Mignolet, Marc P.; Yang, Shuchi; Chen, P. C.
2016-03-01
The focus of this investigation is on the formulation and validation of a modeling strategy of the uncertainty that may exist on the specification of the power spectral density of scalar stationary processes and on the spectral matrices of vector ones. These processes may, for example, be forces on a structure originating from natural phenomena which are coarsely modeled (i.e., with epistemic uncertainty) or are specified by parameters unknown (i.e., with aleatoric uncertainty) in the application considered. The propagation of the uncertainty, e.g., to the response of the structure, may be carried out provided that a stochastic model of the uncertainty in the power spectral density/matrix is available from which admissible samples can be efficiently generated. Such a stochastic model will be developed here through an autoregressive-based parameterization of the specified baseline power spectral density/matrix and of its random samples. Autoregressive (AR) models are particularly well suited for this parametrization since their spectra are known to converge to a broad class of spectra (all non-pathological spectra) as the AR order increases. Note that the characterization of these models is not achieved directly in terms of their coefficients but rather in terms of their reflection coefficients which lie (or their eigenvalues in the vector process case) in the domain [0,1) as a necessary and sufficient condition for stability. Maximum entropy concepts are then employed to formulate the distribution of the reflection coefficients in both scalar and vector process case leading to a small set of hyperparameters of the uncertain model. Depending on the information available, these hyperparameters could either be varied in a parametric study format to assess the effects of uncertainty or could be identified, e.g., in a maximum likelihood format, from observed data. The validation and assessment of these concepts is finally achieved on several examples including the
A complex autoregressive model and application to monthly temperature forecasts
Directory of Open Access Journals (Sweden)
X. Gu
2005-11-01
Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.
Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes
Directory of Open Access Journals (Sweden)
W. Wang
2005-01-01
Full Text Available Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average models for seasonal streamflow series. However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity effect, a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.
Sensor network based solar forecasting using a local vector autoregressive ridge framework
Energy Technology Data Exchange (ETDEWEB)
Xu, J. [Stony Brook Univ., NY (United States); Yoo, S. [Brookhaven National Lab. (BNL), Upton, NY (United States); Heiser, J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Kalb, P. [Brookhaven National Lab. (BNL), Upton, NY (United States)
2016-04-04
The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.
Identification and estimation of non-Gaussian structural vector autoregressions
DEFF Research Database (Denmark)
Lanne, Markku; Meitz, Mika; Saikkonen, Pentti
-Gaussian components is, without any additional restrictions, identified and leads to (essentially) unique impulse responses. We also introduce an identification scheme under which the maximum likelihood estimator of the non-Gaussian SVAR model is consistent and asymptotically normally distributed. As a consequence......, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions....
Testing the Conditional Mean Function of Autoregressive Conditional Duration Models
DEFF Research Database (Denmark)
Hautsch, Nikolaus
This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors. I...
Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity
Directory of Open Access Journals (Sweden)
Isao Ishida
2015-01-01
Full Text Available We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor’s 500 (S&P 500 and several other indices, we obtained good performance using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear heterogeneous autoregressive and other models of realized volatility.
DEFF Research Database (Denmark)
Holt, Matthew T.; Teräsvirta, Timo
This paper examines trends in annual temperature data for the northern and southern hemisphere (1850-2010) by using variants of the shifting-mean autoregressive (SM-AR) model of Gonzalez and Terasvirta (2008). Univariate models are first fitted to each series by using the so called Quick...
Global Hemispheric Temperatures and Co–Shifting: A Vector Shifting–Mean Autoregressive Analysis
DEFF Research Database (Denmark)
Holt, Matthew T.; Terasvirta, Timo
This paper examines local changes in annual temperature data for the northern and southern hemispheres (1850-2014) by using a multivariate generalisation of the shifting-mean autoregressive model of González and Teräsvirta (2008). Univariate models are first fitted to each series by using the Quick...
Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)
DEFF Research Database (Denmark)
Agosto, Arianna; Cavaliere, Guiseppe; Kristensen, Dennis
We develop a class of Poisson autoregressive models with additional covariates (PARX) that can be used to model and forecast time series of counts. We establish the time series properties of the models, including conditions for stationarity and existence of moments. These results are in turn used...
Integer Valued Autoregressive Models for Tipping Bucket Rainfall Measurements
DEFF Research Database (Denmark)
Thyregod, Peter; Carstensen, Niels Jacob; Madsen, Henrik
1999-01-01
A new method for modelling the dynamics of rain sampled by a tipping bucket rain gauge is proposed. The considered models belong to the class of integer valued autoregressive processes. The models take the autocorelation and discrete nature of the data into account. A first order, a second order...
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least squares...
A note on intrinsic Conditional Autoregressive models for disconnected graphs
Freni-Sterrantino, Anna
2017-05-13
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples on disease mapping.
Smooth transition autoregressive models - A survey of recent developments
D.J.C. van Dijk (Dick); T. Terasvirta; Ph.H.B.F. Franses (Philip Hans)
2000-01-01
textabstractThis paper surveys recent developments related to the smooth transition autoregressive [STAR] time series model and several of its variants. We put emphasis on new methods for testing for STAR nonlinearity, model evaluation, and forecasting. Several useful extensions of the basic STAR
Robust bayesian analysis of an autoregressive model with ...
African Journals Online (AJOL)
In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...
Nonparametric Kernel Testing in Semiparametric Autoregressive Conditional Duration Model
Pipat Wongsaart; Jiti Gao
2011-01-01
A crucially important advantage of the semiparametric regression approach to the nonlinear autoregressive conditional duration (ACD) model developed in Wongsaart et al. (2011), i.e. the so-called Semiparametric ACD (SEMI-ACD) model, is the fact that its estimation method does not require a parametric assumption on the conditional distribution of the standardized duration process and, therefore, the shape of the baseline hazard function. The research in this paper complements that of Wongsaart...
Nonlinear models for autoregressive conditional heteroskedasticity
DEFF Research Database (Denmark)
Teräsvirta, Timo
This paper contains a brief survey of nonlinear models of autore- gressive conditional heteroskedasticity. The models in question are parametric nonlinear extensions of the original model by Engle (1982). After presenting the individual models, linearity testing and parameter estimation are discu...
Bias-corrected estimation in potentially mildly explosive autoregressive models
DEFF Research Database (Denmark)
Haufmann, Hendrik; Kruse, Robinson
that the indirect inference approach oers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical...... application to a long annual US Debt/GDP series we consider rolling window estimation of autoregressive models. We find substantial evidence for time-varying persistence and periods of explosiveness during the Civil War and World War II. During the recent years, the series is nearly explosive again. Further......This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is studied...
Processing on weak electric signals by the autoregressive model
Ding, Jinli; Zhao, Jiayin; Wang, Lanzhou; Li, Qiao
2008-10-01
A model of the autoregressive model of weak electric signals in two plants was set up for the first time. The result of the AR model to forecast 10 values of the weak electric signals is well. It will construct a standard set of the AR model coefficient of the plant electric signal and the environmental factor, and can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on agricultural productions.
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Callot, Laurent
We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated consistently...
Changing Dynamics : Time-Varying Autoregressive Models Using Generalized Additive Modeling
Bringmann, L.F.; Hamaker, E.L.; Vigo, D.E.; Aubert, A.; Borsboom, D.; Tuerlinckx, F.
In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that
Ozone Concentration Prediction via Spatiotemporal Autoregressive Model With Exogenous Variables
Kamoun, W.; Senoussi, R.
2009-04-01
Forecast of environmental variables are nowadays of main concern for public health or agricultural management. In this context a large literature is devoted to spatio-temporal modelling of these variables using different statistical approaches. However, most of studies ignored the potential contribution of local (e.g. meteorological and/or geographical) covariables as well as the dynamical characteristics of observations. In this study, we present a spatiotemporal short term forecasting model for ozone concentration based on regularly observed covariables in predefined geographical sites. Our driving system simply combines a multidimensional second order autoregressive structured process with a linear regression model over influent exogenous factors and reads as follows: 2 q j Z (t) = A (Î&,cedil;D )Ã- [ αiZ(t- i)]+ B (Î&,cedil;D )Ã- [ βjX (t)]+ É(t) i=1 j=1 Z(t)=(Z1(t),â¦,Zn(t)) represents the vector of ozone concentration at time t of the n geographical sites, whereas Xj(t)=(X1j(t),â¦,Xnj(t)) denotes the jth exogenous variable observed over these sites. The nxn matrix functions A and B account for the spatial relationships between sites through the inter site distance matrix D and a vector parameter Î&.cedil; Multidimensional white noise É is assumed to be Gaussian and spatially correlated but temporally independent. A covariance structure of Z that takes account of noise spatial dependences is deduced under a stationary hypothesis and then included in the likelihood function. Statistical model and estimation procedure: Contrarily to the widely used choice of a {0,1}-valued neighbour matrix A, we put forward two more natural choices of exponential or power decay. Moreover, the model revealed enough stable to readily accommodate the crude observations without the usual tedious and somewhat arbitrarily variable transformations. Data set and preliminary analysis: In our case, ozone variable represents here the daily maximum ozone
A mixed autoregressive probit model for ordinal longitudinal data.
Varin, Cristiano; Czado, Claudia
2010-01-01
Longitudinal data with binary and ordinal outcomes routinely appear in medical applications. Existing methods are typically designed to deal with short measurement series. In contrast, modern longitudinal data can result in large numbers of subject-specific serial observations. In this framework, we consider multivariate probit models with random effects to capture heterogeneity and autoregressive terms for describing the serial dependence. Since likelihood inference for the proposed class of models is computationally burdensome because of high-dimensional intractable integrals, a pseudolikelihood approach is followed. The methodology is motivated by the analysis of a large longitudinal study on the determinants of migraine severity.
Directory of Open Access Journals (Sweden)
Emmanuel Owusu-Sekyere
2017-07-01
Full Text Available Background: This article adds to scarce sub-Saharan African and South African literature on monetary policy transmission mechanisms by looking into: (1 the Keynesian interest rate channel of monetary policy transmission in South Africa, focussing on the behaviour of household credit and household consumption; (2 using the time-varying parameter vector autoregressive (VAR techniques with stochastic volatility to capture the time-varying nature of the underlying structure of the South African economy to see whether it performs better than the constant parameter VAR in so doing and (3 policy implications emerging from the findings of the study. Aim: In testing the hypotheses of the interest rate channel of monetary policy transmission, the aim is to see how household credit and ultimately household consumption have evolved in South Africa: (1 before inflation targeting (1994–1999, (2 after inflation targeting (2000–2007 and (3 during the global financial crisis (2007–2012 in response to different monetary policy positioning. Setting: We focus on three periods: post transition from apartheid, during inflation targeting and during the global financial crisis, periods which saw changes in the monetary policy stance in South Africa. Methods: Quarterly data from 1994Q1 to 2012Q4, constant parameter VAR and time-varying parameter vector autoregressive (TVP-VAR techniques are used in this study. The use of the TVP-VAR is to capture the time-varying nature of the underlying structure of the South African economy and also to investigate whether it performs better than the constant parameter VAR in so doing. Results: The results show that household credit and consumption declined and stayed negative post transition and after inflation targeting – periods of monetary tightening in South Africa - but increased during the global financial crisis, which saw expansionary monetary policy measures aimed at mitigating the negative output gap in South
4K Video Traffic Prediction using Seasonal Autoregressive Modeling
Directory of Open Access Journals (Sweden)
D. R. Marković
2017-06-01
Full Text Available From the perspective of average viewer, high definition video streams such as HD (High Definition and UHD (Ultra HD are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding. Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.
Least squares estimation in a simple random coefficient autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Lange, Theis
2013-01-01
we prove the curious result that View the MathML source. The proof applies the notion of a tail index of sums of positive random variables with infinite variance to find the order of magnitude of View the MathML source and View the MathML source and hence the limit of View the MathML source......The question we discuss is whether a simple random coefficient autoregressive model with infinite variance can create the long swings, or persistence, which are observed in many macroeconomic variables. The model is defined by yt=stρyt−1+εt,t=1,…,n, where st is an i.i.d. binary variable with p...
Stator Fault Detection in Induction Motors by Autoregressive Modeling
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Francisco M. Garcia-Guevara
2016-01-01
Full Text Available This study introduces a novel methodology for early detection of stator short circuit faults in induction motors by using autoregressive (AR model. The proposed algorithm is based on instantaneous space phasor (ISP module of stator currents, which are mapped to α-β stator-fixed reference frame; then, the module is obtained, and the coefficients of the AR model for such module are estimated and evaluated by order selection criterion, which is used as fault signature. For comparative purposes, a spectral analysis of the ISP module by Discrete Fourier Transform (DFT is performed; a comparison of both methodologies is obtained. To demonstrate the suitability of the proposed methodology for detecting and quantifying incipient short circuit stator faults, an induction motor was altered to induce different-degree fault scenarios during experimentation.
Incorporating measurement error in n=1 psychological autoregressive modeling
Schuurman, Noemi K.; Houtveen, Jan H.; Hamaker, Ellen L.
2015-01-01
Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive
Seasonal autoregressive modeling of a skew storm surge series
Weiss, Jérôme; Bernardara, Pietro; Andreewsky, Marc; Benoit, Michel
Autoregressive (AR) models have been widely used in several geophysical applications, as they represent a simple and practical option for modeling stochastic series. In this paper, we show that AR models can be adapted and are useful for the description of skew surge (i.e., a surge occurring at the time of a high tide) series. Namely, seasonal AR models of skew surge series are built on 35 sites located along the coasts of the European Atlantic Ocean, the English Channel and the Southern part of the North Sea. These models are presented and discussed. The estimation of the distribution of the residuals, modeled using a Normal Inverse Gaussian (NIG) distribution, is also discussed. AR models are advantageous for a number of reasons: (i) they provide information on the correlation length of the surge phenomena, (ii) they can be used to forecast short-term surge occurrences based on a limited set of past observations and (iii) they provide plausible information about longer series, which may have larger extremes than what is observed, permitting a statistical description of simulated extremes. These three characteristics and benefits are examined and discussed for a selected site, the Saint-Nazaire harbor (France), with respect to the storm surge that occurred during the Xynthia storm of February 2010.
The comparison study among several data transformations in autoregressive modeling
Setiyowati, Susi; Waluyo, Ramdhani Try
2015-12-01
In finance, the adjusted close of stocks are used to observe the performance of a company. The extreme prices, which may increase or decrease drastically, are often become particular concerned since it can impact to bankruptcy. As preventing action, the investors have to observe the future (forecasting) stock prices comprehensively. For that purpose, time series analysis could be one of statistical methods that can be implemented, for both stationary and non-stationary processes. Since the variability process of stocks prices tend to large and also most of time the extreme values are always exist, then it is necessary to do data transformation so that the time series models, i.e. autoregressive model, could be applied appropriately. One of popular data transformation in finance is return model, in addition to ratio of logarithm and some others Tukey ladder transformation. In this paper these transformations are applied to AR stationary models and non-stationary ARCH and GARCH models through some simulations with varying parameters. As results, this work present the suggestion table that shows transformations behavior for some condition of parameters and models. It is confirmed that the better transformation is obtained, depends on type of data distributions. In other hands, the parameter conditions term give significant influence either.
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag
2009-01-01
In this article we consider geometric ergodicity and likelihood-based inference for linear and nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly to its past values, as well as to the observed values of the Poisson process. This also applies...... to the conditional variance, making possible interpretation as an integer-valued generalized autoregressive conditional heteroscedasticity process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and past observations. As a particular example, we consider...... an exponential autoregressive Poisson model for time series. Under geometric ergodicity, the maximum likelihood estimators are shown to be asymptotically Gaussian in the linear model. In addition, we provide a consistent estimator of their asymptotic covariance matrix. Our approach to verifying geometric...
Optimized order estimation for autoregressive models to predict respiratory motion.
Dürichen, Robert; Wissel, Tobias; Schweikard, Achim
2013-11-01
To successfully ablate moving tumors in robotic radio-surgery, it is necessary to compensate for motion of inner organs caused by respiration. This can be achieved by tracking the body surface and correlating the external movement with the tumor position as it is implemented in the CyberKnife[Formula: see text] Synchrony system. Tracking errors, originating from system immanent time delays, are typically reduced by time series prediction. Many prediction algorithms exploit autoregressive (AR) properties of the signal. Estimating the optimal model order [Formula: see text] for these algorithms constitutes a challenge often solved via grid search or prior knowledge about the signal. Aiming at a more efficient approach instead, this study evaluates the Akaike information criterion (AIC), the corrected AIC, and the Bayesian information criterion (BIC) on the first minute of the respiratory signal. Exemplarily, we evaluated the approach for a least mean square (LMS) and a wavelet-based LMS (wLMS) predictor. Analyzing 12 motion traces, orders estimated by AIC had the highest prediction accuracy for both prediction algorithms. Extending the investigations to 304 real motion traces, the prediction error of wLMS using AIC was found to decrease significantly by 85.1 % of the data compared to the original implementation The overall results suggest that using AIC to estimate the model order [Formula: see text] for prediction algorithms based on AR properties is a valid method which avoids intensive grid search and leads to high prediction accuracy.
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbæk, Anders; Tjøstheim, Dag
This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, making an interpretation as an integer valued GARCH process possible. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model...
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag
This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, implying an interpretation as an integer valued GARCH process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model for time...
Statistical analysis of the autoregressive modeling of reverberant speech.
Gaubitch, Nikolay D; Ward, Darren B; Naylor, Patrick A
2006-12-01
Hands-free speech input is required in many modern telecommunication applications that employ autoregressive (AR) techniques such as linear predictive coding. When the hands-free input is obtained in enclosed reverberant spaces such as typical office rooms, the speech signal is distorted by the room transfer function. This paper utilizes theoretical results from statistical room acoustics to analyze the AR modeling of speech under these reverberant conditions. Three cases are considered: (i) AR coefficients calculated from a single observation; (ii) AR coefficients calculated jointly from an M-channel observation (M > 1); and (iii) AR coefficients calculated from the output of a delay-and sum beamformer. The statistical analysis, with supporting simulations, shows that the spatial expectation of the AR coefficients for cases (i) and (ii) are approximately equal to those from the original speech, while for case (iii) there is a discrepancy due to spatial correlation between the microphones which can be significant. It is subsequently demonstrated that at each individual source-microphone position (without spatial expectation), the M-channel AR coefficients from case (ii) provide the best approximation to the clean speech coefficients when microphones are closely spaced (<0.3m).
Multilevel Autoregressive Mediation Models: Specification, Estimation, and Applications.
Zhang, Qian; Wang, Lijuan; Bergeman, C S
2017-11-27
In the current study, extending from the cross-lagged panel models (CLPMs) in Cole and Maxwell (2003), we proposed the multilevel autoregressive mediation models (MAMMs) by allowing the coefficients to differ across individuals. In addition, Level-2 covariates can be included to explain the interindividual differences of mediation effects. Given the complexity of the proposed models, Bayesian estimation was used. Both a CLPM and an unconditional MAMM were fitted to daily diary data. The 2 models yielded different statistical conclusions regarding the average mediation effect. A simulation study was conducted to examine the estimation accuracy of Bayesian estimation for MAMMs and consequences of model mis-specifications. Factors considered included the sample size (N), number of time points (T), fixed indirect and direct effect sizes, and Level-2 variances and covariances. Results indicated that the fixed effect estimates for the indirect effect components (a and b) and the fixed effects of Level-2 covariates were accurate when N ≥ 50 and T ≥ 5. For estimating Level-2 variances and covariances, they were accurate provided a sufficiently large N and T (e.g., N ≥ 500 and T ≥ 50). Estimates of the average mediation effect were generally accurate when N ≥ 100 and T ≥ 10, or N ≥ 50 and T ≥ 20. Furthermore, we found that when Level-2 variances were zero, MAMMs yielded valid inferences about the fixed effects, whereas when random effects existed, CLPMs had low coverage rates for fixed effects. DIC can be used for model selection. Limitations and future directions were discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Directory of Open Access Journals (Sweden)
Mohammad Ghahremanzadeh
2014-06-01
Full Text Available Agriculture as one of the major economic sectors of Iran, has an important role in Gross Domestic Production by providing about 14% of GDP. This study attempts to forecast the value of the agriculture GDP using Periodic Autoregressive model (PAR, as the new seasonal time series techniques. To address this aim, the quarterly data were collected from March 1988 to July 1989. The collected data was firstly analyzed using periodic unit root test Franses & Paap (2004. The analysis found non-periodic unit root in the seasonal data. Second, periodic seasonal behavior (Boswijk & Franses, 1996 was examined. The results showed that periodic autoregressive model fits agriculture GDP well. This makes an accurate forecast of agriculture GDP possible. Using the estimated model, the future value of quarter agricultural GDP from March 2011 to July 2012was forecasted. With consideration to the fair fit of this model with agricultural GDP, It is recommended to use periodic autoregressive model for the future studies.
A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory
DEFF Research Database (Denmark)
Nonejad, Nima
We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte Ca...
Multivariate Autoregressive Model Based Heart Motion Prediction Approach for Beating Heart Surgery
Directory of Open Access Journals (Sweden)
Fan Liang
2013-02-01
Full Text Available A robotic tool can enable a surgeon to conduct off-pump coronary artery graft bypass surgery on a beating heart. The robotic tool actively alleviates the relative motion between the point of interest (POI on the heart surface and the surgical tool and allows the surgeon to operate as if the heart were stationary. Since the beating heart's motion is relatively high-band, with nonlinear and nonstationary characteristics, it is difficult to follow. Thus, precise beating heart motion prediction is necessary for the tracking control procedure during the surgery. In the research presented here, we first observe that Electrocardiography (ECG signal contains the causal phase information on heart motion and non-stationary heart rate dynamic variations. Then, we investigate the relationship between ECG signal and beating heart motion using Granger Causality Analysis, which describes the feasibility of the improved prediction of heart motion. Next, we propose a nonlinear time-varying multivariate vector autoregressive (MVAR model based adaptive prediction method. In this model, the significant correlation between ECG and heart motion enables the improvement of the prediction of sharp changes in heart motion and the approximation of the motion with sufficient detail. Dual Kalman Filters (DKF estimate the states and parameters of the model, respectively. Last, we evaluate the proposed algorithm through comparative experiments using the two sets of collected vivo data.
To center or not to center? Investigating inertia with a multilevel autoregressive model
Directory of Open Access Journals (Sweden)
Ellen L. Hamaker
2015-01-01
Full Text Available Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion, cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship. This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction, cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.
Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation
De Haan-Rietdijk, Silvia; Gottman, John M.; Bergeman, Cindy S.; Hamaker, Ellen L.
2014-01-01
Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of
de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M
1998-01-01
The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a
Bias-correction in vector autoregressive models: A simulation study
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
investigate the properties of an iterative scheme when applying the analytical bias formula, and we …find that this can imply slightly better fi…nite-sample properties for very small sample sizes while for larger sample sizes there is no gain by iterating. Finally, we also pay special attention to the risk...
Some identification problems in the cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren
An analysis of some identification problems in the cointegrated VAR is given. We give a new criteria for identification by linear restrictions on indi- vidual relations which is equivalent to the rank condition. We compare the asymptotic distribution of the estimators of α and β; when they are id......An analysis of some identification problems in the cointegrated VAR is given. We give a new criteria for identification by linear restrictions on indi- vidual relations which is equivalent to the rank condition. We compare the asymptotic distribution of the estimators of α and β; when...... they are identified by linear restrictions on β and when they are identified by linear restrictions on α; in which case a component of β is asymptotically Gaussian. Finally we discuss identification of shocks by introducing the contemporaneous and permanent e¤ect of a shock and the distinction between permanent...... and transi- tory shocks, which allows one to identify permanent shocks from the long-run variance and transitory shocks from the short-run variance....
Some Identification Problems in the Cointegrated Vector Autoregressive Model
DEFF Research Database (Denmark)
Johansen, Søren
An analysis of some identification problems in the cointegrated VAR is given. We give a new criteria for identification by linear restrictions on individual relations which is equivalent to the rank condition. We compare the asymptotic distribution of the estimators of a and ß; when they are iden......An analysis of some identification problems in the cointegrated VAR is given. We give a new criteria for identification by linear restrictions on individual relations which is equivalent to the rank condition. We compare the asymptotic distribution of the estimators of a and ß; when...... they are identified by linear restrictions on ß; and when they are identified by linear restrictions on a; in which case a component of ß^ is asymptotically Gaussian. Finally we discuss identification of shocks by introducing the contemporaneous and permanent effect of a shock and the distinction between permanent...... and transitory shocks, which allows one to identify permanent shocks from the long-run variance and transitory shocks from the short-run variance...
Search for periodicities in experimental data using an autoregression data model
Belashev, B Z
2001-01-01
To process data obtained during interference experiments in high-energy physics, methods of spectral analysis are employed. Methods of spectral analysis, in which an autoregression model of experimental data is used, such as the maximum entropy technique as well as Pisarenko and Prony's method, are described. To show the potentials of the methods, experimental and simulated hummed data are discussed as an example.
DEFF Research Database (Denmark)
Becciolini, Diego; Franzosi, Diogo Buarque; Foadi, Roshan
2015-01-01
We analyze the Large Hadron Collider (LHC) phenomenology of heavy vector resonances with a $SU(2)_L\\times SU(2)_R$ spectral global symmetry. This symmetry partially protects the electroweak S-parameter from large contributions of the vector resonances. The resulting custodial vector model spectrum...
Modelling malaria incidence by an autoregressive distributed lag model with spatial component.
Laguna, Francisco; Grillet, María Eugenia; León, José R; Ludeña, Carenne
2017-08-01
The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela. First, we fit an autoregressive distributed lag model (ARDL) to the weekly data, and then, we adjust a linear separable spacial vectorial autoregressive model (VAR) to the residuals of the ARDL. Finally, the model parameters are tuned using a Markov Chain Monte Carlo (MCMC) procedure derived from the Metropolis-Hastings algorithm. Our results show that the best model to account for the variations of malaria incidence from 2001 to 2008 in 10 endemic Municipalities in North-Eastern Venezuela is a logit model that included the accumulated local precipitation in combination with the local maximum temperature of the preceding month as positive regressors. Additionally, we show that although malaria dynamics is highly heterogeneous in space, a detailed analysis of the estimated spatial parameters in our model yield important insights regarding the joint behavior of the disease incidence across the different counties in our study. Copyright © 2017 Elsevier Ltd. All rights reserved.
Lee, Duncan; Rushworth, Alastair; Sahu, Sujit K
2014-06-01
Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models. © 2014, The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.
Image restoration using 2D autoregressive texture model and structure curve construction
Voronin, V. V.; Marchuk, V. I.; Petrosov, S. P.; Svirin, I.; Agaian, S.; Egiazarian, K.
2015-05-01
In this paper an image inpainting approach based on the construction of a composite curve for the restoration of the edges of objects in an image using the concepts of parametric and geometric continuity is presented. It is shown that this approach allows to restore the curved edges and provide more flexibility for curve design in damaged image by interpolating the boundaries of objects by cubic splines. After edge restoration stage, a texture restoration using 2D autoregressive texture model is carried out. The image intensity is locally modeled by a first spatial autoregressive model with support in a strongly causal prediction region on the plane. Model parameters are estimated by Yule-Walker method. Several examples considered in this paper show the effectiveness of the proposed approach for large objects removal as well as recovery of small regions on several test images.
Directory of Open Access Journals (Sweden)
Fei Jin
2013-05-01
Full Text Available This paper studies the generalized spatial two stage least squares (GS2SLS estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the case that the number of instruments K increases with, but at a rate slower than, the sample size, and derive the approximate mean square errors (MSE that account for the trade-offs between the bias and variance, for both the GS2SLS estimator and a bias-corrected GS2SLS estimator. A criterion function for the optimal K selection can be based on the approximate MSEs. Monte Carlo experiments are provided to show the performance of our procedure of choosing K.
Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression
DEFF Research Database (Denmark)
Bork, Lasse
Economy-wide e¤ects of shocks to the US federal funds rate are estimated in a state space model with 120 US macroeconomic and financial time series driven by the dynamics of the federal funds rate and a few dynamic factors. This state space system is denoted a factor-augmented VAR (FAVAR) by Bern......Economy-wide e¤ects of shocks to the US federal funds rate are estimated in a state space model with 120 US macroeconomic and financial time series driven by the dynamics of the federal funds rate and a few dynamic factors. This state space system is denoted a factor-augmented VAR (FAVAR...... and the dynamic factors simultaneously and allows for classical inference. I demonstrate empirically that the same impulse responses but better fit emerge robustly from a low order FAVAR with eight correlated factors compared to a high order FAVAR with fewer correlated factors, for instance four factors...
Oil Price Volatility and Economic Growth in Nigeria: a Vector Auto-Regression (VAR Approach
Directory of Open Access Journals (Sweden)
Edesiri Godsday Okoro
2014-02-01
Full Text Available The study examined oil price volatility and economic growth in Nigeria linking oil price volatility, crude oil prices, oil revenue and Gross Domestic Product. Using quarterly data sourced from the Central Bank of Nigeria (CBN Statistical Bulletin and World Bank Indicators (various issues spanning 1980-2010, a non‐linear model of oil price volatility and economic growth was estimated using the VAR technique. The study revealed that oil price volatility has significantly influenced the level of economic growth in Nigeria although; the result additionally indicated a negative relationship between the oil price volatility and the level of economic growth. Furthermore, the result also showed that the Nigerian economy survived on crude oil, to such extent that the country‘s budget is tied to particular price of crude oil. This is not a good sign for a developing economy, more so that the country relies almost entirely on revenue of the oil sector as a source of foreign exchange earnings. This therefore portends some dangers for the economic survival of Nigeria. It was recommended amongst others that there should be a strong need for policy makers to focus on policy that will strengthen/stabilize the economy with specific focus on alternative sources of government revenue. Finally, there should be reduction in monetization of crude oil receipts (fiscal discipline, aggressive saving of proceeds from oil booms in future in order to withstand vicissitudes of oil price volatility in future.
Directory of Open Access Journals (Sweden)
Hongze Li
2014-01-01
Full Text Available Short-term power load forecasting is one of the most important issues in the economic and reliable operation of electricity power system. Taking the characteristics of randomness, tendency, and periodicity of short-term power load into account, a new method (SSA-AR model which combines the univariate singular spectrum analysis and autoregressive model is proposed. Firstly, the singular spectrum analysis (SSA is employed to decompose and reconstruct the original power load series. Secondly, the autoregressive (AR model is used to forecast based on the reconstructed power load series. The employed data is the hourly power load series of the Mid-Atlantic region in PJM electricity market. Empirical analysis result shows that, compared with the single autoregressive model (AR, SSA-based linear recurrent method (SSA-LRF, and BPNN (backpropagation neural network model, the proposed SSA-AR method has a better performance in terms of short-term power load forecasting.
Tesfaye, Yonas Gebeyehu; Meerschaert, Mark M.; Anderson, Paul L.
2006-01-01
The generation of synthetic river flow samples that can reproduce the essential statistical features of historical river flows is useful for the planning, design, and operation of water resource systems. Most river flow series are periodically stationary; that is, their mean and covariance functions are periodic with respect to time. This article develops model identification and simulation techniques based on a periodic autoregressive moving average (PARMA) model to capture the seasonal variations in river flow statistics. The innovations algorithm is used to obtain parameter estimates. An application to monthly flow data for the Fraser River in British Columbia is included. A careful statistical analysis of the PARMA model residuals, including a truncated Pareto model for the extreme tails, produces a realistic simulation of these river flows.
An application of the Autoregressive Conditional Poisson (ACP) model
CSIR Research Space (South Africa)
Holloway, Jennifer P
2010-11-01
Full Text Available www.csir.co.za Results: ACP & DACP vs Poisson ? Cholera cases modelled using ? Seasonal variables and lag 6 air temperature Parameters ACP DACP Poisson 0.0438 0.0530 0.0214 0.0247 0.2291 0.2628 0.0951 Intercept -0.8624 Lag6 temp 0.1368 0...
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
2012-01-01
optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence......Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour...... and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....
Generalized space time autoregressive with exogenous variable model and its application
Astuti, Dewi; Nurani Ruchjana, Budi; Soemartini
2017-10-01
In this paper we proposed the Generalized Space Time Autoregressive with variable Exogenous, abbreviated GSTARX as GSTAR development with the addition of exogenous variables. GSTARX not only involves the element of time and location, but also the influence of exogenous variables in the model. GSTARX equation can be written as a linear model, so we can estimate parameters of GSTARX model using Ordinary Least Squares (OLS) method. For our case study, we use GSTARX model with uniform and inverse distance weights to predict an export volume of Crude Palm Oil (CPO) in several locations on the island of Sumatera, where X is the international CPO prices.
New GPS-Aided SINU System Modeling using an Autoregressive Model
Directory of Open Access Journals (Sweden)
Chot Hun Lim
2015-09-01
Full Text Available Stochastic error in the Micro-Electro-Mechanical-System (MEMS Strapdown Inertial Navigation Unit (SINU is the primary issue causing sensors to be unable to operate as a standalone device. Conventional implementation of MEMS SINU fuses measurement with a global positioning system (GPS through a Kalman filter in order to achieve long-term accuracy. Such integration is known as a GPS-aided SINU system, and its estimation accuracy relies on how precise the stochastic error prediction is in Kalman filtering operation. In this paper, a comprehensive study on stochastic error modeling and analysis through a Gauss-Markov (GM model and autoregressive (AR model are presented. A wavelet denoising technique is introduced prior to error modeling to remove the MEMS SINU's high frequency noise. Without a wavelet denoising technique, neither the GM model nor AR model can be utilized to represent the stochastic error of SINU. Next, details of the Kalman filter implementation to accommodate the AR model are presented. The modeling outcomes are implemented on an unmanned aerial vehicle (UAV for on-board motion sensing. The experimental results show that AR model implementation, compared to a conventional GM model, significantly reduced the estimated errors while preserving the position, velocity and orientation measurements.
Self-organising mixture autoregressive model for non-stationary time series modelling.
Ni, He; Yin, Hujun
2008-12-01
Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.
Directory of Open Access Journals (Sweden)
Oksana V. Mandrikova
2015-11-01
Full Text Available The paper is devoted to new mathematical tools for ionospheric parameter analysis and anomaly discovery during ionospheric perturbations. The complex structure of processes under study, their a-priori uncertainty and therefore the complex structure of registered data require a set of techniques and technologies to perform mathematical modelling, data analysis, and to make final interpretations. We suggest a technique of ionospheric parameter modelling and analysis based on combining the wavelet transform with autoregressive integrated moving average models (ARIMA models. This technique makes it possible to study ionospheric parameter changes in the time domain, make predictions about variations, and discover anomalies caused by high solar activity and lithospheric processes prior to and during strong earthquakes. The technique was tested on critical frequency foF2 and total electron content (TEC datasets from Kamchatka (a region in the Russian Far East and Magadan (a town in the Russian Far East. The mathematical models introduced in the paper facilitated ionospheric dynamic mode analysis and proved to be efficient for making predictions with time advance equal to 5 hours. Ionospheric anomalies were found using model error estimates, those anomalies arising during increased solar activity and strong earthquakes in Kamchatka.
Application of Motion Correction using 3D Autoregressive Model in Kinect-based Telemedicine
Directory of Open Access Journals (Sweden)
Kim Baek Seob
2017-01-01
Full Text Available In telemedicine, where the convergence of different types of medical treatment occurs, it is very important to establish credibility regarding the mutual communication between patients and medical workers by acquiring and sharing more accurate data. For rehabilitation treatment in particular, where motion data are required, auxiliary equipment such as a Kinect sensor is being more widely used. This study proposes a methodology for improving the motion recognition rate by compensating the noise from a Kinect sensor using a 3D autoregressive model. Moreover, this study investigates the methods applied for vitalizing the area of telemedicine under this particular trend.
Nelson, B K
1998-07-01
Most methods of defining a statistical relationship between variables require that errors in prediction not be correlated. That is, knowledge of the error in one instance should not give information about the likely error in the next measurement. Real data frequently fail this requirement. If a Durbin-Watson statistic reveals that there is autocorrelation of sequential data points, analysis of variance and regression results will be invalid and possibly misleading. Such data sets may be analyzed by time series methodologies such as autoregressive integrated moving average (ARIMA) modeling. This method is demonstrated by an example from a public policy intervention.
Directory of Open Access Journals (Sweden)
Rahul Tripathi
2014-01-01
Full Text Available Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA models and was compared with the forecasted all Indian data. The autoregressive (p and moving average (q parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF and autocorrelation function (ACF of the different time series. ARIMA (2, 1, 0 model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1 was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC and Schwarz-Bayesian information criteria (SBC. The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE, which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.
Riedl, M.; Suhrbier, A.; Malberg, H.; Penzel, T.; Bretthauer, G.; Kurths, J.; Wessel, N.
2008-07-01
The parameters of heart rate variability and blood pressure variability have proved to be useful analytical tools in cardiovascular physics and medicine. Model-based analysis of these variabilities additionally leads to new prognostic information about mechanisms behind regulations in the cardiovascular system. In this paper, we analyze the complex interaction between heart rate, systolic blood pressure, and respiration by nonparametric fitted nonlinear additive autoregressive models with external inputs. Therefore, we consider measurements of healthy persons and patients suffering from obstructive sleep apnea syndrome (OSAS), with and without hypertension. It is shown that the proposed nonlinear models are capable of describing short-term fluctuations in heart rate as well as systolic blood pressure significantly better than similar linear ones, which confirms the assumption of nonlinear controlled heart rate and blood pressure. Furthermore, the comparison of the nonlinear and linear approaches reveals that the heart rate and blood pressure variability in healthy subjects is caused by a higher level of noise as well as nonlinearity than in patients suffering from OSAS. The residue analysis points at a further source of heart rate and blood pressure variability in healthy subjects, in addition to heart rate, systolic blood pressure, and respiration. Comparison of the nonlinear models within and among the different groups of subjects suggests the ability to discriminate the cohorts that could lead to a stratification of hypertension risk in OSAS patients.
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
Directory of Open Access Journals (Sweden)
Lida Barba
2016-01-01
Full Text Available The traffic accidents occurrence urges the intervention of researchers and society; the human losses and material damage could be abated with scientific studies focused on supporting prevention plans. In this paper prediction strategies based on singular values and autoregressive models are evaluated for multistep ahead traffic accidents forecasting. Three time series of injured people in traffic accidents collected in Santiago de Chile from 2000:1 to 2014:12 were used, which were previously classified by causes related to the behavior of drivers, passengers, or pedestrians and causes not related to the behavior as road deficiencies, mechanical failures, and undetermined causes. A simplified form of Singular Spectrum Analysis (SSA, combined with the autoregressive linear (AR method, and a conventional Artificial Neural Network (ANN are proposed. Additionally, equivalent models that combine Hankel Singular Value Decomposition (HSVD, AR, and ANN are evaluated. The comparative analysis shows that the hybrid models SSA-AR and SSA-ANN reach the highest accuracy with an average MAPE of 1.5% and 1.9%, respectively, from 1- to 14-step ahead prediction. However, it was discovered that HSVD-AR shows a higher accuracy in the farthest horizons, from 12- to 14-step ahead prediction, which reaches an average MAPE of 2.2%.
Resistance in a non-linear autoregressive model of pulmonary mechanics
Directory of Open Access Journals (Sweden)
Langdon Ruby
2016-09-01
Full Text Available Respiratory system modelling can enable patient-specific mechanical ventilator settings to be found, and can thus reduce the incidence of ventilator induced lung injury in the intensive care unit. The resistance of a simple first order model (FOM of pulmonary mechanics was compared with a flow dependent term of a non-linear autoregressive (NARX model. Model parameters were identified for consecutive non-overlapping windows of length 20 breaths. The analysis was performed over recruitment manoeuvres for 25 sedated mechanically ventilated patients. The NARX model term, b1, consistently decreased as positive end expiratory pressure (PEEP increased, while the FOM resistance behaviour varied. Overall the NARX b1 behaviour is more in-line with expected trends in airway resistance as PEEP increases. This work has further verified the physiologically descriptive capability of the NARX model.
Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach
Hamid, Mohd Fahmi Abdul; Shabri, Ani
2017-05-01
Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.
Schliep, E. M.; Gelfand, A. E.; Holland, D. M.
2015-12-01
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.
Dhussa, Anil K; Sambi, Surinder S; Kumar, Shashi; Kumar, Sandeep; Kumar, Surendra
2014-10-01
In waste-to-energy plants, there is every likelihood of variations in the quantity and characteristics of the feed. Although intermediate storage tanks are used, but many times these are of inadequate capacity to dampen the variations. In such situations an anaerobic digester treating waste slurry operates under dynamic conditions. In this work a special type of dynamic Artificial Neural Network model, called Nonlinear Autoregressive Exogenous model, is used to model the dynamics of anaerobic digesters by using about one year data collected on the operating digesters. The developed model consists of two hidden layers each having 10 neurons, and uses 18days delay. There are five neurons in input layer and one neuron in output layer for a day. Model predictions of biogas production rate are close to plant performance within ±8% deviation. Copyright © 2014 Elsevier Ltd. All rights reserved.
Wu, Yupeng; Jiao, Baohua; Wu, Zhendong; Zhen, Junli; Jia, Qingzhong; Zhang, Hailin; Guan, Bingcai; Wang, Shuai
2015-11-01
Post-traumatic epilepsy (PTE) is a common consequence of traumatic brain injury (TBI) and significant predictor of poor prognosis in TBI patients. To develop clinical interventions for PTE risk reduction, there is a need to elucidate the epileptogenic mechanisms induced by brain injury. The iron-induced rat model of epilepsy used here mimics many aspects of human PTE. Intracortical injection of iron results in local neuronal damage and the establishment of an epileptic focus, leading to chronic spontaneous electroencephalographic (EEG) signals and motor seizures, with progressively increasing frequency over many months. Identifying unique aspects of PTE seizure semiology for prognosis and treatment may be aided by novel methods of EEG analysis. Here, autoregressive (AR) methods were compared to the conventional fast Fourier transform (FFT) for processing EEG signals in iron-induced epilepsy. Power spectra obtained using AR showed higher frequency resolution over a given epoch than the spectra obtained using FFT. Moreover, changes in total AR spectral power and frequency distribution over brief successive periods provided convenient indexes for long-term monitoring of seizures. Autoregression analysis may prove complementary to FFT for EEG analysis in PTE patients.
Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model
Directory of Open Access Journals (Sweden)
Ahsan Shazaib
2017-01-01
Full Text Available Gas turbine (GT engines are known for their high availability and reliability and are extensively used for power generation, marine and aero-applications. Maintenance of such complex machines should be done proactively to reduce cost and sustain high availability of the GT. The aim of this paper is to explore the use of autoregressive (AR models to predict remaining useful life (RUL of a GT engine. The Turbofan Engine data from NASA benchmark data repository is used as case study. The parametric investigation is performed to check on any effect of changing model parameter on modelling accuracy. Results shows that a single sensory data cannot accurately predict RUL of GT and further research need to be carried out by incorporating multi-sensory data. Furthermore, the predictions made using AR model seems to give highly pessimistic values for RUL of GT.
Ambrosini, Roberto; Borgoni, Riccardo; Rubolini, Diego; Sicurella, Beatrice; Fiedler, Wolfgang; Bairlein, Franz; Baillie, Stephen R.; Robinson, Robert A.; Clark, Jacquie A.; Spina, Fernando; Saino, Nicola
2014-01-01
Migration is a fundamental stage in the life history of several taxa, including birds, and is under strong selective pressure. At present, the only data that may allow for both an assessment of patterns of bird migration and for retrospective analyses of changes in migration timing are the databases of ring recoveries. We used ring recoveries of the Barn Swallow Hirundo rustica collected from 1908–2008 in Europe to model the calendar date at which a given proportion of birds is expected to have reached a given geographical area (‘progression of migration’) and to investigate the change in timing of migration over the same areas between three time periods (1908–1969, 1970–1990, 1991–2008). The analyses were conducted using binomial conditional autoregressive (CAR) mixed models. We first concentrated on data from the British Isles and then expanded the models to western Europe and north Africa. We produced maps of the progression of migration that disclosed local patterns of migration consistent with those obtained from the analyses of the movements of ringed individuals. Timing of migration estimated from our model is consistent with data on migration phenology of the Barn Swallow available in the literature, but in some cases it is later than that estimated by data collected at ringing stations, which, however, may not be representative of migration phenology over large geographical areas. The comparison of median migration date estimated over the same geographical area among time periods showed no significant advancement of spring migration over the whole of Europe, but a significant advancement of autumn migration in southern Europe. Our modelling approach can be generalized to any records of ringing date and locality of individuals including those which have not been recovered subsequently, as well as to geo-referenced databases of sightings of migratory individuals. PMID:25047331
Ambrosini, Roberto; Borgoni, Riccardo; Rubolini, Diego; Sicurella, Beatrice; Fiedler, Wolfgang; Bairlein, Franz; Baillie, Stephen R; Robinson, Robert A; Clark, Jacquie A; Spina, Fernando; Saino, Nicola
2014-01-01
Migration is a fundamental stage in the life history of several taxa, including birds, and is under strong selective pressure. At present, the only data that may allow for both an assessment of patterns of bird migration and for retrospective analyses of changes in migration timing are the databases of ring recoveries. We used ring recoveries of the Barn Swallow Hirundo rustica collected from 1908-2008 in Europe to model the calendar date at which a given proportion of birds is expected to have reached a given geographical area ('progression of migration') and to investigate the change in timing of migration over the same areas between three time periods (1908-1969, 1970-1990, 1991-2008). The analyses were conducted using binomial conditional autoregressive (CAR) mixed models. We first concentrated on data from the British Isles and then expanded the models to western Europe and north Africa. We produced maps of the progression of migration that disclosed local patterns of migration consistent with those obtained from the analyses of the movements of ringed individuals. Timing of migration estimated from our model is consistent with data on migration phenology of the Barn Swallow available in the literature, but in some cases it is later than that estimated by data collected at ringing stations, which, however, may not be representative of migration phenology over large geographical areas. The comparison of median migration date estimated over the same geographical area among time periods showed no significant advancement of spring migration over the whole of Europe, but a significant advancement of autumn migration in southern Europe. Our modelling approach can be generalized to any records of ringing date and locality of individuals including those which have not been recovered subsequently, as well as to geo-referenced databases of sightings of migratory individuals.
Autoregressive hidden Markov models for the early detection of neonatal sepsis.
Stanculescu, Ioan; Williams, Christopher K I; Freer, Yvonne
2014-09-01
Late onset neonatal sepsis is one of the major clinical concerns when premature babies receive intensive care. Current practice relies on slow laboratory testing of blood cultures for diagnosis. A valuable research question is whether sepsis can be reliably detected before the blood sample is taken. This paper investigates the extent to which physiological events observed in the patient's monitoring traces could be used for the early detection of neonatal sepsis. We model the distribution of these events with an autoregressive hidden Markov model (AR-HMM). Both learning and inference carefully use domain knowledge to extract the baby's true physiology from the monitoring data. Our model can produce real-time predictions about the onset of the infection and also handles missing data. We evaluate the effectiveness of the AR-HMM for sepsis detection on a dataset collected from the Neonatal Intensive Care Unit at the Royal Infirmary of Edinburgh.
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate......In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...
Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
Directory of Open Access Journals (Sweden)
Langdon Ruby
2016-09-01
Full Text Available For patients with acute respiratory distress syndrome (ARDS, the use of mathematical models to determine patient-specific ventilator settings can reduce ventilator induced lung injury and improve patient outcomes. A non-linear autoregressive model of pulmonary mechanics was used to identify inspiratory and expiratory pressure-dependent elastance (Ei and Ee as independent variables. The analysis was implemented on 19 data sets of recruitment manoeuvres (RMs that were performed on 10 mechanically ventilated patients. At pressures p = 15–20 cmH2O the agreement between Ei and Ee was low. However, Ei was a well-matched predictor of Ee for p = 25–40 cmH2O, with R2 ≥ 0.78, and there was no significant bias in the difference between Ei and Ee. Since many other models cannot uniquely identify Ei and Ee, the outcome may provide further insight into the characteristics of ARDS lungs in sedated patients.
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, J.; Engsted, T.
1999-07-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)
Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar
2017-02-15
Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. The proposed architecture leads to reliable estimates of EC than the existing latent models. This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process. Copyright © 2016 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Eleftherios Giovanis
2014-12-01
Full Text Available The current study examines the turn of the month effect on stock returns in 20 countries. This will allow us to explore whether the seasonal patterns usually found in global data; America, Australia, Europe and Asia. Ordinary Least Squares (OLS is problematic as it leads to unreliable estimations; because of the autocorrelation and Autoregressive Conditional Heteroskedasticity (ARCH effects existence. For this reason Generalized GARCH models are estimated. Two approaches are followed. The first is the symmetric Generalized ARCH (1,1 model. However, previous studies found that volatility tends to increase more when the stock market index decreases than when the stock market index increases by the same amount. In addition there is higher seasonality in volatility rather on average returns. For this reason the Periodic-GARCH (1,1 is estimated. The findings support the persistence of the specific calendar effect in 19 out of 20 countries examined.
Seifert, Michael; Abou-El-Ardat, Khalil; Friedrich, Betty; Klink, Barbara; Deutsch, Andreas
2014-01-01
Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression
Multivariate Exponential Autoregressive and Autoregressive Moving ...
African Journals Online (AJOL)
Autoregressive (AR) and autoregressive moving average (ARMA) processes with multivariate exponential (ME) distribution are presented and discussed. The theory of positive dependence is used to show that in many cases, multivariate exponential autoregressive (MEAR) and multivariate autoregressive moving average ...
DEFF Research Database (Denmark)
Thomsen, C E; Rosenfalck, A; Nørregaard Christensen, K
1991-01-01
The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG....... The method applied autoregressive modelling of the signal, segmented in 2 s fixed intervals. The features from the EEG segments were used for learning and for classification. The learning process was unsupervised and hierarchical clustering analysis was used to construct a learning set of EEG amplitude......-frequency patterns for each of the three anaesthetic drugs. These EEG patterns were assigned to a colour code corresponding to similar clinical states. A common learning set could be used for all patients anaesthetized with the same drug. The classification process could be performed on-line and the results were...
Statistical classification of gaussian spatial data generated by conditional autoregressive model
Directory of Open Access Journals (Sweden)
Kęstutis Dučinskas
2013-09-01
Full Text Available Given training sample, the problem of classifying Gaussian spatial data into one of two populations specified by conditional autoregressive model (CAR with different mean functions is considered. This paper concerns with classification procedures associated with Bayes Discriminant Function (BDF under deterministic spatial sampling design. In the case of complete parametric certainty, the overall misclassification probability associated with aforementioned BDF is derived. This is the extension of the previous one to the CAR case. Spatial weights based on inverse of Euclidean distance and the second and third order neighbourhood schemes on regular 2-dimensional lattice are used for illustrative examples. The effect of the spatial sampling design, Mahalanobis distances and prior probabilities on the performance of proposed classification procedure is numerically evaluated.
Khan, A M; Lee, Y K; Kim, T S
2008-01-01
Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.
Generalizing smooth transition autoregressions
DEFF Research Database (Denmark)
Chini, Emilio Zanetti
We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail...
Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.
2016-01-01
The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…
DEFF Research Database (Denmark)
Li, Chunjian; Andersen, Søren Vang
2007-01-01
We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussian...... outputs. The signal models are general and suitable to numerous important signals, such as speech signals and base-band communication signals. Applications to speech analysis and blind channel equalization are given to exemplify the efficiency of the new methods....
Directory of Open Access Journals (Sweden)
Jaime Buitrago
2017-01-01
Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.
DEFF Research Database (Denmark)
Kock, Anders Bredahl
2016-01-01
We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...
Directory of Open Access Journals (Sweden)
Lorenzo G. Tanadini
2016-11-01
Full Text Available Abstract Background A number of potential therapeutic approaches for neurological disorders have failed to provide convincing evidence of efficacy, prompting pharmaceutical and health companies to discontinue their involvement in drug development. Limitations in the statistical analysis of complex endpoints have very likely had a negative impact on the translational process. Methods We propose a transitional ordinal model with an autoregressive component to overcome previous limitations in the analysis of Upper Extremity Motor Scores, a relevant endpoint in the field of Spinal Cord Injury. Statistical power and clinical interpretation of estimated treatment effects of the proposed model were compared to routinely employed approaches in a large simulation study of two-arm randomized clinical trials. A revisitation of a key historical trial provides further comparison between the different analysis approaches. Results The proposed model outperformed all other approaches in virtually all simulation settings, achieving on average 14 % higher statistical power than the respective second-best performing approach (range: -1 %, +34 %. Only the transitional model allows treatment effect estimates to be interpreted as conditional odds ratios, providing clear interpretation and visualization. Conclusion The proposed model takes into account the complex ordinal nature of the endpoint under investigation and explicitly accounts for relevant prognostic factors such as lesion level and baseline information. Superior statistical power, combined with clear clinical interpretation of estimated treatment effects and widespread availability in commercial software, are strong arguments for clinicians and trial scientists to adopt, and further extend, the proposed approach.
Santosa, H.; Hobara, Y.
2017-01-01
The electric field amplitude of very low frequency (VLF) transmitter from Hawaii (NPM) has been continuously recorded at Chofu (CHF), Tokyo, Japan. The VLF amplitude variability indicates lower ionospheric perturbation in the D region (60-90 km altitude range) around the NPM-CHF propagation path. We carried out the prediction of daily nighttime mean VLF amplitude by using Nonlinear Autoregressive with Exogenous Input Neural Network (NARX NN). The NARX NN model, which was built based on the daily input variables of various physical parameters such as stratospheric temperature, total column ozone, cosmic rays, Dst, and Kp indices possess good accuracy during the model building. The fitted model was constructed within the training period from 1 January 2011 to 4 February 2013 by using three algorithms, namely, Bayesian Neural Network (BRANN), Levenberg Marquardt Neural Network (LMANN), and Scaled Conjugate Gradient (SCG). The LMANN has the largest Pearson correlation coefficient (r) of 0.94 and smallest root-mean-square error (RMSE) of 1.19 dB. The constructed models by using LMANN were applied to predict the VLF amplitude from 5 February 2013 to 31 December 2013. As a result the one step (1 day) ahead predicted nighttime VLF amplitude has the r of 0.93 and RMSE of 2.25 dB. We conclude that the model built according to the proposed methodology provides good predictions of the electric field amplitude of VLF waves for NPM-CHF (midlatitude) propagation path.
CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors
Directory of Open Access Journals (Sweden)
Duncan Lee
2013-11-01
Full Text Available Conditional autoregressive models are commonly used to represent spatial autocorrelation in data relating to a set of non-overlapping areal units, which arise in a wide variety of applications including agriculture, education, epidemiology and image analysis. Such models are typically specified in a hierarchical Bayesian framework, with inference based on Markov chain Monte Carlo (MCMC simulation. The most widely used software to fit such models is WinBUGS or OpenBUGS, but in this paper we introduce the R package CARBayes. The main advantage of CARBayes compared with the BUGS software is its ease of use, because: (1 the spatial adjacency information is easy to specify as a binary neighbourhood matrix; and (2 given the neighbourhood matrix the models can be implemented by a single function call in R. This paper outlines the general class of Bayesian hierarchical models that can be implemented in the CARBayes software, describes their implementation via MCMC simulation techniques, and illustrates their use with two worked examples in the fields of house price analysis and disease mapping.
Mac Nally, Ralph; Thomson, James R.; Kimmerer, Wim J.; Feyrer, Frederick; Newman, Ken B.; Sih, Andy; Bennett, William A.; Brown, Larry; Fleishman, Erica; Culberson, Steven D.; Castillo, Gonzalo
2010-01-01
Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2 isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey). Our results were relatively robust with respect to the form of stock–recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state–space models that describe more fully the life-history dynamics of the declining species.
A time series model: First-order integer-valued autoregressive (INAR(1))
Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.
2017-07-01
Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.
Zhang, Xujun; Pang, Yuanyuan; Cui, Mengjing; Stallones, Lorann; Xiang, Huiyun
2015-02-01
Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China. Copyright © 2015 Elsevier Inc. All rights reserved.
Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model
Directory of Open Access Journals (Sweden)
Chengcheng Xu
2017-08-01
Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.
Directory of Open Access Journals (Sweden)
Tuti Purwaningsih
2017-07-01
Full Text Available China as a country became the economic center of the world. However, with a population of 1.3 billion, China's per capita income is still at number 80 in the world. In the world, considering the imbalance between town and country with 100 million people still living in poverty. Thus, to address this imbalance, it is necessary to study the condition in depth, because income per capita is often used as a benchmark to measure the prosperity of a country. With greater and equitable income per capita, the country will be judged increasingly affluent. Two factors, mainly industry and tourism, play an important role in the economic progress in China. These are include Per capita Disposable Income Nationwide (yuan, Total Value of Exports of operating units (1,000 USD, Registered Unemployed Person in Urban Area (10000 person, Foreign Exchange Earning from International tourism(in millions USD and Number of Overseas Visitor Arrivals (million person/time. Thus, it is necessary to investigate the influence of these factors to increase per capita income. Since the economic development of a region usually affect the surrounding area, this study aims to include spatial effects, using Spatial Autoregressive (SAR Model. The results suggest that the per capita income affected by the Tourism factor is about 58.65% (R-squared.
Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman
2017-08-01
Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.
Directory of Open Access Journals (Sweden)
Ryan Louise
2007-11-01
Full Text Available Abstract Background The Conditional Autoregressive (CAR model is widely used in many small-area ecological studies to analyse outcomes measured at an areal level. There has been little evaluation of the influence of different neighbourhood weight matrix structures on the amount of smoothing performed by the CAR model. We examined this issue in detail. Methods We created several neighbourhood weight matrices and applied them to a large dataset of births and birth defects in New South Wales (NSW, Australia within 198 Statistical Local Areas. Between the years 1995–2003, there were 17,595 geocoded birth defects and 770,638 geocoded birth records with available data. Spatio-temporal models were developed with data from 1995–2000 and their fit evaluated within the following time period: 2001–2003. Results We were able to create four adjacency-based weight matrices, seven distance-based weight matrices and one matrix based on similarity in terms of a key covariate (i.e. maternal age. In terms of agreement between observed and predicted relative risks, categorised in epidemiologically relevant groups, generally the distance-based matrices performed better than the adjacency-based neighbourhoods. In terms of recovering the underlying risk structure, the weight-7 model (smoothing by maternal-age 'Covariate model' was able to correctly classify 35/47 high-risk areas (sensitivity 74% with a specificity of 47%, and the 'Gravity' model had sensitivity and specificity values of 74% and 39% respectively. Conclusion We found considerable differences in the smoothing properties of the CAR model, depending on the type of neighbours specified. This in turn had an effect on the models' ability to recover the observed risk in an area. Prior to risk mapping or ecological modelling, an exploratory analysis of the neighbourhood weight matrix to guide the choice of a suitable weight matrix is recommended. Alternatively, the weight matrix can be chosen a priori
Extrapolation of a non-linear autoregressive model of pulmonary mechanics.
Langdon, Ruby; Docherty, Paul D; Chiew, Yeong Shiong; Chase, J Geoffrey
2017-02-01
For patients with acute respiratory distress syndrome (ARDS), mechanical ventilation (MV) is an essential therapy in the intensive care unit (ICU). Suboptimal PEEP levels in MV can cause ventilator induced lung injury, which is associated with increased mortality, extended ICU stay, and high cost. The ability to predict the outcome of respiratory mechanics in response to changes in PEEP would thus provide a critical advantage in personalising and improving care. Testing the potentially dangerous high pressures would not be required to assess their impact. A nonlinear autoregressive (NARX) model was used to predict airway pressure in 19 data sets from 10 mechanically ventilated ARDS patients. Patient-specific NARX models were identified from pressure and flow data over one, two, three, or four adjacent PEEP levels in a recruitment manoeuvre. Extrapolation of NARX model elastance functions allowed prediction of patient responses to PEEP changes to higher or lower pressures. NARX model predictions were more successful than those using a well validated first order model (FOM). The most clinically important results were for extrapolation up one PEEP step of 2cmH2O from the highest PEEP in the training data. When the NARX model was trained on one PEEP level, the mean RMS residual for the extrapolation PEEP level was 0.52 (90% CI: 0.47-0.57) cmH2O, compared to 1.50 (90% CI: 1.38-1.62) cmH2O for the FOM. When trained on four PEEP levels, the NARX result was 0.50 (90% CI: 0.42-0.58) cmH2O, and was 1.95 (90% CI: 1.71-2.19) cmH2O for the FOM. The results suggest that a full recruitment manoeuvre may not be required for the NARX model to obtain a useful estimate of the pressure waveform at higher PEEP levels. The methodology could thus allow clinicians to make informed decisions about ventilator PEEP settings while reducing the risk associated with high PEEP, and subsequent high peak airway pressures. Copyright © 2016. Published by Elsevier Inc.
Cai, Jiji; Jung, Cheolkon
2017-09-01
We propose image-guided depth propagation for two-dimensional (2-D)-to-three-dimensional (3-D) video conversion using superpixel matching and the adaptive autoregressive (AR) model. We adopt key frame-based depth propagation that propagates the depth map in the key frame to nonkey frames. Moreover, we use the adaptive AR model for depth refinement to penalize depth-color inconsistency. First, we perform superpixel matching to estimate motion vectors at the superpixel level instead of block matching based on the fixed block size. Then, we conduct depth compensation based on motion vectors to generate the depth map in the nonkey frame. However, the size of two superpixels is not exactly the same due to the segment-based matching, which causes matching errors in the compensated depth map. Thus, we introduce an adaptive image-guided AR model to minimize matching errors and produce the final depth map by minimizing AR prediction errors. Finally, we employ depth-image-based rendering to generate stereoscopic views from 2-D videos and their depth maps. Experimental results demonstrate that the proposed method successfully performs depth propagation and produces high-quality depth maps for 2-D-to-3-D video conversion.
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Mei-Yu LEE
2014-11-01
Full Text Available This paper investigates the effect of the nonzero autocorrelation coefficients on the sampling distributions of the Durbin-Watson test estimator in three time-series models that have different variance-covariance matrix assumption, separately. We show that the expected values and variances of the Durbin-Watson test estimator are slightly different, but the skewed and kurtosis coefficients are considerably different among three models. The shapes of four coefficients are similar between the Durbin-Watson model and our benchmark model, but are not the same with the autoregressive model cut by one-lagged period. Second, the large sample case shows that the three models have the same expected values, however, the autoregressive model cut by one-lagged period explores different shapes of variance, skewed and kurtosis coefficients from the other two models. This implies that the large samples lead to the same expected values, 2(1 – ρ0, whatever the variance-covariance matrix of the errors is assumed. Finally, comparing with the two sample cases, the shape of each coefficient is almost the same, moreover, the autocorrelation coefficients are negatively related with expected values, are inverted-U related with variances, are cubic related with skewed coefficients, and are U related with kurtosis coefficients.
Varouchakis, Emmanouil
2017-04-01
Reliable temporal modelling of groundwater level is significant for efficient water resources management in hydrological basins and for the prevention of possible desertification effects. In this work we propose a stochastic data driven approach of temporal monitoring and prediction that can incorporate auxiliary information. More specifically, we model the temporal (mean annual and biannual) variation of groundwater level by means of a discrete time autoregressive exogenous variable model (ARX model). The ARX model parameters and its predictions are estimated by means of the Kalman filter adaptation algorithm (KFAA). KFAA is suitable for sparsely monitored basins that do not allow for an independent estimation of the ARX model parameters. Three new modified versions of the original form of the ARX model are proposed and investigated: the first considers a larger time scale, the second a larger time delay in terms of the groundwater level input and the third considers the groundwater level difference between the last two hydrological years, which is incorporated in the model as a third input variable. We apply KFAA to time series of groundwater level values from Mires basin in the island of Crete. In addition to precipitation measurements, we use pumping data as exogenous variables. We calibrate the ARX model based on the groundwater level for the years 1981 to 2006 and use it to successfully predict the mean annual and biannual groundwater level for recent years (2007-2010).
Directory of Open Access Journals (Sweden)
MEHDI AMIAN
2013-10-01
Full Text Available Functional near infrared spectroscopy (fNIRS is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2 and deoxyhemoglobin (HHb concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR is about 2 dB higher for ARMA based method.
Directory of Open Access Journals (Sweden)
habib salami
2014-03-01
Full Text Available The presence of yield systematic risk in agricultural sector is one of the main reasons for facing this sector with huge damages and is one of the restricting factors in developing agricultural insurance in Iran. This study explores the presence of systematic yield risk and the extent and severity of yield spatial dependence for apple production in Iran. To this end, the apple production regions were grouped into two climatic regions based on their thermal regimes. In the second step, systematic yield risk was explored using the first order spatial autoregressive (FAR model in each of the two climatic regions.Finally, the effects of climatic variables on the yield of apple have been estimated using more general spatial autoregressive models. Results indicate that apple production regions can be classified into two mountainous and plain regions. Apple yields are correlated across space in each of the two regions. Frost in the first region and drought in the second region is accounted for the presence of systematic yield risk in apple production in Iran. Results from more general models revealed that one year lag of drought, the occurrence of frost in March, average of temperature in June and July, total annual precipitation, and variation of precipitation are important climate variables that affect apple yield in Iran.
Optimal transformations for categorical autoregressive time series
Buuren, S. van
1996-01-01
This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze
Su, Fei; Deng, Bin; Li, Hongji; Yang, Shuangming; Qin, Yingmei; Wang, Jiang; Liu, Chen
2017-12-01
This study explores the implementation of the nonlinear autoregressive Volterra (NARV) model using a field programmable gate arrays (FPGAs)-based hardware simulation platform and accomplishes the identification process of the Hodgkin-Huxley (HH) model. First, a physiological detailed single-compartment HH model is applied to generate experiment data sets and the electrical behavior of neurons are described by the membrane potential. Then, based on the injected input current and the output membrane potential, a second-order NARV model is constructed and implemented on FPGA-based simulation platforms. The NARV modeling method is data-driven, requiring no accurate physiological information and the FPGA-based hardware simulation can provide a real time and high-performance platform to deal with the drawbacks of software simulation. Therefore, the proposed method in this paper is capable of handling the nonlinearities and uncertainties in nonlinear neural systems and may help promote the development of clinical treatment devices.
Directory of Open Access Journals (Sweden)
Eric L Garland
2015-02-01
Full Text Available Recent theory suggests that positive psychological processes integral to health may be energized through the self-reinforcing dynamics of an upward spiral to counter emotion dysregulation. The present study examined positive emotion-cognition interactions among individuals in partial remission from depression who had been randomly assigned to treatment with mindfulness-based cognitive therapy (MBCT; n = 64 or a wait-list control condition (n = 66. We hypothesized that MBCT stimulates upward spirals by increasing positive affect and positive cognition. Experience sampling assessed changes in affect and cognition during 6 days before and after treatment, which were analyzed with a series of multilevel and autoregressive latent trajectory models. Findings suggest that MBCT was associated with significant increases in trait positive affect and momentary positive cognition, which were preserved through autoregressive and cross-lagged effects driven by global emotional tone. Findings suggest that daily positive affect and cognition are maintained by an upward spiral that might be promoted by mindfulness training.
Energy Technology Data Exchange (ETDEWEB)
Geraldo, Issa Cherif [Laboratoire d’Automatique, Génie Informatique et Signal (LAGIS UMR CNRS 8219), Université Lille 1, Sciences et technologies, Avenue Paul Langevin, BP 48, 59651 Villeneuve d’Ascq CEDEX (France); Bose, Tanmoy [Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal (India); Pekpe, Komi Midzodzi, E-mail: midzodzi.pekpe@univ-lille1.fr [Laboratoire d’Automatique, Génie Informatique et Signal (LAGIS UMR CNRS 8219), Université Lille 1, Sciences et technologies, Avenue Paul Langevin, BP 48, 59651 Villeneuve d’Ascq CEDEX (France); Cassar, Jean-Philippe [Laboratoire d’Automatique, Génie Informatique et Signal (LAGIS UMR CNRS 8219), Université Lille 1, Sciences et technologies, Avenue Paul Langevin, BP 48, 59651 Villeneuve d’Ascq CEDEX (France); Mohanty, A.R. [Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal (India); Paumel, Kévin [CEA, DEN, Nuclear Technology Department, F-13108 Saint-Paul-lez-Durance (France)
2014-10-15
Highlights: • The work deals with sodium boiling detection in a liquid metal fast breeder reactor. • The authors choose to use acoustic data instead of thermal data. • The method is designed to not to be disturbed by the environment noises. • A real time boiling detection methods are proposed in the paper. - Abstract: This paper deals with acoustic monitoring of sodium boiling in a liquid metal fast breeder reactor (LMFBR) based on auto regressive (AR) models which have low computational complexities. Some authors have used AR models for sodium boiling or sodium–water reaction detection. These works are based on the characterization of the difference between fault free condition and current functioning of the system. However, even in absence of faults, it is possible to observe a change in the AR models due to the change of operating mode of the LMFBR. This sets up the delicate problem of how to distinguish a change in operating mode in absence of faults and a change due to presence of faults. In this paper we propose a new approach for boiling detection based on the estimation of AR models on sliding windows. Afterwards, classification of the models into boiling or non-boiling models is made by comparing their coefficients by two statistical methods, multiple linear regression (LR) and support vectors machines (SVM). The proposed approach takes into account operating mode information in order to avoid false alarms. Experimental data include non-boiling background noise data collected from Phenix power plant (France) and provided by the CEA (Commissariat à l’Energie Atomique et aux énergies alternatives, France) and boiling condition data generated in laboratory. High boiling detection rates as well as low false alarms rates obtained on these experimental data show that the proposed method is efficient for boiling detection. Most importantly, it shows that the boiling phenomenon introduces a disturbance into the AR models that can be clearly detected.
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
2008-01-01
Better modelling and forecasting of very short-term power fluctuations at large offshore wind farms may significantly enhance control and management strategies of their power output. The paper introduces a new methodology for modelling and forecasting such very short-term fluctuations. The proposed...... consists in 1-step ahead forecasting exercise on time-series of wind generation with a time resolution of 10 minute. The quality of the introduced forecasting methodology and its interest for better understanding power fluctuations are finally discussed....... methodology is based on a Markov-switching autoregressive model with time-varying coefficients. An advantage of the method is that one can easily derive full predictive densities. The quality of this methodology is demonstrated from the test case of 2 large offshore wind farms in Denmark. The exercise...
Szolgayova, Elena
2010-05-01
Hybrid modelling, used for simulation and forecasting of hydrological time series, involving both process-based and data-driven types of models combines the available domain knowledge and process physics with the recent advances in data driven tools. In this way, complex hydrological processes can be modelled and forecasted by decomposing the problem into several smaller sub - problems and using process physics based models where these are most appropriate, and data dictated tools (such as ANN, time series models or traditional statistics) for the residual data, when necessary. The fitting and forecasting performance of such models have to be explored case based. So far, only a few attempts to apply various nonlinear time series models within such a framework were reported in the hydrological modelling literature. This contribution presents results concerning the possibility to use GARCH type of models for such purposes. More specifically, error time series from two hydrological conceptual models were analyzed (applied on time series measured from the Hron and Morava Rivers in Slovakia), concentrating on the improvement of the modelling and forecasting performance of these models. The goal of investigation was to try to expand the knowledge in the time series modelling of hydrological model error time series with the aim to test and develop appropriate methods for various time steps from the GARCH family of models. In order to achieve this, following steps were taken: 1. The presence of heteroscedasticity was verified in time series. 2. A model from the GARCH family was fitted on the data, comparing the fit with a fit of an ARMA model. 3. One - step - ahead forecasts from the fitted models were produced, performing comparisons. The investigation of model properties and performances was thoroughly tested under various conditions of their future practical applications. In general, heteroscedasticity was present in the majority of the error time series of the
Large N Expansion. Vector Models
Nissimov, Emil; Pacheva, Svetlana
2006-01-01
Preliminary version of a contribution to the "Quantum Field Theory. Non-Perturbative QFT" topical area of "Modern Encyclopedia of Mathematical Physics" (SELECTA), eds. Aref'eva I, and Sternheimer D, Springer (2007). Consists of two parts - "main article" (Large N Expansion. Vector Models) and a "brief article" (BPHZL Renormalization).
Tan, Li; Jiang, Hongbo; Wang, Ying; Wei, Sheng; Nie, Shaofa
2014-01-01
Background Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. Method In this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012. Results The best-fitted hybrid model was combined with seasonal ARIMA and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively −965.03, −1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend. Conclusion The model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information. PMID:24893000
DEFF Research Database (Denmark)
Silvennoinen, Annastiina; Teräsvirta, Timo
In this paper we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new Double Smooth Transition Conditional Correlation GARCH model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen and Ter¨asvirta (2005) by including...... another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition...
Tongal, Hakan; Booij, Martijn J.
2016-01-01
A nonlinear stochastic self-exciting threshold autoregressive (SETAR) model and a chaotic k-nearest neighbour (k-nn) model, for the first time, were compared in one and multi-step ahead daily flow forecasting for nine rivers with low, medium, and high flows in the western United States. The
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
optimized is based on penalized maximum-likelihood, with exponential forgetting of past observations. MSAR models are then employed for 1-step-ahead point forecasting of 10-minute resolution time-series of wind power at two large offshore wind farms. They are favourably compared against persistence and Auto......Wind power production data at temporal resolutions of a few minutes exhibits successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour......Regressive (AR) models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....
Directory of Open Access Journals (Sweden)
Cristhian Moreno-Chaparro
2011-12-01
Full Text Available This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA with Discrete Wavelet Transform (DWT; a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing. A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in prediction.
DEFF Research Database (Denmark)
Meng, Anders; Shawe-Taylor, John
2005-01-01
In music genre classification the decision time is typically of the order of several seconds however most automatic music genre classification systems focus on short time features derived from 10-50ms. This work investigates two models, the multivariate gaussian model and the multivariate...... autoregressive model for modelling short time features. Furthermore, it was investigated how these models can be integrated over a segment of short time features into a kernel such that a support vector machine can be applied. Two kernels with this property were considered, the convolution kernel and product...
A Vector Autoregressive Model for Electricity Prices Subject to Long Memory and Regime Switching
DEFF Research Database (Denmark)
Haldrup, Niels; Nielsen, Frank; Nielsen, Morten Ørregaard
2007-01-01
to occasional congestion periods. For a system of bilat- eral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between essen- tially a univariate price process under non-congestion and a bivariate price...... process under congestion. At the same time it is an empirical regularity that electricity prices tend to show a high degree of fractional integration, and thus that prices may be fractionally cointegrated. An empirical analysis using Nord Pool data shows that even though the prices strongly co-move under...
Directory of Open Access Journals (Sweden)
Chieh-Fan Chen
2011-01-01
Full Text Available This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.
Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro
2017-01-01
Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.
Bildirici, Melike; Ersin, Özgür Ömer
2018-01-01
The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.
Wang, Zheng; Roe, Anna W
2011-01-15
Gamma band synchronization has drawn increasing interest with respect to its potential role in neuronal encoding strategy and behavior in awake, behaving animals. However, contamination of these recordings by power line noise can confound the analysis and interpretation of cortical local field potential (LFP). Existing denoising methods are plagued by inadequate noise reduction, inaccuracies, and even introduction of new noise components. To carefully and more completely remove such contamination, we propose an automatic method based on the concept of adaptive noise cancellation that utilizes the correlative features of common noise sources, and implement with AutoRegressive model with eXogenous Input (ARX). We apply this technique to both simulated data and LFPs recorded in the primary visual cortex of awake macaque monkeys. The analyses here demonstrate a greater degree of accurate noise removal than conventional notch filters. Our method leaves desired signal intact and does not introduce artificial noise components. Application of this method to awake monkey V1 recordings reveals a significant power increase in the gamma range evoked by visual stimulation. Our findings suggest that the ARX denoising procedure will be an important pre-processing step in the analysis of large volumes of cortical LFP data as well as high frequency (gamma-band related) electroencephalography/magnetoencephalography (EEG/MEG) applications, one which will help to convincingly dissociate this notorious artifact from gamma-band activity. Copyright © 2010 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Ali Akbar Akbari
2014-08-01
Full Text Available Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG, as an experimental technique,is concerned with the development, recording, and analysis of myoelectric signals. EMG-based research is making progress in the development of simple, robust, user-friendly, and efficient interface devices for the amputees. Materials and Methods Prediction of muscular activity and motion patterns is a common, practical problem in prosthetic organs. Recurrent neural network (RNN models are not only applicable for the prediction of time series, but are also commonly used for the control of dynamical systems. The prediction can be assimilated to identification of a dynamic process. An architectural approach of RNN with embedded memory is Nonlinear Autoregressive Exogenous (NARX model, which seems to be suitable for dynamic system applications. Results Performance of NARX model is verified for several chaotic time series, which are applied as input for the neural network. The results showed that NARX has the potential to capture the model of nonlinear dynamic systems. The R-value and MSE are and , respectively. Conclusion EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX network. It is possible to obtain EMG signals of muscles in other arm motions to predict the lost functions of the absent arm in above-elbow amputees, using NARX model.
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
In this paper we investigate the effects of careful modelling the long-run dynamics of the volatilities of stock market returns on the conditional correlation structure. To this end we allow the individual unconditional variances in Conditional Correlation GARCH models to change smoothly over time...... by incorporating a nonstationary component in the variance equations. The modelling technique to determine the parametric structure of this time-varying component is based on a sequence of specification Lagrange multiplier-type tests derived in Amado and Teräsvirta (2011). The variance equations combine the long....... The results suggest that accounting for deterministic changes in the unconditional variances considerably improves the fit of the multivariate Conditional Correlation GARCH models to the data. The effect of careful specification of the variance equations on the estimated correlations is variable: in some...
Semantic Vector Space Model: Implementation and Evaluation.
Liu, Geoffrey Z.
1997-01-01
Presents the Semantic Vector Space Model, a text representation and searching technique based on the combination of Vector Space Model with heuristic syntax parsing and distributed representation of semantic case structures. In this model, both documents and queries are represented as semantic matrices, and retrieval is achieved by computing…
A Realistic Process Example for MIMO MPC based on Autoregressive Models
DEFF Research Database (Denmark)
Huusom, Jakob Kjøbsted; Jørgensen, John Bagterp
2014-01-01
Advanced controllers such as model predictive control are i n use for a wide range of application in the process industry. The potential utili zation of such advanced predictive controllers is far from exhausted. One barrier for more wide spread implementation is the lack of simple methodologies ...
Directory of Open Access Journals (Sweden)
Shuntaro Okazaki
Full Text Available People's behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction--two individuals influencing one another--or in one direction--one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another's head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR, the measure of influence using a multivariate autoregressive model, was also computed to identify the degree to which one's postural sway is explained by that of the other's and how visual information (sighted vs. blindfolded interacts with paired participants' postural sway. It was found that for synchronization to take place, it is crucial that paired participants be sighted and exert equal influence on one another by simultaneously exchanging visual information. Furthermore, a simulation for the proposed system with a wider range of visual input showed a pattern of results similar to the
Altomare, Albino; Cesario, Eugenio; Mastroianni, Carlo
2016-10-01
The opportunity of using Cloud resources on a pay-as-you-go basis and the availability of powerful data centers and high bandwidth connections are speeding up the success and popularity of Cloud systems, which is making on-demand computing a common practice for enterprises and scientific communities. The reasons for this success include natural business distribution, the need for high availability and disaster tolerance, the sheer size of their computational infrastructure, and/or the desire to provide uniform access times to the infrastructure from widely distributed client sites. Nevertheless, the expansion of large data centers is resulting in a huge rise of electrical power consumed by hardware facilities and cooling systems. The geographical distribution of data centers is becoming an opportunity: the variability of electricity prices, environmental conditions and client requests, both from site to site and with time, makes it possible to intelligently and dynamically (re)distribute the computational workload and achieve as diverse business goals as: the reduction of costs, energy consumption and carbon emissions, the satisfaction of performance constraints, the adherence to Service Level Agreement established with users, etc. This paper proposes an approach that helps to achieve the business goals established by the data center administrators. The workload distribution is driven by a fitness function, evaluated for each data center, which weighs some key parameters related to business objectives, among which, the price of electricity, the carbon emission rate, the balance of load among the data centers etc. For example, the energy costs can be reduced by using a "follow the moon" approach, e.g. by migrating the workload to data centers where the price of electricity is lower at that time. Our approach uses data about historical usage of the data centers and data about environmental conditions to predict, with the help of regressive models, the values of the
Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai
2013-01-01
Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated significant positive spatial correlation (p < 0.05). The results also imply that the clustering trend of ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model. PMID:24384778
Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika
2017-06-01
Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.
Residual Analysis of Generalized Autoregressive Integrated Moving ...
African Journals Online (AJOL)
In this study, analysis of residuals of generalized autoregressive integrated moving average bilinear time series model was considered. The adequacy of this model was based on testing the estimated residuals for whiteness. Jarque-Bera statistic and squared-residual autocorrelations were used to test the estimated ...
Tan, Ting; Chen, Lizhang; Liu, Fuqiang
2014-11-01
To establish multiple seasonal autoregressive integrated moving average model (ARIMA) according to the hand-foot-mouth disease incidence in Changsha, and to explore the feasibility of the multiple seasonal ARIMA in predicting the hand-foot-mouth disease incidence. EVIEWS 6.0 was used to establish multiple seasonal ARIMA according to the hand-foot- mouth disease incidence from May 2008 to August 2013 in Changsha, and the data of the hand- foot-mouth disease incidence from September 2013 to February 2014 were served as the examined samples of the multiple seasonal ARIMA, then the errors were compared between the forecasted incidence and the real value. Finally, the incidence of hand-foot-mouth disease from March 2014 to August 2014 was predicted by the model. After the data sequence was handled by smooth sequence, model identification and model diagnosis, the multiple seasonal ARIMA (1, 0, 1)×(0, 1, 1)12 was established. The R2 value of the model fitting degree was 0.81, the root mean square prediction error was 8.29 and the mean absolute error was 5.83. The multiple seasonal ARIMA is a good prediction model, and the fitting degree is good. It can provide reference for the prevention and control work in hand-foot-mouth disease.
A vector model for error propagation
Energy Technology Data Exchange (ETDEWEB)
Smith, D.L.; Geraldo, L.P.
1989-03-01
A simple vector model for error propagation, which is entirely equivalent to the conventional statistical approach, is discussed. It offers considerable insight into the nature of error propagation while, at the same time, readily demonstrating the significance of uncertainty correlations. This model is well suited to the analysis of error for sets of neutron-induced reaction cross sections. 7 refs., 1 fig.
Autoregressive smoothing of GOMOS transmittances
Fussen, D.; Vanhellemont, F.; Bingen, C.; Kyrölä, B.; Tamminen, J.; Sofieva, V.; Hassinen, S.; Seppälä, A.; Verronen, P. T.; Bertaux, J. L.; Hauchecorne, A.; Dalaudier, F.; d'Andon, O. Fanton; Barrot, G.; Mangin, A.; Theodore, B.; Guirlet, M.; Renard, J. B.; Fraisse, R.; Snoeij, P.; Koopman, R.; Saavedra, L.
GOMOS is a stellar occultation instrument onboard ENVISAT. It has already measured several hundreds of thousands occultations since March 2002. In some circumstances, the obliqueness of the star setting causes the remote sounding of possible horizontal turbulence that cannot be adequately corrected by using the fast photometer signals, leading to the presence of residual scintillation in the atmospheric transmittance. We investigate the mechanism that produces this spurious signal that may cause the retrieval of wavy constituent profiles. A special algorithm of vertical autoregressive smoothing (VAS) is proposed that takes into account the physical correlation between adjacent measurements at different tangent altitudes. A regularization parameter of the method may be optimized on basis of the minimal correlation between the residuals as prescribed by the Durbin-Watson statistics. The improvements obtained in the retrieval of both O 3 and NO 2 number density profiles is presented and discussed with respect to the results of the official data processing model.
Vector difference calculus for physical lattice models
Schwalm, W.; Moritz, B.; Giona, M.; Schwalm, M.
1999-01-01
A vector difference calculus is developed for physical models defined on a general triangulating graph G, which may be a regular or an extremely irregular lattice, using discrete field quantities roughly analogous to differential forms. The role of the space Λp of p-forms at a point is taken on by the linear space generated at a graph vertex by the geometrical p-simplices which contain it. The vector operations divergence, gradient, and curl are developed using the boundary ∂ and coboundary d. Dot, cross, and scalar products are defined in such a way that discrete analogs of the vector integral theorems, including theorems of Gauss-Ostrogradski, Stokes, and Green, as well as most standard vector identities hold exactly, not as approximations to a continuum limit. Physical conservation laws for the models become theorems satisfied by the discrete fields themselves. Three discrete lattice models are constructed as examples, namely a discrete version of the Maxwell equations, the Navier-Stokes equation for incompressible flow, and the Navier linearized model for a homogeneous, isotropic elastic medium. Weight factors needed for obtaining quantitative agreement with continuum calculations are derived for the special case of a regular triangular lattice. Green functions are developed using a generalized Helmholtz decomposition of the fields.
DEFF Research Database (Denmark)
Jensen, E W; Lindholm, P; Henneberg, S W
1996-01-01
Obtaining an adequate depth of anesthesia is a continuous challenge to the anesthetist. With the introduction of muscle-relaxing agents the traditional signs of awareness are often obscured, or difficult to interpret. These signs include blood pressure, heart rate, pupil size, etc. However......, these factors do not describe the depth of anesthesia (DA) in a cerebral activity sense. Hence, a better measure of the DA is required. It has been suggested that Auditory-Evoked Potentials (AEP) can provide additional information about the DA. The general method of extracting AEP is by use of a Moving Time...... Average (MTA). However, the MTA is time consuming because a large number of repetitions is needed to produce an estimate of the AEP. Hence, changes occurring over a small number of sweeps will not be detected by the MTA average. We describe a system-identification method, an autoregressive model...
Vector quarks in the Higgs triplet model
Bahrami, Sahar; Frank, Mariana
2014-08-01
We analyze the effects of introducing vector fermions in the Higgs triplet model. In this scenario, the model contains, in addition to the Standard Model particle content, one triplet Higgs representation and a variety of vectorlike fermion states, including singlet, doublet, and triplet states. We investigate the electroweak precision variables and impose restrictions on model parameters. We show that, for some representations, introducing vector quarks significantly alters the constraints on the mass of the doubly charged Higgs boson, bringing it in closer agreement with present experimental constraints. We also study the effects of introducing the vectorlike fermions on neutral Higgs phenomenology, in particular on the loop-dominated decays H→γγ and H→Zγ, and the restrictions they impose on the parameter space.
Techie Quaicoe, Michael; Twenefour, Frank B K; Baah, Emmanuel M; Nortey, Ezekiel N N
2015-01-01
This research article aimed at modeling the variations in the dollar/cedi exchange rate. It examines the applicability of a range of ARCH/GARCH specifications for modeling volatility of the series. The variants considered include the ARMA, GARCH, IGARCH, EGARCH and M-GARCH specifications. The results show that the series was non stationary which resulted from the presence of a unit root in it. The ARMA (1, 1) was found to be the most suitable model for the conditional mean. From the Box-Ljung test statistics x-squared of 1476.338 with p value 0.00217 for squared returns and 16.918 with 0.0153 p values for squared residuals, the null hypothesis of no ARCH effect was rejected at 5% significance level indicating the presence of an ARCH effect in the series. ARMA (1, 1) + GARCH (1, 1) which has all parameters significant was found to be the most suitable model for the conditional mean with conditional variance, thus showing adequacy in describing the conditional mean with variance of the return series at 5% significant level. A 24 months forecast for the mean actual exchange rates and mean returns from January, 2013 to December, 2014 made also showed that the fitted model is appropriate for the data and a depreciating trend of the cedi against the dollar for forecasted period respectively.
Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza
2017-09-01
In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.
Estimation of Time Varying Autoregressive Symmetric Alpha Stable
National Aeronautics and Space Administration — In this work, we present a novel method for modeling time-varying autoregressive impulsive signals driven by symmetric alpha stable distributions. The proposed...
Directory of Open Access Journals (Sweden)
Juan D Velásquez
2008-12-01
Full Text Available Una red neuronal autorregresiva es estimada para el precio mensual brasileño de corto plazo de la electricidad, la cual describe mejor la dinámica de los precios que un modelo lineal autorregresivo y que un perceptrón multicapa clásico que usan las mismas entradas y neuronas en la capa oculta. El modelo propuesto es especificado usando un procedimiento estadístico basado en el contraste del radio de verosimilitud. El modelo pasa una batería de pruebas de diagnóstico. El procedimiento de especificación propuesto permite seleccionar el número de unidades en la capa oculta y las entradas a la red neuronal, usando pruebas estadísticas que tienen en cuenta la cantidad de los datos y el ajuste del modelo a la serie de precios. La especificación del modelo final demuestra que el precio para el próximo mes es una función no lineal del precio actual, de la energía afluente actual y de la energía almacenada en el embalse equivalente en el mes actual y dos meses atrás.An autoregressive neural network model is estimated for the monthly Brazilian electricity spot price, which describes the prices dynamics better than a linear autoregressive model and a classical multilayer perceptron using the same input and neurons in the hidden layer. The proposed model is specified using a statistical procedure based on a likelihood ratio test. The model passes a battery of diagnostic tests. The proposed specification procedure allows us to select the number of units in hidden layer and the inputs to the neural network based on statistical tests, taking into account the number of data and the model fitting to the price time series. The final model specification demonstrates that the price for the next month is a nonlinear function of the current price, the current energy inflow, and the energy saved in the equivalent reservoir in the current month and two months ago.
Liang, Hao; Gao, Lian; Liang, Bingyu; Huang, Jiegang; Zang, Ning; Liao, Yanyan; Yu, Jun; Lai, Jingzhen; Qin, Fengxiang; Su, Jinming; Ye, Li; Chen, Hui
2016-01-01
Background Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. Methods The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models. Results The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models. Conclusions The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County. PMID:27258555
Energy Technology Data Exchange (ETDEWEB)
Biyanto, Totok R. [Department of Engineering Physics, Institute Technology of Sepuluh Nopember Surabaya, Surabaya, Indonesia 60111 (Indonesia)
2016-06-03
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Directory of Open Access Journals (Sweden)
Jane Law
2016-03-01
Full Text Available Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s for each area; and (spatial weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended.
Directory of Open Access Journals (Sweden)
Naohiro Nakamura
2017-12-01
Full Text Available Many seismic records were obtained during the 2011 off the Pacific coast of Tohoku earthquake. These records can be used to improve the seismic design and disaster prevention capabilities of buildings. In this paper, seismic simulation analyses of a steel-reinforced concrete high-rise building located in the Tokyo Bay area are conducted based on the seismic record of the Tohoku earthquake. A non-linear sway-rocking model is used in the analysis, and comparisons are drawn between the observed records and analytical results of the pre-shock, main shock, and earthquake after 1 month. The analytical results correspond well with the seismic records, and the effect of the non-linear nature of the main shock is retained in the building. This is an important consideration when conducting response evaluation. An auto-regressive exogenous model is used to identify the first and second natural periods, and the damping ratios, of both the records and the analytical results. Although the first and second damping ratios are similar in value to the observed results, the second damping ratio is overestimated in the analytical results because of the stiffness damping model.
Directory of Open Access Journals (Sweden)
E. Pishbahar
2015-05-01
Full Text Available There are different ideas and opinions about the effects of macroeconomic variables on real and nominal variables. To answer the question of whether changes in macroeconomic variables as a political tool is useful over a business cycle, understanding the effect of macroeconomic variables on economic growth is important. In the present study, the Bayesian Vector autoregresive model and seasonality data for the years between 1991 and 2013 was used to determine the impact of monetary policy on value-added agriculture. Predicts of Vector autoregresive model are usually divertaed due to a lot of parameters in the model. Bayesian vector autoregresive model estimates more reliable predictions due to reducing the number of included parametrs and considering the former models. Compared to the Vector Autoregressive model, the coefficients are estimated more accurately. Based on the results of RMSE in this study, previous function Nrmal-Vyshart was identified as a suitable previous disteribution. According to the results of the impulse response function, the sudden effects of shocks in macroeconomic variables on the value added in agriculture and domestic venture capital are stable. The effects on the exchange rates, tax revenues and monetary will bemoderated after 7, 5 and 4periods. Monetary policy shocks ,in the first half of the year, increased the value added of agriculture, while in the second half of the year had a depressing effect on the value added.
Feng, Yongjiu; Chen, Xinjun; Liu, Yang
2017-09-01
The spatiotemporal distribution and relationship between nominal catch-per-unit-effort (CPUE) and environment for the jumbo flying squid (Dosidicus gigas) were examined in offshore Peruvian waters during 2009-2013. Three typical oceanographic factors affecting the squid habitat were investigated in this research, including sea surface temperature (SST), sea surface salinity (SSS) and sea surface height (SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive (SAR) model and a generalized additive model (GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°-82.7°W and 11.9°-17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°-81.2°W and 14.3°-15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°-21.9°C for SST, 35.16-35.32 for SSS and 27.2-31.5 cm for SSH in the areas bounded by 78°-80°W/82-84°W and 15°-18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas offshore Peru, and offer a new SAR modeling method for advancing fishery science.
Xiloyannis, Michele; Gavriel, Constantinos; Thomik, Andreas A C; Faisal, A Aldo
2017-10-01
Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( ) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our approach achieves high levels of performance (RMSE of 8°/s and ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a hand
Precessing Asteroids ftrom Radius Vector Models?
Drummond, Jack D.
2014-11-01
Examining a sample of asteroids (the first 99) for which radius vector models have been constructed from mostly lightcurves, located on a web site where such models are listed (http://astro.troja.mff.cuni.cz/projects/damit ; see Durech et al. (2010), DAMIT: a database of asteroid models, A&A, 513, A46), we fit their surfaces as triaxial ellipsoids and provide their three dimensions. In the process we also derive an Euler angular offset θ between each model's spin axis and its axis of maximum moment of inertia assuming a uniform distribution of mass. Most θ's conform to a chi-squared distribution having a maximum at 3° and a mean at 5°, and with the square root of the variance being 3°. However, seven models produce θ>20°, which we interpret as indicating possibly strong precessors, tumblers, or due to incorrect models: asteroids (68), (89), (125), (162), (167), (222), and (230). Nine others produce an excess over the distribution at 12°probability of an impact sufficient to change the angular momentum of the asteroid implied by θ during the damping time to return to rotation about the small axis is vanishingly small (less than 1 in 10000) for the 8 out of 16 asteroids with absolute dimensions. The most likely resolution, then, is that the rotational pole for the 16 asteroid models with high θ needs to be adjusted by θ degrees.
Sugiyanto; Zukhronah, Etik; Susanti, Yuliana; Rahma Dwi, Sisca
2017-06-01
A country is said to be a crisis when the financial system is experiencing a disruption that affects systems that can not function efficiently. The performance efficiency of macroeconomic indicators especially in imports and exports can be used to detect the financial crisis in Indonesia. Based on the import and export indicators from 1987 to 2015, the movement of these indicators can be modelled using SWARCH three states. The results showed that SWARCH (3,1) model was able to detect the crisis that occurred in Indonesia in 1997 and 2008. Using this model, it can be concluded that Indonesia is prone to financial crisis in 2016.
DEFF Research Database (Denmark)
Boeriis, Morten; van Leeuwen, Theo
2017-01-01
This article revisits the concept of vectors, which, in Kress and van Leeuwen’s Reading Images (2006), plays a crucial role in distinguishing between ‘narrative’, action-oriented processes and ‘conceptual’, state-oriented processes. The use of this concept in image analysis has usually focused...... on the most salient vectors, and this works well, but many images contain a plethora of vectors, which makes their structure quite different from the linguistic transitivity structures with which Kress and van Leeuwen have compared ‘narrative’ images. It can also be asked whether facial expression vectors...... should be taken into account in discussing ‘reactions’, which Kress and van Leeuwen link only to eyeline vectors. Finally, the question can be raised as to whether actions are always realized by vectors. Drawing on a re-reading of Rudolf Arnheim’s account of vectors, these issues are outlined...
Energy Technology Data Exchange (ETDEWEB)
Castillo D, R.; Ortiz V, J.; Ruiz E, J.A. [ININ, 52750 La Marquesa, Estado de Mexico (Mexico)
2008-07-01
The method of the response to the impulse of an autoregressive model for stability analysis of the nuclear boiling water reactors had one of the best behaviors in a range of stable operation conditions to quasi stables during the benchmark of stability of the Forsmark reactors. The method was developed in Mat lab and it uses the Gauss-Newton optimization method for to carry out the adjustment from the response to the impulse. In this work a program in Fortran of the response method to the impulse of an autoregressive model it was developed, which uses an adaptive optimization algorithm called NL2SOL, instead of the original method. This change is due that Gauss-Newton method doesn't converge in some cases to the best adjustment parameters for what the method has been substituted in the more recent Mat lab versions. Among the main obtained results it has that the programmed autoregressive model converges to a smaller order that the original method and while less stable is the reactor it is more big the difference in the order. Also was found an important difference in the first adjustment parameter being caused by the response magnitude to the impulse. As to for the decay ratio and oscillation frequency both programs presented acceptable results. (Author)
Directory of Open Access Journals (Sweden)
Ihsan Mohd Yassin
2014-12-01
Full Text Available System Identification (SI is a discipline concerned with inference of mathematical models from dynamic systems based on their input and output measurements. Among the many types of SI models, the superior NARMAX model and its derivatives (NARX and NARMA are powerful, efficient and unified representations of a variety of nonlinear systems. The identification process of NARX/NARMA/NARMAX is typically performed using the established Orthogonal Least Squares (OLS. Weaknesses of the OLS model are known, leading to various alternatives and modifications of the original algorithm. This paper extends the findings of previous research in application of the Binary Particle Swarm Optimization (BPSO for structure selection of a polynomial NARX model on a DC Motor (DCM dataset. The contributions of this paper involve the implementation and analysis of a MySQL database to serve as a lookup table for the BPSO optimization process. Additional analysis regarding the frequencies of term selection is also made possible by the database. An analysis of different preprocessing methods was also performed leading to the best model. The results show that the BPSO structure selection method is improved by the presence of the database, while the magnitude scaling approach was the best preprocessing method for NARX identification of the DCM dataset.
The Prediction of Exchange Rates with the Use of Auto-Regressive Integrated Moving-Average Models
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Daniela Spiesová
2014-10-01
Full Text Available Currency market is recently the largest world market during the existence of which there have been many theories regarding the prediction of the development of exchange rates based on macroeconomic, microeconomic, statistic and other models. The aim of this paper is to identify the adequate model for the prediction of non-stationary time series of exchange rates and then use this model to predict the trend of the development of European currencies against Euro. The uniqueness of this paper is in the fact that there are many expert studies dealing with the prediction of the currency pairs rates of the American dollar with other currency but there is only a limited number of scientific studies concerned with the long-term prediction of European currencies with the help of the integrated ARMA models even though the development of exchange rates has a crucial impact on all levels of economy and its prediction is an important indicator for individual countries, banks, companies and businessmen as well as for investors. The results of this study confirm that to predict the conditional variance and then to estimate the future values of exchange rates, it is adequate to use the ARIMA (1,1,1 model without constant, or ARIMA [(1,7,1,(1,7] model, where in the long-term, the square root of the conditional variance inclines towards stable value.
Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao; Li, Li
2011-10-01
Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and
E. M. Schliep; A. E. Gelfand; Holland, D. M.
2015-01-01
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additio...
Dean, Roger T; Dunsmuir, William T M
2016-06-01
Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.
Zhai, Shiyan; Song, Genxin; Qin, Yaochen; Ye, Xinyue; Lee, Jay
2017-01-01
This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention.
Directory of Open Access Journals (Sweden)
Athenia Bongani Sibindi
2014-12-01
Full Text Available The life insurance sector may contribute to economic growth by its very mechanism of savings mobilisation and thereby performing an intermediation role in the economy. This ensures that capital is provided to deficient units who are in need of capital to finance their working capital requirements and invest in technology thereby resulting in an increase in output. In this way, it could be argued that life insurance development spurs financial development. In this article we investigate the causal relationship between the life insurance sector, financial development and economic growth in South Africa for the period 1990 to 2012 by applying the ARDL bounds testing procedure. We make use of life insurance density as the proxy for life insurance development, real per capita growth domestic product as the proxy for economic growth and real broad money per capita as the proxy for financial development. We test for cointegration amongst the variables by applying the bounds test and then proceed to test for Granger causality based on the error correction model. Our results confirm that the variables are cointegrated and move in tandem to each other in the long-run. The results also indicate that the direction of causality runs from the economy to the life insurance sector in the short-run which is consistent with the “demand-following” insurance-growth hypothesis. There is also evidence of bidirectional Granger causality running from the economy to financial development and vice versa, both in the long-run and short-run. The results also reveal that life insurance complements financial development in bringing about economic growth further lending credence to the “complementarity” hypothesis
Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei
2018-01-01
Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.
Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
Khan, Muhammad Yousaf; Mittnik, Stefan
2017-11-01
In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.
Spontaneous symmetry breaking in the composite-vector-boson model
Energy Technology Data Exchange (ETDEWEB)
Garavaglia, T.
1986-11-15
Spontaneous symmetry breaking is discussed in the Abelian, QED-like, composite-vector-boson model. When the auxiliary vector field has a nonzero vacuum expectation value, a global symmetry, Lorentz invariance, is broken. It is shown that the regularization of the saddle-point conditions for the quantum fluctuation generating functional is consistent only with a spacelike vacuum expectation value for the auxiliary vector field.
Vector space model for document representation in information retrieval
Directory of Open Access Journals (Sweden)
Dan MUNTEANU
2007-12-01
Full Text Available This paper presents the basics of information retrieval: the vector space model for document representation with Boolean and term weighted models, ranking methods based on the cosine factor and evaluation measures: recall, precision and combined measure.
Directory of Open Access Journals (Sweden)
João Domingos Scalon
2010-07-01
Full Text Available The dairy yield is one of the most important activities for the Brazilian economy and the use of statistical models may improve the decision making in this productive sector. The aim of this paper was to compare the performance of both the traditional linear regression model and the spatial regression model called conditional autoregressive (CAR to explain how some covariates may contribute for the dairy yield. This work used a database on dairy yield supplied by the Brazilian Institute of Geography and Statistics (IBGE and another database on geographical information of the state of Minas Gerais provided by the Integrated Program of Technological Use of Geographical Information (GEOMINAS. The results showed the superiority of the CAR model over the traditional linear regression model to explain the dairy yield. The CAR model allowed the identification of two different spatial clusters of counties related to the dairy yield in the state of Minas Gerais. The first cluster represents the region where one observes the biggest levels of dairy yield. It is formed by the counties of the Triângulo Mineiro. The second cluster is formed by the northern counties of the state that present the lesser levels of dairy yield. A produção de leite é uma das atividades mais importantes para a economia brasileira e o uso de modelos estatísticos pode auxiliar a tomada de decisão neste setor produtivo. O objetivo deste artigo foi comparar o desempenho do modelo de regressão linear tradicional e do modelo de regressão espacial, denominado de autoregressivo condicional (CAR, para explicar como algumas variáveis preditoras contribuem para a quantidade de leite produzido. Este trabalho usou uma base de dados sobre a produção de leite fornecida pelo Instituto Brasileiro de Geografia e Estatística (IBGE e outra base de dados sobre informações geográficas do estado de Minas Gerais, fornecida pelo Programa Integrado de Uso da Tecnologia de Geoprocessamento
Vector meson photoproduction — model independent aspects
Kloet, W. M.; Tabakin, F.
2000-07-01
The rich spin structure of vector meson photoproduction allows for a systematic analysis of the angular and energy dependence of the spin observables in the photon-nucleon c.m. frame. Constraints for spin observables based on positivity of the spin density matrix, are discussed and should be part of any future analysis of experimental data.
Vector meson photoproduction - model independent aspects
Energy Technology Data Exchange (ETDEWEB)
Kloet, W. M.; Tabakin, F
2000-07-31
The rich spin structure of vector meson photoproduction allows for a systematic analysis of the angular and energy dependence of the spin observables in the photon-nucleon c.m. frame. Constraints for spin observables based on positivity of the spin density matrix, are discussed and should be part of any future analysis of experimental data.
A Model of Medium Depolarisation Effects on Polarisation Vector Position
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J. Moc
1994-07-01
Full Text Available The paper presents the results of modelling the influence of real atmosphere with precipitation clouds on the radar signal polarisation status. The relationship between the signal vector depolarisation shift value and the initial wave polarisation is examined.
Scattering vector mesons in D4/D8 model
Energy Technology Data Exchange (ETDEWEB)
Ballon Bayona, C.A., E-mail: ballon@cbpf.b [Centro Brasileiro de Pesquisas Fisicas, Rua Dr. Xavier Sigaud 150, Urca, 22290-180 Rio de Janeiro, RJ (Brazil); Boschi-Filho, Henrique, E-mail: boschi@if.ufrj.b [Instituto de Fisica, Universidade Federal do Rio de Janeiro, Caixa Postal 68528, 21941-972 Rio de Janeiro, RJ (Brazil); Braga, Nelson R.F., E-mail: braga@if.ufrj.b [Instituto de Fisica, Universidade Federal do Rio de Janeiro, Caixa Postal 68528, 21941-972 Rio de Janeiro, RJ (Brazil); Torres, Marcus A.C., E-mail: mtorres@if.ufrj.b [Instituto de Fisica, Universidade Federal do Rio de Janeiro, Caixa Postal 68528, 21941-972 Rio de Janeiro, RJ (Brazil)
2010-02-15
We review in this proceedings some recent results for vector meson form factors obtained using the holographic D4-D8 brane model. The D4-D8 brane model, proposed by Sakai and Sugimoto, is a holographic dual of a semi-realistic strongly coupled large N{sub c} QCD since it breaks supersymmetry and incorporates chiral symmetry breaking. We analyze the vector meson wave functions and Regge trajectories as well.
Stable Parameter Estimation for Autoregressive Equations with Random Coefficients
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V. B. Goryainov
2014-01-01
Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross
Speech Segmentation Using Bayesian Autoregressive Changepoint Detector
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P. Sovka
1998-12-01
Full Text Available This submission is devoted to the study of the Bayesian autoregressive changepoint detector (BCD and its use for speech segmentation. Results of the detector application to autoregressive signals as well as to real speech are given. BCD basic properties are described and discussed. The novel two-step algorithm consisting of cepstral analysis and BCD for automatic speech segmentation is suggested.
Eastin, Matthew D; Delmelle, Eric; Casas, Irene; Wexler, Joshua; Self, Cameron
2014-09-01
Dengue fever transmission results from complex interactions between the virus, human hosts, and mosquito vectors-all of which are influenced by environmental factors. Predictive models of dengue incidence rate, based on local weather and regional climate parameters, could benefit disease mitigation efforts. Time series of epidemiological and meteorological data for the urban environment of Cali, Colombia are analyzed from January of 2000 to December of 2011. Significant dengue outbreaks generally occur during warm-dry periods with extreme daily temperatures confined between 18°C and 32°C--the optimal range for mosquito survival and viral transmission. Two environment-based, multivariate, autoregressive forecast models are developed that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. These models have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts. © The American Society of Tropical Medicine and Hygiene.
Nordic climate change: data for modeling vector borne diseases
DEFF Research Database (Denmark)
Kristensen, Birgit; Bødker, Rene
The distribution of vector species is generally restricted by a range of different climatic and geographical factors, while the development and spread of the vector-borne diseases (veterinary and zoonotic) is often primarily temperature driven. Thus temperature and its derivatives are key factors...... derivatives were calculated in order to assess the geographical and seasonal variation in the area. In order to evaluate the response of vector borne diseases to possible future climate changes and the subsequent potential spread into new areas, daily temperature predictions (mean, min and max) for three 20...... in the modelling of vector-borne diseases. This puts a high demand on the quality and accuracy of the temperature data to be used as input in such models. In order to best capture the local temporal and spatial variation in the temperature surfaces, accurate daily temperature data were used in the present project...
Labour motivation : an axiomatic vector model
Kotliarov, Ivan
2008-01-01
En el presente artículo se da una lista de axiomas necesarios para la construcción de una teoría matemática de la motivación humana. Se propone un modelo matemático de la motivación en el trabajo. La motivación se representa como un vector resultante de la motivación parcial generada por grupos específicos de necesidades. El modelo de Vroom se incluye en el modelo propuesto como ejemplo de motivación. Se establece una correlación entre los gastos de motivación, el nivel de motivación y el niv...
Multidimensional Vector Model of Stimulus-Response Compatibility
Yamaguchi, Motonori; Proctor, Robert W.
2012-01-01
The present study proposes and examines the multidimensional vector (MDV) model framework as a modeling schema for choice response times. MDV extends the Thurstonian model, as well as signal detection theory, to classification tasks by taking into account the influence of response properties on stimulus discrimination. It is capable of accounting…
Penalised Complexity Priors for Stationary Autoregressive Processes
Sørbye, Sigrunn Holbek
2017-05-25
The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR(p) model. Although it is easy to write down some prior, it is not at all obvious how to understand and interpret the prior distribution, to ensure that it behaves according to the users\\' prior knowledge. In this article, we approach this problem using the recently developed ideas of penalised complexity (PC) priors. These prior have important properties like robustness and invariance to reparameterisations, as well as a clear interpretation. A PC prior is computed based on specific principles, where model component complexity is penalised in terms of deviation from simple base model formulations. In the AR(1) case, we discuss two natural base model choices, corresponding to either independence in time or no change in time. The latter case is illustrated in a survival model with possible time-dependent frailty. For higher-order processes, we propose a sequential approach, where the base model for AR(p) is the corresponding AR(p-1) model expressed using the partial autocorrelations. The properties of the new prior distribution are compared with the reference prior in a simulation study.
A surface hydrology model for regional vector borne disease models
Tompkins, Adrian; Asare, Ernest; Bomblies, Arne; Amekudzi, Leonard
2016-04-01
Small, sun-lit temporary pools that form during the rainy season are important breeding sites for many key mosquito vectors responsible for the transmission of malaria and other diseases. The representation of this surface hydrology in mathematical disease models is challenging, due to their small-scale, dependence on the terrain and the difficulty of setting soil parameters. Here we introduce a model that represents the temporal evolution of the aggregate statistics of breeding sites in a single pond fractional coverage parameter. The model is based on a simple, geometrical assumption concerning the terrain, and accounts for the processes of surface runoff, pond overflow, infiltration and evaporation. Soil moisture, soil properties and large-scale terrain slope are accounted for using a calibration parameter that sets the equivalent catchment fraction. The model is calibrated and then evaluated using in situ pond measurements in Ghana and ultra-high (10m) resolution explicit simulations for a village in Niger. Despite the model's simplicity, it is shown to reproduce the variability and mean of the pond aggregate water coverage well for both locations and validation techniques. Example malaria simulations for Uganda will be shown using this new scheme with a generic calibration setting, evaluated using district malaria case data. Possible methods for implementing regional calibration will be briefly discussed.
Lee, Cameron C.; Sheridan, Scott C.; Barnes, Brian B.; Hu, Chuanmin; Pirhalla, Douglas E.; Ransibrahmanakul, Varis; Shein, Karsten
2017-10-01
The coastal waters of the southeastern USA contain important protected habitats and natural resources that are vulnerable to climate variability and singular weather events. Water clarity, strongly affected by atmospheric events, is linked to substantial environmental impacts throughout the region. To assess this relationship over the long-term, this study uses an artificial neural network-based time series modeling technique known as non-linear autoregressive models with exogenous input (NARX models) to explore the relationship between climate and a water clarity index (KDI) in this area and to reconstruct this index over a 66-year period. Results show that synoptic-scale circulation patterns, weather types, and precipitation all play roles in impacting water clarity to varying degrees in each region of the larger domain. In particular, turbid water is associated with transitional weather and cyclonic circulation in much of the study region. Overall, NARX model performance also varies—regionally, seasonally and interannually—with wintertime estimates of KDI along the West Florida Shelf correlating to the actual KDI at r > 0.70. Periods of extreme (high) KDI in this area coincide with notable El Niño events. An upward trend in extreme KDI events from 1948 to 2013 is also present across much of the Florida Gulf coast.
The Mixed Effects Trend Vector Model
de Rooij, Mark; Schouteden, Martijn
2012-01-01
Maximum likelihood estimation of mixed effect baseline category logit models for multinomial longitudinal data can be prohibitive due to the integral dimension of the random effects distribution. We propose to use multidimensional unfolding methodology to reduce the dimensionality of the problem. As a by-product, readily interpretable graphical…
Probability output modeling for support vector machines
Zhang, Xiang; Xiao, Xiaoling; Tian, Jinwen; Liu, Jian
2007-11-01
In this paper we propose an approach to model the posterior probability output of multi-class SVMs. The sigmoid function is used to estimate the posterior probability output in binary classification. This approach modeling the posterior probability output of multi-class SVMs is achieved by directly solving the equations that are based on the combination of the probability outputs of binary classifiers using the Bayes's rule. The differences and different weights among these two-class SVM classifiers, based on the posterior probability, are considered and given for the combination of the probability outputs among these two-class SVM classifiers in this method. The comparative experiment results show that our method achieves the better classification precision and the better probability distribution of the posterior probability than the pairwise couping method and the Hastie's optimization method.
Vector machine techniques for modeling of seismic liquefaction data
Directory of Open Access Journals (Sweden)
Pijush Samui
2014-06-01
Full Text Available This article employs three soft computing techniques, Support Vector Machine (SVM; Least Square Support Vector Machine (LSSVM and Relevance Vector Machine (RVM, for prediction of liquefaction susceptibility of soil. SVM and LSSVM are based on the structural risk minimization (SRM principle which seeks to minimize an upper bound of the generalization error consisting of the sum of the training error and a confidence interval. RVM is a sparse Bayesian kernel machine. SVM, LSSVM and RVM have been used as classification tools. The developed SVM, LSSVM and RVM give equations for prediction of liquefaction susceptibility of soil. A comparative study has been carried out between the developed SVM, LSSVM and RVM models. The results from this article indicate that the developed SVM gives the best performance for prediction of liquefaction susceptibility of soil.
Some models for epidemics of vector-transmitted diseases
Directory of Open Access Journals (Sweden)
Fred Brauer
2016-10-01
Full Text Available Vector-transmitted diseases such as dengue fever and chikungunya have been spreading rapidly in many parts of the world. The Zika virus has been known since 1947 and invaded South America in 2013. It can be transmitted not only by (mosquito vectors but also directly through sexual contact. Zika has developed into a serious global health problem because, while most cases are asymptomatic or very light, babies born to Zika - infected mothers may develop microcephaly and other very serious birth defects.We formulate and analyze two epidemic models for vector-transmitted diseases, one appropriate for dengue and chikungunya fever outbreaks and one that includes direct transmission appropriate for Zika virus outbreaks. This is especially important because the Zika virus is the first example of a disease that can be spread both indirectly through a vector and directly (through sexual contact. In both cases, we obtain expressions for the basic reproduction number and show how to use the initial exponential growth rate to estimate the basic reproduction number. However, for the model that includes direct transmission some additional data would be needed to identify the fraction of cases transmitted directly. Data for the 2015 Zika virus outbreak in Barranquilla, Colombia has been used to fit parameters to the model developed here and to estimate the basic reproduction number.
Vector-model-supported approach in prostate plan optimization.
Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Lehman, Margot; Pryor, David; Chan, Lawrence Wing Chi
2017-01-01
Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number
Directory of Open Access Journals (Sweden)
Nurul Huda
2015-04-01
Full Text Available Objective - Islamic banks are banks which its activities, both fund raising and funds distribution are on the basis of Islamic principles, namely buying and selling and profit sharing. Islamic banking is aimed at supporting the implementation of national development in order to improve justice, togetherness, and equitable distribution of welfare. In pursuit of supporting the implementation of national development, Islamic banking often faced stability problems of financing instruments being operated. In this case, it is measured by the gap between the actual rate of return and the expected rate of return. The individual actual RoR of this instrument will generate an expected rate of return. This raises the gap or difference between the actual rate of return and the expected rate of return of individual instruments, which in this case is called the abnormal rate of return. The stability of abnormal rate of return of individual instruments is certainly influenced by the stability of the expected rate of return. Expected rate of return has a volatility or fluctuation levels for each financing instrument. It is also a key element or material basis for the establishment of a variance of individual instruments. Variance in this case indicates the level of uncertainty of the rate of return. Individual variance is the origin of the instrument base for variance in the portfolio finance that further a portfolio analysis. So, this paper is going to analyze the level of expected RoR volatility as an initial step to see and predict the stability of the fluctuations in the rate of return of Indonesian Islamic financing instruments.Methods – Probability of Occurence, Expected Rate of Return (RoR and GARCH (Generalized Autoregressive Conditional Heteroscedasticity.Results - The expected RoR volatility of the murabaha and istishna financing instruments tend to be more volatile than expected RoR volatility of musharaka and qardh financing instruments
Preclinical models to assess the immunogenicity of AAV vectors.
Ertl, Hildegund C J
2017-11-23
Although gene transfer using adeno-associated virus (AAV) vectors has made tremendous progress in recent years, challenges remain due to vector-specific adaptive immune responses. Specifically, AAV-neutralizing antibodies reduce AAV-transduction rates, while CD8+ T cells directed to AAV capsid antigens cause rejection of AAV-transduced cells. This has been addressed clinically by excluding humans with pre-existing AAV-neutralizing antibodies from gene transfer trials or by using immunosuppression or reduced doses of vectors expressing improved transgene products to blunt or circumvent destructive T cell responses. Although these approaches have met with success for treatment of some diseases, most notably hemophilia B, they may not be suitable for others. Pre-clinical models are thus needed to test alternative options to sidestep pre-existing AAV-neutralizing antibodies, to prevent their induction following gene transfer and to block the detrimental effects of CD8+ T cells directed to AAV capsid antigens. This chapter describes some of the available, although not yet perfect, models that can assess immune responses to AAV gene transfer. Copyright © 2017 Elsevier Inc. All rights reserved.
Twin support vector machines models, extensions and applications
Jayadeva; Chandra, Suresh
2017-01-01
This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.
Implications of vector boson scattering unitarity in composite Higgs models
Buarque Franzosi, Diogo; Ferrarese, Piero
2017-09-01
The strong nature of composite Higgs models manifests at high energies through the growing behavior of the scattering amplitudes of longitudinally polarized weak bosons that leads to the formation of composite resonances as well as nonresonant strong effects. In this work the unitarity of these scattering amplitudes is used as a tool to assess the profile of the composite spectrum of the theory, including nonresonant enhancements, vector resonances and the C P -even scalar excitation. These three signatures are then studied in realistic scattering processes at hadron colliders, aiming to estimate the potential to exclude dynamically motivated scenarios of composite Higgs models.
Using support vector machine models for crash injury severity analysis.
Li, Zhibin; Liu, Pan; Wang, Wei; Xu, Chengcheng
2012-03-01
The study presented in this paper investigated the possibility of using support vector machine (SVM) models for crash injury severity analysis. Based on crash data collected at 326 freeway diverge areas, a SVM model was developed for predicting the injury severity associated with individual crashes. An ordered probit (OP) model was also developed using the same dataset. The research team compared the performance of the SVM model and the OP model. It was found that the SVM model produced better prediction performance for crash injury severity than did the OP model. The percent of correct prediction for the SVM model was found to be 48.8%, which was higher than that produced by the OP model (44.0%). Even though the SVM model may suffer from the multi-class classification problem, it still provides better prediction results for small proportion injury severities than the OP model does. The research also investigated the potential of using the SVM model for evaluating the impacts of external factors on crash injury severities. The sensitivity analysis results show that the SVM model produced comparable results regarding the impacts of variables on crash injury severity as compared to the OP model. For several variables such as the length of the exit ramp and the shoulder width of the freeway mainline, the results of the SVM model are more reasonable than those of the OP model. Copyright © 2011 Elsevier Ltd. All rights reserved.
Model Checking Vector Addition Systems with one zero-test
Bonet, Rémi; Leroux, Jérôme; Zeitoun, Marc
2012-01-01
We design a variation of the Karp-Miller algorithm to compute, in a forward manner, a finite representation of the cover (i.e., the downward closure of the reachability set) of a vector addition system with one zero-test. This algorithm yields decision procedures for several problems for these systems, open until now, such as place-boundedness or LTL model-checking. The proof techniques to handle the zero-test are based on two new notions of cover: the refined and the filtered cover. The refined cover is a hybrid between the reachability set and the classical cover. It inherits properties of the reachability set: equality of two refined covers is undecidable, even for usual Vector Addition Systems (with no zero-test), but the refined cover of a Vector Addition System is a recursive set. The second notion of cover, called the filtered cover, is the central tool of our algorithms. It inherits properties of the classical cover, and in particular, one can effectively compute a finite representation of this set, e...
Energy Technology Data Exchange (ETDEWEB)
Cheng, Yanjie; Tang, Youmin; Jackson, Peter [University of Northern British Columbia, Environmental Science and Engineering, Prince George, BC (Canada); Zhou, Xiaobing [University of Northern British Columbia, Environmental Science and Engineering, Prince George, BC (Canada); Centre for Australian Weather and Climate Research (CAWCR), Bureau of Meteorology, Melbourne, VIC (Australia); Chen, Dake [Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY (United States); State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou (China)
2010-10-15
In this study, singular vector analysis was performed for the period from 1856 to 2003 using the latest Zebiak-Cane model version LDEO5. The singular vector, representing the optimal growth pattern of initial perturbations/errors, was obtained by perturbing the constructed tangent linear model of the Zebiak-Cane model. Variations in the singular vector and singular value, as a function of initial time, season, ENSO states, and optimal period, were investigated. Emphasis was placed on exploring relative roles of linear and nonlinear processes in the optimal perturbation growth of ENSO, and deriving statistically robust conclusions using long-term singular vector analysis. It was found that the first singular vector is dominated by a west-east dipole spanning most of the equatorial Pacific, with one center located in the east and the other in the central Pacific. Singular vectors are less sensitive to initial conditions, i.e., independence of seasons and decades; while singular values exhibit a strong sensitivity to initial conditions. The dynamical diagnosis shows that the total linear and nonlinear heating terms play opposite roles in controlling the optimal perturbation growth, and that the linear optimal perturbation is more than twice as large as the nonlinear one. The total linear heating causes a warming effect and controls two positive perturbation growth regions: one in the central Pacific and the other in the eastern Pacific; whereas the total linearized nonlinear advection brings a cooling effect controlling the negative perturbation growth in the central Pacific. (orig.)
SO(10) models for flavor with vector-like fermions
Saad, Shaikh
2017-11-01
In this work, unified models based on S O(10) symmetry is presented which provides insights into the flavor observables of charged fermions and the neutrinos. Unlike the conventional S O(10) models, the Higgs boson 10H belonging to the fundamental representation is not present in this new class of models. Instead vector-like fermions in the 16 + 16 ¯ representation is introduced to induce the flavor mixing. A variety of scenarios, both non-supersymmetric and supersymmetric, are studied involving a 126 ¯H Higgs boson. For symmetry breaking purpose, 126 ¯H Higgs is accompanied by either a 45H or a 210H of Higgs boson. Our analysis shows that this framework, by utilizing either type-I or type-II seesaw mechanism, an excellent fit to the fermion masses and mixings can be obtained with a limited number of parameters. To test and distinguish these flavor models, proton decay branching ratios are also computed.
Higgs boson phenomenology in a simple model with vector resonances
Energy Technology Data Exchange (ETDEWEB)
Castillo-Felisola, Oscar; Corral, Cristobal; Gonzalez, Marcela; Moreno, Gaston; Neill, Nicolas A.; Rojas, Felipe; Zamora, Jilberto; Zerwekh, Alfonso R. [Universidad Tecnica Federico Santa Maria, Departamento de Fisica, Valparaiso (Chile); Universidad Tecnica Federico Santa Maria, Centro Cientifico-Tecnologico de Valparaiso, Valparaiso (Chile)
2013-12-15
In this paper we consider a simple scenario where the Higgs boson and two vector resonances are supposed to arise from a new strong interacting sector. We use the ATLAS measurements of the dijet spectrum to set limits on the masses of the resonances. Additionally we compute the Higgs boson decay to two photons and found, when compare to the Standard Model prediction, a small excess which is compatible with ATLAS measurements. Finally we make prediction for Higgs-strahlung processes for the LHC running at 14 TeV. (orig.)
Testing and inference in nonlinear cointegrating vector error correction models
DEFF Research Database (Denmark)
Kristensen, D.; Rahbek, A.
2013-01-01
We analyze estimators and tests for a general class of vector error correction models that allows for asymmetric and nonlinear error correction. For a given number of cointegration relationships, general hypothesis testing is considered, where testing for linearity is of particular interest. Under...... the null of linearity, parameters of nonlinear components vanish, leading to a nonstandard testing problem. We apply so-called sup-tests to resolve this issue, which requires development of new(uniform) functional central limit theory and results for convergence of stochastic integrals. We provide a full...
Structure of Vector Mesons in Holographic Model with Linear Confinement
Energy Technology Data Exchange (ETDEWEB)
Anatoly Radyushkin; Hovhannes Grigoryan
2007-11-01
We investigate wave functions and form factors of vector mesons in the holographic dual model of QCD with oscillator-like infrared cutoff. We introduce wave functions conjugate to solutions of the 5D equation of motion and develop a formalism based on these wave functions, which are very similar to those of a quantum-mechanical oscillator. For the lowest bound state (rho-meson), we show that all its elastic form factors can be built from the basic form factor which, in this model, exhibits a perfect vector meson dominance, i.e., is given by the rho-pole contribution alone. We calculate the electric radius of the rho-meson and find the value _C = 0.655 fm, which is larger than in the case of the hard-wall cutoff. We calculate the coupling constant f_rho and find that the experimental value is in the middle between the values given by the oscillator and hard-wall models.
Methodology for the AutoRegressive Planet Search (ARPS) Project
Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration
2018-01-01
The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.
Support Vector Regression Model for Direct Methanol Fuel Cell
Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.
2012-07-01
The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R2) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.
Vector cylindrical harmonics for low-dimensional convection models
Kelley, Douglas H; Knox, Catherine A
2016-01-01
Approximate empirical models of thermal convection can allow us to identify the essential properties of the flow in simplified form, and to produce empirical estimates using only a few parameters. Such "low-dimensional" empirical models can be constructed systematically by writing numerical or experimental measurements as superpositions of a set of appropriate basis modes, a process known as Galerkin projection. For Boussinesq convection in a cylinder, those basis modes should be defined in cylindrical coordinates, vector-valued, divergence-free, and mutually orthogonal. Here we construct two such basis sets, one using Bessel functions in the radial direction, and one using Chebyshev polynomials. We demonstrate that each set has those desired characteristics and demonstrate the advantages and drawbacks of each set. We show their use for representing sample simulation data and point out their potential for low-dimensional convection models.
Comparing SVARs and SEMs : Two models of the UK economy
Jacobs, J.P.A.M.; Wallis, K.F.
2005-01-01
The structural vector autoregression (SVAR) and simultaneous equation macroeconometric model (SEM) styles of empirical macroeconomic modelling are compared and contrasted, with reference to two models of the UK economy, namely the long-run structural VAR model of Garratt, Lee, Pesaran and Shin and
Environmental statistical modelling of mosquito vectors at different geographical scales
Cianci, D.
2015-01-01
Vector-borne diseases are infections transmitted by the bite of infected arthropod vectors, such as mosquitoes, ticks, fleas, midges and flies. Vector-borne diseases pose an increasingly wider threat to global public health, both in terms of people affected and their geographical spread. Mosquitoes
Energy Technology Data Exchange (ETDEWEB)
Castro, Jorge Henrique de [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil); Silva, Alexandre Pinto Alves da [Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Eletrica
2010-07-01
Develop the natural gas network is critical success factor for the distribution company. It is a decision that employs the demand given location 'x' and a future time 't' so that the net allows the best conditions for the return of the capital. In this segment, typical network industry, the spatial infra-structure vision associated to the market allows better evaluation of the business because to mitigate costs and risks. In fact, economic models little developed in order to assess the question of the location, due to its little employment by economists. The objective of this article is to analyze the application of spatial perspective in natural gas demand forecasting and to identify the models that can be employed observing issues of dependency and spatial heterogeneity; as well as the capacity of mapping of variables associated with the problem. (author)
DEFF Research Database (Denmark)
Græsbøll, Kaare
The main outcome of this PhD project is a generic model for non-contagious infectious vector-borne disease spread by one vector species between up to two species of hosts distributed on farms and pasture. The model features a within-herd model of disease, combined with a triple movement kernel...... that describes spread of disease using vectors or hosts as agents of the spread. The model is run with bluetongue as the primary case study, and it is demonstrated how an epidemic outbreak of bluetongue 8 in Denmark is sensitive to the use of pasture, climate, vaccination, vector abundance, and flying parameters....... In constructing a more process oriented agent-based approach to spread modeling new parameters describing vector behavior were introduced. When these vector flying parameters have been quantified by experiments, this model can be implemented on areas naïve to the modeled disease with a high predictive power...
Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression
Directory of Open Access Journals (Sweden)
Lili Li
2016-01-01
Full Text Available We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7 and China. The quantile autoregression model (QAR enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.
Testing and Inference in Nonlinear Cointegrating Vector Error Correction Models
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbek, Anders
In this paper, we consider a general class of vector error correction models which allow for asymmetric and non-linear error correction. We provide asymptotic results for (quasi-)maximum likelihood (QML) based estimators and tests. General hypothesis testing is considered, where testing...... for linearity is of particular interest as parameters of non-linear components vanish under the null. To solve the latter type of testing, we use the so-called sup tests, which here requires development of new (uniform) weak convergence results. These results are potentially useful in general for analysis...... symmetric non-linear error correction are considered. A simulation study shows that the finite sample properties of the bootstrapped tests are satisfactory with good size and power properties for reasonable sample sizes....
Testing and Inference in Nonlinear Cointegrating Vector Error Correction Models
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbæk, Anders
In this paper, we consider a general class of vector error correction models which allow for asymmetric and non-linear error correction. We provide asymptotic results for (quasi-)maximum likelihood (QML) based estimators and tests. General hypothesis testing is considered, where testing...... for linearity is of particular interest as parameters of non-linear components vanish under the null. To solve the latter type of testing, we use the so-called sup tests, which here requires development of new (uniform) weak convergence results. These results are potentially useful in general for analysis...... symmetric non-linear error correction considered. A simulation study shows that the fi…nite sample properties of the bootstrapped tests are satisfactory with good size and power properties for reasonable sample sizes....
Analysis of a vector-bias model on malaria transmission.
Chamchod, Farida; Britton, Nicholas F
2011-03-01
We incorporate a vector-bias term into a malaria-transmission model to account for the greater attractiveness of infectious humans to mosquitoes in terms of differing probabilities that a mosquito arriving at a human at random picks that human depending on whether he is infectious or susceptible. We prove that transcritical bifurcation occurs at the basic reproductive ratio equalling 1 by projecting the flow onto the extended centre manifold. We next study the dynamics of the system when incubation time of malaria parasites in mosquitoes is included, and find that the longer incubation time reduces the prevalence of malaria. Also, we incorporate a random movement of mosquitoes as a diffusion term and a chemically directed movement of mosquitoes to humans expressed in terms of sweat and body odour as a chemotaxis term to study the propagation of infected population to uninfected population. We find that a travelling wave occurs; its speed is calculated numerically and estimated for the lower bound analytically.
Periodically integrated subset autoregressions for Dutch industrial production and money stock
Ph.H.B.F. Franses (Philip Hans)
1993-01-01
textabstractThe univariate quarterly Dutch series of industrial production and money stock are both modelled with a periodically integrated subset autoregression (PISA). This model for a non-stationary series allows the lag orders, the values of the parameters and the cyclical patterns to vary over
Use of a mixture statistical model in studying malaria vectors density.
Directory of Open Access Journals (Sweden)
Olayidé Boussari
Full Text Available Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with "village" as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%. The NPMP model had a good aptitude to predict the observed values and showed that: i proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.
THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...
African Journals Online (AJOL)
for any input (feed intake). Existing knowledge indicates that effIciency of feed utilization varies ... are evaluated. If consumer preferences and future demand remain static, this will be no problem: it ..... The physiology of growth: Animals as input - output devices. Proc. Intern. Summer School on the Computer and Res. in Nutr.
Multivariate autoregressive algorithms for ocean wave modelling
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Lyons, G.J.; Witz, J.A.
stream_size 8 stream_content_type text/plain stream_name 2_Int_Offshore_Polar_Eng_Conf_Proc_1992_77.pdf.txt stream_source_info 2_Int_Offshore_Polar_Eng_Conf_Proc_1992_77.pdf.txt Content-Encoding ISO-8859-1 Content-Type text...
Ecological footprint model using the support vector machine technique.
Ma, Haibo; Chang, Wenjuan; Cui, Guangbai
2012-01-01
The per capita ecological footprint (EF) is one of the most widely recognized measures of environmental sustainability. It aims to quantify the Earth's biological resources required to support human activity. In this paper, we summarize relevant previous literature, and present five factors that influence per capita EF. These factors are: National gross domestic product (GDP), urbanization (independent of economic development), distribution of income (measured by the Gini coefficient), export dependence (measured by the percentage of exports to total GDP), and service intensity (measured by the percentage of service to total GDP). A new ecological footprint model based on a support vector machine (SVM), which is a machine-learning method based on the structural risk minimization principle from statistical learning theory was conducted to calculate the per capita EF of 24 nations using data from 123 nations. The calculation accuracy was measured by average absolute error and average relative error. They were 0.004883 and 0.351078% respectively. Our results demonstrate that the EF model based on SVM has good calculation performance.
Zhou, Lim Yi; Shan, Fam Pei; Shimizu, Kunio; Imoto, Tomoaki; Lateh, Habibah; Peng, Koay Swee
2017-08-01
A comparative study of logistic regression, support vector machine (SVM) and least square support vector machine (LSSVM) models has been done to predict the slope failure (landslide) along East-West Highway (Gerik-Jeli). The effects of two monsoon seasons (southwest and northeast) that occur in Malaysia are considered in this study. Two related factors of occurrence of slope failure are included in this study: rainfall and underground water. For each method, two predictive models are constructed, namely SOUTHWEST and NORTHEAST models. Based on the results obtained from logistic regression models, two factors (rainfall and underground water level) contribute to the occurrence of slope failure. The accuracies of the three statistical models for two monsoon seasons are verified by using Relative Operating Characteristics curves. The validation results showed that all models produced prediction of high accuracy. For the results of SVM and LSSVM, the models using RBF kernel showed better prediction compared to the models using linear kernel. The comparative results showed that, for SOUTHWEST models, three statistical models have relatively similar performance. For NORTHEAST models, logistic regression has the best predictive efficiency whereas the SVM model has the second best predictive efficiency.
Trend stationarity in the I(2) cointegration model
DEFF Research Database (Denmark)
Rahbek, Anders; Kongsted, Hans Christian; Jørgensen, Clara
1999-01-01
A vector autoregressive model for I(2) processes which allows for trend-stationary components and restricts the deterministic part of the process to be at most linear is defined. A two-step statistical analysis of the model is derived. The joint test of I(1) and I(2) cointegrating ranks is shown...
On a Numerical and Graphical Technique for Evaluating some Models Involving Rational Expectations
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
Campbell and Shiller (1987) proposed a graphical technique for the present value model which consists of plotting the spread and theoretical spread as calculated from the cointegrated vector autoregressive model. We extend these techniques to a number of rational expectation models and give...
On a numerical and graphical technique for evaluating some models involving rational expectations
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
Campbell and Shiller (1987) proposed a graphical technique for the present value model which consists of plotting the spread and theoretical spread as calculated from the cointegrated vector autoregressive model. We extend these techniques to a number of rational expectation models and give...
Mathematical modelling of vector-borne diseases and insecticide resistance evolution.
Gabriel Kuniyoshi, Maria Laura; Pio Dos Santos, Fernando Luiz
2017-01-01
Vector-borne diseases are important public health issues and, consequently, in silico models that simulate them can be useful. The susceptible-infected-recovered (SIR) model simulates the population dynamics of an epidemic and can be easily adapted to vector-borne diseases, whereas the Hardy-Weinberg model simulates allele frequencies and can be used to study insecticide resistance evolution. The aim of the present study is to develop a coupled system that unifies both models, therefore enabling the analysis of the effects of vector population genetics on the population dynamics of an epidemic. Our model consists of an ordinary differential equation system. We considered the populations of susceptible, infected and recovered humans, as well as susceptible and infected vectors. Concerning these vectors, we considered a pair of alleles, with complete dominance interaction that determined the rate of mortality induced by insecticides. Thus, we were able to separate the vectors according to the genotype. We performed three numerical simulations of the model. In simulation one, both alleles conferred the same mortality rate values, therefore there was no resistant strain. In simulations two and three, the recessive and dominant alleles, respectively, conferred a lower mortality. Our numerical results show that the genetic composition of the vector population affects the dynamics of human diseases. We found that the absolute number of vectors and the proportion of infected vectors are smaller when there is no resistant strain, whilst the ratio of infected people is larger in the presence of insecticide-resistant vectors. The dynamics observed for infected humans in all simulations has a very similar shape to real epidemiological data. The population genetics of vectors can affect epidemiological dynamics, and the presence of insecticide-resistant strains can increase the number of infected people. Based on the present results, the model is a basis for development of
On detecting and modeling periodic correlation in financial data
Broszkiewicz-Suwaj, E.; Makagon, A.; Weron, R.; Wyłomańska, A.
2004-05-01
For many economic problems standard statistical analysis, based on the notion of stationarity, is not adequate. These include modeling seasonal decisions of consumers, forecasting business cycles and-as we show in the present article-modeling wholesale power market prices. We apply standard methods and a novel spectral domain technique to conclude that electricity price returns exhibit periodic correlation with daily and weekly periods. As such they should be modeled with periodically correlated processes. We propose to apply periodic autoregression models which are closely related to the standard instruments in econometric analysis-vector autoregression models.
Modelling bluetongue virus transmission between farms using animal and vector movements
Turner, Joanne; Bowers, Roger G.; Baylis, Matthew
2012-01-01
Bluetongue is a notifiable disease of ruminants which, in 2007, occurred for the first time in England. We present the first model for bluetongue that explicitly incorporates farm to farm movements of the two main hosts, as well as vector dispersal. The model also includes a seasonal vector to host ratio and dynamic restriction zones that evolve as infection is detected. Batch movements of sheep were included by modelling degree of mixing at markets. We investigate the transmission of bluetongue virus between farms in eastern England (the focus of the outbreak). Results indicate that most parameters affecting outbreak size relate to vectors and that the infection generally cannot be maintained without between-herd vector transmission. Movement restrictions are effective at reducing outbreak size, and a targeted approach would be as effective as a total movement ban. The model framework is flexible and can be adapted to other vector-borne diseases of livestock. PMID:22432051
Gene therapy model of X-linked severe combined immunodeficiency using a modified foamy virus vector.
Directory of Open Access Journals (Sweden)
Satoshi Horino
Full Text Available X-linked severe combined immunodeficiency (SCID-X1 is an inherited genetic immunodeficiency associated with mutations in the common cytokine receptor γ chain (γc gene, and characterized by a complete defect of T and natural killer (NK cells. Gene therapy for SCID-X1 using conventional retroviral (RV vectors carrying the γc gene results in the successful reconstitution of T cell immunity. However, the high incidence of vector-mediated T cell leukemia, caused by vector insertion near or within cancer-related genes has been a serious problem. In this study, we established a gene therapy model of mouse SCID-X1 using a modified foamy virus (FV vector expressing human γc. Analysis of vector integration in a human T cell line demonstrated that the FV vector integration sites were significantly less likely to be located within or near transcriptional start sites than RV vector integration sites. To evaluate the therapeutic efficacy, bone marrow cells from γc-knockout (γc-KO mice were infected with the FV vector and transplanted into γc-KO mice. Transplantation of the FV-treated cells resulted in the successful reconstitution of functionally active T and B cells. These data suggest that FV vectors can be effective and may be safer than conventional RV vectors for gene therapy for SCID-X1.
Directory of Open Access Journals (Sweden)
David M Poché
2016-08-01
Full Text Available Visceral leishmaniasis (VL is a disease caused by two known vector-borne parasite species (Leishmania donovani, L. infantum, transmitted to man by phlebotomine sand flies (species: Phlebotomus and Lutzomyia, resulting in ≈50,000 human fatalities annually, ≈67% occurring on the Indian subcontinent. Indoor residual spraying is the current method of sand fly control in India, but alternative means of vector control, such as the treatment of livestock with systemic insecticide-based drugs, are being evaluated. We describe an individual-based, stochastic, life-stage-structured model that represents a sand fly vector population within a village in India and simulates the effects of vector control via fipronil-based drugs orally administered to cattle, which target both blood-feeding adults and larvae that feed on host feces.Simulation results indicated efficacy of fipronil-based control schemes in reducing sand fly abundance depended on timing of drug applications relative to seasonality of the sand fly life cycle. Taking into account cost-effectiveness and logistical feasibility, two of the most efficacious treatment schemes reduced population peaks occurring from April through August by ≈90% (applications 3 times per year at 2-month intervals initiated in March and >95% (applications 6 times per year at 2-month intervals initiated in January relative to no control, with the cumulative number of sand fly days occurring April-August reduced by ≈83% and ≈97%, respectively, and more specifically during the summer months of peak human exposure (June-August by ≈85% and ≈97%, respectively.Our model should prove useful in a priori evaluation of the efficacy of fipronil-based drugs in controlling leishmaniasis on the Indian subcontinent and beyond.
ANALISIS PERILAKU SEKTOR PERTANIAN INDONESIA: APLIKASI VECTOR ERROR CORRECTION MODEL
Directory of Open Access Journals (Sweden)
Andi Irawan
2011-08-01
Full Text Available Normal 0 false false false MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} The specific goals of this study were:Firstly, in the long run perspective, the goal is to analyze impact of policythat inflate agriculture price to growth, employment, and investment inagriculture sector. Secondly, in the short run perspective, the goals are: (1to analyze which economic blocks that have most producing instability toagriculture sector, (2 to analyze behaviour af inflation in agriculture sectorand causality relationship both among output price and input prices and amonginput prices. Quantitative methods used ini this studywere Vector Error Correction Model, Johansen Cointegration Test, and GrangerCausality Test. Data used in this study come from several sources such as BankIndonesia, BPS Statistic, International Financial Statistic and CEIC dataCompany Limited, series data from first monthly of 1993 (1993:01 up to thelast monthly of 2002 (2002:12. In the agriculture sector, production(output and capital are responsive to change in the output price. This meanthat inflating the output price effectively help generate output and newinvestment in this sector. Nevertheless because shock in price can be source ofinstability to agriculture sector, so government should be carefully applypolicies that can inflating the price in agriculture. To solve unemploymentproblem in agriculture sector, government should apply cost strategy such assubsidy policy of input price.
Lentiviral vectors in neurodegenrative disorders - Aspects in gene therapy and disease models
DEFF Research Database (Denmark)
Nielsen, Troels Tolstrup
2009-01-01
, which is most often only satisfactory in the initial phase of the disease. Gene therapy is a novel treatment strategy intended to treat or alleviate disease by genetically modifying cells by introducing nucleic acids into the cells. Lentiviral vectors hold great promise as gene transfer vectors...... expression and escape transgene silencing during differentiation of neural stem cell lines. However, insulator vectors appeared to be impaired in functionality, which has importance for the future use of insulators in viral vectors. Finally, cell based models of HD was constructed to elucidate...
Skin injury model classification based on shape vector analysis.
Röhrich, Emil; Thali, Michael; Schweitzer, Wolf
2012-11-06
Skin injuries can be crucial in judicial decision making. Forensic experts base their classification on subjective opinions. This study investigates whether known classes of simulated skin injuries are correctly classified statistically based on 3D surface models and derived numerical shape descriptors. Skin injury surface characteristics are simulated with plasticine. Six injury classes - abrasions, incised wounds, gunshot entry wounds, smooth and textured strangulation marks as well as patterned injuries - with 18 instances each are used for a k-fold cross validation with six partitions. Deformed plasticine models are captured with a 3D surface scanner. Mean curvature is estimated for each polygon surface vertex. Subsequently, distance distributions and derived aspect ratios, convex hulls, concentric spheres, hyperbolic points and Fourier transforms are used to generate 1284-dimensional shape vectors. Subsequent descriptor reduction maximizing SNR (signal-to-noise ratio) result in an average of 41 descriptors (varying across k-folds). With non-normal multivariate distribution of heteroskedastic data, requirements for LDA (linear discriminant analysis) are not met. Thus, shrinkage parameters of RDA (regularized discriminant analysis) are optimized yielding a best performance with λ = 0.99 and γ = 0.001. Receiver Operating Characteristic of a descriptive RDA yields an ideal Area Under the Curve of 1.0 for all six categories. Predictive RDA results in an average CRR (correct recognition rate) of 97,22% under a 6 partition k-fold. Adding uniform noise within the range of one standard deviation degrades the average CRR to 71,3%. Digitized 3D surface shape data can be used to automatically classify idealized shape models of simulated skin injuries. Deriving some well established descriptors such as histograms, saddle shape of hyperbolic points or convex hulls with subsequent reduction of dimensionality while maximizing SNR seem to work well for the data at hand, as
Modeling Dynamics of Wikipedia: An Empirical Analysis Using a Vector Error Correction Model
Directory of Open Access Journals (Sweden)
Liu Feng-Jun
2017-01-01
Full Text Available In this paper, we constructed a system dynamic model of Wikipedia based on the co-evolution theory, and investigated the interrelationships among topic popularity, group size, collaborative conflict, coordination mechanism, and information quality by using the vector error correction model (VECM. This study provides a useful framework for analyzing the dynamics of Wikipedia and presents a formal exposition of the VECM methodology in the information system research.
Global properties of vector-host disease models with time delays.
Cai, Li-Ming; Li, Xue-Zhi; Fang, Bin; Ruan, Shigui
2017-05-01
Since there exist extrinsic and intrinsic incubation periods of pathogens in the feedback interactions between the vectors and hosts, it is necessary to consider the incubation delays in vector-host disease transmission dynamics. In this paper, we propose vector-host disease models with two time delays, one describing the incubation period in the vector population and another representing the incubation period in the host population. Both distributed and discrete delays are used. By constructing suitable Liapunov functions, we obtain sufficient conditions for the global stability of the endemic equilibria of these models. The analytic results reveal that the global dynamics of such vector-host disease models with time delays are completely determined by the basic reproduction number. Some specific cases with discrete delay are studied and the corresponding results are improved.
Eigenvalues of Bethe vectors in the Gaudin model
Molev, A. I.; Mukhin, E. E.
2017-09-01
According to the Feigin-Frenkel-Reshetikhin theorem, the eigenvalues of higher Gaudin Hamiltonians on Bethe vectors can be found using the center of an affine vertex algebra at the critical level. We recently calculated explicit Harish-Chandra images of the generators of the center in all classical types. Combining these results leads to explicit formulas for the eigenvalues of higher Gaudin Hamiltonians on Bethe vectors. The Harish-Chandra images can be interpreted as elements of classical W-algebras. By calculating classical limits of the corresponding screening operators, we elucidate a direct connection between the rings of q-characters and classical W-algebras.
Estimating transmitted waves of floating breakwater using support vector regression model
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Hegde, A.V.; Kumar, V.; Patil, S.G.
to diameter of pipes (S/D). The radial basis functions performed well than the polynomial function in the regressive support vector machine as the kernel function for the given set of data. The support vector regression model gives the correlation coefficients...
The Japanese Economic Model: JEM
Fujiwara, Ippei; Hara, Naoko; Hirose, Yasuo; Teranishi, Yuki
2004-01-01
In this paper, we set out the Japanese Economic Model (JEM), a large-scale macroeconomic model of the Japanese economy. Although the JEM is a theoretical model designed with a view to overcoming the Lucas ( 1976) critique of traditional large-scale macroeconomic models, it can also be used for both projection and simulation analysis. This is achieved by embedding a mechanism within which "short-run dynamics," basically captured by a vector autoregression model, eventually converge to a "short...
A Hierarchical Bayes Error Correction Model to Explain Dynamic Effects of Price Changes
D. Fok (Dennis); R. Paap (Richard); C. Horváth (Csilla); Ph.H.B.F. Franses (Philip Hans)
2005-01-01
textabstractThe authors put forward a sales response model to explain the differences in immediate and dynamic effects of promotional prices and regular prices on sales. The model consists of a vector autoregression rewritten in error-correction format which allows to disentangle the immediate
System Identification of Civil Engineering Structures using State Space and ARMAV Models
DEFF Research Database (Denmark)
Andersen, P.; Kirkegaard, Poul Henning; Brincker, Rune
In this paper the relations between an ambient excited structural system, represented by an innovation state space system, and the Auto-Regressive Moving Average Vector (ARMAV) model are considered. It is shown how to obtain a multivariate estimate of the ARMAV model from output measurements, usi...
Modeling the two-point correlation of the vector stream function
Oberlack, M.; Rogers, M. M.; Reynolds, W. C.
1994-01-01
A new model for the two-point vector stream function correlation has been developed using tensor invariant arguments and evaluated by the comparison of model predictions with DNS data for incompressible homogeneous turbulent shear flow. This two-point vector stream function model correlation can then be used to calculate the two-point velocity correlation function and other quantities useful in turbulence modeling. The model assumes that the two-point vector stream function correlation can be written in terms of the separation vector and a new tensor function that depends only on the magnitude of the separation vector. The model has a single free model coefficient, which has been chosen by comparison with the DNS data. The relative error of the model predictions of the two-point vector stream function correlation is only a few percent for a broad range of the model coefficient. Predictions of the derivatives of this correlation, which are of interest in turbulence modeling, may not be this accurate.
Multivariate time series modeling of selected childhood diseases in ...
African Journals Online (AJOL)
This process revealed that except tetanus; malaria, URTI, Pneumonia and anaemia series are interrelated. Hence, the four interrelated time series were considered in the multivariate analysis. Order selection criteria were employed to determine the order of the vector autoregressive (VAR) model to be fitted to these series.
A vector model for off-axis hysteresis loops using anisotropy field
Energy Technology Data Exchange (ETDEWEB)
Jamali, Ali, E-mail: alijamal@gwu.edu [Electrical and Computer Engineering Department, The George Washington University, Washington, D.C. 20052 (United States); Torre, Edward Della [Electrical and Computer Engineering Department, The George Washington University, Washington, D.C. 20052 (United States); Cardelli, Ermanno [Department of Engineering, University of Perugia, Perugia (Italy); ElBidweihy, Hatem [Electrical and Computer Engineering Department, United States Naval Academy, Annapolis, MD 21402 (United States); Bennett, Lawrence H. [Electrical and Computer Engineering Department, The George Washington University, Washington, D.C. 20052 (United States)
2016-11-15
A model for the off-axis vector magnetization of a distribution of uniaxial particles is presented. Recent work by the authors decomposed the magnetization into two components and modeled the total vector magnetization as their vector sum. In this paper, to account for anisotropy, the direction of the reversible magnetization component is specified by the vector sum of the applied field and an effective anisotropy field. The formulation of the new anisotropy field (AF) model is derived and its results are discussed considering (i) oscillation and rotational modes, (ii) lag angle, and (iii) unitary magnetization. The advantages of the AF model are outlined by comparing its results to the results of the classical Stoner–Wohlfarth model.
Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya
Directory of Open Access Journals (Sweden)
Alfred O. Ochieng
2016-11-01
Full Text Available Background: Rift Valley fever (RVF is a vector-borne zoonotic disease that has an impact on human health and animal productivity. Here, we explore the use of vector presence modelling to predict the distribution of RVF vector species under climate change scenario to demonstrate the potential for geographic spread of Rift Valley fever virus (RVFV. Objectives: To evaluate the effect of climate change on RVF vector distribution in Baringo County, Kenya, with an aim of developing a risk map for spatial prediction of RVF outbreaks. Methodology: The study used data on vector presence and ecological niche modelling (MaxEnt algorithm to predict the effect of climatic change on habitat suitability and the spatial distribution of RVF vectors in Baringo County. Data on species occurrence were obtained from longitudinal sampling of adult mosquitoes and larvae in the study area. We used present (2000 and future (2050 Bioclim climate databases to model the vector distribution. Results: Model results predicted potential suitable areas with high success rates for Culex quinquefasciatus, Culex univitattus, Mansonia africana, and Mansonia uniformis. Under the present climatic conditions, the lowlands were found to be highly suitable for all the species. Future climatic conditions indicate an increase in the spatial distribution of Cx. quinquefasciatus and M. africana. Model performance was statistically significant. Conclusion: Soil types, precipitation in the driest quarter, precipitation seasonality, and isothermality showed the highest predictive potential for the four species.
Robert, Michael A; Okamoto, Kenichi; Lloyd, Alun L; Gould, Fred
2013-01-01
Genetic approaches for controlling disease vectors have aimed either to reduce wild-type populations or to replace wild-type populations with insects that cannot transmit pathogens. Here, we propose a Reduce and Replace (R&R) strategy in which released insects have both female-killing and anti-pathogen genes. We develop a mathematical model to numerically explore release strategies involving an R&R strain of the dengue vector Aedes aegypti. We show that repeated R&R releases may lead to a temporary decrease in mosquito population density and, in the absence of fitness costs associated with the anti-pathogen gene, a long-term decrease in competent vector population density. We find that R&R releases more rapidly reduce the transient and long-term competent vector densities than female-killing releases alone. We show that releases including R&R females lead to greater reduction in competent vector density than male-only releases. The magnitude of reduction in total and competent vectors depends upon the release ratio, release duration, and whether females are included in releases. Even when the anti-pathogen allele has a fitness cost, R&R releases lead to greater reduction in competent vectors than female-killing releases during the release period; however, continued releases are needed to maintain low density of competent vectors long-term. We discuss the results of the model as motivation for more detailed studies of R&R strategies.
Dynamic Model Based Vector Control of Linear Induction Motor
2012-05-01
sensorless control is critical for LIM control in some special case. Reference [13] introduces a direct torque and flux control based on space...Industry Applications, IEEE Transactions on, vol. 28, no. 5, pp. 1054–1061, 1992. [4] J. Nash, “ Direct torque control , induction motor vector ...13] C. Lascu, I. Boldea, and F. Blaabjerg, “A modified direct torque control for induction motor sensorless drive,” Industry Applications,
Vector and axial-vector resonances in composite models of the Higgs boson
Energy Technology Data Exchange (ETDEWEB)
Franzosi, Diogo Buarque [II. Physikalisches Institut, Universität Göttingen,Friedrich-Hund-Platz 1, 37077 Göttingen (Germany); Cacciapaglia, Giacomo; Cai, Haiying; Deandrea, Aldo [Univ Lyon, Université Lyon 1, CNRS/IN2P3, IPNL,F-69622, Villeurbanne (France); Frandsen, Mads [CP-Origins & Danish Institute for Advanced Study DIAS, University of Southern Denmark,Campusvej 55, DK-5230 Odense M (Denmark)
2016-11-11
We provide a non-linear realisation of composite Higgs models in the context of the SU(4)/Sp(4) symmetry breaking pattern, where the effective Lagrangian of the spin-0 and spin-1 resonances is constructed via the CCWZ prescription using the Hidden Symmetry formalism. We investigate the EWPT constraints by accounting the effects from reduced Higgs couplings and integrating out heavy spin-1 resonances. This theory emerges from an underlying theory of gauge interactions with fermions, thus first principle lattice results predict the massive spectrum in composite Higgs models. This model can be used as a template for the phenomenology of composite Higgs models at the LHC and at future 100 TeV colliders, as well as for other application. In this work, we focus on the formalism for spin-1 resonances and their bounds from di-lepton and di-boson searches at the LHC.
Tiled vector data model for the geographical features of symbolized maps.
Li, Lin; Hu, Wei; Zhu, Haihong; Li, You; Zhang, Hang
2017-01-01
Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and 'addition' (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity.
Multivariable integrated evaluation of model performance with the vector field evaluation diagram
Xu, Zhongfeng; Han, Ying; Fu, Congbin
2017-10-01
This paper develops a multivariable integrated evaluation (MVIE) method to measure the overall performance of climate model in simulating multiple fields. The general idea of MVIE is to group various scalar fields into a vector field and compare the constructed vector field against the observed one using the vector field evaluation (VFE) diagram. The VFE diagram was devised based on the cosine relationship between three statistical quantities: root mean square length (RMSL) of a vector field, vector field similarity coefficient, and root mean square vector deviation (RMSVD). The three statistical quantities can reasonably represent the corresponding statistics between two multidimensional vector fields. Therefore, one can summarize the three statistics of multiple scalar fields using the VFE diagram and facilitate the intercomparison of model performance. The VFE diagram can illustrate how much the overall root mean square deviation of various fields is attributable to the differences in the root mean square value and how much is due to the poor pattern similarity. The MVIE method can be flexibly applied to full fields (including both the mean and anomaly) or anomaly fields depending on the application. We also propose a multivariable integrated evaluation index (MIEI) which takes the amplitude and pattern similarity of multiple scalar fields into account. The MIEI is expected to provide a more accurate evaluation of model performance in simulating multiple fields. The MIEI, VFE diagram, and commonly used statistical metrics for individual variables constitute a hierarchical evaluation methodology, which can provide a more comprehensive evaluation of model performance.
R.W. Strachan (Rodney); H.K. van Dijk (Herman)
2008-01-01
textabstractA Bayesian model averaging procedure is presented that makes use of a finite mixture of many model structures within the class of vector autoregressive (VAR) processes. It is applied to two empirical issues. First, stability of the Great Ratios in U.S. macro-economic time series is
Spherical cap modelling of Orsted magnetic field vectors over southern Africa
CSIR Research Space (South Africa)
Kotze, PB
2001-01-01
Full Text Available Vector magnetic field observations by the Orsted satellite during geomagnetic quiet conditions around January 1, 2000, have been employed to derive a spherical cap harmonic model (Haines, 1985) over the southern African region between 10 degrees...
Autoregressive logistic regression applied to atmospheric circulation patterns
Guanche, Y.; Mínguez, R.; Méndez, F. J.
2014-01-01
Autoregressive logistic regression models have been successfully applied in medical and pharmacology research fields, and in simple models to analyze weather types. The main purpose of this paper is to introduce a general framework to study atmospheric circulation patterns capable of dealing simultaneously with: seasonality, interannual variability, long-term trends, and autocorrelation of different orders. To show its effectiveness on modeling performance, daily atmospheric circulation patterns identified from observed sea level pressure fields over the Northeastern Atlantic, have been analyzed using this framework. Model predictions are compared with probabilities from the historical database, showing very good fitting diagnostics. In addition, the fitted model is used to simulate the evolution over time of atmospheric circulation patterns using Monte Carlo method. Simulation results are statistically consistent with respect to the historical sequence in terms of (1) probability of occurrence of the different weather types, (2) transition probabilities and (3) persistence. The proposed model constitutes an easy-to-use and powerful tool for a better understanding of the climate system.
Vector and Axial-vector resonances in composite models of the Higgs boson
DEFF Research Database (Denmark)
Franzosi, Diogo Buarque; Cacciapaglia, Giacomo; Cai, Haiying
2016-01-01
We provide a non-linear realisation of composite Higgs models in the context of the SU(4)/Sp(4) symmetry breaking pattern, where the effective Lagrangian of the spin-0 and spin-1 resonances is constructed via the CCWZ prescription using the Hidden Symmetry formalism. We investigate the EWPT const...... as a template for the phenomenology of composite Higgs models at the LHC and at future 100 TeV colliders, as well as for other application. In this work, we focus on the formalism for spin-1 resonances and their bounds from di-lepton and di-boson searches at the LHC....
Multifrequency spiral vector model for the brushless doubly-fed induction machine
DEFF Research Database (Denmark)
Han, Peng; Cheng, Ming; Zhu, Xinkai
2017-01-01
into consideration the low-order space harmonic coupling by incorporating nonsinusoidal inductances into modeling process. A new model order reduction approach is then proposed to transform the nested-loop rotor into an equivalent single-loop one. The effectiveness of the proposed modelling method is verified by 2D...... analytically, revealing the underlying relationship between harmonic components of stator-rotor mutual inductances and the airgap magnetic field distribution. Different from existing vector models, which only model the fundamental components of mutual inductances, the proposed vector model takes...
The double-gradient model of flapping instability with oblique wave vector
Korovinskiy, Daniil; Kiehas, Stefan
2017-04-01
The double-gradient model of magnetotail flapping oscillations/instability is generalized for the case of oblique propagation in the equatorial plane. The transversal direction Y (in GSM reference system) of the wave vector is found to be preferable, showing the highest growth rates of kink and sausage double-gradient unstable modes. Growth rates decrease with the wave vector rotating toward the X direction. It is found that neither waves nor instability with a wave vector pointing toward the Earth/magnetotail can develop.
Grass, David; Jasinski, Michael F.; Govere, John
2003-01-01
There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.
Improved Likelihood Ratio Tests for Cointegration Rank in the VAR Model
DEFF Research Database (Denmark)
Boswijk, H. Peter; Jansson, Michael; Nielsen, Morten Ørregaard
We suggest improved tests for cointegration rank in the vector autoregressive (VAR) model and develop asymptotic distribution theory and local power results. The tests are (quasi-)likelihood ratio tests based on a Gaussian likelihood, but of course the asymptotic results apply more generally...
Evaluation of the impacts of climate change on disease vectors through ecological niche modelling.
Carvalho, B M; Rangel, E F; Vale, M M
2017-08-01
Vector-borne diseases are exceptionally sensitive to climate change. Predicting vector occurrence in specific regions is a challenge that disease control programs must meet in order to plan and execute control interventions and climate change adaptation measures. Recently, an increasing number of scientific articles have applied ecological niche modelling (ENM) to study medically important insects and ticks. With a myriad of available methods, it is challenging to interpret their results. Here we review the future projections of disease vectors produced by ENM, and assess their trends and limitations. Tropical regions are currently occupied by many vector species; but future projections indicate poleward expansions of suitable climates for their occurrence and, therefore, entomological surveillance must be continuously done in areas projected to become suitable. The most commonly applied methods were the maximum entropy algorithm, generalized linear models, the genetic algorithm for rule set prediction, and discriminant analysis. Lack of consideration of the full-known current distribution of the target species on models with future projections has led to questionable predictions. We conclude that there is no ideal 'gold standard' method to model vector distributions; researchers are encouraged to test different methods for the same data. Such practice is becoming common in the field of ENM, but still lags behind in studies of disease vectors.
Data Transformation and Forecasting in Models with Unit Roots and Cointegration
John C. Chao; Valentina Corradi; Norman R. Swanson
2001-01-01
We perform a series of Monte Carlo experiments in order to evaluate the impact of data transformation on forecasting models, and find that vector error-corrections dominate differenced data vector autoregressions when the correct data transformation is used, but not when data are incorrectly tansformed, even if the true model contains cointegrating restrictions. We argue that one reason for this is the failure of standard unit root and cointegration tests under incorrect data transformation.
Vector meson quasinormal modes in a finite-temperature AdS/QCD model
Energy Technology Data Exchange (ETDEWEB)
Mamani, Luis A.H. [Departamento de Ciências Exatas e Tecnológicas,Universidade Estadual de Santa Cruz,Rodovia Jorge Amado, km 16, 45662-900, Ilhéus, BA (Brazil); Centro de Ciências Naturais e Humanas, Universidade Federal do ABC,Rua Santa Adélia 166, 09210-170, Santo André, SP (Brazil); Miranda, Alex S. [Departamento de Ciências Exatas e Tecnológicas,Universidade Estadual de Santa Cruz,Rodovia Jorge Amado, km 16, 45662-900, Ilhéus, BA (Brazil); Boschi-Filho, Henrique; Braga, Nelson R.F. [Instituto de Física, Universidade Federal do Rio de Janeiro,Caixa Postal 68528, RJ 21941-972 (Brazil)
2014-03-12
We study the spectrum of vector mesons in a finite temperature plasma. The plasma is holographically described by a black hole AdS/QCD model. We compute the boundary retarded Green’s functions using AdS/CFT prescriptions. The corresponding thermal spectral functions show quasiparticle peaks at low temperatures. Then we calculate the quasinormal modes of vector mesons in the soft-wall black hole geometry and analyse their temperature and momentum dependences.
Prediction of municipal solid waste generation using nonlinear autoregressive network.
Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A
2015-12-01
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.
MODEL PERMINTAAN UANG DI INDONESIA DENGAN PENDEKATAN VECTOR ERROR CORRECTION MODEL
Directory of Open Access Journals (Sweden)
imam mukhlis
2016-09-01
Full Text Available This research aims to estimate the demand for money model in Indonesia for 2005.2-2015.12. The variables used in this research are ; demand for money, interest rate, inflation, and exchange rate (IDR/US$. The stationary test with ADF used to test unit root in the data. Cointegration test applied to estimate the long run relationship berween variables. This research employed the Vector Error Correction Model (VECM to estimate the money demand model in Indonesia. The results showed that all the data was stationer at the difference level (1%. There were long run relationship between interest rate, inflation and exchange rate to demand for money in Indonesia. The VECM model could not explaine interaction between explanatory variables to independent variables. In the short run, there were not relationship between interest rate, inflation and exchange rate to demand for money in Indonesia for 2005.2-2015.12
Directory of Open Access Journals (Sweden)
Akhsyim Afandi
2017-03-01
Full Text Available There was a question whether monetary policy works through bank lending channelrequired a monetary-induced change in bank loans originates from the supply side. Mostempirical studies that employed vector autoregressive (VAR models failed to fulfill thisrequirement. Aiming to offer a solution to this identification problem, this paper developed afive-variable vector error correction (VEC model of two separate bank credit markets inIndonesia. Departing from previous studies, the model of each market took account of onestructural break endogenously determined by implementing a unit root test. A cointegrationtest that took account of one structural break suggested two cointegrating vectors identifiedas bank lending supply and demand relations. The estimated VEC system for both marketssuggested that bank loans adjusted more strongly in the direction of the supply equation.
A Corporate Credit Rating Model Using Support Vector Domain Combined with Fuzzy Clustering Algorithm
Directory of Open Access Journals (Sweden)
Xuesong Guo
2012-01-01
Full Text Available Corporate credit-rating prediction using statistical and artificial intelligence techniques has received considerable attentions in the literature. Different from the thoughts of various techniques for adopting support vector machines as binary classifiers originally, a new method, based on support vector domain combined with fuzzy clustering algorithm for multiclassification, is proposed in the paper to accomplish corporate credit rating. By data preprocessing using fuzzy clustering algorithm, only the boundary data points are selected as training samples to accomplish support vector domain specification to reduce computational cost and also achieve better performance. To validate the proposed methodology, real-world cases are used for experiments, with results compared with conventional multiclassification support vector machine approaches and other artificial intelligence techniques. The results show that the proposed model improves the performance of corporate credit-rating with less computational consumption.
Modeling Earth Albedo Currents on Sun Sensors for Improved Vector Observations
DEFF Research Database (Denmark)
Bhanderi, Dan
2006-01-01
Earth albedo influences vector measurements of the solar line of sight vector, due to the induced current on in the photo voltaics of Sun sensors. Although advanced digital Sun sensors exist, these are typically expensive and may not be suited for satellites in the nano or pico-class. Previously...... an Earth albedo model, based on reflectivity data from NASA's Total Ozone Mapping Spectrometer project, has been published. In this paper the proposed model is presented, and the model is sought validated by comparing simulated data with telemetry from the Danish Ørsted satellite. A novel method...
The Creation of Space Vector Models of Buildings From RPAS Photogrammetry Data
Trhan, Ondrej
2017-06-01
The results of Remote Piloted Aircraft System (RPAS) photogrammetry are digital surface models and orthophotos. The main problem of the digital surface models obtained is that buildings are not perpendicular and the shape of roofs is deformed. The task of this paper is to obtain a more accurate digital surface model using building reconstructions. The paper discusses the problem of obtaining and approximating building footprints, reconstructing the final spatial vector digital building model, and modifying the buildings on the digital surface model.
Use of a Modified Vector Model for Odor Intensity Prediction of Odorant Mixtures
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Luchun Yan
2015-03-01
Full Text Available Odor intensity (OI indicates the perceived intensity of an odor by the human nose, and it is usually rated by specialized assessors. In order to avoid restrictions on assessor participation in OI evaluations, the Vector Model which calculates the OI of a mixture as the vector sum of its unmixed components’ odor intensities was modified. Based on a detected linear relation between the OI and the logarithm of odor activity value (OAV—a ratio between chemical concentration and odor threshold of individual odorants, OI of the unmixed component was replaced with its corresponding logarithm of OAV. The interaction coefficient (cosα which represented the degree of interaction between two constituents was also measured in a simplified way. Through a series of odor intensity matching tests for binary, ternary and quaternary odor mixtures, the modified Vector Model provided an effective way of relating the OI of an odor mixture with the lnOAV values of its constituents. Thus, OI of an odor mixture could be directly predicted by employing the modified Vector Model after usual quantitative analysis. Besides, it was considered that the modified Vector Model was applicable for odor mixtures which consisted of odorants with the same chemical functional groups and similar molecular structures.
Use of a Modified Vector Model for Odor Intensity Prediction of Odorant Mixtures
Yan, Luchun; Liu, Jiemin; Fang, Di
2015-01-01
Odor intensity (OI) indicates the perceived intensity of an odor by the human nose, and it is usually rated by specialized assessors. In order to avoid restrictions on assessor participation in OI evaluations, the Vector Model which calculates the OI of a mixture as the vector sum of its unmixed components’ odor intensities was modified. Based on a detected linear relation between the OI and the logarithm of odor activity value (OAV—a ratio between chemical concentration and odor threshold) of individual odorants, OI of the unmixed component was replaced with its corresponding logarithm of OAV. The interaction coefficient (cosα) which represented the degree of interaction between two constituents was also measured in a simplified way. Through a series of odor intensity matching tests for binary, ternary and quaternary odor mixtures, the modified Vector Model provided an effective way of relating the OI of an odor mixture with the lnOAV values of its constituents. Thus, OI of an odor mixture could be directly predicted by employing the modified Vector Model after usual quantitative analysis. Besides, it was considered that the modified Vector Model was applicable for odor mixtures which consisted of odorants with the same chemical functional groups and similar molecular structures. PMID:25760055
An Information Retrieval Model Based on Vector Space Method by Supervised Learning.
Tai, Xiaoying; Ren, Fuji; Kita, Kenji
2002-01-01
Proposes a method to improve retrieval performance of the vector space model by using users' relevance feedback. Discusses the use of singular value decomposition and the latent semantic indexing model, and reports the results of two experiments that show the effectiveness of the proposed method. (Author/LRW)
Models for the effects of host movement in vector-borne disease systems.
Cosner, Chris
2015-12-01
Host and/or vector movement patterns have been shown to have significant effects in both empirical studies and mathematical models of vector-borne diseases. The processes of economic development and globalization seem likely to make host movement even more important in the future. This article is a brief survey of some of the approaches that have been used to study the effects of host movement in analytic mathematical models for vector-borne diseases. It describes the formulation and interpretation of various types of spatial models and describes a few of the conclusions that can be drawn from them. It is not intended to be comprehensive but rather to provide sufficient background material and references to the literature to serve as an entry point into this area of research for interested readers. Copyright © 2015 Elsevier Inc. All rights reserved.
Modeling vector-borne disease risk in migratory animals under climate change.
Hall, Richard J; Brown, Leone M; Altizer, Sonia
2016-08-01
Recent theory suggests that animals that migrate to breed at higher latitudes may benefit from reduced pressure from natural enemies, including pathogens ("migratory escape"), and that migration itself weeds out infected individuals and lowers infection prevalence ("migratory culling"). The distribution and activity period of arthropod disease vectors in temperate regions is expected to respond rapidly to climate change, which could reduce the potential for migratory escape. However, climate change could have the opposite effect of reducing transmission if differential responses in the phenology and distribution of migrants and disease vectors reduce their overlap in space and time. Here we outline a simple modeling framework for exploring the influence of climate change on vector-borne disease dynamics in a migratory host. We investigate two scenarios under which pathogen transmission dynamics might be mediated by climate change: (1) vectors respond more rapidly than migrants to advancing phenology at temperate breeding sites, causing peak susceptible host density and vector emergence to diverge ("migratory mismatch") and (2) reduced migratory propensity allows increased nonbreeding survival of infected hosts and larger breeding-site epidemics (loss of migratory culling, here referred to as "sedentary amplification"). Our results highlight the need for continued surveillance of climate-induced changes to migratory behavior and vector activity to predict pathogen prevalence and its impacts on migratory animals. © The Author 2016. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.
Notes toward a verifiable vector algebraic basis for colorimetric modeling
Oulton, David P.
2009-01-01
The presented notes aim toward improved models of the scalar visual response to flat-field stimuli, and are prompted by unease over the complexity of existing colour difference models. Some of the basic assumptions of colorimetry are examined in detail, and analytical methods whereby these assumptions can be investigated experimentally are presented. A key finding is that the standard CIE colorimetric model is verifiably correct as a predictor of point colour identity and metameric visual equ...
Equivalent magnetic vector potential model for low-frequency magnetic exposure assessment
Diao, Y. L.; Sun, W. N.; He, Y. Q.; Leung, S. W.; Siu, Y. M.
2017-10-01
In this paper, a novel source model based on a magnetic vector potential for the assessment of induced electric field strength in a human body exposed to the low-frequency (LF) magnetic field of an electrical appliance is presented. The construction of the vector potential model requires only a single-component magnetic field to be measured close to the appliance under test, hence relieving considerable practical measurement effort—the radial basis functions (RBFs) are adopted for the interpolation of discrete measurements; the magnetic vector potential model can then be directly constructed by summing a set of simple algebraic functions of RBF parameters. The vector potentials are then incorporated into numerical calculations as the equivalent source for evaluations of the induced electric field in the human body model. The accuracy and effectiveness of the proposed model are demonstrated by comparing the induced electric field in a human model to that of the full-wave simulation. This study presents a simple and effective approach for modelling the LF magnetic source. The result of this study could simplify the compliance test procedure for assessing an electrical appliance regarding LF magnetic exposure.
Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine
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Hang-cheong Wong
2012-01-01
Full Text Available Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM, to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN and decremental least-squares support vector machine (DLSSVM. Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.
Hollingsworth, T Déirdre; Pulliam, Juliet R C; Funk, Sebastian; Truscott, James E; Isham, Valerie; Lloyd, Alun L
2015-03-01
Many of the challenges which face modellers of directly transmitted pathogens also arise when modelling the epidemiology of pathogens with indirect transmission--whether through environmental stages, vectors, intermediate hosts or multiple hosts. In particular, understanding the roles of different hosts, how to measure contact and infection patterns, heterogeneities in contact rates, and the dynamics close to elimination are all relevant challenges, regardless of the mode of transmission. However, there remain a number of challenges that are specific and unique to modelling vector-borne diseases and macroparasites. Moreover, many of the neglected tropical diseases which are currently targeted for control and elimination are vector-borne, macroparasitic, or both, and so this article includes challenges which will assist in accelerating the control of these high-burden diseases. Here, we discuss the challenges of indirect measures of infection in humans, whether through vectors or transmission life stages and in estimating the contribution of different host groups to transmission. We also discuss the issues of "evolution-proof" interventions against vector-borne disease. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.
Modeling boundary vector cell firing given optic flow as a cue.
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Florian Raudies
Full Text Available Boundary vector cells in entorhinal cortex fire when a rat is in locations at a specific distance from walls of an environment. This firing may originate from memory of the barrier location combined with path integration, or the firing may depend upon the apparent visual input image stream. The modeling work presented here investigates the role of optic flow, the apparent change of patterns of light on the retina, as input for boundary vector cell firing. Analytical spherical flow is used by a template model to segment walls from the ground, to estimate self-motion and the distance and allocentric direction of walls, and to detect drop-offs. Distance estimates of walls in an empty circular or rectangular box have a mean error of less than or equal to two centimeters. Integrating these estimates into a visually driven boundary vector cell model leads to the firing patterns characteristic for boundary vector cells. This suggests that optic flow can influence the firing of boundary vector cells.
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T. Déirdre Hollingsworth
2015-03-01
Full Text Available Many of the challenges which face modellers of directly transmitted pathogens also arise when modelling the epidemiology of pathogens with indirect transmission – whether through environmental stages, vectors, intermediate hosts or multiple hosts. In particular, understanding the roles of different hosts, how to measure contact and infection patterns, heterogeneities in contact rates, and the dynamics close to elimination are all relevant challenges, regardless of the mode of transmission. However, there remain a number of challenges that are specific and unique to modelling vector-borne diseases and macroparasites. Moreover, many of the neglected tropical diseases which are currently targeted for control and elimination are vector-borne, macroparasitic, or both, and so this article includes challenges which will assist in accelerating the control of these high-burden diseases. Here, we discuss the challenges of indirect measures of infection in humans, whether through vectors or transmission life stages and in estimating the contribution of different host groups to transmission. We also discuss the issues of “evolution-proof” interventions against vector-borne disease.
Design of vaccination and fumigation on Host-Vector Model by input-output linearization method
Nugraha, Edwin Setiawan; Naiborhu, Janson; Nuraini, Nuning
2017-03-01
Here, we analyze the Host-Vector Model and proposed design of vaccination and fumigation to control infectious population by using feedback control especially input-output liniearization method. Host population is divided into three compartments: susceptible, infectious and recovery. Whereas the vector population is divided into two compartment such as susceptible and infectious. In this system, vaccination and fumigation treat as input factors and infectious population as output result. The objective of design is to stabilize of the output asymptotically tend to zero. We also present the examples to illustrate the design model.
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Yan, Yuan
2017-11-20
When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
Modeling the spread of vector-borne diseases on bipartite networks.
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Donal Bisanzio
Full Text Available BACKGROUND: Vector-borne diseases for which transmission occurs exclusively between vectors and hosts can be modeled as spreading on a bipartite network. METHODOLOGY/PRINCIPAL FINDINGS: In such models the spreading of the disease strongly depends on the degree distribution of the two classes of nodes. It is sufficient for one of the classes to have a scale-free degree distribution with a slow enough decay for the network to have asymptotically vanishing epidemic threshold. Data on the distribution of Ixodes ricinus ticks on mice and lizards from two independent studies are well described by a scale-free distribution compatible with an asymptotically vanishing epidemic threshold. The commonly used negative binomial, instead, cannot describe the right tail of the empirical distribution. CONCLUSIONS/SIGNIFICANCE: The extreme aggregation of vectors on hosts, described by the power-law decay of the degree distribution, makes the epidemic threshold decrease with the size of the network and vanish asymptotically.
A survey of basic reproductive ratios in vector-borne disease transmission modeling
Soewono, E.; Aldila, D.
2015-03-01
Vector-borne diseases are commonly known in tropical and subtropical countries. These diseases have contributed to more than 10% of world infectious disease cases. Among the vectors responsible for transmitting the diseases are mosquitoes, ticks, fleas, flies, bugs and worms. Several of the diseases are known to contribute to the increasing threat to human health such as malaria, dengue, filariasis, chikungunya, west nile fever, yellow fever, encephalistis, and anthrax. It is necessary to understand the real process of infection, factors which contribute to the complication of the transmission in order to come up with a good and sound mathematical model. Although it is not easy to simulate the real transmission process of the infection, we could say that almost all models have been developed from the already long known Host-Vector model. It constitutes the main transmission processes i.e. birth, death, infection and recovery. From this simple model, the basic concepts of Disease Free and Endemic Equilibria and Basic Reproductive Ratio can be well explained and understood. Theoretical, modeling, control and treatment aspects of disease transmission problems have then been developed for various related diseases. General construction as well as specific forms of basic reproductive ratios for vector-borne diseases are discusses here.
Learned Vector-Space Models for Document Retrieval.
Caid, William R.; And Others
1995-01-01
The Latent Semantic Indexing and MatchPlus systems examine similar contexts in which words appear and create representational models that capture the similarity of meaning of terms and then use the representation for retrieval. Text Retrieval Conference experiments using these systems demonstrate the computational feasibility of using…
Limbach, Keith; Stefaniak, Maureen; Chen, Ping; Patterson, Noelle B; Liao, Grant; Weng, Shaojie; Krepkiy, Svetlana; Ekberg, Greg; Torano, Holly; Ettyreddy, Damodar; Gowda, Kalpana; Sonawane, Sharvari; Belmonte, Arnel; Abot, Esteban; Sedegah, Martha; Hollingdale, Michael R; Moormann, Ann; Vulule, John; Villasante, Eileen; Richie, Thomas L; Brough, Douglas E; Bruder, Joseph T
2017-07-03
A DNA-human Ad5 (HuAd5) prime-boost malaria vaccine has been shown to protect volunteers against a controlled human malaria infection. The potency of this vaccine, however, appeared to be affected by the presence of pre-existing immunity against the HuAd5 vector. Since HuAd5 seroprevalence is very high in malaria-endemic areas of the world, HuAd5 may not be the most appropriate malaria vaccine vector. This report describes the evaluation of the seroprevalence, immunogenicity and efficacy of three newly identified gorilla adenoviruses, GC44, GC45 and GC46, as potential malaria vaccine vectors. The seroprevalence of GC44, GC45 and GC46 is very low, and the three vectors are not efficiently neutralized by human sera from Kenya and Ghana, two countries where malaria is endemic. In mice, a single administration of GC44, GC45 and GC46 vectors expressing a murine malaria gene, Plasmodium yoelii circumsporozoite protein (PyCSP), induced robust PyCSP-specific T cell and antibody responses that were at least as high as a comparable HuAd5-PyCSP vector. Efficacy studies in a murine malaria model indicated that a prime-boost regimen with DNA-PyCSP and GC-PyCSP vectors can protect mice against a malaria challenge. Moreover, these studies indicated that a DNA-GC46-PyCSP vaccine regimen was significantly more efficacious than a DNA-HuAd5-PyCSP regimen. These data suggest that these gorilla-based adenovectors have key performance characteristics for an effective malaria vaccine. The superior performance of GC46 over HuAd5 highlights its potential for clinical development.
Modelling of a double star induction motor for space vector PWM control
Energy Technology Data Exchange (ETDEWEB)
Hadiouche, D.; Razik, H.; Rezzoug, A. [Groupe de Recherche en Electrotechnique et Electronique de Nancy, G.R.E.E.N. - CNRS UPRES, University H. Poincare, Vandoeuvre-les-Nancy (France)
2000-08-01
In this paper, we present the analysis and the modelling of a Double-Star Induction Motor (DSIM). A steady-state model is first established in order to analyse its harmonic behaviour. Then, a new transformation matrix is proposed in order to develop a suitable dynamic model. In both cases, the study is made using an arbitrary shift angle between the two stars. At last, a space vector Pulse Width Modulation (PWM) control or the DSIM is simulated. (orig.)
A Bayesian vector error corrections model of the U.S. economy
Tom Stark
1998-01-01
This paper presents a small-scale macroeconometric time-series model that can be used to generate short-term forecasts for U.S. output, inflation, and the rate of unemployment. Drawing on both the Bayesian VAR and vector error corrections (VEC) literature, the author specifies the baseline model as a Bayesian VEC. The author documents the model's forecasting ability over various periods, examines its impulse responses, and considers several reasonable alternative specifications. Based on a ro...
Genetic algorithm identification of a H-moving vector hysteresis model
Energy Technology Data Exchange (ETDEWEB)
Cardelli, E. [Department of Industrial Engineering, University of Perugia, I-06125 Perugia (Italy); Center for Electric and Magnetic Applied Research (Italy); Faba, A., E-mail: faba@unipg.it [Department of Industrial Engineering, University of Perugia, I-06125 Perugia (Italy); Center for Electric and Magnetic Applied Research (Italy)
2014-02-15
In this work we present an identification procedure for a vector hysteresis model defined by a H-moving approach. The model parameters are identified by means of a suitable implementation of a genetic algorithm with a set of experimental data. The analytical formulation of the model and the characteristics of the genetic algorithm used are described. A comparison between computed data and experimental measurements for a not oriented grain Si–Fe magnetic steel with a weak lamination anisotropy are reported.
Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform
Xu, S.; Xue, W.; Lin, H.X.
2011-01-01
In this article, we discuss the performance modeling and optimization of Sparse Matrix-Vector Multiplication (SpMV) on NVIDIA GPUs using CUDA. SpMV has a very low computation-data ratio and its performance is mainly bound by the memory bandwidth. We propose optimization of SpMV based on ELLPACK from
Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases
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Jean-Marie Aerts
2012-11-01
Full Text Available The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the physical habitat of pathogens’ vectors and/or hosts, and urban areas, thus humans. This study implements the concept behind gravity models in the spatial spread of two vector-borne diseases, nephropathia epidemica and Lyme borreliosis, based on current knowledge on the transmission mechanism of these diseases. Two sources of information on vegetated systems were tested: the CORINE land cover map and MODIS NDVI. The size of vegetated areas near urban centers and a local indicator of occupation-related exposure were found significant predictors of disease risk. Both the land cover map and the space-borne dataset were suited yet not equivalent input sources to locate and measure vegetated areas of importance for disease spread. The overall results point at the compatibility of the gravity model concept and the spatial spread of vector-borne diseases.
Mc Cann, R.S.; Messina, J.P.; MacFarlane, D.W.; Bayoh, M.N.; Vulule, J.M.; Gimnig, J.E.; Walker, E.D.
2014-01-01
BACKGROUND: Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission. METHODS: We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and
Vector dark energy models with quadratic terms in the Maxwell tensor derivatives
Energy Technology Data Exchange (ETDEWEB)
Haghani, Zahra; Shahidi, Shahab [Damghan University, School of Physics, Damghan (Iran, Islamic Republic of); Harko, Tiberiu [Babes-Bolyai University, Department of Physics, Cluj-Napoca (Romania); University College London, Department of Mathematics, London (United Kingdom); Sepangi, Hamid Reza [Shahid Beheshti University, Department of Physics, Tehran (Iran, Islamic Republic of)
2017-03-15
We consider a vector-tensor gravitational model with terms quadratic in the Maxwell tensor derivatives, called the Bopp-Podolsky term. The gravitational field equations of the model and the equations describing the evolution of the vector field are obtained and their Newtonian limit is investigated. The cosmological implications of a Bopp-Podolsky type dark energy term are investigated for a Bianchi type I homogeneous and anisotropic geometry for two models, corresponding to the absence and presence of the self-interacting potential of the field, respectively. The time evolutions of the Hubble function, of the matter energy density, of the shear scalar, of the mean anisotropy parameter, and of the deceleration parameter, respectively, as well as the field potentials are obtained for both cases by numerically integrating the cosmological evolution equations. In the presence of the vector type dark energy with quadratic terms in the Maxwell tensor derivatives, depending on the numerical values of the model parameters, the Bianchi type I Universe experiences a complex dynamical evolution, with the dust Universes ending in an isotropic phase. The presence of the self-interacting potential of the vector field significantly shortens the time interval necessary for the full isotropization of the Universe. (orig.)
Spatial coincidence modelling, automated database updating and data consistency in vector GIS
Kufoniyi, O.
1995-01-01
This thesis presents formal approaches for automated database updating and consistency control in vector- structured spatial databases. To serve as a framework, a conceptual data model is formalized for the representation of geo-data from multiple map layers in which a map layer denotes a
Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine
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Lutao Liu
2013-01-01
Full Text Available A new method of transformer fault diagnosis based on relevance vector machine (RVM is proposed. Bayesian estimation is applied to support vector machine (SVM in the novel algorithm, which made fault diagnosis system work more effectively. In the paper, the analysis model is presented that the solutions of RVM have the feature of sparsity and RVM can obtain global solutions under finite samples. The process of transformer fault diagnosis for four working statuses is given in experiments and simulations. The results validated that this method has obvious advantages of diagnosis time and accuracy compared with backpropagation (BP neural networks and general SVM methods.
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Hyun M Yang
2000-06-01
Full Text Available OBJECTIVE: Describe the overall transmission of malaria through a compartmental model, considering the human host and mosquito vector. METHODS: A mathematical model was developed based on the following parameters: human host immunity, assuming the existence of acquired immunity and immunological memory, which boosts the protective response upon reinfection; mosquito vector, taking into account that the average period of development from egg to adult mosquito and the extrinsic incubation period of parasites (transformation of infected but non-infectious mosquitoes into infectious mosquitoes are dependent on the ambient temperature. RESULTS: The steady state equilibrium values obtained with the model allowed the calculation of the basic reproduction ratio in terms of the model's parameters. CONCLUSIONS: The model allowed the calculation of the basic reproduction ratio, one of the most important epidemiological variables.
Directory of Open Access Journals (Sweden)
Yang Hyun M
2000-01-01
Full Text Available OBJECTIVE: Describe the overall transmission of malaria through a compartmental model, considering the human host and mosquito vector. METHODS: A mathematical model was developed based on the following parameters: human host immunity, assuming the existence of acquired immunity and immunological memory, which boosts the protective response upon reinfection; mosquito vector, taking into account that the average period of development from egg to adult mosquito and the extrinsic incubation period of parasites (transformation of infected but non-infectious mosquitoes into infectious mosquitoes are dependent on the ambient temperature. RESULTS: The steady state equilibrium values obtained with the model allowed the calculation of the basic reproduction ratio in terms of the model's parameters. CONCLUSIONS: The model allowed the calculation of the basic reproduction ratio, one of the most important epidemiological variables.
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Alastair G Kerr
2016-01-01
Full Text Available Familial hypercholesterolemia (FH is a life-threatening genetic disorder characterized by elevated levels of plasma low-density lipoprotein cholesterol (LDL-cholesterol. Current attempts at gene therapy for FH have been limited by the use of strong heterologous promoters which lack genomic DNA elements essential for regulated expression. Here, we have combined a minigene vector expressing the human LDLR cDNA from a 10 kb native human LDLR locus genomic DNA promoter element, with an efficient miRNA targeting 3-hydroxy-3-methylgutaryl-coenzyme A reductase (Hmgcr, to further enhance LDLR expression. We show that the combined vector suppresses endogenous Hmgcr transcripts in vivo, leading to an increase in LDLR transgene expression. In a diet-induced Ldlr-/- mouse model of FH, we show that administration of the combined vector reduces atherogenic plasma lipids by ≃32%. Finally, we demonstrate that our episomal nonviral vectors are able to reduce atherosclerosis by ≃40% after 12 weeks in vivo. Taken together, the vector system we describe exploits the normal cellular regulation of the LDLR to provide prolonged expression of LDLR through targeted knockdown of Hmgcr. This novel gene therapy system could act alone, or in synergy with current therapies that modulate intracellular cholesterol, such as statins, greatly enhancing its therapeutic application for FH.
Further results on the h-test of Durbin for stable autoregressive processes
National Research Council Canada - National Science Library
Frédéric Proïa
2013-01-01
The purpose of this paper is to investigate the asymptotic behavior of the Durbin-Watson statistic for the stable p-order autoregressive process when the driven noise is given by a first-order autoregressive process...
Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Foote, Matthew; Lehman, Margot; Chan, Lawrence Wing Chi
2017-01-01
Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, following the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality. Copyright © 2017 American Association of Medical Dosimetrists. All rights reserved.
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Cheikh Talla
Full Text Available Rift Valley fever is an emerging mosquito-borne disease that represents a threat to human and animal health. The exophilic and exophagic behavior of the two main vector in West Africa (Aedes vexans and Culex poicilipes, adverse events post-vaccination, and lack of treatment, render ineffective the disease control. Therefore it is essential to develop an information system that facilitates decision-making and the implementation of adaptation strategies. In East Africa, RVF outbreaks are linked with abnormally high rainfall, and can be predicted up to 5 months in advance by modeling approaches using climatic and environmental parameters. However, the application of these models in West Africa remains unsatisfactory due to a lack of data for animal and human cases and differences in the dynamics of the disease emergence and the vector species involved in transmission. Models have been proposed for West Africa but they were restricted to rainfall impact analysis without a spatial dimension. In this study, we developed a mixed Bayesian statistical model to evaluate the effects of climatic and ecological determinants on the spatiotemporal dynamics of the two main vectors. Adult mosquito abundance data were generated from July to December every fortnight in 2005-2006 at 79 sites, including temporary ponds, bare soils, shrubby savannah, wooded savannah, steppes, and villages in the Barkédji area. The results demonstrate the importance of environmental factors and weather conditions for predicting mosquito abundance. The rainfall and minimum temperature were positively correlated with the abundance of Cx. poicilipes, whereas the maximum temperature had negative effects. The rainfall was negatively correlated with the abundance of Ae. vexans. After combining land cover classes, weather conditions, and vector abundance, our model was used to predict the areas and periods with the highest risks of vector pressure. This information could support decision
A late time accelerated FRW model with scalar and vector fields via Noether symmetry
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Babak Vakili
2014-11-01
Full Text Available We study the evolution of a three-dimensional minisuperspace cosmological model by the Noether symmetry approach. The phase space variables turn out to correspond to the scale factor of a flat Friedmann–Robertson–Walker (FRW model, a scalar field with potential function V(ϕ with which the gravity part of the action is minimally coupled and a vector field of its kinetic energy is coupled with the scalar field by a coupling function f(ϕ. Then, the Noether symmetry of such a cosmological model is investigated by utilizing the behavior of the corresponding Lagrangian under the infinitesimal generator of the desired symmetry. We explicitly calculate the form of the coupling function between the scalar and the vector fields and also the scalar field potential function for which such symmetry exists. Finally, by means of the corresponding Noether current, we integrate the equations of motion and obtain exact solutions for the scale factor, scalar and vector fields. It is shown that the resulting cosmology is an accelerated expansion universe for which its expansion is due to the presence of the vector field in the early times, while the scalar field is responsible of its late time expansion.
Souris, Marc; Marcombe, Sébastien; Laforet, Julie; Brey, Paul T; Corbel, Vincent; Overgaard, Hans J
2017-01-01
Climatic, sociological and environmental conditions are known to affect the spatial distribution of malaria vectors and disease transmission. Intensive use of insecticides in the agricultural and public health sectors exerts a strong selective pressure on resistance genes in malaria vectors. Spatio-temporal models of favorable conditions for Anopheles species' presence were developed to estimate the probability of presence of malaria vectors and insecticide resistance in Lao PDR. These models were based on environmental and meteorological conditions, and demographic factors. GIS software was used to build and manage a spatial database with data collected from various geographic information providers. GIS was also used to build and run the models. Results showed that potential insecticide use and therefore the probability of resistance to insecticide is greater in the southwestern part of the country, specifically in Champasack province and where malaria incidence is already known to be high. These findings can help national authorities to implement targeted and effective vector control strategies for malaria prevention and elimination among populations most at risk. Results can also be used to focus the insecticide resistance surveillance in Anopheles mosquito populations in more restricted area, reducing the area of surveys, and making the implementation of surveillance system for Anopheles mosquito insecticide resistance possible.
Directory of Open Access Journals (Sweden)
Marc Souris
Full Text Available Climatic, sociological and environmental conditions are known to affect the spatial distribution of malaria vectors and disease transmission. Intensive use of insecticides in the agricultural and public health sectors exerts a strong selective pressure on resistance genes in malaria vectors. Spatio-temporal models of favorable conditions for Anopheles species' presence were developed to estimate the probability of presence of malaria vectors and insecticide resistance in Lao PDR. These models were based on environmental and meteorological conditions, and demographic factors. GIS software was used to build and manage a spatial database with data collected from various geographic information providers. GIS was also used to build and run the models. Results showed that potential insecticide use and therefore the probability of resistance to insecticide is greater in the southwestern part of the country, specifically in Champasack province and where malaria incidence is already known to be high. These findings can help national authorities to implement targeted and effective vector control strategies for malaria prevention and elimination among populations most at risk. Results can also be used to focus the insecticide resistance surveillance in Anopheles mosquito populations in more restricted area, reducing the area of surveys, and making the implementation of surveillance system for Anopheles mosquito insecticide resistance possible.
A late time accelerated FRW model with scalar and vector fields via Noether symmetry
Energy Technology Data Exchange (ETDEWEB)
Vakili, Babak, E-mail: b-vakili@iauc.ac.ir
2014-11-10
We study the evolution of a three-dimensional minisuperspace cosmological model by the Noether symmetry approach. The phase space variables turn out to correspond to the scale factor of a flat Friedmann–Robertson–Walker (FRW) model, a scalar field with potential function V(ϕ) with which the gravity part of the action is minimally coupled and a vector field of its kinetic energy is coupled with the scalar field by a coupling function f(ϕ). Then, the Noether symmetry of such a cosmological model is investigated by utilizing the behavior of the corresponding Lagrangian under the infinitesimal generator of the desired symmetry. We explicitly calculate the form of the coupling function between the scalar and the vector fields and also the scalar field potential function for which such symmetry exists. Finally, by means of the corresponding Noether current, we integrate the equations of motion and obtain exact solutions for the scale factor, scalar and vector fields. It is shown that the resulting cosmology is an accelerated expansion universe for which its expansion is due to the presence of the vector field in the early times, while the scalar field is responsible of its late time expansion.
Double sampling control chart for a first order autoregressive process
Directory of Open Access Journals (Sweden)
Fernando A. E. Claro
2008-12-01
Full Text Available In this paper we propose the Double Sampling control chart for monitoring processes in which the observations follow a first order autoregressive model. We consider sampling intervals that are sufficiently long to meet the rational subgroup concept. The Double Sampling chart is substantially more efficient than the Shewhart chart and the Variable Sample Size chart. To study the properties of these charts we derived closed-form expressions for the average run length (ARL taking into account the within-subgroup correlation. Numerical results show that this correlation has a significant impact on the chart properties.Neste artigo propomos o gráfico de controle de amostragem dupla para monitoramento de processos nos quais as observações seguem um modelo autoregressivo de primeira ordem. Nós consideramos intervalos de amostragem suficientemente longos em linha com o conceito de subgrupos racionais. O gráfico de controle de amostragem dupla é substancialmente mais eficiente que o Gráfico de Shewhart e do que o Gráfico com Amostra de Tamanho Variável. Para estudar as propriedades destes gráficos nós derivamos expressões de forma-fechada para o Numero Médio de Amostras até o Sinal (NMA levando em conta a correlação dentro do subgrupo. Os resultados numéricos mostram que esta correlação tem impacto significante sobre as propriedades do gráfico.
Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H; Bisanzio, Donal; Liu, Yang; Remais, Justin V
2014-01-01
Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.
Directory of Open Access Journals (Sweden)
Zhang Jing
2016-01-01
Full Text Available To assist physicians to quickly find the required 3D model from the mass medical model, we propose a novel retrieval method, called DRFVT, which combines the characteristics of dimensionality reduction (DR and feature vector transformation (FVT method. The DR method reduces the dimensionality of feature vector; only the top M low frequency Discrete Fourier Transform coefficients are retained. The FVT method does the transformation of the original feature vector and generates a new feature vector to solve the problem of noise sensitivity. The experiment results demonstrate that the DRFVT method achieves more effective and efficient retrieval results than other proposed methods.
Jing, Zhang; Sheng, Kang Bao
2015-01-01
To assist physicians to quickly find the required 3D model from the mass medical model, we propose a novel retrieval method, called DRFVT, which combines the characteristics of dimensionality reduction (DR) and feature vector transformation (FVT) method. The DR method reduces the dimensionality of feature vector; only the top M low frequency Discrete Fourier Transform coefficients are retained. The FVT method does the transformation of the original feature vector and generates a new feature vector to solve the problem of noise sensitivity. The experiment results demonstrate that the DRFVT method achieves more effective and efficient retrieval results than other proposed methods.
Recchia, Gabriel; Jones, Michael; Sahlgren, Magnus; Kanerva, Pentti
2010-01-01
Encoding information about the order in which words typically appear has been shown to improve the performance of high-dimensional semantic space models. This requires an encoding operation capable of binding together vectors in an order-sensitive way, and efficient enough to scale to large text corpora. Although both circular convolution and random permutations have been enlisted for this purpose in semantic models, these operations have never been systematically compared. In Experiment 1 we...
Interpreting linear support vector machine models with heat map molecule coloring
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Rosenbaum Lars
2011-03-01
Full Text Available Abstract Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.
The Creation of Space Vector Models of Buildings From RPAS Photogrammetry Data
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Trhan Ondrej
2017-06-01
Full Text Available The results of Remote Piloted Aircraft System (RPAS photogrammetry are digital surface models and orthophotos. The main problem of the digital surface models obtained is that buildings are not perpendicular and the shape of roofs is deformed. The task of this paper is to obtain a more accurate digital surface model using building reconstructions. The paper discusses the problem of obtaining and approximating building footprints, reconstructing the final spatial vector digital building model, and modifying the buildings on the digital surface model.
Directory of Open Access Journals (Sweden)
Patricia Illoldi-Rangel
2012-01-01
Full Text Available Species distribution models were constructed for ten Ixodes species and Amblyomma cajennense for a region including Mexico and Texas. The model was based on a maximum entropy algorithm that used environmental layers to predict the relative probability of presence for each taxon. For Mexico, species geographic ranges were predicted by restricting the models to cells which have a higher probability than the lowest probability of the cells in which a presence record was located. There was spatial nonconcordance between the distributions of Amblyomma cajennense and the Ixodes group with the former restricted to lowlands and mainly the eastern coast of Mexico and the latter to montane regions with lower temperature. The risk of Lyme disease is, therefore, mainly present in the highlands where some Ixodes species are known vectors; if Amblyomma cajennense turns out to be a competent vector, the area of risk also extends to the lowlands and the east coast.
Relaxed singular vectors, Jack symmetric functions and fractional level slˆ(2 models
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David Ridout
2015-05-01
Full Text Available The fractional level models are (logarithmic conformal field theories associated with affine Kac–Moody (superalgebras at certain levels k∈Q. They are particularly noteworthy because of several longstanding difficulties that have only recently been resolved. Here, Wakimoto's free field realisation is combined with the theory of Jack symmetric functions to analyse the fractional level slˆ(2 models. The first main results are explicit formulae for the singular vectors of minimal grade in relaxed Wakimoto modules. These are closely related to the minimal grade singular vectors in relaxed (parabolic Verma modules. Further results include an explicit presentation of Zhu's algebra and an elegant new proof of the classification of simple relaxed highest weight modules over the corresponding vertex operator algebra. These results suggest that generalisations to higher rank fractional level models are now within reach.
Gamma and Exponential Autoregressive Moving Average (ARMA ...
African Journals Online (AJOL)
The distributions of developed models, unlike those studied by Lawrance and Lewis (1980), can be determined given either the distribution of the innovation sequence {et} or that of the process itself. The autocorrelation structure, which is a major identification tool in time series, is discussed for each of the proposed models.
Ismail, S.; Samsudin, R.; Shabri, A.
2010-10-01
Successful river flow time series forecasting is a major goal and an essential procedure that is necessary in water resources planning and management. This study introduced a new hybrid model based on a combination of two familiar non-linear method of mathematical modeling: Self Organizing Map (SOM) and Least Square Support Vector Machine (LSSVM) model referred as SOM-LSSVM model. The hybrid model uses the SOM algorithm to cluster the training data into several disjointed clusters and the individual LSSVM is used to forecast the river flow. The feasibility of this proposed model is evaluated to actual river flow data from Bernam River located in Selangor, Malaysia. Their results have been compared to those obtained using LSSVM and artificial neural networks (ANN) models. The experiment results show that the SOM-LSSVM model outperforms other models for forecasting river flow. It also indicates that the proposed model can forecast more precisely and provides a promising alternative technique in river flow forecasting.
Mathematical analysis of a power-law form time dependent vector-borne disease transmission model.
Sardar, Tridip; Saha, Bapi
2017-06-01
In the last few years, fractional order derivatives have been used in epidemiology to capture the memory phenomena. However, these models do not have proper biological justification in most of the cases and lack a derivation from a stochastic process. In this present manuscript, using theory of a stochastic process, we derived a general time dependent single strain vector borne disease model. It is shown that under certain choice of time dependent transmission kernel this model can be converted into the classical integer order system. When the time-dependent transmission follows a power law form, we showed that the model converted into a vector borne disease model with fractional order transmission. We explicitly derived the disease-free and endemic equilibrium of this new fractional order vector borne disease model. Using mathematical properties of nonlinear Volterra type integral equation it is shown that the unique disease-free state is globally asymptotically stable under certain condition. We define a threshold quantity which is epidemiologically known as the basic reproduction number (R0). It is shown that if R0 > 1, then the derived fractional order model has a unique endemic equilibrium. We analytically derived the condition for the local stability of the endemic equilibrium. To test the model capability to capture real epidemic, we calibrated our newly proposed model to weekly dengue incidence data of San Juan, Puerto Rico for the time period 30th April 1994 to 23rd April 1995. We estimated several parameters, including the order of the fractional derivative of the proposed model using aforesaid data. It is shown that our proposed fractional order model can nicely capture real epidemic. Copyright © 2017 Elsevier Inc. All rights reserved.
Modified multiple measurement vectors model for squinted synthetic aperture radar imaging
Chen, Yichang; Zhang, Qun; Chen, Yong-An; Sun, Li
2017-05-01
Inspired by the theoretical advances of compressed sensing, lots of sparsity-aware methods have been proposed for squinted synthetic aperture radar (SAR) imaging based on the single-measurement vector (SMV) model. Compared with SMV, the multiple measurement vectors (MMV) model has been demonstrated to have better reconstruction performance. In fact, echo received by SAR at different azimuth positions can be viewed as MMVs. However, the MMV model cannot be directly used in squinted SAR imaging, because MMV requires multiple sparse vectors of the common sparse structures, while the high-resolution range profiles (HRRPs) obtained by squinted SAR at different azimuth positions have different sparse structures due to range migration effect. A squinted SAR imaging method is proposed based on MMV. First, a modified MMV model that considers range migration is built to realize sparse representation of echo. Additionally, an improved orthogonal matching pursuit algorithm is developed to reconstruct HRRPs. Finally, a high-resolution two-dimensional image result can be easily achieved via traditional azimuth match filtering. Experimental results based on both simulated and real data demonstrate that the proposed MMV-based method can provide better computational efficiency and antinoise ability compared to the SMV-based method.
Vector model for mapping of visual space to subjective 4-D sphere
Matuzevicius, Dalius; Vaitkevicius, Henrikas
2014-03-01
Here we present a mathematical model of binocular vision that maps a visible physical world to a subjective perception of it. The subjective space is a set of 4-D vectors whose components are outputs of four monocular neurons from each of the two eyes. Monocular neurons have one of the four types of concentric receptive fields with Gabor-like weighting coefficients. Next this vector representation of binocular vision is implemented as a pool of neurons where each of them is selective to the object's particular location in a 3-D visual space. Formally each point of the visual space is being projected onto a 4-D sphere. Proposed model allows determination of subjective distances in depth and direction, provides computational means for determination of Panum's area and explains diplopia and allelotropia.
Directory of Open Access Journals (Sweden)
Wolfgang Witteveen
2014-01-01
Full Text Available The mechanical response of multilayer sheet structures, such as leaf springs or car bodies, is largely determined by the nonlinear contact and friction forces between the sheets involved. Conventional computational approaches based on classical reduction techniques or the direct finite element approach have an inefficient balance between computational time and accuracy. In the present contribution, the method of trial vector derivatives is applied and extended in order to obtain a-priori trial vectors for the model reduction which are suitable for determining the nonlinearities in the joints of the reduced system. Findings show that the result quality in terms of displacements and contact forces is comparable to the direct finite element method but the computational effort is extremely low due to the model order reduction. Two numerical studies are presented to underline the method’s accuracy and efficiency. In conclusion, this approach is discussed with respect to the existing body of literature.
Wei, W. B.; Tan, L.; Jia, M. Q.; Pan, Z. K.
2017-01-01
The variational level set method is one of the main methods of image segmentation. Due to signed distance functions as level sets have to keep the nature of the functions through numerical remedy or additional technology in an evolutionary process, it is not very efficient. In this paper, a normal vector projection method for image segmentation using Chan-Vese model is proposed. An equivalent formulation of Chan-Vese model is used by taking advantage of property of binary level set functions and combining with the concept of convex relaxation. Threshold method and projection formula are applied in the implementation. It can avoid the above problems and obtain a global optimal solution. Experimental results on both synthetic and real images validate the effects of the proposed normal vector projection method, and show advantages over traditional algorithms in terms of computational efficiency.
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Gabriel Parra-Henao
2016-01-01
Full Text Available Ecological niche modeling of Triatominae bugs allow us to establish the local risk of transmission of the parasite Trypanosoma cruzi, which causes Chagas disease. This information could help to guide health authority recommendations on infection monitoring, prevention, and control. In this study, we estimated the geographic distribution of triatomine species in Colombia and identified the relationship between landscape structure and climatic factors influencing their occurrence. A total of 2451 records of 4 triatomine species (Panstrongylus geniculatus, Rhodnius pallescens, R. prolixus, and Triatoma maculata were analyzed. The variables that provided more information to explain the ecologic niche of these vectors were related to precipitation, altitude, and temperature. We found that the species with the broadest potential geographic distribution were P. geniculatus, R. pallescens, and R. prolixus. In general, the models predicted the highest occurrence probability of these vectors in the eastern slope of the Eastern Cordillera, the southern region of the Magdalena valley, and the Sierra Nevada of Santa Marta.
LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION
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IMEN TRABELSI
2017-05-01
Full Text Available Speaker Identification (SI aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been proposed based on Gaussian mixture model-universal background model (GMM-UBM and Learning vector quantization (LVQ for automatic text-independent speaker identification. Features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC extracted from the speech signal are used to train the New England subset of the TIMIT database. The best results obtained (90% for gender- independent speaker identification, 97 % for male speakers and 93% for female speakers for test data using 36 MFCC features.
Gamma and Exponential Autoregressive Moving Average (ARMA ...
African Journals Online (AJOL)
Time series data encountered in practice depict properties that deviate from those of gaussian processes. The gamma and exponentially distributed processes which are used as basic models for positive time series fall in the class of non-gaussian processes. In this paper, we develop new and simpler representations of the ...
Ramilo, David W; Nunes, Telmo; Madeira, Sara; Boinas, Fernando; da Fonseca, Isabel Pereira
2017-01-01
Vector-borne diseases are not only accounted responsible for their burden on human health-care systems, but also known to cause economic constraints to livestock and animal production. Animals are affected directly by the transmitted pathogens and indirectly when animal movement is restricted. Distribution of such diseases depends on climatic and social factors, namely, environmental changes, globalization, trade and unplanned urbanization. Culicoides biting midges are responsible for the transmission of several pathogenic agents with relevant economic impact. Due to a fragmentary knowledge of their ecology, occurrence is difficult to predict consequently, limiting the control of these arthropod vectors. In order to understand the distribution of Culicoides species, in mainland Portugal, data collected during the National Entomologic Surveillance Program for Bluetongue disease (2005-2013), were used for statistical evaluation. Logistic regression analysis was preformed and prediction maps (per season) were obtained for vector and potentially vector species. The variables used at the present study were selected from WorldClim (two climatic variables) and CORINE databases (twenty-two land cover variables). This work points to an opposite distribution of C. imicola and species from the Obsoletus group within mainland Portugal. Such findings are evidenced in autumn, with the former appearing in Central and Southern regions. Although appearing northwards, on summer and autumn, C. newsteadi reveals a similar distribution to C. imicola. The species C. punctatus appears in all Portuguese territory throughout the year. Contrary, C. pulicaris is poorly caught in all areas of mainland Portugal, being paradoxical present near coastal areas and higher altitude regions.
Space vector-based modeling and control of a modular multilevel converter in HVDC applications
DEFF Research Database (Denmark)
Bonavoglia, M.; Casadei, G.; Zarri, L.
2013-01-01
Modular multilevel converter (MMC) is an emerging multilevel topology for high-voltage applications that has been developed in recent years. In this paper, the modeling and the control of MMCs are restated in terms of space vectors, which may allow a deeper understanding of the converter behavior....... As a result, a control scheme for three-phase MMCs based on the previous theoretical analysis is presented. Numerical simulations are used to test its feasibility....
Expansion of the Variational Garrote to a Multiple Measurement Vectors Model
DEFF Research Database (Denmark)
Hansen, Sofie Therese; Stahlhut, Carsten; Hansen, Lars Kai
2013-01-01
The recovery of sparse signals in underdetermined systems is the focus of this paper. We propose an expanded version of the Variational Garrote, originally presented by Kappen (2011), which can use multiple measurement vectors (MMVs) to further improve source retrieval performance. We show its...... superiority compared to the original formulation and demonstrate its ability to correctly estimate both the sources’ location and their magnitude. Finally evidence is given of the high performance of the proposed algorithm compared to other MMV models....
Perancangan Email Client Dengan Pengklasifikasian Email Menggunakan Algoritma Vector Space Model
Christian, Moses
2015-01-01
On today's age of technology, widely used email to send information throughout the world. During the classification of the email is still done manually and less objective. So in this study, the authors apply the method of Vector Space Model (VSM) to make an automatic email classification and more objective. With this method of email classification can be done automatically based on address, subject, and body of an email that allows users to email in the organization of every incoming email in...
Modeling the impact of global warming on vector-borne infections.
Massad, Eduardo; Coutinho, Francisco Antonio Bezerra; Lopez, Luis Fernandez; da Silva, Daniel Rodrigues
2011-06-01
Global warming will certainly affect the abundance and distribution of disease vectors. The effect of global warming, however, depends on the complex interaction between the human host population and the causative infectious agent. In this work we review some mathematical models that were proposed to study the impact of the increase in ambient temperature on the spread and gravity of some insect-transmitted diseases. Copyright © 2011 Elsevier B.V. All rights reserved.
Explaining earnings persistence: a threshold autoregressive panel unit root approach
Galimberti, Jaqueson Kingeski; Cupertino, César Medeiros
2009-01-01
This paper proposes a reassessment to the hypothesis that the persistence of current earnings performance is decreasing in the magnitude of the accrual component of earnings and increasing in the magnitude of the cash flow component of earnings. For this purpose, a threshold autoregressive panel unit root approach is proposed using a Fisher-type. This approach allowed us to distinguish between unconditioned and conditioned measures of persistence, making it possible to infer whether the earni...
Vector-borne diseases models with residence times - A Lagrangian perspective.
Bichara, Derdei; Castillo-Chavez, Carlos
2016-11-01
A multi-patch and multi-group modeling framework describing the dynamics of a class of diseases driven by the interactions between vectors and hosts structured by groups is formulated. Hosts' dispersal is modeled in terms of patch-residence times with the nonlinear dynamics taking into account the effective patch-host size. The residence times basic reproduction number R0 is computed and shown to depend on the relative environmental risk of infection. The model is robust, that is, the disease free equilibrium is globally asymptotically stable (GAS) if R0≤1 and a unique interior endemic equilibrium is shown to exist that is GAS whenever R0>1 whenever the configuration of host-vector interactions is irreducible. The effects of patchiness and groupness, a measure of host-vector heterogeneous structure, on the basic reproduction number R0, are explored. Numerical simulations are carried out to highlight the effects of residence times on disease prevalence. Copyright © 2016 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
A hybrid least squares support vector machines and GMDH approach for river flow forecasting
Samsudin, R.; Saad, P.; Shabri, A.
2010-06-01
This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.
Characterization of autoregressive processes using entropic quantifiers
Traversaro, Francisco; Redelico, Francisco O.
2018-01-01
The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.
Vector Flow Visualization of Urinary Flow Dynamics in a Bladder Outlet Obstruction Model.
Ishii, Takuro; Yiu, Billy Y S; Yu, Alfred C H
2017-11-01
Voiding dysfunction that results from bladder outlet (BO) obstruction is known to alter significantly the dynamics of urine passage through the urinary tract. To non-invasively image this phenomenon on a time-resolved basis, we pursued the first application of a recently developed flow visualization technique called vector projectile imaging (VPI) that can track the spatiotemporal dynamics of flow vector fields at a frame rate of 10,000 fps (based on plane wave excitation and least-squares Doppler vector estimation principles). For this investigation, we designed a new anthropomorphic urethral tract phantom to reconstruct urinary flow dynamics under controlled conditions (300 mm H2O inlet pressure and atmospheric outlet pressure). Both a normal model and a diseased model with BO obstruction were developed for experimentation. VPI cine loops were derived from these urinary flow phantoms. Results show that VPI is capable of depicting differences in the flow dynamics of normal and diseased urinary tracts. In the case with BO obstruction, VPI depicted the presence of BO flow jet and vortices in the prostatic urethra. The corresponding spatial-maximum flow velocity magnitude was estimated to be 2.43 m/s, and it is significantly faster than that for the normal model (1.52 m/s) and is in line with values derived from computational fluid dynamics simulations. Overall, this investigation demonstrates the feasibility of using vector flow visualization techniques to non-invasively examine internal flow characteristics related to voiding dysfunction in the urethral tract. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.
Simplified models of vector control impact upon malaria transmission by zoophagic mosquitoes.
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Samson S Kiware
Full Text Available BACKGROUND: High coverage of personal protection measures that kill mosquitoes dramatically reduce malaria transmission where vector populations depend upon human blood. However, most primary malaria vectors outside of sub-Saharan Africa can be classified as "very zoophagic," meaning they feed occasionally (<10% of blood meals upon humans, so personal protection interventions have negligible impact upon their survival. METHODS AND FINDINGS: We extended a published malaria transmission model to examine the relationship between transmission, control, and the baseline proportion of bloodmeals obtained from humans (human blood index. The lower limit of the human blood index enables derivation of simplified models for zoophagic vectors that (1 Rely on only three field-measurable parameters. (2 Predict immediate and delayed (with and without assuming reduced human infectivity, respectively impacts of personal protection measures upon transmission. (3 Illustrate how appreciable indirect communal-level protection for non-users can be accrued through direct personal protection of users. (4 Suggest the coverage and efficacy thresholds required to attain epidemiological impact. The findings suggest that immediate, indirect, community-wide protection of users and non-users alike may linearly relate to the efficacy of a user's direct personal protection, regardless of whether that is achieved by killing or repelling mosquitoes. High protective coverage and efficacy (≥80% are important to achieve epidemiologically meaningful impact. Non-users are indirectly protected because the two most common species of human malaria are strict anthroponoses. Therefore, the small proportion of mosquitoes that are killed or diverted while attacking humans can represent a large proportion of those actually transmitting malaria. CONCLUSIONS: Simplified models of malaria transmission by very zoophagic vectors may be used by control practitioners to predict intervention impact
Moiroux, Nicolas; Djènontin, Armel; Bio-Bangana, Abdul S; Chandre, Fabrice; Corbel, Vincent; Guis, Hélène
2014-03-12
A better understanding of the ecology and spatial-temporal distribution of malaria vectors is essential to design more effective and sustainable strategies for malaria control and elimination. In a previous study, we analyzed presence-absence data of An. funestus, An. coluzzii, and An. gambiae s.s. in an area of southern Benin with high coverage of vector control measures. Here, we further extend the work by analysing the positive values of the dataset to assess the determinants of the abundance of these three vectors and to produce predictive maps of vector abundance. Positive counts of the three vectors were assessed using negative-binomial zero-truncated (NBZT) mixed-effect models according to vector control measures and environmental covariates derived from field and remote sensing data. After 8-fold cross-validation of the models, predictive maps of abundance of the sympatric An. funestus, An. coluzzii, and An. gambiae s.s. were produced. Cross-validation of the NBZT models showed a satisfactory predictive accuracy. Almost all changes in abundance between two surveys in the same village were well predicted by the models but abundances for An. gambiae s.s. were slightly underestimated. During the dry season, predictive maps showed that abundance greater than 1 bite per person per night were observed only for An. funestus and An. coluzzii. During the rainy season, we observed both increase and decrease in abundance of An. funestus, which are dependent on the ecological setting. Abundances of both An. coluzzii and An. gambiae s.s. increased during the rainy season but not in the same areas. Our models helped characterize the ecological preferences of three major African malaria vectors. This works highlighted the importance to study independently the binomial and the zero-truncated count processes when evaluating vector control strategies. The study of the bio-ecology of malaria vector species in time and space is critical for the implementation of timely and
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Ayse KOCALMIS BILHAN
2013-01-01
Full Text Available A space vector PWM method for a two level inverter is proposed in this paper. A two level inverter using space vector modulation strategy has been modeled and simulated with a passive R-L load. Photovoltaic cells are used as DC source for input of two-level inverter. Simulation results are presented for various operation conditions to verify the system model. In this paper, MATLAB/Simulink package program has been used for modeling and simulation of PV cells and two-level space vector pulse width modulation (SVPWM inverter.
Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F
2014-06-01
To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified). © 2013 Elsevier B.V. All rights reserved.
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Edwin Eduardo Millán Rojas
2018-02-01
Full Text Available Context: Management to care for the environment and the Earth (geo can be source of inspiration for developing models that allow addressing complexity issues; the objective of this research was to develop an additional aspect of the inspired models. The geoinspired model has two features, the first covering aspects related to environmental management and the behavior of natural resources, and the second has a component of spatial location associated with existing objects on the Earth's surface. Method: The approach developed in the research is descriptive and its main objective is the representation or characterization of a case study within a particular context. Results: The result was the design of a model to emulate the natural behavior of the water tributaries of the Amazon foothills, in order to extend the application of the inspired models and allow the use of elements such as geo-referencing and environmental management. The proposed geoinspired model is called “natural vectors agents inspired in environmental management”. Conclusions: The agents vectors naturals inspired by the environmental are polyform elements that can assume the behavior of environmental entities, which makes it possible to achieve progress in other fields of environmental management (use of soil, climate, flora, fauna, and link environmental issues with the structure of the proposed model.
Statistical analysis of hypotheses on the cointegrating relations in the I(2) model
DEFF Research Database (Denmark)
Johansen, Søren
2006-01-01
The cointegrated vector autoregressive model for I(2) variables is a non-linear parametric restriction on the linear I(2) regression model for variables of order I(0), I(1) and I(2). In this paper we discuss non-linear submodels given by smooth parametrizations. We give conditions......) and the reformulation is applied to show that some hypotheses on the cointegrating coefficients in the cointegrated I(2) model give asymptotic ¿² inference....
de Sitter symmetries and inflationary correlators in parity violating scalar-vector models
Beltrán Almeida, Juan P.; Motoa-Manzano, Josué; Valenzuela-Toledo, César A.
2017-11-01
In this paper we use conformal field theory techniques to constrain the form of the correlations functions of an inflationary scalar-vector model described by the interaction term f1(phi)Fμ νFμ ν + f2(phi)~Fμ νFμ ν. We use the fact that the conformal group is the relevant symmetry group acting on super horizon scales in an inflationary de Sitter background. As a result, we find that super horizon conformal symmetry, constraints the form of the coupling functions f1, f2 to be homogeneous functions of the same degree. We derive the general form of the correlators involving scalar and vector perturbations in this model and determine its squeezed limit scaling behaviour for super horizon scales. The approach followed here is useful to constraint the shape of scalar-vector correlators, and our results agree with recent literature on the subject, but don't allow us to determine amplitude factors of the correlators.
Probing vector-like quark models with Higgs-boson pair production
Cacciapaglia, Giacomo; Cai, Haiying; Carvalho, Alexandra; Deandrea, Aldo; Flacke, Thomas; Fuks, Benjamin; Majumder, Devdatta; Shao, Hua-Sheng
2017-07-01
We investigate Higgs-boson pair production at the LHC when the final state system arises from decays of vector-like quarks coupling to the Higgs boson and the Standard Model quarks. Our phenomenological study includes next-to-leading-order QCD corrections, which are important to guarantee accurate predictions, and focuses on a detailed analysis of a di-Higgs signal in the four b-jet channel. Whereas existing Run II CMS and ATLAS analyses are not specifically designed for probing non-resonant, vector-like-quark induced, di-Higgs production, we show that they nevertheless offer some potential for these modes. We then investigate the possibility of distinguishing between the various di-Higgs production mechanisms by exploiting the kinematic properties of the signal.
Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks.
Hartemink, Nienke; Vanwambeke, Sophie O; Purse, Bethan V; Gilbert, Marius; Van Dyck, Hans
2015-11-01
Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional habitat of vectors or hosts, and hence of the pathogen. Empirical-statistical methods do not explicitly incorporate biological mechanisms, whereas current mechanistic models are rarely spatially explicit; both methods ignore the way animals use the landscape (i.e. movement ecology). We argue that applying a functional concept for habitat, i.e. the resource-based habitat concept (RBHC), can solve these issues. The RBHC offers a framework to identify systematically the different ecological resources that are necessary for the completion of the transmission cycle and to relate these resources to (combinations of) landscape features and other environmental factors. The potential of the RBHC as a framework for identifying suitable habitats for vector-borne pathogens is explored and illustrated with the case of bluetongue virus, a midge-transmitted virus affecting ruminants. The concept facilitates the study of functional habitats of the interacting species (vectors as well as hosts) and provides new insight into spatial and temporal variation in transmission opportunities and exposure that ultimately determine disease risks. It may help to identify knowledge gaps and control options arising from changes in the spatial configuration of key resources across the landscape. The RBHC framework may act as a bridge between existing mechanistic and statistical modelling approaches. © 2014 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.
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Changhao Fan
2017-01-01
Full Text Available In modeling, only information from the deviation between the output of the support vector regression (SVR model and the training sample is considered, whereas the other prior information of the training sample, such as probability distribution information, is ignored. Probabilistic distribution information describes the overall distribution of sample data in a training sample that contains different degrees of noise and potential outliers, as well as helping develop a high-accuracy model. To mine and use the probability distribution information of a training sample, a new support vector regression model that incorporates probability distribution information weight SVR (PDISVR is proposed. In the PDISVR model, the probability distribution of each sample is considered as the weight and is then introduced into the error coefficient and slack variables of SVR. Thus, the deviation and probability distribution information of the training sample are both used in the PDISVR model to eliminate the influence of noise and outliers in the training sample and to improve predictive performance. Furthermore, examples with different degrees of noise were employed to demonstrate the performance of PDISVR, which was then compared with those of three SVR-based methods. The results showed that PDISVR performs better than the three other methods.
Composite Weak Vector Bosons in a Left-Right Symmetric Preon Model : Particles and fields
Motoo, SEKIGUTI; Shin, ISHIDA; Hiroaki, WADA; Atomic Energy Research Institute, College of Science and Technology Nihon University
1996-01-01
We take the viewpoint that the standard model is a low energy effective theory among composite quarks, leptons and weak bosons in a left-right (LR) symmetric preon model with a hypercolor SU(N)_ gauge interaction. Starting from NJL-type interactions with glohal SU(2)_L × SU(2)_R symmetry, we construct the composite weak vector bosons from a pair of spinor preons and derive their effective interactions with quarks and leptons, which are essentially identical, at the tree diagram level, to thos...
Analysis of the sensitivity properties of a model of vector-borne bubonic plague.
Buzby, Megan; Neckels, David; Antolin, Michael F; Estep, Donald
2008-09-06
Model sensitivity is a key to evaluation of mathematical models in ecology and evolution, especially in complex models with numerous parameters. In this paper, we use some recently developed methods for sensitivity analysis to study the parameter sensitivity of a model of vector-borne bubonic plague in a rodent population proposed by Keeling & Gilligan. The new sensitivity tools are based on a variational analysis involving the adjoint equation. The new approach provides a relatively inexpensive way to obtain derivative information about model output with respect to parameters. We use this approach to determine the sensitivity of a quantity of interest (the force of infection from rats and their fleas to humans) to various model parameters, determine a region over which linearization at a specific parameter reference point is valid, develop a global picture of the output surface, and search for maxima and minima in a given region in the parameter space.
McCann, Robert S; Messina, Joseph P; MacFarlane, David W; Bayoh, M Nabie; Vulule, John M; Gimnig, John E; Walker, Edward D
2014-06-06
Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission. We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and accumulated precipitation to model larval habitat locations in a region of western Kenya through two methods: logistic regression and random forest. Additionally, we used two separate data sets to account for variation in habitat locations across space and over time. Larval habitats were more likely to be present in locations with a lower slope to contributing area ratio (i.e. TWI), closer to streams, with agricultural land use relative to nonagricultural land use, and in friable clay/sandy clay loam soil and firm, silty clay/clay soil relative to friable clay soil. The probability of larval habitat presence increased with increasing accumulated precipitation. The random forest models were more accurate than the logistic regression models, especially when accumulated precipitation was included to account for seasonal differences in precipitation. The most accurate models for the two data sets had area under the curve (AUC) values of 0.864 and 0.871, respectively. TWI, distance to the nearest stream, and precipitation had the greatest mean decrease in Gini impurity criteria in these models. This study demonstrates the usefulness of random forest models for larval malaria vector habitat modeling. TWI and distance to the nearest stream were the two most important landscape variables in these models. Including accumulated precipitation in our models improved the accuracy of larval habitat location predictions by accounting for seasonal variation in the precipitation. Finally, the sampling strategy employed here for model parameterization could serve as a framework for creating predictive larval habitat models to assist in larval control efforts.
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Joel K Kelso
Full Text Available The spread of Bluetongue virus (BTV among ruminants is caused by movement of infected host animals or by movement of infected Culicoides midges, the vector of BTV. Biologically plausible models of Culicoides dispersal are necessary for predicting the spread of BTV and are important for planning control and eradication strategies.A spatially-explicit simulation model which captures the two underlying population mechanisms, population dynamics and movement, was developed using extensive data from a trapping program for C. brevitarsis on the east coast of Australia. A realistic midge flight sub-model was developed and the annual incursion and population establishment of C. brevitarsis was simulated. Data from the literature was used to parameterise the model.The model was shown to reproduce the spread of C. brevitarsis southwards along the east Australian coastline in spring, from an endemic population to the north. Such incursions were shown to be reliant on wind-dispersal; Culicoides midge active flight on its own was not capable of achieving known rates of southern spread, nor was re-emergence of southern populations due to overwintering larvae. Data from midge trapping programmes were used to qualitatively validate the resulting simulation model.The model described in this paper is intended to form the vector component of an extended model that will also include BTV transmission. A model of midge movement and population dynamics has been developed in sufficient detail such that the extended model may be used to evaluate the timing and extent of BTV outbreaks. This extended model could then be used as a platform for addressing the effectiveness of spatially targeted vaccination strategies or animal movement bans as BTV spread mitigation measures, or the impact of climate change on the risk and extent of outbreaks. These questions involving incursive Culicoides spread cannot be simply addressed with non-spatial models.
On Diagnostic Checking of Vector ARMA-GARCH Models with Gaussian and Student-t Innovations
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Yongning Wang
2013-04-01
Full Text Available This paper focuses on the diagnostic checking of vector ARMA (VARMA models with multivariate GARCH errors. For a fitted VARMA-GARCH model with Gaussian or Student-t innovations, we derive the asymptotic distributions of autocorrelation matrices of the cross-product vector of standardized residuals. This is different from the traditional approach that employs only the squared series of standardized residuals. We then study two portmanteau statistics, called Q1(M and Q2(M, for model checking. A residual-based bootstrap method is provided and demonstrated as an effective way to approximate the diagnostic checking statistics. Simulations are used to compare the performance of the proposed statistics with other methods available in the literature. In addition, we also investigate the effect of GARCH shocks on checking a fitted VARMA model. Empirical sizes and powers of the proposed statistics are investigated and the results suggest a procedure of using jointly Q1(M and Q2(M in diagnostic checking. The bivariate time series of FTSE 100 and DAX index returns is used to illustrate the performance of the proposed portmanteau statistics. The results show that it is important to consider the cross-product series of standardized residuals and GARCH effects in model checking.
SVMQA: support-vector-machine-based protein single-model quality assessment.
Manavalan, Balachandran; Lee, Jooyoung
2017-08-15
The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these problems can be grouped into two categories: consensus methods and single-model methods. Consensus methods in general perform better and attain higher correlation between predicted and true quality measures. However, these methods frequently fail to generate proper quality scores for native-like structures which are distinct from the rest of the pool. Conversely, single-model methods do not suffer from this drawback and are better suited for real-life applications where many models from various sources may not be readily available. In this study, we developed a support-vector-machine-based single-model global quality assessment (SVMQA) method. For a given protein model, the SVMQA method predicts TM-score and GDT_TS score based on a feature vector containing statistical potential energy terms and consistency-based terms between the actual structural features (extracted from the three-dimensional coordinates) and predicted values (from primary sequence). We trained SVMQA using CASP8, CASP9 and CASP10 targets and determined the machine parameters by 10-fold cross-validation. We evaluated the performance of our SVMQA method on various benchmarking datasets. Results show that SVMQA outperformed the existing best single-model QA methods both in ranking provided protein models and in selecting the best model from the pool. According to the CASP12 assessment, SVMQA was the best method in selecting good-quality models from decoys in terms of GDTloss. SVMQA method can be freely downloaded from http://lee.kias.re.kr/SVMQA/SVMQA_eval.tar.gz. jlee@kias.re.kr. Supplementary data are available at Bioinformatics online.
Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model
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Shaojiang Dong
2014-01-01
Full Text Available Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Energy Technology Data Exchange (ETDEWEB)
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan [Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)
2014-09-12
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
Assi, Kondo Claude; Gay, Etienne; Chnafa, Christophe; Mendez, Simon; Nicoud, Franck; Abascal, Juan F. P. J.; Lantelme, Pierre; Tournoux, François; Garcia, Damien
2017-09-01
We propose a regularized least-squares method for reconstructing 2D velocity vector fields within the left ventricular cavity from single-view color Doppler echocardiographic images. Vector flow mapping is formulated as a quadratic optimization problem based on an {{\\ell }2} -norm minimization of a cost function composed of a Doppler data-fidelity term and a regularizer. The latter contains three physically interpretable expressions related to 2D mass conservation, Dirichlet boundary conditions, and smoothness. A finite difference discretization of the continuous problem was adopted in a polar coordinate system, leading to a sparse symmetric positive-definite system. The three regularization parameters were determined automatically by analyzing the L-hypersurface, a generalization of the L-curve. The performance of the proposed method was numerically evaluated using (1) a synthetic flow composed of a mixture of divergence-free and curl-free flow fields and (2) simulated flow data from a patient-specific CFD (computational fluid dynamics) model of a human left heart. The numerical evaluations showed that the vector flow fields reconstructed from the Doppler components were in good agreement with the original velocities, with a relative error less than 20%. It was also demonstrated that a perturbation of the domain contour has little effect on the rebuilt velocity fields. The capability of our intraventricular vector flow mapping (iVFM) algorithm was finally illustrated on in vivo echocardiographic color Doppler data acquired in patients. The vortex that forms during the rapid filling was clearly deciphered. This improved iVFM algorithm is expected to have a significant clinical impact in the assessment of diastolic function.
Using remote sensing and machine learning for the spatial modelling of a bluetongue virus vector
Van doninck, J.; Peters, J.; De Baets, B.; Ducheyne, E.; Verhoest, N. E. C.
2012-04-01
Bluetongue is a viral vector-borne disease transmitted between hosts, mostly cattle and small ruminants, by some species of Culicoides midges. Within the Mediterranean basin, C. imicola is the main vector of the bluetongue virus. The spatial distribution of this species is limited by a number of environmental factors, including temperature, soil properties and land cover. The identification of zones at risk of bluetongue outbreaks thus requires detailed information on these environmental factors, as well as appropriate epidemiological modelling techniques. We here give an overview of the environmental factors assumed to be constraining the spatial distribution of C. imicola, as identified in different studies. Subsequently, remote sensing products that can be used as proxies for these environmental constraints are presented. Remote sensing data are then used together with species occurrence data from the Spanish Bluetongue National Surveillance Programme to calibrate a supervised learning model, based on Random Forests, to model the probability of occurrence of the C. imicola midge. The model will then be applied for a pixel-based prediction over the Iberian peninsula using remote sensing products for habitat characterization.
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Baghdad BELABES
2008-12-01
Full Text Available In this paper a hybrid controller combining a linear model following controller (LMFC and fuzzy logic control (FLC for speed vector controlled permanent magnet synchronous motor (PMSM is described on this study. The FLC is introduced at the adaptive mechanism level. First, an LMFC system is designed to allow the plant states to be controlled to follow the states produced by a reference model. In the nominal conditions, the model following is perfect and the adaptive mechanism based on the fuzzy logic is idle. Secondly, when parameter variations or external disturbances occur, an augmented signal will be generated by FLC mechanism to preserve the desired model following control performance. The effectiveness and robustness of the proposed controller is demonstrated by some simulation results.
An improved wave-vector frequency-domain method for nonlinear wave modeling.
Jing, Yun; Tao, Molei; Cannata, Jonathan
2014-03-01
In this paper, a recently developed wave-vector frequency-domain method for nonlinear wave modeling is improved and verified by numerical simulations and underwater experiments. Higher order numeric schemes are proposed that significantly increase the modeling accuracy, thereby allowing for a larger step size and shorter computation time. The improved algorithms replace the left-point Riemann sum in the original algorithm by the trapezoidal or Simpson's integration. Plane waves and a phased array were first studied to numerically validate the model. It is shown that the left-point Riemann sum, trapezoidal, and Simpson's integration have first-, second-, and third-order global accuracy, respectively. A highly focused therapeutic transducer was then used for experimental verifications. Short high-intensity pulses were generated. 2-D scans were conducted at a prefocal plane, which were later used as the input to the numerical model to predict the acoustic field at other planes. Good agreement is observed between simulations and experiments.
Three-dimensional vector modeling and restoration of flat finite wave tank radiometric measurements
Truman, W. M.; Balanis, C. A.
1977-01-01
The three-dimensional vector interaction between a microwave radiometer and a wave tank was modeled. Computer programs for predicting the response of the radiometer to the brightness temperature characteristics of the surroundings were developed along with a computer program that can invert (restore) the radiometer measurements. It is shown that the computer programs can be used to simulate the viewing of large bodies of water, and is applicable to radiometer measurements received from satellites monitoring the ocean. The water temperature, salinity, and wind speed can be determined.
DEFF Research Database (Denmark)
Franchi, Massimo; Johansen, Søren
It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper...... formulates a CVAR model allowing for many near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then a critical value adjustment suggested by McCloskey for the test on the cointegrating relations is implemented, and it is found by simulation that it eliminates...... size distortions and has reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs....
Bitseki Penda, Valère; Djellout, Hacène; Proïa, Frédéric
2012-01-01
24 pages.; The purpose of this paper is to investigate moderate deviations for the Durbin-Watson statistic associated with the stable first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We first establish a moderate deviation principle for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated with the driven noise. It enables us to provide a...
Adeno-associated viral vector serotype 5 poorly transduces liver in rat models.
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Paula S Montenegro-Miranda
Full Text Available Preclinical studies in mice and non-human primates showed that AAV serotype 5 provides efficient liver transduction and as such seems a promising vector for liver directed gene therapy. An advantage of AAV5 compared to serotype 8 already shown to provide efficient correction in a phase 1 trial in patients suffering from hemophilia B, is its lower seroprevalence in the general population. Our goal is liver directed gene therapy for Crigler-Najjar syndrome type I, inherited severe unconjugated hyperbilirubinemia caused by UGT1A1 deficiency. In a relevant animal model, the Gunn rat, we compared the efficacy of AAV 5 and 8 to that of AAV1 previously shown to be effective. Ferrying a construct driving hepatocyte specific expression of UGT1A1, both AAV8 and AAV1 provided an efficient correction of hyperbilirubinemia. In contrast to these two and to other animal models AAV5 failed to provide any correction. To clarify whether this unexpected finding was due to the rat model used or due to a problem with AAV5, the efficacy of this serotype was compared in a mouse and two additional rat strains. Administration of an AAV5 vector expressing luciferase under the control of a liver specific promoter confirmed that this serotype poorly performed in rat liver, rendering it not suitable for proof of concept studies in this species.
Modeling and path-following control of a vector-driven stratospheric satellite
Zheng, Zewei; Chen, Tian; Xu, Ming; Zhu, Ming
2016-05-01
The stratospheric satellite driven by steady prevailing winds in the stratosphere must be controlled in its longitudinal excursion to keep a latitudinal orbital flight. In a reliable and high-precision control system, an available system model must come first. In this paper, we study the 6 degree-of-freedom (DOF) modeling and path-following problem of a novel stratospheric satellite which consists of a high-altitude helium balloon, a truss and two vector-motor-driven propellers. To keep a latitudinal flight orbit, an algorithm for accurate latitudinal path following is proposed based on the theories of vector field and sliding mode control. Moreover, a forward velocity controller is added to the control algorithm to maintain a constant velocity. Finally, a series of open-loop control simulations are completed to verify the effectiveness of the model in the performance of the stratospheric satellite dynamics, and path-following control simulation results demonstrate the effectiveness of the proposed control algorithm.
Tadesse, T.; Wiegelmann, T.; Gosain, S.; MacNeice, P.; Pevtsov, A. A.
2014-01-01
Context. The magnetic field permeating the solar atmosphere is generally thought to provide the energy for much of the activity seen in the solar corona, such as flares, coronal mass ejections (CMEs), etc. To overcome the unavailability of coronal magnetic field measurements, photospheric magnetic field vector data can be used to reconstruct the coronal field. Currently, there are several modelling techniques being used to calculate three-dimensional field lines into the solar atmosphere. Aims. For the first time, synoptic maps of a photospheric-vector magnetic field synthesized from the vector spectromagnetograph (VSM) on Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used to model the coronal magnetic field and estimate free magnetic energy in the global scale. The free energy (i.e., the energy in excess of the potential field energy) is one of the main indicators used in space weather forecasts to predict the eruptivity of active regions. Methods. We solve the nonlinear force-free field equations using an optimization principle in spherical geometry. The resulting threedimensional magnetic fields are used to estimate the magnetic free energy content E(sub free) = E(sub nlfff) - E(sub pot), which is the difference of the magnetic energies between the nonpotential field and the potential field in the global solar corona. For comparison, we overlay the extrapolated magnetic field lines with the extreme ultraviolet (EUV) observations by the atmospheric imaging assembly (AIA) on board the Solar Dynamics Observatory (SDO). Results. For a single Carrington rotation 2121, we find that the global nonlinear force-free field (NLFFF) magnetic energy density is 10.3% higher than the potential one. Most of this free energy is located in active regions.
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Mahmudul Mannan Toy
2011-01-01
Full Text Available The broad objective of this study is to empirically estimate the export supply model of Bangladesh. The techniques of cointegration, Engle-Granger causality and Vector Error Correction are applied to estimate the export supply model. The econometric analysis is done by using the time series data of the variables of interest which is collected from various secondary sources. The study has empirically tested the hypothesis, long run relationship and casualty between variables of the model. The cointegration analysis shows that all the variables of the study are co-integrated at their first differences meaning that there exists long run relationship among the variables. The VECM estimation shows the dynamics of variables in the export supply function and the short run and long run elasticities of export supply with respect to each independent variable. The error correction term is found negative which indicates that any short run disequilibrium will be turned into equilibrium in the long run.
Hettiarachchi, Imali T; Mohamed, Shady; Nyhof, Luke; Nahavandi, Saeid
2013-01-01
Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.
Modeling a ground-coupled heat pump system by a support vector machine
Energy Technology Data Exchange (ETDEWEB)
Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)
2008-08-15
This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for generalization, and this capability is independent of the dimensionality of the input data. In this study, a SVM based method was intended to adopt GCHP system for efficient modeling. The Lin-kernel SVM method was quite efficient in modeling purposes and did not require a pre-knowledge about the system. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that the root-mean squared (RMS) value is 0.002722, the coefficient of multiple determinations (R{sup 2}) value is 0.999999, coefficient of variation (cov) value is 0.077295, and mean error function (MEF) value is 0.507437 for the proposed Lin-kernel SVM method. The optimum parameters of the SVM method were determined by using a greedy search algorithm. This search algorithm was effective for obtaining the optimum parameters. The simulation results show that the SVM is a good method for prediction of the COP of the GCHP system. The computation of SVM model is faster compared with other machine learning techniques (artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS)); because there are fewer free parameters and only support vectors (only a fraction of all data) are used in the generalization process. (author)
Relativistic scalar-vector models of the N-N and N-nuclear interactions
Energy Technology Data Exchange (ETDEWEB)
Green, A.E.S.
1985-01-01
This paper for the Proceedings of Conference an Anti-Nucleon and Nucleon-Nucleus Interactions summarizes work by the principal investigator and his collaborators on the nucleon-nucleon (N-N) and nucleon-nuclear (N-eta) interactions. It draws heavily on a paper presented at the Many Body Conference in Rome in 1972 but also includes a brief review of our phenomenological N-eta interaction studies. We first summarize our 48-49 generalized scalar-vector meson field theory model of the N-N interactions. This is followed by a brief description of our phenomenological work in the 50's on the N-eta interaction sponsored by the Atomic Energy Commission (the present DOE). This work finally led to strong velocity dependent potentials with spin orbit and isospin terms for shell and optical model applications. This is followed by a section on the Emergence of One-Boson Exchange Models describing developments in the 60's of quantitative generalized one boson exchange potentials (GOBEP) including our purely relativistic N-N analyses. Then follows a section on the application of this meson field model to the N-eta interaction, in particular to spherical closed shell nuclei. This work was sponsored by AFOSR but funding was halted with the Mansfield amendment. We conclude with a discussion of subsequent collateral work by former colleagues and by others who have converged upon scalar-vector relativistic models of N-N, antiN-N, N-eta and antiN-eta interactions and some lessons learned from this extended endeavor. 61 refs.
Wang, Jinke; Shi, Changfa
2017-04-24
In the active shape model framework, principal component analysis (PCA) based statistical shape models (SSMs) are widely employed to incorporate high-level a priori shape knowledge of the structure to be segmented to achieve robustness. A crucial component of building SSMs is to establish shape correspondence between all training shapes, which is a very challenging task, especially in three dimensions. We propose a novel mesh-to-volume registration based shape correspondence establishment method to improve the accuracy and reduce the computational cost. Specifically, we present a greedy algorithm based deformable simplex mesh that uses vector field convolution as the external energy. Furthermore, we develop an automatic shape initialization method by using a Gaussian mixture model based registration algorithm, to derive an initial shape that has high overlap with the object of interest, such that the deformable models can then evolve more locally. We apply the proposed deformable surface model to the application of femur statistical shape model construction to illustrate its accuracy and efficiency. Extensive experiments on ten femur CT scans show that the quality of the constructed femur shape models via the proposed method is much better than that of the classical spherical harmonics (SPHARM) method. Moreover, the proposed method achieves much higher computational efficiency than the SPHARM method. The experimental results suggest that our method can be employed for effective statistical shape model construction.
Morin, Cory W.; Comrie, Andrew C.
2010-09-01
Climate can strongly influence the population dynamics of disease vectors and is consequently a key component of disease ecology. Future climate change and variability may alter the location and seasonality of many disease vectors, possibly increasing the risk of disease transmission to humans. The mosquito species Culex quinquefasciatus is a concern across the southern United States because of its role as a West Nile virus vector and its affinity for urban environments. Using established relationships between atmospheric variables (temperature and precipitation) and mosquito development, we have created the Dynamic Mosquito Simulation Model (DyMSiM) to simulate Cx. quinquefasciatus population dynamics. The model is driven with climate data and validated against mosquito count data from Pasco County, Florida and Coachella Valley, California. Using 1-week and 2-week filters, mosquito trap data are reproduced well by the model ( P < 0.0001). Dry environments in southern California produce different mosquito population trends than moist locations in Florida. Florida and California mosquito populations are generally temperature-limited in winter. In California, locations are water-limited through much of the year. Using future climate projection data generated by the National Center for Atmospheric Research CCSM3 general circulation model, we applied temperature and precipitation offsets to the climate data at each location to evaluate mosquito population sensitivity to possible future climate conditions. We found that temperature and precipitation shifts act interdependently to cause remarkable changes in modeled mosquito population dynamics. Impacts include a summer population decline from drying in California due to loss of immature mosquito habitats, and in Florida a decrease in late-season mosquito populations due to drier late summer conditions.
DEFF Research Database (Denmark)
Litvan, Héctor; Jensen, Erik W; Galan, Josefina
2002-01-01
The extraction of the middle latency auditory evoked potentials (MLAEP) is usually done by moving time averaging (MTA) over many sweeps (often 250-1,000), which could produce a delay of more than 1 min. This problem was addressed by applying an autoregressive model with exogenous input (ARX) that...
Support vector regression model for predicting the sorption capacity of lead (II
Directory of Open Access Journals (Sweden)
Nusrat Parveen
2016-09-01
Full Text Available Biosorption is supposed to be an economical process for the treatment of wastewater containing heavy metals like lead (II. In this research paper, the support vector regression (SVR has been used to predict the sorption capacity of lead (II ions with the independent input parameters being: initial lead ion concentration, pH, temperature and contact time. Tree fern, an agricultural by-product, has been employed as a low cost biosorbent. Comparison between multiple linear regression (MLR and SVR-based models has been made using statistical parameters. It has been found that the SVR model is more accurate and generalized for prediction of the sorption capacity of lead (II ions.
Modelling a Voice Activated Speaker Identification System using MFCC-Pitch-Formant Vector
Sengupta, Avik; Ghosh, Rabindranath
2012-03-01
The paper presents the model of an automatic speaker identification system which will recognize users based on their voice. The system will be relatively independent of spoken words but will rely on the voice quality of a user i.e. use speech independent voice recognition. The basic approach was to create a front end system which will identify speech parameters of particular users and create speech feature vectors which will later be used to train a back-propagation neural network for the recognition phase. Mel-frequency cepstrum coefficients and linear predictive coding coefficients have been used, along with Pitch and Formants, for feature extraction. The main area of focus of the paper is to outline the optimum set of speech features which form the most reliable model for an automatic speaker identification system.
Wang, Zhimeng; Jiang, Lin; Li, Menglong; Sun, Lina; Lin, Rongying
2007-09-01
There are approximately 10(9) proteins in a cell. A hotspot in bioinformatics is how to identify a protein subcellular localization, if its sequence is known. In this paper, a method using fast Fourier transform-based support vector machine is developed to predict the subcellular localization of proteins from their physicochemical properties and structural parameters. The prediction accuracies reached 83% in prokaryotic organisms and 84% in eukaryotic organisms with the substitution model of the c-p-v matrix (c, composition; p, polarity; and v, molecular volume). The overall prediction accuracy was also evaluated using the "leave-one-out" jackknife procedure. The influence of the substitution model on prediction accuracy has also been discussed in the work. The source code of the new program is available on request from the authors.
Coexistence of two vector order parameters: a holographic model for ferromagnetic superconductivity
Energy Technology Data Exchange (ETDEWEB)
Amoretti, Andrea [Dipartimento di Fisica, Università di Genova, and I.N.F.N. - Sezione di Genova, via Dodecaneso 33, 16146, Genova (Italy); Braggio, Alessandro [CNR-SPIN, via Dodecaneso 33, 16146, Genova (Italy); Maggiore, Nicola; Magnoli, Nicodemo [Dipartimento di Fisica, Università di Genova, and I.N.F.N. - Sezione di Genova, via Dodecaneso 33, 16146, Genova (Italy); Musso, Daniele [Physique Théorique et Mathématique, Université Libre de Bruxelles, C.P. 231, 1050 Bruxelles (Belgium)
2014-01-13
We study a generalization of the standard holographic p-wave superconductor featuring two interacting vector order parameters. Basing our argument on the symmetry and linear response properties of the model, we propose it as a holographic effective theory describing a strongly coupled ferromagnetic superconductor. We show that the two order parameters undergo concomitant condensations as a manifestation of an intrinsically interlaced charge/spin dynamics. Such intertwined dynamics is confirmed by the study of the transport properties. We characterize thoroughly the equilibrium and the linear response (i.e. optical conductivity and spin susceptibility) of the model at hand by means of a probe approximation analysis. Some insight about the effects of backreaction in the normal phase can be gained by analogy with the s-wave unbalanced holographic superconductor.
Integer valued autoregressive processes with generalized discrete Mittag-Leffler marginals
Directory of Open Access Journals (Sweden)
Kanichukattu K. Jose
2013-05-01
Full Text Available In this paper we consider a generalization of discrete Mittag-Leffler distributions. We introduce and study the properties of a new distribution called geometric generalized discrete Mittag-Leffler distribution. Autoregressive processes with geometric generalized discrete Mittag-Leffler distributions are developed and studied. The distributions are further extended to develop a more general class of geometric generalized discrete semi-Mittag-Leffler distributions. The processes are extended to higher orders also. An application with respect to an empirical data on customer arrivals in a bank counter is also given. Various areas of potential applications like human resource development, insect growth, epidemic modeling, industrial risk modeling, insurance and actuaries, town planning etc are also discussed.
Directory of Open Access Journals (Sweden)
Babangida Nuraddeen Muhammad
2016-01-01
Full Text Available Pore-water pressure (PWP is influenced by climatic changes, especially rainfall. These changes may affect the stability of, particularly unsaturated slopes. Thus monitoring the changes in PWP resulting from climatic factors has become an important part of effective slope management. However, this monitoring requires field instrumentation program, which is resource and labour expensive. Recently, soft computing modelling has become an alternative. Low degree polynomial kernel support vector machine (SVM was evaluated in modelling the PWP changes. The developed model used pore-water pressure and rainfall data collected from an instrumented slope. Wrapper technique was used to select input features and k-fold cross validation was used to calibrate the model parameters. The developed model showed great promise in modelling the pore-water pressure changes. High correlation, with coefficient of determination of 0.9694 between the predicted and observed changes was obtained. The one degree polynomial SVM model yielded competitive result, and can be used to provide lead time records of PWP which can aid in better slope management.
Unsteady aerodynamic modeling at high angles of attack using support vector machines
Directory of Open Access Journals (Sweden)
Wang Qing
2015-06-01
Full Text Available Accurate aerodynamic models are the basis of flight simulation and control law design. Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism. Support vector machines (SVMs based on statistical learning theory provide a novel tool for nonlinear system modeling. The work presented here examines the feasibility of applying SVMs to high angle-of-attack unsteady aerodynamic modeling field. Mainly, after a review of SVMs, several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail, such as selection of input variables, selection of output variables and determination of SVM parameters. The least squares SVM (LS-SVM models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration, and then used to predict the aerodynamic responses in other tests. The predictions are in good agreement with the test data, which indicates the satisfying learning and generalization performance of LS-SVMs.
Support vector machine-based open crop model (SBOCM): Case of rice production in China.
Su, Ying-Xue; Xu, Huan; Yan, Li-Jiao
2017-03-01
Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.
Support vector machine-based open crop model (SBOCM: Case of rice production in China
Directory of Open Access Journals (Sweden)
Ying-xue Su
2017-03-01
Full Text Available Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.
An environmental data set for vector-borne disease modeling and epidemiology.
Chabot-Couture, Guillaume; Nigmatulina, Karima; Eckhoff, Philip
2014-01-01
Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases. Low- and middle-income countries bear a significant portion of the disease burden; but data about weather conditions in those countries can be sparse and difficult to reconstruct. Here, we describe methods to assemble high-resolution gridded time series data sets of air temperature, relative humidity, land temperature, and rainfall for such areas; and we test these methods on the island of Madagascar. Air temperature and relative humidity were constructed using statistical interpolation of weather station measurements; the resulting median 95th percentile absolute errors were 2.75°C and 16.6%. Missing pixels from the MODIS11 remote sensing land temperature product were estimated using Fourier decomposition and time-series analysis; thus providing an alternative to the 8-day and 30-day aggregated products. The RFE 2.0 remote sensing rainfall estimator was characterized by comparing it with multiple interpolated rainfall products, and we observed significant differences in temporal and spatial heterogeneity relevant to vector-borne disease modeling.
Production of transgenic pig as an Alzheimer's disease model using a multi-cistronic vector system.
Directory of Open Access Journals (Sweden)
Seung-Eun Lee
Full Text Available Alzheimer's disease (AD is a progressive neurodegenerative disease associated with memory loss and cognitive impairments. An AD transgenic (Tg pig model would be useful for preclinical testing of therapeutic agents. We generated an AD Tg pig by somatic cell nuclear transfer (SCNT using a multi-cistronic vector that harbored three AD-related genes with a total of six well-characterized mutations: hAPP (K670N/M671L, I716V, and V717I, hTau (P301L, and hPS1 (M146V and L286P. Four AD Tg cell lines were established from Jeju black pig ear fibroblasts (JB-PEFs; the resultant JB-PEFAD cells harbored transgene integration, expressed transgene mRNAs, and had normal karyotypes. Tg line #2-1, which expressed high levels of the transgenes, was used for SCNT; cleavage and blastocyst rates of embryos derived from this line were lower than those of Non-Tg. These embryos yielded three piglets (Jeju National University AD-Tg pigs, JNUPIGs revealed by microsatellite testing to be genetically identical to JB-PEFAD. Transgenes were expressed in multiple tissues, and at especially high levels in brain, and Aβ-40/42, total Tau, and GFAP levels were high in brains of the Tg animals. Five or more copies of transgenes were inserted into chromosome X. This is the first report of an AD Tg pig derived from a multi-cistronic vector.
Support vector machines for seizure detection in an animal model of chronic epilepsy
Nandan, Manu; Talathi, Sachin S.; Myers, Stephen; Ditto, William L.; Khargonekar, Pramod P.; Carney, Paul R.
2010-06-01
We compare the performance of three support vector machine (SVM) types: weighted SVM, one-class SVM and support vector data description (SVDD) for the application of seizure detection in an animal model of chronic epilepsy. Large EEG datasets (273 h and 91 h respectively, with a sampling rate of 1 kHz) from two groups of rats with chronic epilepsy were used in this study. For each of these EEG datasets, we extracted three energy-based seizure detection features: mean energy, mean curve length and wavelet energy. Using these features we performed twofold cross-validation to obtain the performance statistics: sensitivity (S), specificity (K) and detection latency (τ) as a function of control parameters for the given SVM. Optimal control parameters for each SVM type that produced the best seizure detection statistics were then identified using two independent strategies. Performance of each SVM type is ranked based on the overall seizure detection performance through an optimality index metric (O). We found that SVDD not only performed better than the other SVM types in terms of highest value of the mean optimality index metric (\\skew3\\bar{O} ) but also gave a more reliable performance across the two EEG datasets.
An environmental data set for vector-borne disease modeling and epidemiology.
Directory of Open Access Journals (Sweden)
Guillaume Chabot-Couture
Full Text Available Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases. Low- and middle-income countries bear a significant portion of the disease burden; but data about weather conditions in those countries can be sparse and difficult to reconstruct. Here, we describe methods to assemble high-resolution gridded time series data sets of air temperature, relative humidity, land temperature, and rainfall for such areas; and we test these methods on the island of Madagascar. Air temperature and relative humidity were constructed using statistical interpolation of weather station measurements; the resulting median 95th percentile absolute errors were 2.75°C and 16.6%. Missing pixels from the MODIS11 remote sensing land temperature product were estimated using Fourier decomposition and time-series analysis; thus providing an alternative to the 8-day and 30-day aggregated products. The RFE 2.0 remote sensing rainfall estimator was characterized by comparing it with multiple interpolated rainfall products, and we observed significant differences in temporal and spatial heterogeneity relevant to vector-borne disease modeling.
Modelling and Simulation of SVPWM Based Vector Controlled HVDC Light Systems
Directory of Open Access Journals (Sweden)
Ajay Kumar MOODADLA
2012-11-01
Full Text Available Recent upgrades in power electronics technology have lead to the improvements of insulated gate bipolar transistor (IGBT based Voltage source converter High voltage direct current (VSC HVDC transmission systems. These are also commercially known as HVDC Light systems, which are popular in renewable, micro grid, and electric power systems. Out of different pulse width modulation (PWM schemes, Space vector PWM (SVPWM control scheme finds growing importance in power system applications because of its better dc bus utilization. In this paper, modelling of the converter is described, and SVPWM scheme is utilized to control the HVDC Light system in order to achieve better DC bus utilization, harmonic reduction, and for reduced power fluctuations. The simulations are carried out in the MATLAB/SIMULINK environment and the results are provided for steady state and dynamic conditions. Finally, the performance of SVPWM based vector controlled HVDC Light transmission system is compared with sinusoidal pulse width modulation (SPWM based HVDC Light system in terms of output voltage and total harmonic distortion (THD.
Steven K. Buigut; Neven Valev
2004-01-01
The treaty of 1999 to revive the defunct East African Community (EAC) ratified by Kenya, Uganda, and Tanzania came into force on July 2000 with the objective of fostering a closer co-operation in political, economic, social, and cultural fields. To achieve this, an East Africa Customs Union protocol was signed in March 2004. A Common Market, a Monetary Union, and ultimately a Political Federation of East Africa states is planned. Though the question of a monetary union has been discussed in t...
A Vector AutoRegressive (VAR) Approach to the Credit Channel for ...
African Journals Online (AJOL)
user1
control of the money supply due to its ease of use, and the relatively smooth interaction it has with the economy as a whole. Monetary policy is related to a number of fundamental economic indicators such as exchange rate, inflation, asset prices, credit, etc. The monetary transmission mechanism is the term used to denote ...
Determinants of Deviation from Inflation Targets in Pakistan: A Vector Autoregressive Approach
National Research Council Canada - National Science Library
Sunila Jabeen; Farzana Shaheen; Azad Haider
2015-01-01
.... However, inflation deviation from the announced target is common. Employing impulse response functions and variance decomposition, over the period, 1991Q1-2007Q4,this paper attempts to explain why inflation targets have been missed in Pakistan...
Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.
Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627
Stratton, Margaret D.; Ehrlich, Hanna Y.; Mor, Siobhan M.; Naumova, Elena N.
2017-01-01
Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.
An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression
Directory of Open Access Journals (Sweden)
Deepak Bhatt
2012-07-01
Full Text Available Micro Electro Mechanical System (MEMS-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.
An enhanced MEMS error modeling approach based on Nu-Support Vector Regression.
Bhatt, Deepak; Aggarwal, Priyanka; Bhattacharya, Prabir; Devabhaktuni, Vijay
2012-01-01
Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10-35% for gyroscopes and 61-76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.
Directory of Open Access Journals (Sweden)
Gabere MN
2016-06-01
Full Text Available Musa Nur Gabere,1 Mohamed Aly Hussein,1 Mohammad Azhar Aziz2 1Department of Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Colorectal Cancer Research Program, Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia Purpose: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC. The selection of important features is a crucial step before training a classifier.Methods: In this study, we built a model that uses support vector machine (SVM to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid.Results: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF, Bayes net (BN, multilayer perceptron (MLP, naïve Bayes (NB, reduced error pruning tree (REPT, and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP. Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1
Modeling horizontal gene transfer (HGT in the gut of the Chagas disease vector Rhodnius prolixus
Directory of Open Access Journals (Sweden)
Durvasula Ravi V
2011-05-01
Full Text Available Abstract Background Paratransgenesis is an approach to reducing arthropod vector competence using genetically modified symbionts. When applied to control of Chagas disease, the symbiont bacterium Rhodococcus rhodnii, resident in the gut lumen of the triatomine vector Rhodnius prolixus (Hemiptera: Reduviidae, is transformed to export cecropin A, an insect immune peptide. Cecropin A is active against Trypanosoma cruzi, the causative agent of Chagas disease. While proof of concept has been achieved in laboratory studies, a rigorous and comprehensive risk assessment is required prior to consideration of field release. An important part of this assessment involves estimating probability of transgene horizontal transfer to environmental organisms (HGT. This article presents a two-part risk assessment methodology: a theoretical model predicting HGT in the gut of R. prolixus from the genetically transformed symbiont R. rhodnii to a closely related non-target bacterium, Gordona rubropertinctus, in the absence of selection pressure, and a series of laboratory trials designed to test the model. Results The model predicted an HGT frequency of less than 1.14 × 10-16 per 100,000 generations at the 99% certainty level. The model was iterated twenty times, with the mean of the ten highest outputs evaluated at the 99% certainty level. Laboratory trials indicated no horizontal gene transfer, supporting the conclusions of the model. Conclusions The model treats HGT as a composite event, the probability of which is determined by the joint probability of three independent events: gene transfer through the modalities of transformation, transduction, and conjugation. Genes are represented in matrices and Monte Carlo method and Markov chain analysis are used to simulate and evaluate environmental conditions. The model is intended as a risk assessment instrument and predicts HGT frequency of less than 1.14 × 10-16 per 100,000 generations. With laboratory studies that
International Stock Market Efficiency: A Non-Bayesian Time-Varying Model Approach
Mikio Ito; Akihiko Noda; Tatsuma Wada
2012-01-01
This paper develops a non-Bayesian methodology to analyze the time-varying structure of international linkages and market efficiency in G7 countries. We consider a non-Bayesian time-varying vector autoregressive (TV-VAR) model, and apply it to estimate the joint degree of market efficiency in the sense of Fama (1970, 1991). Our empirical results provide a new perspective that the international linkages and market efficiency change over time and that their behaviors correspond well to historic...
Predicting and understanding law-making with word vectors and an ensemble model.
Nay, John J
2017-01-01
Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill's sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.
Directory of Open Access Journals (Sweden)
Antonio Blanco-Oliver
2014-10-01
Full Text Available Despite the leading role that micro-entrepreneurship plays in economic development, and the high failure rate of microenterprise start-ups in their early years, very few studies have designed financial distress models to detect the financial problems of micro-entrepreneurs. Moreover, due to a lack of research, nothing is known about whether non-financial information and nonparametric statistical techniques improve the predictive capacity of these models. Therefore, this paper provides an innovative financial distress model specifically designed for microenterprise startups via support vector machines (SVMs that employs financial, non-financial, and macroeconomic variables. Based on a sample of almost 5,500 micro- entrepreneurs from a Peruvian Microfinance Institution (MFI, our findings show that the introduction of non-financial information related to the zone in which the entrepreneurs live and situate their business, the duration of the MFI-entrepreneur relationship, the number of loans granted by the MFI in the last year, the loan destination, and the opinion of experts on the probability that microenterprise start-ups may experience financial problems, significantly increases the accuracy performance of our financial distress model. Furthermore, the results reveal that the models that use SVMs outperform those which employ traditional logistic regression (LR analysis.
Oka, K; Belalcazar, L M; Dieker, C; Nour, E A; Nuno-Gonzalez, P; Paul, A; Cormier, S; Shin, J-K; Finegold, M; Chan, L
2007-02-01
We examined the efficacy and host response to the adenovirus (Ad)-mediated delivery of human apolipoprotein A-I (APOA1) gene to the liver of APOA1(-/-) mice. Administration of a first-generation vector (FGAd-AI) resulted in a transient appearance of APOA1 in plasma and induced an anti-APOA1 antibody titer, whereas treatment with a helper-dependent vector (HDAd-AI) resulted in sustained APOA1 expression without inducing an antibody titer. With these results, we studied the effects of FGAd vectors on APOAI expression by HDAd-AI vector. Co-treatment with an FGAd vector inhibited HDAd-AI- mediated APOA1 expression independent of transgene cassettes, but only FGAd-AI induced a humoral response. Furthermore, APOA1 mRNA levels in mice co-treated with FGAd vectors were much lower than those expected from the vector copy number, suggesting that DNA of FGAd vectors interferes with the HDAd-AI vector's APOA1 promoter. A single treatment with an HDAd-AI vector produced a supraphysiological plasma APOA1 level that gradually declined to about half the normal human level over the course of 2 years, associated with a plasma cholesterol level that is persistently higher than that in controls. This investigation provides the proof of principle that liver-directed HDAd gene delivery is effective for the long-term phenotypic correction of monogenic hypoalphalipoproteinemia.
Data on Support Vector Machines (SVM model to forecast photovoltaic power
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M. Malvoni
2016-12-01
Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.
Noise Correlation Effect on Detection: Signals in Equicorrelated or Autoregressive(1) Gaussian.
Kasasbeh, Hadi; Viswanathan, Ramanarayanan; Cao, Lei
2017-07-01
In this letter, we consider the effect of noise correlation on the error performance of binary hypothesis signal detection, when one of two deterministic signals is received in correlated Gaussian noise. For the likelihood ratio detection scheme, analytical performance results are derived for equicorrelated and autoregressive order one models. Although it is known previously that the best signal lies in the direction of eigenvector corresponding to the minimum eigenvalue of the noise covariance matrix, our investigation of the variation of mean signal-to-noise power ratio as a function of correlation parameter (i) shows how correlation leads to increased probability of error up to a point, beyond which monotonic decrease in error probability with increasing correlation is possible and (ii) provides a max-min signal design solution for the unknown correlation parameter case. Numerical results are also included for some specific signals.
Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters
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V. B. Goryainov
2017-01-01
Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the
Lucarini, Valerio; Vannitsem, Stephane
2016-04-01
We study a simplified coupled atmosphere-ocean model using the formalism of covariant Lyapunov vectors (CLVs), which link physically-based directions of perturbations to growth/decay rates. The model is obtained via a severe truncation of quasi-geostrophic equations for the two fluids, and includes a simple yet physically meaningful representation of their dynamical/thermodynamical coupling. The model has 36 degrees of freedom, and the parameters are chosen so that a chaotic behaviour is observed. One finds two positive Lyapunov exponents (LEs), sixteen negative LEs, and eighteen near-zero LEs. The presence of many near-zero LEs results from the vast time-scale separation between the characteristic time scales of the two fluids, and leads to nontrivial error growth properties in the tangent space spanned by the corresponding CLVs, which are geometrically very degenerate. Such CLVs correspond to two different classes of ocean/atmosphere coupled modes. The tangent space spanned by the CLVs corresponding to the positive and negative LEs has, instead, a non-pathological behaviour, and one can construct robust large deviations laws for the finite time LEs, thus providing a universal model for assessing predictability on long to ultra-long scales along such directions. Finally, it is somewhat surprising to find that the tangent space of the unstable manifold has strong projection on both atmospheric and oceanic components, thus giving evidence that coupled modes are responsible for the instability of the flow.
A Case of Multi-vector and Multi-host Epidemiological Model: Bartonella Infection
Anguelov, R.; Brettschneider, H.; Bastos, A. D. S.
2010-11-01
We consider a compartmental model for the Bartonella infection on rodents. More precisely, on the co-occurring populations of Rattus rattus and Rattus norvegicus where the vectors are two species of ectoparasites, namely ticks and fleas. As usual for such models a key stage is the modelling of the forces of infection. While the vital dynamics and the progression of the infection within each of the four species are sufficiently well known to determine the rest of the transfer rates, there is practically no data on the probability of infection. In order to determine appropriate values for the coefficients of the forces of infection we solve an optimal control problem where the objective function is the norm of the difference between the observed and the predicted by the model equilibrium infection prevalence rates in the four species. Within this setting the conjecture that the higher prevalence of the infection in Rattus norvegicus can be explained solely by their higher ectoparasite load is tested and disproved.
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Sheng Zhou
Full Text Available Hematopoietic stem cell gene therapy requires the use of integrating retroviral vectors in order to stably transmit a therapeutic gene to mature blood cells. Human clinical trials have shown that some vector integration events lead to disrupted regulation of proto-oncogenes resulting in disordered hematopoiesis including T-cell leukemia. Newer vectors have been designed to decrease the incidence of these adverse events but require appropriate pre-clinical assays to demonstrate safety. We have used two distinct mouse serial transplant assays to evaluate the safety of a self-inactivating lentiviral vector intended for use in X-linked severe combined immunodeficiency (XSCID gene therapy trials. These experiments entailed 28 months of total follow-up and included 386 mice. There were no cases in which the XSCID lentiviral vector clearly caused hematopoietic malignancies, although a single case of B cell malignancy was observed that contained the lentiviral vector as a likely passenger event. In contrast, a SFFV-DsRed γ-retroviral vector resulted in clonal transformation events in multiple secondary recipients. Non-specific pathology not related to vector insertions was noted including T cell leukemias arising from irradiated recipient cells. Overall, this comprehensive study of mouse transplant safety assays demonstrate the relative safety of the XSCID lentiviral vector but also highlight the limitations of these assays.
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Yan Hong Chen
2016-01-01
Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.
Lu, Xinjiang; Liu, Wenbo; Zhou, Chuang; Huang, Minghui
2017-06-13
The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers. In this paper, a robust LS-SVM method is proposed and is shown to have more reliable performance when modeling a nonlinear system under conditions where Gaussian or non-Gaussian noise is present. The construction of a new objective function allows for a reduction of the mean of the modeling error as well as the minimization of its variance, and it does not constrain the mean of the modeling error to zero. This differs from the traditional LS-SVM, which uses a worst-case scenario approach in order to minimize the modeling error and constrains the mean of the modeling error to zero. In doing so, the proposed method takes the modeling error distribution information into consideration and is thus less conservative and more robust in regards to random noise. A solving method is then developed in order to determine the optimal parameters for the proposed robust LS-SVM. An additional analysis indicates that the proposed LS-SVM gives a smaller weight to a large-error training sample and a larger weight to a small-error training sample, and is thus more robust than the traditional LS-SVM. The effectiveness of the proposed robust LS-SVM is demonstrated using both artificial and real life cases.
RECONSTRUCTION OF 3D VECTOR MODELS OF BUILDINGS BY COMBINATION OF ALS, TLS AND VLS DATA
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H. Boulaassal
2012-09-01
Full Text Available Airborne Laser Scanning (ALS, Terrestrial Laser Scanning (TLS and Vehicle based Laser Scanning (VLS are widely used as data acquisition methods for 3D building modelling. ALS data is often used to generate, among others, roof models. TLS data has proven its effectiveness in the geometric reconstruction of building façades. Although the operating algorithms used in the processing chain of these two kinds of data are quite similar, their combination should be more investigated. This study explores the possibility of combining ALS and TLS data for simultaneously producing 3D building models from bird point of view and pedestrian point of view. The geometric accuracy of roofs and façades models is different due to the acquisition techniques. In order to take these differences into account, the surfaces composing roofs and façades are extracted with the same algorithm of segmentation. Nevertheless the segmentation algorithm must be adapted to the properties of the different point clouds. It is based on the RANSAC algorithm, but has been applied in a sequential way in order to extract all potential planar clusters from airborne and terrestrial datasets. Surfaces are fitted to planar clusters, allowing edge detection and reconstruction of vector polygons. Models resulting from TLS data are obviously more accurate than those generated from ALS data. Therefore, the geometry of the roofs is corrected and adapted according to the geometry of the corresponding façades. Finally, the effects of the differences between raw ALS and TLS data on the results of the modeling process are analyzed. It is shown that such combination could be used to produce reliable 3D building models.
Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M.
2017-02-01
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ˜60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.
Aslan, Hamide; Dey, Ranadhir; Meneses, Claudio; Castrovinci, Philip; Jeronimo, Selma Maria Bezerra; Oliva, Gætano; Fischer, Laurent; Duncan, Robert C.; Nakhasi, Hira L.; Valenzuela, Jesus G.; Kamhawi, Shaden
2013-01-01
Background. Visceral leishmaniasis (VL) is transmitted by sand flies. Protection of needle-challenged vaccinated mice was abrogated in vector-initiated cutaneous leishmaniasis, highlighting the importance of developing natural transmission models for VL. Methods. We used Lutzomyia longipalpis to transmit Leishmania infantum or Leishmania donovani to hamsters. Vector-initiated infections were monitored and compared with intracardiac infections. Body weights were recorded weekly. Organ parasite loads and parasite pick-up by flies were assessed in sick hamsters. Results. Vector-transmitted L. infantum and L. donovani caused ≥5-fold increase in spleen weight compared with uninfected organs and had geometric mean parasite loads (GMPL) comparable to intracardiac inoculation of 107–108 parasites, although vector-initiated disease progression was slower and weight loss was greater. Only vector-initiated L. infantum infections caused cutaneous lesions at transmission and distal sites. Importantly, 45.6%, 50.0%, and 33.3% of sand flies feeding on ear, mouth, and testicular lesions, respectively, were parasite-positive. Successful transmission was associated with a high mean percent of metacyclics (66%–82%) rather than total GMPL (2.0 × 104–8.0 × 104) per midgut. Conclusions. This model provides an improved platform to study initial immune events at the bite site, parasite tropism, and pathogenesis and to test drugs and vaccines against naturally acquired VL. PMID:23288926
Aslan, Hamide; Dey, Ranadhir; Meneses, Claudio; Castrovinci, Philip; Jeronimo, Selma Maria Bezerra; Oliva, Gætano; Fischer, Laurent; Duncan, Robert C; Nakhasi, Hira L; Valenzuela, Jesus G; Kamhawi, Shaden
2013-04-15
Visceral leishmaniasis (VL) is transmitted by sand flies. Protection of needle-challenged vaccinated mice was abrogated in vector-initiated cutaneous leishmaniasis, highlighting the importance of developing natural transmission models for VL. We used Lutzomyia longipalpis to transmit Leishmania infantum or Leishmania donovani to hamsters. Vector-initiated infections were monitored and compared with intracardiac infections. Body weights were recorded weekly. Organ parasite loads and parasite pick-up by flies were assessed in sick hamsters. Vector-transmitted L. infantum and L. donovani caused ≥5-fold increase in spleen weight compared with uninfected organs and had geometric mean parasite loads (GMPL) comparable to intracardiac inoculation of 10(7)-10(8) parasites, although vector-initiated disease progression was slower and weight loss was greater. Only vector-initiated L. infantum infections caused cutaneous lesions at transmission and distal sites. Importantly, 45.6%, 50.0%, and 33.3% of sand flies feeding on ear, mouth, and testicular lesions, respectively, were parasite-positive. Successful transmission was associated with a high mean percent of metacyclics (66%-82%) rather than total GMPL (2.0 × 10(4)-8.0 × 10(4)) per midgut. This model provides an improved platform to study initial immune events at the bite site, parasite tropism, and pathogenesis and to test drugs and vaccines against naturally acquired VL.
QCD next-to-leading-order predictions matched to parton showers for vector-like quark models.
Fuks, Benjamin; Shao, Hua-Sheng
2017-01-01
Vector-like quarks are featured by a wealth of beyond the Standard Model theories and are consequently an important goal of many LHC searches for new physics. Those searches, as well as most related phenomenological studies, however, rely on predictions evaluated at the leading-order accuracy in QCD and consider well-defined simplified benchmark scenarios. Adopting an effective bottom-up approach, we compute next-to-leading-order predictions for vector-like-quark pair production and single production in association with jets, with a weak or with a Higgs boson in a general new physics setup. We additionally compute vector-like-quark contributions to the production of a pair of Standard Model bosons at the same level of accuracy. For all processes under consideration, we focus both on total cross sections and on differential distributions, most these calculations being performed for the first time in our field. As a result, our work paves the way to precise extraction of experimental limits on vector-like quarks thanks to an accurate control of the shapes of the relevant observables and emphasise the extra handles that could be provided by novel vector-like-quark probes never envisaged so far.
Miller, Aaron M; Mims, James L
2006-08-01
To develop a new pulley-based torque vector mathematical model for medial rectus muscle recessions and compare it based on known clinical characteristics, to the currently accepted nonpulley length-tension model. The following quantitative characteristics of the results of bilateral medial rectus muscle recessions were chosen for study to see whether the new torque vector model or the classic length-tension model would better predict these characteristics: (1) larger bilateral medial rectus muscle recessions produce more effect per millimeter, with the dose-response curve approximating an exponential shape; (2) the exact location of the preplaced medial rectus muscle suture prior to muscle disinsertion in recessions has minimal effect on the postoperative ocular alignment; and (3) medial rectus muscle recessions of more than eight mm are likely to produce an early consecutive exotropia. Based on the documented location of the medial rectus muscle pulley, the change in the torque vector per millimeter of medial rectus muscle recession was calculated and shown to have an exponential shape. For all three of the quantitative characteristics chosen, the torque vector model appears to better predict the results of medial rectus muscle recessions when compared with the length-tension model. Many quantitative characteristics of medial rectus muscle recessions are better explained by the torque vector model, instead of the classical length-tension model, and support the presence, location, and function of the medial rectus muscle pulley. This new understanding of ocular motility mechanics may influence surgical technique and introduce new surgical considerations for correction of ocular motility disorders.
Kazemi, Maryam; Moghimbeigi, Abbas; Kiani, Javad; Mahjub, Hossein; Faradmal, Javad
2016-01-01
Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients' demographic characteristics and clinical features. In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used. For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy. The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases.
Huang, Mengmeng; Wang, Qiao; Chen, Xinyu; Zhang, Yu
2017-04-15
This study investigated the effect of flavanols and their derivatives on acrylamide formation under low-moisture conditions via prediction using the support vector regression (SVR) approach. Acrylamide was generated in a potato-based equimolar asparagine-reducing sugar model system through oven heating. Both positive and negative effects were observed when the flavonoid treatment ranged 1-10,000μmol/L. Flavanols and derivatives (100μmol/L) suppress the acrylamide formation within a range of 59.9-78.2%, while their maximal promotion effects ranged from 2.15-fold to 2.84-fold for the control at a concentration of 10,000μmol/L. The correlations between inhibition rates and changes in Trolox-equivalent antioxidant capacity (ΔTEAC) (RTEAC-DPPH=0.878, RTEAC-ABTS=0.882, RTEAC-FRAP=0.871) were better than promotion rates (RTEAC-DPPH=0.815, RTEAC-ABTS=0.749, RTEAC-FRAP=0.841). Using ΔTEAC as variables, an optimized SVR model could robustly serve as a new predictive tool for estimating the effect (R: 0.783-0.880), the fitting performance of which was slightly better than that of multiple linear regression model (R: 0.754-0.880). Copyright © 2016 Elsevier Ltd. All rights reserved.
River flow time series using least squares support vector machines
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R. Samsudin
2011-06-01
Full Text Available This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH and the least squares support vector machine (LSSVM. The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN models, Autoregressive Integrated Moving Average (ARIMA, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE and coefficient of correlation (R are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.
River flow time series using least squares support vector machines
Samsudin, R.; Saad, P.; Shabri, A.
2011-06-01
This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.
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Man Zhu
2017-03-01
Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.
Guo, Hua; Jiang, Bin
2014-12-16
CONSPECTUS: Mode specificity is defined by the differences in reactivity due to excitations in various reactant modes, while bond selectivity refers to selective bond breaking in a reaction. These phenomena not only shed light on reaction dynamics but also open the door for laser control of reactions. The existence of mode specificity and bond selectivity in a reaction indicates that not all forms of energy are equivalent in promoting the reactivity, thus defying a statistical treatment. They also allow the enhancement of reactivity and control product branching ratio. As a result, they are of central importance in chemistry. This Account discusses recent advances in our understanding of these nonstatistical phenomena. In particular, the newly proposed sudden vector projection (SVP) model and its applications are reviewed. The SVP model is based on the premise that the collision in many direct reactions is much faster than intramolecular vibrational energy redistribution in the reactants. In such a sudden limit, the coupling of a reactant mode with the reaction coordinate at the transition state, which dictates its ability to promote the reaction, is approximately quantified by the projection of the former onto the latter. The SVP model can be considered as a generalization of the venerable Polanyi's rules, which are based on the location of the barrier. The SVP model is instead based on properties of the saddle point and as a result capable of treating the translational, rotational, and multiple vibrational modes in reactions involving polyatomic reactants. In case of surface reactions, the involvement of surface atoms can also be examined. Taking advantage of microscopic reversibility, the SVP model has also been used to predict product energy disposal in reactions. This simple yet powerful rule of thumb has been successfully demonstrated in many reactions including uni- and bimolecular reactions in the gas phase and gas-surface reactions. The success of the SVP
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Carl Ceasar F. Talungon
2016-12-01
Full Text Available This study was conducted to estimate the diesel oil price volatility of the three big oil companies (Caltex, Petron and Shell in the Philippines using Generalized Autoregressive Conditional Heteroscedasticity (GARCH model. The diesel price of the three big oil companies in the Philippines had exhibited wild fluctuations at various times which confirmed that the diesel prices are wildly volatile. The ARMA model was used to model the data series of the big three oil companies’ diesel prices based on the modified Box-Jenkins approach. The big three oil companies’ diesel prices fluctuated wildly indicating that the series are volatile. ADF test revealed that all series are stationary at level except Shell. Among the three diesel prices, only the ARMA model for Petron diesel price possessed residuals with white noise process and significant in ARCH LM test. The residuals of ARMA model for Caltex and Shell diesel prices failed to satisfy white noise process and ARCH LM test. Thus, only Petron diesel price proceeded to GARCH model identification for price volatility estimation. ARMA (2,2 model was chosen for Petron diesel price based on selected criteria while GARCH (1,1 model, came out to be the best model to estimate Petron diesel price volatility.
Gilbertsen, Noreen D.; Belytschko, Ted
1990-01-01
The implementation of a nonlinear explicit program on a vectorized, concurrent computer with shared memory is described and studied. The conflict between vectorization and concurrency is described and some guidelines are given for optimal block sizes. Several example problems are summarized to illustrate the types of speed-ups which can be achieved by reprogramming as compared to compiler optimization.
Is outdoor vector control needed for malaria elimination? An individual-based modelling study.
Zhu, Lin; Müller, Günter C; Marshall, John M; Arheart, Kristopher L; Qualls, Whitney A; Hlaing, WayWay M; Schlein, Yosef; Traore, Sekou F; Doumbia, Seydou; Beier, John C
2017-07-03
Residual malaria transmission has been reported in many areas even with adequate indoor vector control coverage, such as long-lasting insecticidal nets (LLINs). The increased insecticide resistance in Anopheles mosquitoes has resulted in reduced efficacy of the widely used indoor tools and has been linked with an increase in outdoor malaria transmission. There are considerations of incorporating outdoor interventions into integrated vector management (IVM) to achieve malaria elimination; however, more information on the combination of tools for effective control is needed to determine their utilization. A spatial individual-based model was modified to simulate the environment and malaria transmission activities in a hypothetical, isolated African village setting. LLINs and outdoor attractive toxic sugar bait (ATSB) stations were used as examples of indoor and outdoor interventions, respectively. Different interventions and lengths of efficacy periods were tested. Simulations continued for 420 days, and each simulation scenario was repeated 50 times. Mosquito populations, entomologic inoculation rates (EIRs), probabilities of local mosquito extinction, and proportion of time when the annual EIR was reduced below one were compared between different intervention types and efficacy periods. In the village setting with clustered houses, the combinational intervention of 50% LLINs plus outdoor ATSBs significantly reduced mosquito population and EIR in short term, increased the probability of local mosquito extinction, and increased the time when annual EIR is less than one per person compared to 50% LLINs alone; outdoor ATSBs alone significantly reduced mosquito population in short term, increased the probability of mosquito extinction, and increased the time when annual EIR is less than one compared to 50% LLINs alone, but there was no significant difference in EIR in short term between 50% LLINs and outdoor ATSBs. In the village setting with dispersed houses, the
Support Vector Machine Model for Automatic Detection and Classification of Seismic Events
Barros, Vesna; Barros, Lucas
2016-04-01
The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support-vector
Jian, Y.; Silvestri, S.; Marani, M.; Saltarin, A.; Chillemi, G.
2012-12-01
We applied a hierarchical state space model to predict the abundance of Cx.pipiens (a West Nile Virus vector) in the Po River Delta Region, Northeastern Italy. The study area has large mosquito abundance, due to a favorable environment and climate as well as dense human population. Mosquito data were collected on a weekly basis at more than 20 sites from May to September in 2010 and 2011. Cx.pipiens was the dominant species in our samples, accounting for about 90% of the more than 300,000 total captures. The hydrological component of the model accounted for evapotranspiration, infiltration and deep percolation to infer, in a 0D context, the local dynamics of soil moisture as a direct exogenous forcing of mosquito dynamics. The population model had a Gompertz structure, which included exogenous meteorological forcings and delayed internal dynamics. The models were coupled within a hierarchical statistical structure to overcome the relatively short length of the samples by exploiting the large number of concurrent observations available. The results indicated that Cx.pipiens abundance had significant density dependence at 1 week lag, which approximately matched its development time from larvae to adult. Among the exogenous controls, temperature, daylight hours, and soil moisture explained most of the dynamics. Longer daylight hours and lower soil moisture values resulted in higher abundance. The negative correlation of soil moisture and mosquito population can be explained with the abundance of water in the region (e.g. due to irrigation) and the preference for eutrophic habitats by Cx.pipien. Variations among sites were explained by land use factors as represented by distance to the nearest rice field and NDVI values: the carrying capacity decreased with increased distance to the nearest rice filed, while the maximum growth rate was positively related with NDVI. The model shows a satisfactory performance in predicting (potentially one week in advance) mosquito
Nekrasov, A.; Hoogeboom, P.
2005-01-01
A Ka-band backscatter model and an algorithm for measurement of the wind speed and direction over the sea surface by a frequency-modulated continous-wave radar demonstrator system operated in scatterometer mode have been developed. To evaluate the proposed algorithm, a simulation of the wind vector
DEFF Research Database (Denmark)
Yang, Yukay
I consider multivariate (vector) time series models in which the error covariance matrix may be time-varying. I derive a test of constancy of the error covariance matrix against the alternative that the covariance matrix changes over time. I design a new family of Lagrange-multiplier tests against...
Chen, Chau-Kuang
2010-01-01
Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…
Lochbaum, Karen E.; Streeter, Lynn A.
1989-01-01
Describes experiments that compared a new method for automatically analyzing semantic structures in text by statistical means with the standard vector space model. Findings indicate that combining both methods improved performance over either alone. The effects of other experimental variables on retrieval performance (term weighting, suffix…
Probing emergent geometry through phase transitions in free vector and matrix models
Energy Technology Data Exchange (ETDEWEB)
Amado, Irene; Sundborg, Bo [The Oskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University,AlbaNova, 106 91 Stockholm (Sweden); Thorlacius, Larus [The Oskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University,AlbaNova, 106 91 Stockholm (Sweden); Science Institute, University of Iceland, Dunhaga 3, 107 Reykjavik (Iceland); Wintergerst, Nico [The Oskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University,AlbaNova, 106 91 Stockholm (Sweden)
2017-02-01
Boundary correlation functions provide insight into the emergence of an effective geometry in higher spin gravity duals of O(N) or U(N) symmetric field theories. On a compact manifold, the singlet constraint leads to nontrivial dynamics at finite temperature and large N phase transitions even at vanishing ’t Hooft coupling. At low temperature, the leading behavior of boundary two-point functions is consistent with propagation through a bulk thermal anti de Sitter space. Above the phase transition, the two-point function shows significant departure from thermal AdS space and the emergence of localized black hole like objects in the bulk. In adjoint models, these objects appear at length scales of order of the AdS radius, consistent with a Hawking-Page transition, but in vector models they are parametrically larger than the AdS scale. In low dimensions, we find another crossover at large distances beyond which the correlation function again takes a thermal AdS form, albeit with a temperature dependent normalization factor.
Directory of Open Access Journals (Sweden)
Abazar Solgi
2017-06-01
Full Text Available Introduction: Chemical pollution of surface water is one of the serious issues that threaten the quality of water. This would be more important when the surface waters used for human drinking supply. One of the key parameters used to measure water pollution is BOD. Because many variables affect the water quality parameters and a complex nonlinear relationship between them is established conventional methods can not solve the problem of quality management of water resources. For years, the Artificial Intelligence methods were used for prediction of nonlinear time series and a good performance of them has been reported. Recently, the wavelet transform that is a signal processing method, has shown good performance in hydrological modeling and is widely used. Extensive research has been globally provided in use of Artificial Neural Network and Adaptive Neural Fuzzy Inference System models to forecast the BOD. But support vector machine has not yet been extensively studied. For this purpose, in this study the ability of support vector machine to predict the monthly BOD parameter based on the available data, temperature, river flow, DO and BOD was evaluated. Materials and Methods: SVM was introduced in 1992 by Vapnik that was a Russian mathematician. This method has been built based on the statistical learning theory. In recent years the use of SVM, is highly taken into consideration. SVM was used in applications such as handwriting recognition, face recognition and has good results. Linear SVM is simplest type of SVM, consists of a hyperplane that dataset of positive and negative is separated with maximum distance. The suitable separator has maximum distance from every one of two dataset. So about this machine that its output groups label (here -1 to +1, the aim is to obtain the maximum distance between categories. This is interpreted to have a maximum margin. Wavelet transform is one of methods in the mathematical science that its main idea was
An UWB LNA Design with PSO Using Support Vector Microstrip Line Model
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Salih Demirel
2015-01-01
Full Text Available A rigorous and novel design procedure is constituted for an ultra-wideband (UWB low noise amplifier (LNA by exploiting the 3D electromagnetic simulator based support vector regression machine (SVRM microstrip line model. First of all, in order to design input and output matching circuits (IMC-OMC, source ZS and load ZL termination impedance of matching circuit, which are necessary to obtain required input VSWR (Vireq, noise (Freq, and gain (GTreq, are determined using performance characterisation of employed transistor, NE3512S02, between 3 and 8 GHz frequencies. After the determination of the termination impedance, to provide this impedance with IMC and OMC, dimensions of microstrip lines are obtained with simple, derivative-free, easily implemented algorithm Particle Swarm Optimization (PSO. In the optimization of matching circuits, highly accurate and fast SVRM model of microstrip line is used instead of analytical formulations. ADCH-80a is used to provide ultra-wideband RF choking in DC bias. During the design process, it is aimed that Vireq = 1.85, Freq = Fmin, and GTreq = GTmax all over operating frequency band. Measurements taken from the realized LNA demonstrate the success of this approximation over the band.
Energy Technology Data Exchange (ETDEWEB)
Ablakulov, Kh., E-mail: ablakulov@inp.uz; Narzikulov, Z., E-mail: narzikulov@inp.uz [Uzbek Academy of Sciences, Institute of Nuclear Physics (Uzbekistan)
2015-01-15
A phenomenological model is developed in terms of bilocal meson fields in order to describe a vector meson and its leptonic decays. A new Salpeter equation for this particle and the Schwinger-Dyson equation allowing for the presence of an arbitrary potential and for a modification associated with the renormalization of the quark (antiquark ) wave function within the meson are given. An expression for the constant of the leptonic decay of the charged rho meson is obtained from an analysis of the decay process τ → ρν via parametrizing in it the hadronization of intermediate charged weak W bosons into a bilocal vector meson. The potential is chosen in the form of the sum of harmonic-oscillator and Coulomb potentials, and the respective boundary-value problem is formulated. It is shown that the solutions to this problem describe both the mass spectrum of vector mesons and their leptonic-decay constants.
Directory of Open Access Journals (Sweden)
Chiu-Keng Lai
2017-10-01
Full Text Available In this paper, we present the modeling, analysis, and realization of current vector control for a permanent magnet synchronous motor (PMSM drive using MATLAB/Simulink and a field programmable gate array (FPGA. In AC motor drive systems, most of the current vector controls are realized by digital signal processors (DSPs because of their complete and compact hardware functions. However, the performances of drive systems realized by low-cost DSP are limited by the hardware structure and computation capacity, which may lead to the difficulty of reaching a fast enough response, above all, for those motors with a small electrical time constant. Therefore, we use FPGA to speed up the calculation about the current vector control to attain a fast response. Simulations and practical experimental results are used to verify the correctness and performance of the designed full hardware system.
Directory of Open Access Journals (Sweden)
Nildimar Alves Honório
2009-11-01
Full Text Available Rio de Janeiro, Brazil, experienced a severe dengue fever epidemic in 2008. This was the worst epidemic ever, characterized by a sharp increase in case-fatality rate, mainly among younger individuals. A combination of factors, such as climate, mosquito abundance, buildup of the susceptible population, or viral evolution, could explain the severity of this epidemic. The main objective of this study is to model the spatial patterns of dengue seroprevalence in three neighborhoods with different socioeconomic profiles in Rio de Janeiro. As blood sampling coincided with the peak of dengue transmission, we were also able to identify recent dengue infections and visually relate them to Aedes aegypti spatial distribution abundance. We analyzed individual and spatial factors associated with seroprevalence using Generalized Additive Model (GAM.Three neighborhoods were investigated: a central urban neighborhood, and two isolated areas characterized as a slum and a suburban area. Weekly mosquito collections started in September 2006 and continued until March 2008. In each study area, 40 adult traps and 40 egg traps were installed in a random sample of premises, and two infestation indexes calculated: mean adult density and mean egg density. Sera from individuals living in the three neighborhoods were collected before the 2008 epidemic (July through November 2007 and during the epidemic (February through April 2008. Sera were tested for DENV-reactive IgM, IgG, Nested RT-PCR, and Real Time RT-PCR. From the before-after epidemics paired data, we described seroprevalence, recent dengue infections (asymptomatic or not, and seroconversion. Recent dengue infection varied from 1.3% to 14.1% among study areas. The highest IgM seropositivity occurred in the slum, where mosquito abundance was the lowest, but household conditions were the best for promoting contact between hosts and vectors. By fitting spatial GAM we found dengue seroprevalence hotspots located at the
GeoLearn: Prediction Modeling Using Large Size Geospatial Raster and Vector Data
Bajcsy, P.; Kooper, R.; Keselman, Y.; Li, Q.; Feng, W.; Mehra, V.; Richard, R.; Kumar, P.
2006-12-01
This research develops informatics technologies that support hydrologic scientists in predictive modeling using large size geospatial raster and vector data. The technology helps scientists understand inter-relationships between land surface variables such as radiation, precipitation, temperature, and the presence and amount of vegetation. We have been studying how these relationships manifest themselves at relatively large scales of regions, continents, and the entire globe. In the past, the class of such scientific studies usually has been limited to small spatial regions because of the lack of informatics solutions and the challenges associated with integration and modeling using large size geospatial raster and vector data. The motivation for our work was to provide a desktop software solution for understanding the inter-relationships among multiple variables at spatial scales and resolutions than have not been modeled in the past. Examples of such studies include the understanding of the most important drivers of vegetation growth across a variety of ecosystems at the US continental scale in search of common elements, or finding commonalities and differences within existing eco-regions. The studies conducted in our work considered a large number of input variables for predicting vegetation greenness and are broadly characterized into meteorology, topography, and soil categories. Most of the variables of interest are acquired using remote sensing because otherwise large spatial coverage and sufficient spatial resolution could not be achieved. The data sets are accessible from NASA web sites, for instance, the data products acquired by the MODIS satellite. These data products in HDF EOS file format are characterized by a large size, variable temporal and spatial resolution, spatially varying quality control and quality assurance (QA/QC) parameters and by a set of geographic projections. Additional data sets might come from other agencies (e.g., soil type maps or
Modeling of Soil Aggregate Stability using Support Vector Machines and Multiple Linear Regression
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Ali Asghar Besalatpour
2016-02-01
stability. Conclusion: The pixel-scale soil aggregate stability predicted that using the developed SVM and MLR models demonstrates the usefulness of incorporating topographic and vegetation information along with the soil properties as predictors. However, the SVM model achieved more accuracy in predicting soil aggregate stability compared to the MLR model. Therefore, it appears that support vector machines can be used for prediction of some soil physical properties such as geometric mean diameter of soil aggregates in the study area. Furthermore, despite the high predictive accuracy of the SVM method compared to the MLR technique which was confirmed by the obtained results in the current study, the advantages of the SVM method such as its intrinsic effectiveness with respect to traditional prediction methods, less effort in setting up the control parameters for architecture design, the possibility of solving the learning problem according to constrained quadratic programming methods, etc., should motivate soil scientists to work on it further in the future.
Directory of Open Access Journals (Sweden)
Fabyano Fonseca e Silva
2011-04-01
Full Text Available The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p, panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested by a simulation study using three priors: hierarchical Multivariate Normal-Inverse Gamma (model 1, independent Multivariate Student's t Inverse Gamma (model 2 and Jeffrey's (model 3. Comparisons by Pseudo-Bayes Factor favored model 2. The proposed methodology was applied to longitudinal data relative to Expected Progeny Difference (EPD of beef cattle sires. The forecast efficiency was around 80%. Regarding the mean width of the EPD interval estimation (95% in a future time, a great advantage was observed for the proposed Bayesian methodology over usual asymptotic frequentist method.A previsão dos valores genéticos de animais em tempos futuros constitui importante inovação tecnológica para a área de Zootecnia, uma vez que possibilita planejar com antecedência o descarte ou a manutenção de animais no rebanho. No presente estudo considerou-se uma análise Bayesiana de modelos auto-regressivos de ordem p, AR(p, para dados em painel, de forma a utilizar a função de verossimilhança exata, a análise de comparação de distribuições a priori e a obtenção de distribuições preditivas de dados futuros. A metodologia utilizada foi testada mediante um estudo de simulação usando a priori hierárquica Normal multivariada-Gama inversa (modelo 1, a priori independente t-Student Gama inversa (modelo 2 e a priori de Jeffreys (modelo 3. As compara
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Bubnov A.F.
2016-01-01
Full Text Available In this paper, we consider vacuum polarization effects in the model of charged fermions with anomalous magnetic moment and axial-vector interaction term in a uniform magnetic field. Nontrivial orrections to the effective Lagrangian from the anomalous moment and axial-vector term are calculated with account for various configurations of parameters of the model.
Geographic Distribution of Chagas Disease Vectors in Brazil Based on Ecological Niche Modeling
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Rodrigo Gurgel-Gonçalves
2012-01-01
Full Text Available Although Brazil was declared free from Chagas disease transmission by the domestic vector Triatoma infestans, human acute cases are still being registered based on transmission by native triatomine species. For a better understanding of transmission risk, the geographic distribution of Brazilian triatomines was analyzed. Sixteen out of 62 Brazilian species that both occur in >20 municipalities and present synanthropic tendencies were modeled based on their ecological niches. Panstrongylus geniculatus and P. megistus showed broad ecological ranges, but most of the species sort out by the biome in which they are distributed: Rhodnius pictipes and R. robustus in the Amazon; R. neglectus, Triatoma sordida, and T. costalimai in the Cerrado; R. nasutus, P. lutzi, T. brasiliensis, T. pseudomaculata, T. melanocephala, and T. petrocchiae in the Caatinga; T. rubrovaria in the southern pampas; T. tibiamaculata and T. vitticeps in the Atlantic Forest. Although most occurrences were recorded in open areas (Cerrado and Caatinga, our results show that all environmental conditions in the country are favorable to one or more of the species analyzed, such that almost nowhere is Chagas transmission risk negligible.
Gas detonation cell width prediction model based on support vector regression
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Jiyang Yu
2017-10-01
Full Text Available Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, λ, is highly correlated with the characteristic reaction zone width, δ. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of λ/δ. The obtained correlations formulate the dependency of the ratio λ/δ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR, which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.
Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model
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Ji-Long Liu
2015-03-01
Full Text Available Protein-protein interaction (PPI is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, predicting protein partners for a single protein is likely a simpler problem. Given enough data for a particular protein, the results can be more accurate than general PPI predictors. In the present study, we assessed the potential of using the support vector machine (SVM model with selected features centered on a particular protein for PPI prediction. As a proof-of-concept study, we applied this method to identify the interactome of progesterone receptor (PR, a protein which is essential for coordinating female reproduction in mammals by mediating the actions of ovarian progesterone. We achieved an accuracy of 91.9%, sensitivity of 92.8% and specificity of 91.2%. Our method is generally applicable to any other proteins and therefore may be of help in guiding biomedical experiments.
Development and evaluation of a biomedical search engine using a predicate-based vector space model.
Kwak, Myungjae; Leroy, Gondy; Martinez, Jesse D; Harwell, Jeffrey
2013-10-01
Although biomedical information available in articles and patents is increasing exponentially, we continue to rely on the same information retrieval methods and use very few keywords to search millions of documents. We are developing a fundamentally different approach for finding much more precise and complete information with a single query using predicates instead of keywords for both query and document representation. Predicates are triples that are more complex datastructures than keywords and contain more structured information. To make optimal use of them, we developed a new predicate-based vector space model and query-document similarity function with adjusted tf-idf and boost function. Using a test bed of 107,367 PubMed abstracts, we evaluated the first essential function: retrieving information. Cancer researchers provided 20 realistic queries, for which the top 15 abstracts were retrieved using a predicate-based (new) and keyword-based (baseline) approach. Each abstract was evaluated, double-blind, by cancer researchers on a 0-5 point scale to calculate precision (0 versus higher) and relevance (0-5 score). Precision was significantly higher (psearching than keywords, laying the foundation for rich and sophisticated information search. Copyright © 2013 Elsevier Inc. All rights reserved.
Perturbative and non-perturbative aspects in vector model/higher spin duality
Jevicki, Antal; Jin, Kewang; Ye, Qibin
2013-05-01
We review some recent work on AdS/CFT duality involving the 3D O(N) vector model and AdS4 higher spin gravity. Our construction is based on bi-local collective field theory which provides an off-shell formulation of higher spin gravity with G = 1/N playing the role of a coupling constant. Consequently, perturbative and non-perturbative issues of the theory can be studied. For the correspondence based on free CFTs we discuss the nature of bulk 1/N interactions through an S-matrix which is argued to be equal to 1 (Coleman-Mandula theorem). As a consequence in this class of theories nonlinearities are removable, through a nonlinear field transformation which we show at the exact level. We also describe a geometric (Kähler space) framework for the bi-local theory which applies equally simply to Sp(2N) fermions and the de Sitter correspondence. We discuss in this framework the structure and size of the bi-local Hilbert space and the implementation of (finite N) exclusion principle. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Higher spin theories and holography’.
LENUS (Irish Health Repository)
McGinley, Lisa
2011-03-07
Abstract Introduction A combination of gene and cell therapies has the potential to significantly enhance the therapeutic value of mesenchymal stem cells (MSCs). The development of efficient gene delivery methods is essential if MSCs are to be of benefit using such an approach. Achieving high levels of transgene expression for the required period of time, without adversely affecting cell viability and differentiation capacity, is crucial. In the present study, we investigate lentiviral vector-mediated genetic modification of rat bone-marrow derived MSCs and examine any functional effect of such genetic modification in an in vitro model of ischaemia. Methods Transduction efficiency and transgene persistence of second and third generation rHIV-1 based lentiviral vectors were tested using reporter gene constructs. Use of the rHIV-pWPT-EF1-α-GFP-W vector was optimised in terms of dose, toxicity, cell species, and storage. The in vivo condition of ischaemia was modelled in vitro by separation into its associated constituent parts i.e. hypoxia, serum and glucose deprivation, in which the effect of therapeutic gene over-expression on MSC survival was investigated. Results The second generation lentiviral vector rHIV-pWPT-EF1-α-GFP-W, was the most efficient and provided the most durable transgene expression of the vectors tested. Transduction with this vector did not adversely affect MSC morphology, viability or differentiation potential, and transgene expression levels were unaffected by cryopreservation of transduced cells. Over-expression of HSP70 resulted in enhanced MSC survival and increased resistance to apoptosis in conditions of hypoxia and ischaemia. MSC differentiation capacity was significantly reduced after oxygen deprivation, but was preserved with HSP70 over-expression. Conclusions Collectively, these data validate the use of lentiviral vectors for efficient in vitro gene delivery to MSCs and suggest that lentiviral vector transduction can facilitate
LENUS (Irish Health Repository)
McGinley, Lisa
2012-01-31
INTRODUCTION: A combination of gene and cell therapies has the potential to significantly enhance the therapeutic value of mesenchymal stem cells (MSCs). The development of efficient gene delivery methods is essential if MSCs are to be of benefit using such an approach. Achieving high levels of transgene expression for the required period of time, without adversely affecting cell viability and differentiation capacity, is crucial. In the present study, we investigate lentiviral vector-mediated genetic modification of rat bone-marrow derived MSCs and examine any functional effect of such genetic modification in an in vitro model of ischaemia. METHODS: Transduction efficiency and transgene persistence of second and third generation rHIV-1 based lentiviral vectors were tested using reporter gene constructs. Use of the rHIV-pWPT-EF1-alpha-GFP-W vector was optimised in terms of dose, toxicity, cell species, and storage. The in vivo condition of ischaemia was modelled in vitro by separation into its associated constituent parts i.e. hypoxia, serum and glucose deprivation, in which the effect of therapeutic gene over-expression on MSC survival was investigated. RESULTS: The second generation lentiviral vector rHIV-pWPT-EF1-alpha-GFP-W, was the most efficient and provided the most durable transgene expression of the vectors tested. Transduction with this vector did not adversely affect MSC morphology, viability or differentiation potential, and transgene expression levels were unaffected by cryopreservation of transduced cells. Over-expression of HSP70 resulted in enhanced MSC survival and increased resistance to apoptosis in conditions of hypoxia and ischaemia. MSC differentiation capacity was significantly reduced after oxygen deprivation, but was preserved with HSP70 over-expression. CONCLUSIONS: Collectively, these data validate the use of lentiviral vectors for efficient in vitro gene delivery to MSCs and suggest that lentiviral vector transduction can facilitate
Ren, Xianqiang
2008-01-01
The growing popularity of analogue and mixed-signal (AMS) ASIC and SoC designs for communication applications has led to an increasing requirement for high efficiency performance modelling and optimisation methodologies in AMS synthesis systems. Recently, the support vector machine (SVM) method has been introduced into this challenging field. This research has studied the application of SVMs to AMS performance modelling in terms of the computational cost and prediction accuracy. A novel...
A priori estimates for nonvariational operators modeled on Hörmander's vector fields with drift
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Marco Bramanti
2013-12-01
Full Text Available For a nonvariational operator structured on Hörmander's vector fields with drift, where the matrix of coffiecients is real, symmetric and uniformly positive, we prove local a priori estimates on the second order derivatives with respect to the vector fields, in Hölder spaces if the coecients are Holder continuous, in Lp spaces if the coefficients are bounded, measurable and locally VMO.
Optimal Sparse Matrix Dense Vector Multiplication in the I/O-Model
DEFF Research Database (Denmark)
Bender, Michael A.; Brodal, Gerth Stølting; Fagerberg, Rolf
2010-01-01
We study the problem of sparse-matrix dense-vector multiplication (SpMV) in external memory. The task of SpMV is to compute y:=Ax, where A is a sparse Nx N matrix and x is a vector. We express sparsity by a parameter k, and for each choice of k consider the class of matrices where the number...
Hoffmann, Banesh
1975-01-01
From his unusual beginning in ""Defining a vector"" to his final comments on ""What then is a vector?"" author Banesh Hoffmann has written a book that is provocative and unconventional. In his emphasis on the unresolved issue of defining a vector, Hoffmann mixes pure and applied mathematics without using calculus. The result is a treatment that can serve as a supplement and corrective to textbooks, as well as collateral reading in all courses that deal with vectors. Major topics include vectors and the parallelogram law; algebraic notation and basic ideas; vector algebra; scalars and scalar p
Newell, Homer E
2006-01-01
When employed with skill and understanding, vector analysis can be a practical and powerful tool. This text develops the algebra and calculus of vectors in a manner useful to physicists and engineers. Numerous exercises (with answers) not only provide practice in manipulation but also help establish students' physical and geometric intuition in regard to vectors and vector concepts.Part I, the basic portion of the text, consists of a thorough treatment of vector algebra and the vector calculus. Part II presents the illustrative matter, demonstrating applications to kinematics, mechanics, and e
QCD next-to-leading order predictions matched to parton showers for vector-like quark models
Fuks, Benjamin
2017-02-27
Vector-like quarks are featured by a wealth of beyond the Standard Model theories and are consequently an important goal of many LHC searches for new physics. Those searches, as well as most related phenomenological studies, however rely on predictions evaluated at the leading-order accuracy in QCD and consider well-defined simplified benchmark scenarios. Adopting an effective bottom-up approach, we compute next-to-leading-order predictions for vector-like-quark pair-production and single production in association with jets, with a weak or with a Higgs boson in a general new physics setup. We additionally compute vector-like-quark contributions to the production of a pair of Standard Model bosons at the same level of accuracy. For all processes under consideration, we focus both on total cross sections and on differential distributions, most these calculations being performed for the first time in our field. As a result, our work paves the way to precise extraction of experimental limits on vector-like quarks...
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Kyle A McQuisten
Full Text Available BACKGROUND: Exogenous short interfering RNAs (siRNAs induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models. PRINCIPAL FINDINGS: Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs, General Linear Models (GLMs and Support Vector Machines (SVMs. Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation. CONCLUSIONS: The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features
Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan
2017-07-01
Soil temperature (T s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T s. For this aim, the monthly mean data of the T s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T min, T max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R s, R n, R a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u 2); and water vapor pressure (Vp) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R 2) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T max, T min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R 2 = 0.995) and for RH, V p, P, and u 2 inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R 2 = 0.996), respectively.
Singular vector decomposition of the internal variability of the Canadian Regional Climate Model
Energy Technology Data Exchange (ETDEWEB)
Diaconescu, Emilia Paula; Laprise, Rene [University of Quebec at Montreal (UQAM), Department of Earth and Atmospheric Sciences, Canadian Network for Regional Climate Modelling and Diagnostics, P.O. Box 8888, Montreal, QC (Canada); Centre ESCER (Etude et Simulation du Climat a l' Echelle Regionale), Montreal, QC (Canada); Zadra, Ayrton [University of Quebec at Montreal (UQAM), Department of Earth and Atmospheric Sciences, Canadian Network for Regional Climate Modelling and Diagnostics, P.O. Box 8888, Montreal, QC (Canada); Environment Canada, Meteorological Research Division, Montreal, QC (Canada); Centre ESCER (Etude et Simulation du Climat a l' Echelle Regionale), Montreal, QC (Canada)
2012-03-15
Previous studies have shown that Regional Climate Models (RCM) internal variability (IV) fluctuates in time depending on synoptic events. This study focuses on the physical understanding of episodes with rapid growth of IV. An ensemble of 21 simulations, differing only in their initial conditions, was run over North America using version 5 of the Canadian RCM (CRCM). The IV is quantified in terms of energy of CRCM perturbations with respect to a reference simulation. The working hypothesis is that IV is arising through rapidly growing perturbations developed in dynamically unstable regions. If indeed IV is triggered by the growth of unstable perturbations, a large proportion of the CRCM perturbations must project onto the most unstable singular vectors (SVs). A set of ten SVs was computed to identify the orthogonal set of perturbations that provide the maximum growth with respect to the dry total-energy norm during the course of the CRCM ensemble of simulations. CRCM perturbations were then projected onto the subspace of SVs. The analysis of one episode of rapid growth of IV is presented in detail. It is shown that a large part of the IV growth is explained by initially small-amplitude unstable perturbations represented by the ten leading SVs, the SV subspace accounting for over 70% of the CRCM IV growth in 36 h. The projection on the leading SV at final time is greater than the projection on the remaining SVs and there is a high similarity between the CRCM perturbations and the leading SV after 24-36 h tangent-linear model integration. The vertical structure of perturbations revealed that the baroclinic conversion is the dominant process in IV growth for this particular episode. (orig.)