Monthly streamflow forecasting with auto-regressive integrated moving average
Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani
2017-09-01
Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.
International Nuclear Information System (INIS)
Che Jinxing; Wang Jianzhou
2010-01-01
In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
Directory of Open Access Journals (Sweden)
DT Wiyanti
2013-07-01
Full Text Available Salah satu metode peramalan yang paling dikembangkan saat ini adalah time series, yakni menggunakan pendekatan kuantitatif dengan data masa lampau yang dijadikan acuan untuk peramalan masa depan. Berbagai penelitian telah mengusulkan metode-metode untuk menyelesaikan time series, di antaranya statistik, jaringan syaraf, wavelet, dan sistem fuzzy. Metode-metode tersebut memiliki kekurangan dan keunggulan yang berbeda. Namun permasalahan yang ada dalam dunia nyata merupakan masalah yang kompleks. Satu metode saja mungkin tidak mampu mengatasi masalah tersebut dengan baik. Dalam artikel ini dibahas penggabungan dua buah metode yaitu Auto Regressive Integrated Moving Average (ARIMA dan Radial Basis Function (RBF. Alasan penggabungan kedua metode ini adalah karena adanya asumsi bahwa metode tunggal tidak dapat secara total mengidentifikasi semua karakteristik time series. Pada artikel ini dibahas peramalan terhadap data Indeks Harga Perdagangan Besar (IHPB dan data inflasi komoditi Indonesia; kedua data berada pada rentang tahun 2006 hingga beberapa bulan di tahun 2012. Kedua data tersebut masing-masing memiliki enam variabel. Hasil peramalan metode ARIMA-RBF dibandingkan dengan metode ARIMA dan metode RBF secara individual. Hasil analisa menunjukkan bahwa dengan metode penggabungan ARIMA dan RBF, model yang diberikan memiliki hasil yang lebih akurat dibandingkan dengan penggunaan salah satu metode saja. Hal ini terlihat dalam visual plot, MAPE, dan RMSE dari semua variabel pada dua data uji coba.Â The accuracy of time series forecasting is the subject of many decision-making processes. Time series use a quantitative approach to employ data from the past to make forecast for the future. Many researches have proposed several methods to solve time series, such as using statistics, neural networks, wavelets, and fuzzy systems. These methods have different advantages and disadvantages. But often the problem in the real world is just too complex that a
Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes
International Nuclear Information System (INIS)
Faranda, Davide; Dubrulle, Bérengère; Daviaud, François; Pons, Flavio Maria Emanuele; Saint-Michel, Brice; Herbert, Éric; Cortet, Pierre-Philippe
2014-01-01
We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressive Moving Average (ARMA) statistical analysis. Such analysis goes well beyond the analysis of the mean flow and of the fluctuations and links the behavior of the recorded time series to a discrete version of a stochastic differential equation which is able to describe the correlation structure in the dataset. We introduce a new index Υ that measures the difference between the resulting analysis and the Obukhov model of turbulence, the simplest stochastic model reproducing both Richardson law and the Kolmogorov spectrum. We test the method on datasets measured in a von Kármán swirling flow experiment. We found that the ARMA analysis is well correlated with spatial structures of the flow, and can discriminate between two different flows with comparable mean velocities, obtained by changing the forcing. Moreover, we show that the Υ is highest in regions where shear layer vortices are present, thereby establishing a link between deviations from the Kolmogorov model and coherent structures. These deviations are consistent with the ones observed by computing the Hurst exponents for the same time series. We show that some salient features of the analysis are preserved when considering global instead of local observables. Finally, we analyze flow configurations with multistability features where the ARMA technique is efficient in discriminating different stability branches of the system
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
Energy Technology Data Exchange (ETDEWEB)
Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)
2008-09-15
This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)
Autoregressive Moving Average Graph Filtering
Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert
2016-01-01
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. The design phi...
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina
2013-01-01
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Directory of Open Access Journals (Sweden)
Razana Alwee
2013-01-01
Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Tani, Yuji; Ogasawara, Katsuhiko
2012-01-01
This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.
Directory of Open Access Journals (Sweden)
Wudi Wei
Full Text Available 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.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.The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2(1,1,112 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.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.
A dynamic analysis of moving average rules
Chiarella, C.; He, X.Z.; Hommes, C.H.
2006-01-01
The use of various moving average (MA) rules remains popular with financial market practitioners. These rules have recently become the focus of a number empirical studies, but there have been very few studies of financial market models where some agents employ technical trading rules of the type
on the performance of Autoregressive Moving Average Polynomial
African Journals Online (AJOL)
Timothy Ademakinwa
Distributed Lag (PDL) model, Autoregressive Polynomial Distributed Lag ... Moving Average Polynomial Distributed Lag (ARMAPDL) model. ..... Global Journal of Mathematics and Statistics. Vol. 1. ... Business and Economic Research Center.
Interpreting Bivariate Regression Coefficients: Going beyond the Average
Halcoussis, Dennis; Phillips, G. Michael
2010-01-01
Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…
Self-similarity of higher-order moving averages
Arianos, Sergio; Carbone, Anna; Türk, Christian
2011-10-01
In this work, higher-order moving average polynomials are defined by straightforward generalization of the standard moving average. The self-similarity of the polynomials is analyzed for fractional Brownian series and quantified in terms of the Hurst exponent H by using the detrending moving average method. We prove that the exponent H of the fractional Brownian series and of the detrending moving average variance asymptotically agree for the first-order polynomial. Such asymptotic values are compared with the results obtained by the simulations. The higher-order polynomials correspond to trend estimates at shorter time scales as the degree of the polynomial increases. Importantly, the increase of polynomial degree does not require to change the moving average window. Thus trends at different time scales can be obtained on data sets with the same size. These polynomials could be interesting for those applications relying on trend estimates over different time horizons (financial markets) or on filtering at different frequencies (image analysis).
A note on moving average models for Gaussian random fields
DEFF Research Database (Denmark)
Hansen, Linda Vadgård; Thorarinsdottir, Thordis L.
The class of moving average models offers a flexible modeling framework for Gaussian random fields with many well known models such as the Matérn covariance family and the Gaussian covariance falling under this framework. Moving average models may also be viewed as a kernel smoothing of a Lévy...... basis, a general modeling framework which includes several types of non-Gaussian models. We propose a new one-parameter spatial correlation model which arises from a power kernel and show that the associated Hausdorff dimension of the sample paths can take any value between 2 and 3. As a result...
Bivariate copulas on the exponentially weighted moving average control chart
Directory of Open Access Journals (Sweden)
Sasigarn Kuvattana
2016-10-01
Full Text Available This paper proposes four types of copulas on the Exponentially Weighted Moving Average (EWMA control chart when observations are from an exponential distribution using a Monte Carlo simulation approach. The performance of the control chart is based on the Average Run Length (ARL which is compared for each copula. Copula functions for specifying dependence between random variables are used and measured by Kendall’s tau. The results show that the Normal copula can be used for almost all shifts.
Moving average rules as a source of market instability
Chiarella, C.; He, X.Z.; Hommes, C.H.
2006-01-01
Despite the pervasiveness of the efficient markets paradigm in the academic finance literature, the use of various moving average (MA) trading rules remains popular with financial market practitioners. This paper proposes a stochastic dynamic financial market model in which demand for traded assets
Capillary Electrophoresis Sensitivity Enhancement Based on Adaptive Moving Average Method.
Drevinskas, Tomas; Telksnys, Laimutis; Maruška, Audrius; Gorbatsova, Jelena; Kaljurand, Mihkel
2018-06-05
In the present work, we demonstrate a novel approach to improve the sensitivity of the "out of lab" portable capillary electrophoretic measurements. Nowadays, many signal enhancement methods are (i) underused (nonoptimal), (ii) overused (distorts the data), or (iii) inapplicable in field-portable instrumentation because of a lack of computational power. The described innovative migration velocity-adaptive moving average method uses an optimal averaging window size and can be easily implemented with a microcontroller. The contactless conductivity detection was used as a model for the development of a signal processing method and the demonstration of its impact on the sensitivity. The frequency characteristics of the recorded electropherograms and peaks were clarified. Higher electrophoretic mobility analytes exhibit higher-frequency peaks, whereas lower electrophoretic mobility analytes exhibit lower-frequency peaks. On the basis of the obtained data, a migration velocity-adaptive moving average algorithm was created, adapted, and programmed into capillary electrophoresis data-processing software. Employing the developed algorithm, each data point is processed depending on a certain migration time of the analyte. Because of the implemented migration velocity-adaptive moving average method, the signal-to-noise ratio improved up to 11 times for sampling frequency of 4.6 Hz and up to 22 times for sampling frequency of 25 Hz. This paper could potentially be used as a methodological guideline for the development of new smoothing algorithms that require adaptive conditions in capillary electrophoresis and other separation methods.
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.
Modeling methane emission via the infinite moving average process
Czech Academy of Sciences Publication Activity Database
Jordanova, D.; Dušek, Jiří; Stehlík, M.
2013-01-01
Roč. 122, - (2013), s. 40-49 ISSN 0169-7439 R&D Projects: GA MŠk(CZ) ED1.1.00/02.0073; GA ČR(CZ) GAP504/11/1151 Institutional support: RVO:67179843 Keywords : Environmental chemistry * Pareto tails * t-Hill estimator * Weak consistency * Moving average process * Methane emission model Subject RIV: EH - Ecology, Behaviour Impact factor: 2.381, year: 2013
Spatial analysis based on variance of moving window averages
Wu, B M; Subbarao, K V; Ferrandino, F J; Hao, J J
2006-01-01
A new method for analysing spatial patterns was designed based on the variance of moving window averages (VMWA), which can be directly calculated in geographical information systems or a spreadsheet program (e.g. MS Excel). Different types of artificial data were generated to test the method. Regardless of data types, the VMWA method correctly determined the mean cluster sizes. This method was also employed to assess spatial patterns in historical plant disease survey data encompassing both a...
Pan, Chu-Dong; Yu, Ling; Liu, Huan-Lin
2017-08-01
Traffic-induced moving force identification (MFI) is a typical inverse problem in the field of bridge structural health monitoring. Lots of regularization-based methods have been proposed for MFI. However, the MFI accuracy obtained from the existing methods is low when the moving forces enter into and exit a bridge deck due to low sensitivity of structural responses to the forces at these zones. To overcome this shortcoming, a novel moving average Tikhonov regularization method is proposed for MFI by combining with the moving average concepts. Firstly, the bridge-vehicle interaction moving force is assumed as a discrete finite signal with stable average value (DFS-SAV). Secondly, the reasonable signal feature of DFS-SAV is quantified and introduced for improving the penalty function (∣∣x∣∣2 2) defined in the classical Tikhonov regularization. Then, a feasible two-step strategy is proposed for selecting regularization parameter and balance coefficient defined in the improved penalty function. Finally, both numerical simulations on a simply-supported beam and laboratory experiments on a hollow tube beam are performed for assessing the accuracy and the feasibility of the proposed method. The illustrated results show that the moving forces can be accurately identified with a strong robustness. Some related issues, such as selection of moving window length, effect of different penalty functions, and effect of different car speeds, are discussed as well.
Analysis of nonlinear systems using ARMA [autoregressive moving average] models
International Nuclear Information System (INIS)
Hunter, N.F. Jr.
1990-01-01
While many vibration systems exhibit primarily linear behavior, a significant percentage of the systems encountered in vibration and model testing are mildly to severely nonlinear. Analysis methods for such nonlinear systems are not yet well developed and the response of such systems is not accurately predicted by linear models. Nonlinear ARMA (autoregressive moving average) models are one method for the analysis and response prediction of nonlinear vibratory systems. In this paper we review the background of linear and nonlinear ARMA models, and illustrate the application of these models to nonlinear vibration systems. We conclude by summarizing the advantages and disadvantages of ARMA models and emphasizing prospects for future development. 14 refs., 11 figs
Application of autoregressive moving average model in reactor noise analysis
International Nuclear Information System (INIS)
Tran Dinh Tri
1993-01-01
The application of an autoregressive (AR) model to estimating noise measurements has achieved many successes in reactor noise analysis in the last ten years. The physical processes that take place in the nuclear reactor, however, are described by an autoregressive moving average (ARMA) model rather than by an AR model. Consequently more correct results could be obtained by applying the ARMA model instead of the AR model to reactor noise analysis. In this paper the system of the generalised Yule-Walker equations is derived from the equation of an ARMA model, then a method for its solution is given. Numerical results show the applications of the method proposed. (author)
Generalized Heteroskedasticity ACF for Moving Average Models in Explicit Forms
Samir Khaled Safi
2014-01-01
The autocorrelation function (ACF) measures the correlation between observations at different distances apart. We derive explicit equations for generalized heteroskedasticity ACF for moving average of order q, MA(q). We consider two cases: Firstly: when the disturbance term follow the general covariance matrix structure Cov(wi, wj)=S with si,j ¹ 0 " i¹j . Secondly: when the diagonal elements of S are not all identical but sij = 0 " i¹j, i.e. S=diag(s11, s22,&hellip...
Generalized Heteroskedasticity ACF for Moving Average Models in Explicit Forms
Directory of Open Access Journals (Sweden)
Samir Khaled Safi
2014-02-01
Full Text Available Normal 0 false false false MicrosoftInternetExplorer4 The autocorrelation function (ACF measures the correlation between observations at different distances apart. We derive explicit equations for generalized heteroskedasticity ACF for moving average of order q, MA(q. We consider two cases: Firstly: when the disturbance term follow the general covariance matrix structure Cov(wi, wj=S with si,j ¹ 0 " i¹j . Secondly: when the diagonal elements of S are not all identical but sij = 0 " i¹j, i.e. S=diag(s11, s22,…,stt. The forms of the explicit equations depend essentially on the moving average coefficients and covariance structure of the disturbance terms. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"جدول عادي"; 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;}
Image compression using moving average histogram and RBF network
International Nuclear Information System (INIS)
Khowaja, S.; Ismaili, I.A.
2015-01-01
Modernization and Globalization have made the multimedia technology as one of the fastest growing field in recent times but optimal use of bandwidth and storage has been one of the topics which attract the research community to work on. Considering that images have a lion share in multimedia communication, efficient image compression technique has become the basic need for optimal use of bandwidth and space. This paper proposes a novel method for image compression based on fusion of moving average histogram and RBF (Radial Basis Function). Proposed technique employs the concept of reducing color intensity levels using moving average histogram technique followed by the correction of color intensity levels using RBF networks at reconstruction phase. Existing methods have used low resolution images for the testing purpose but the proposed method has been tested on various image resolutions to have a clear assessment of the said technique. The proposed method have been tested on 35 images with varying resolution and have been compared with the existing algorithms in terms of CR (Compression Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), computational complexity. The outcome shows that the proposed methodology is a better trade off technique in terms of compression ratio, PSNR which determines the quality of the image and computational complexity. (author)
An Exponentially Weighted Moving Average Control Chart for Bernoulli Data
DEFF Research Database (Denmark)
Spliid, Henrik
2010-01-01
of the transformation is given and its limit for small values of p is derived. Control of high yield processes is discussed and the chart is shown to perform very well in comparison with both the most common alternative EWMA chart and the CUSUM chart. The construction and the use of the proposed EWMA chart......We consider a production process in which units are produced in a sequential manner. The units can, for example, be manufactured items or services, provided to clients. Each unit produced can be a failure with probability p or a success (non-failure) with probability (1-p). A novel exponentially...... weighted moving average (EWMA) control chart intended for surveillance of the probability of failure, p, is described. The chart is based on counting the number of non-failures produced between failures in combination with a variance-stabilizing transformation. The distribution function...
Directory of Open Access Journals (Sweden)
Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
International Nuclear Information System (INIS)
Wang, Jianzhou; Hu, Jianming
2015-01-01
With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT (Empirical Wavelet Transform) is employed to extract meaningful information from a wind speed series by designing an appropriate wavelet filter bank. The GPR (Gaussian Process Regression) model is utilized to combine independent forecasts generated by various forecasting engines (ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM)) in a nonlinear way rather than the commonly used linear way. The proposed approach provides more probabilistic information for wind speed predictions besides improving the forecasting accuracy for single-value predictions. The effectiveness of the proposed approach is demonstrated with wind speed data from two wind farms in China. The results indicate that the individual forecasting engines do not consistently forecast short-term wind speed for the two sites, and the proposed combination method can generate a more reliable and accurate forecast. - Highlights: • The proposed approach can make probabilistic modeling for wind speed series. • The proposed approach adapts to the time-varying characteristic of the wind speed. • The hybrid approach can extract the meaningful components from the wind speed series. • The proposed method can generate adaptive, reliable and more accurate forecasting results. • The proposed model combines four independent forecasting engines in a nonlinear way.
A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction
Danandeh Mehr, Ali; Kahya, Ercan
2017-06-01
Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.
Focused information criterion and model averaging based on weighted composite quantile regression
Xu, Ganggang; Wang, Suojin; Huang, Jianhua Z.
2013-01-01
We study the focused information criterion and frequentist model averaging and their application to post-model-selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non
Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies.
Directory of Open Access Journals (Sweden)
Jacinta Chan Phooi M'ng
Full Text Available The objective of this research is to examine the trends in the exchange rate markets of the ASEAN-5 countries (Indonesia (IDR, Malaysia (MYR, the Philippines (PHP, Singapore (SGD, and Thailand (THB through the application of dynamic moving average trading systems. This research offers evidence of the usefulness of the time-varying volatility technical analysis indicator, Adjustable Moving Average (AMA' in deciphering trends in these ASEAN-5 exchange rate markets. This time-varying volatility factor, referred to as the Efficacy Ratio in this paper, is embedded in AMA'. The Efficacy Ratio adjusts the AMA' to the prevailing market conditions by avoiding whipsaws (losses due, in part, to acting on wrong trading signals, which generally occur when there is no general direction in the market in range trading and by entering early into new trends in trend trading. The efficacy of AMA' is assessed against other popular moving-average rules. Based on the January 2005 to December 2014 dataset, our findings show that the moving averages and AMA' are superior to the passive buy-and-hold strategy. Specifically, AMA' outperforms the other models for the United States Dollar against PHP (USD/PHP and USD/THB currency pairs. The results show that different length moving averages perform better in different periods for the five currencies. This is consistent with our hypothesis that a dynamic adjustable technical indicator is needed to cater for different periods in different markets.
Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies.
Chan Phooi M'ng, Jacinta; Zainudin, Rozaimah
2016-01-01
The objective of this research is to examine the trends in the exchange rate markets of the ASEAN-5 countries (Indonesia (IDR), Malaysia (MYR), the Philippines (PHP), Singapore (SGD), and Thailand (THB)) through the application of dynamic moving average trading systems. This research offers evidence of the usefulness of the time-varying volatility technical analysis indicator, Adjustable Moving Average (AMA') in deciphering trends in these ASEAN-5 exchange rate markets. This time-varying volatility factor, referred to as the Efficacy Ratio in this paper, is embedded in AMA'. The Efficacy Ratio adjusts the AMA' to the prevailing market conditions by avoiding whipsaws (losses due, in part, to acting on wrong trading signals, which generally occur when there is no general direction in the market) in range trading and by entering early into new trends in trend trading. The efficacy of AMA' is assessed against other popular moving-average rules. Based on the January 2005 to December 2014 dataset, our findings show that the moving averages and AMA' are superior to the passive buy-and-hold strategy. Specifically, AMA' outperforms the other models for the United States Dollar against PHP (USD/PHP) and USD/THB currency pairs. The results show that different length moving averages perform better in different periods for the five currencies. This is consistent with our hypothesis that a dynamic adjustable technical indicator is needed to cater for different periods in different markets.
Directory of Open Access Journals (Sweden)
Ernest Kissi
2018-03-01
Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.
DEFF Research Database (Denmark)
Chon, K H; Cohen, R J; Holstein-Rathlou, N H
1997-01-01
A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving...... average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre...
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 (...
Kwon, Yong-Seok; Naeem, Khurram; Jeon, Min Yong; Kwon, Il-bum
2017-04-01
We analyze the relations of parameters in moving average method to enhance the event detectability of phase sensitive optical time domain reflectometer (OTDR). If the external events have unique frequency of vibration, then the control parameters of moving average method should be optimized in order to detect these events efficiently. A phase sensitive OTDR was implemented by a pulsed light source, which is composed of a laser diode, a semiconductor optical amplifier, an erbium-doped fiber amplifier, a fiber Bragg grating filter, and a light receiving part, which has a photo-detector and high speed data acquisition system. The moving average method is operated with the control parameters: total number of raw traces, M, number of averaged traces, N, and step size of moving, n. The raw traces are obtained by the phase sensitive OTDR with sound signals generated by a speaker. Using these trace data, the relation of the control parameters is analyzed. In the result, if the event signal has one frequency, then the optimal values of N, n are existed to detect the event efficiently.
Using exponentially weighted moving average algorithm to defend against DDoS attacks
CSIR Research Space (South Africa)
Machaka, P
2016-11-01
Full Text Available This paper seeks to investigate the performance of the Exponentially Weighted Moving Average (EWMA) for mining big data and detection of DDoS attacks in Internet of Things (IoT) infrastructure. The paper will investigate the tradeoff between...
Czech Academy of Sciences Publication Activity Database
Hynek, M.; Smetanová, D.; Stejskal, D.; Zvárová, Jana
2014-01-01
Roč. 34, č. 4 (2014), s. 367-376 ISSN 0197-3851 Institutional support: RVO:67985807 Keywords : nuchal translucency * exponentially weighted moving average model * statistics Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.268, year: 2014
A RED modified weighted moving average for soft real-time application
Directory of Open Access Journals (Sweden)
Domanśka Joanna
2014-09-01
Full Text Available The popularity of TCP/IP has resulted in an increase in usage of best-effort networks for real-time communication. Much effort has been spent to ensure quality of service for soft real-time traffic over IP networks. The Internet Engineering Task Force has proposed some architecture components, such as Active Queue Management (AQM. The paper investigates the influence of the weighted moving average on packet waiting time reduction for an AQM mechanism: the RED algorithm. The proposed method for computing the average queue length is based on a difference equation (a recursive equation. Depending on a particular optimality criterion, proper parameters of the modified weighted moving average function can be chosen. This change will allow reducing the number of violations of timing constraints and better use of this mechanism for soft real-time transmissions. The optimization problem is solved through simulations performed in OMNeT++ and later verified experimentally on a Linux implementation
MARD—A moving average rose diagram application for the geosciences
Munro, Mark A.; Blenkinsop, Thomas G.
2012-12-01
MARD 1.0 is a computer program for generating smoothed rose diagrams by using a moving average, which is designed for use across the wide range of disciplines encompassed within the Earth Sciences. Available in MATLAB®, Microsoft® Excel and GNU Octave formats, the program is fully compatible with both Microsoft® Windows and Macintosh operating systems. Each version has been implemented in a user-friendly way that requires no prior experience in programming with the software. MARD conducts a moving average smoothing, a form of signal processing low-pass filter, upon the raw circular data according to a set of pre-defined conditions selected by the user. This form of signal processing filter smoothes the angular dataset, emphasising significant circular trends whilst reducing background noise. Customisable parameters include whether the data is uni- or bi-directional, the angular range (or aperture) over which the data is averaged, and whether an unweighted or weighted moving average is to be applied. In addition to the uni- and bi-directional options, the MATLAB® and Octave versions also possess a function for plotting 2-dimensional dips/pitches in a single, lower, hemisphere. The rose diagrams from each version are exportable as one of a selection of common graphical formats. Frequently employed statistical measures that determine the vector mean, mean resultant (or length), circular standard deviation and circular variance are also included. MARD's scope is demonstrated via its application to a variety of datasets within the Earth Sciences.
Li, Qingchen; Cao, Guangxi; Xu, Wei
2018-01-01
Based on a multifractal detrending moving average algorithm (MFDMA), this study uses the fractionally autoregressive integrated moving average process (ARFIMA) to demonstrate the effectiveness of MFDMA in the detection of auto-correlation at different sample lengths and to simulate some artificial time series with the same length as the actual sample interval. We analyze the effect of predictable and unpredictable meteorological disasters on the US and Chinese stock markets and the degree of long memory in different sectors. Furthermore, we conduct a preliminary investigation to determine whether the fluctuations of financial markets caused by meteorological disasters are derived from the normal evolution of the financial system itself or not. We also propose several reasonable recommendations.
Cawyer, Chase R; Anderson, Sarah B; Szychowski, Jeff M; Neely, Cherry; Owen, John
2018-03-01
To compare the accuracy of a new regression-derived formula developed from the National Fetal Growth Studies data to the common alternative method that uses the average of the gestational ages (GAs) calculated for each fetal biometric measurement (biparietal diameter, head circumference, abdominal circumference, and femur length). This retrospective cross-sectional study identified nonanomalous singleton pregnancies that had a crown-rump length plus at least 1 additional sonographic examination with complete fetal biometric measurements. With the use of the crown-rump length to establish the referent estimated date of delivery, each method's (National Institute of Child Health and Human Development regression versus Hadlock average [Radiology 1984; 152:497-501]), error at every examination was computed. Error, defined as the difference between the crown-rump length-derived GA and each method's predicted GA (weeks), was compared in 3 GA intervals: 1 (14 weeks-20 weeks 6 days), 2 (21 weeks-28 weeks 6 days), and 3 (≥29 weeks). In addition, the proportion of each method's examinations that had errors outside prespecified (±) day ranges was computed by using odds ratios. A total of 16,904 sonograms were identified. The overall and prespecified GA range subset mean errors were significantly smaller for the regression compared to the average (P < .01), and the regression had significantly lower odds of observing examinations outside the specified range of error in GA intervals 2 (odds ratio, 1.15; 95% confidence interval, 1.01-1.31) and 3 (odds ratio, 1.24; 95% confidence interval, 1.17-1.32) than the average method. In a contemporary unselected population of women dated by a crown-rump length-derived GA, the National Institute of Child Health and Human Development regression formula produced fewer estimates outside a prespecified margin of error than the commonly used Hadlock average; the differences were most pronounced for GA estimates at 29 weeks and later.
International Nuclear Information System (INIS)
Dunn, L.F.; Taylor, M.L.; Kron, T.; Franich, R.
2010-01-01
Full text: Anatomic motion during a radiotherapy treatment is one of the more significant challenges in contemporary radiation therapy. For tumours of the lung, motion due to patient respiration makes both accurate planning and dose delivery difficult. One approach is to use the maximum intensity projection (MIP) obtained from a 40 computed tomography (CT) scan and then use this to determine the treatment volume. The treatment is then planned on a 4DCT average reco struction, rather than assuming the entire ITY has a uniform tumour density. This raises the question: how well does planning on a 'blurred' distribution of density with CT values greater than lung density but less than tumour density match the true case of a tumour moving within lung tissue? The aim of this study was to answer this question, determining the dosimetric impact of using a 4D-CT average reconstruction as the basis for a radiotherapy treatment plan. To achieve this, Monte-Carlo sim ulations were undertaken using GEANT4. The geometry consisted of a tumour (diameter 30 mm) moving with a sinusoidal pattern of amplitude = 20 mm. The tumour's excursion occurs within a lung equivalent volume beyond a chest wall interface. Motion was defined parallel to a 6 MY beam. This was then compared to a single oblate tumour of a magnitude determined by the extremes of the tumour motion. The variable density of the 4DCT average tumour is simulated by a time-weighted average, to achieve the observed density gradient. The generic moving tumour geometry is illustrated in the Figure.
Focused information criterion and model averaging based on weighted composite quantile regression
Xu, Ganggang
2013-08-13
We study the focused information criterion and frequentist model averaging and their application to post-model-selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non-parametric functions approximated by polynomial splines, we show that, under certain conditions, the asymptotic distribution of the frequentist model averaging WCQR-estimator of a focused parameter is a non-linear mixture of normal distributions. This asymptotic distribution is used to construct confidence intervals that achieve the nominal coverage probability. With properly chosen weights, the focused information criterion based WCQR estimators are not only robust to outliers and non-normal residuals but also can achieve efficiency close to the maximum likelihood estimator, without assuming the true error distribution. Simulation studies and a real data analysis are used to illustrate the effectiveness of the proposed procedure. © 2013 Board of the Foundation of the Scandinavian Journal of Statistics..
I PUTU YUDI PRABHADIKA; NI KETUT TARI TASTRAWATI; LUH PUTU IDA HARINI
2018-01-01
Infusion supplies are an important thing that must be considered by the hospital in meeting the needs of patients. This study aims to predict the need for infusion of 0.9% 500 ml of NaCl and 5% 500 ml glucose infusion at Sanglah General Hospital (RSUP) Sanglah so that the hospital can estimate the many infusions needed for the next six months. The forecasting method used in this research is the autoregressive integrated moving average (ARIMA) time series method. The results of this study indi...
A generalization of the preset count moving average algorithm for digital rate meters
International Nuclear Information System (INIS)
Arandjelovic, Vojislav; Koturovic, Aleksandar; Vukanovic, Radomir
2002-01-01
A generalized definition of the preset count moving average algorithm for digital rate meters has been introduced. The algorithm is based on the knowledge of time intervals between successive pulses in random-pulse sequences. The steady state and transient regimes of the algorithm have been characterized. A measure for statistical fluctuations of the successive measurement results has been introduced. The versatility of the generalized algorithm makes it suitable for application in the design of the software of modern measuring/control digital systems
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%.
Liu, Xiaojia; An, Haizhong; Wang, Lijun; Guan, Qing
2017-09-01
The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions.
Meaney, Christopher; Moineddin, Rahim
2014-01-24
In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the
Huang, Lei
2015-01-01
To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409
International Nuclear Information System (INIS)
Ueki, Taro
2010-01-01
The noise propagation of tallies in the Monte Carlo power method can be represented by the autoregressive moving average process of orders p and p-1 (ARMA(p,p-1)], where p is an integer larger than or equal to two. The formula of the autocorrelation of ARMA(p,q), p≥q+1, indicates that ARMA(3,2) fitting is equivalent to lumping the eigenmodes of fluctuation propagation in three modes such as the slow, intermediate and fast attenuation modes. Therefore, ARMA(3,2) fitting was applied to the real standard deviation estimation of fuel assemblies at particular heights. The numerical results show that straightforward ARMA(3,2) fitting is promising but a stability issue must be resolved toward the incorporation in the distributed version of production Monte Carlo codes. The same numerical results reveal that the average performance of ARMA(3,2) fitting is equivalent to that of the batch method in MCNP with a batch size larger than one hundred and smaller than two hundred cycles for a 1100 MWe pressurized water reactor. The bias correction of low lag autocovariances in MVP/GMVP is demonstrated to have the potential of improving the average performance of ARMA(3,2) fitting. (author)
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
International Nuclear Information System (INIS)
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-01-01
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
Shape and depth determinations from second moving average residual self-potential anomalies
International Nuclear Information System (INIS)
Abdelrahman, E M; El-Araby, T M; Essa, K S
2009-01-01
We have developed a semi-automatic method to determine the depth and shape (shape factor) of a buried structure from second moving average residual self-potential anomalies obtained from observed data using filters of successive window lengths. The method involves using a relationship between the depth and the shape to source and a combination of windowed observations. The relationship represents a parametric family of curves (window curves). For a fixed window length, the depth is determined for each shape factor. The computed depths are plotted against the shape factors, representing a continuous monotonically increasing curve. The solution for the shape and depth is read at the common intersection of the window curves. The validity of the method is tested on a synthetic example with and without random errors and on two field examples from Turkey and Germany. In all cases examined, the depth and the shape solutions obtained are in very good agreement with the true ones
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-09-01
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.
Moving Average Filter-Based Phase-Locked Loops: Performance Analysis and Design Guidelines
DEFF Research Database (Denmark)
Golestan, Saeed; Ramezani, Malek; Guerrero, Josep M.
2014-01-01
this challenge, incorporating moving average filter(s) (MAF) into the PLL structure has been proposed in some recent literature. A MAF is a linear-phase finite impulse response filter which can act as an ideal low-pass filter, if certain conditions hold. The main aim of this paper is to present the control...... design guidelines for a typical MAF-based PLL. The paper starts with the general description of MAFs. The main challenge associated with using the MAFs is then explained, and its possible solutions are discussed. The paper then proceeds with a brief overview of the different MAF-based PLLs. In each case......, the PLL block diagram description is shown, the advantages and limitations are briefly discussed, and the tuning approach (if available) is evaluated. The paper then presents two systematic methods to design the control parameters of a typical MAF-based PLL: one for the case of using a proportional...
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.
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
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...... 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...
Statistical aspects of autoregressive-moving average models in the assessment of radon mitigation
International Nuclear Information System (INIS)
Dunn, J.E.; Henschel, D.B.
1989-01-01
Radon values, as reflected by hourly scintillation counts, seem dominated by major, pseudo-periodic, random fluctuations. This methodological paper reports a moderate degree of success in modeling these data using relatively simple autoregressive-moving average models to assess the effectiveness of radon mitigation techniques in existing housing. While accounting for the natural correlation of successive observations, familiar summary statistics such as steady state estimates, standard errors, confidence limits, and tests of hypothesis are produced. The Box-Jenkins approach is used throughout. In particular, intervention analysis provides an objective means of assessing the effectiveness of an active mitigation measure, such as a fan off/on cycle. Occasionally, failure to declare a significant intervention has suggested a means of remedial action in the data collection procedure
A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships
Directory of Open Access Journals (Sweden)
Shuang Guan
2017-10-01
Full Text Available Many of the existing autoregressive moving average (ARMA forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs for a two-factor first-order autoregressive (AR(1 model and forecasting the training data with the AR(1 model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m model. Lastly, we forecasted test data with the ARMA(1,m model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI from 2001 to 2015 and the international gold price from 2000 to 2010.
Modified Exponential Weighted Moving Average (EWMA) Control Chart on Autocorrelation Data
Herdiani, Erna Tri; Fandrilla, Geysa; Sunusi, Nurtiti
2018-03-01
In general, observations of the statistical process control are assumed to be mutually independence. However, this assumption is often violated in practice. Consequently, statistical process controls were developed for interrelated processes, including Shewhart, Cumulative Sum (CUSUM), and exponentially weighted moving average (EWMA) control charts in the data that were autocorrelation. One researcher stated that this chart is not suitable if the same control limits are used in the case of independent variables. For this reason, it is necessary to apply the time series model in building the control chart. A classical control chart for independent variables is usually applied to residual processes. This procedure is permitted provided that residuals are independent. In 1978, Shewhart modification for the autoregressive process was introduced by using the distance between the sample mean and the target value compared to the standard deviation of the autocorrelation process. In this paper we will examine the mean of EWMA for autocorrelation process derived from Montgomery and Patel. Performance to be investigated was investigated by examining Average Run Length (ARL) based on the Markov Chain Method.
Godolphin, E. J.
1980-01-01
It is shown that the estimation procedure of Walker leads to estimates of the parameters of a Gaussian moving average process which are asymptotically equivalent to the maximum likelihood estimates proposed by Whittle and represented by Godolphin.
Average equilibrium charge state of 278113 ions moving in a helium gas
International Nuclear Information System (INIS)
Kaji, D.; Morita, K.; Morimoto, K.
2005-01-01
Difficulty to identify a new heavy element comes from the small production cross section. For example, the production cross section was about 0.5 pb in the case of searching for the 112th element produced by the cold fusion reaction of 208 Pb( 70 Zn,n) 277 ll2. In order to identify heavier elements than element 112, the experimental apparatus with a sensitivity of sub-pico barn level is essentially needed. A gas-filled recoil separator, in general, has a large collection efficiency compared with other recoil separators as seen from the operation principle of a gas-filled recoil separator. One of the most important parameters for a gas-filled recoil separator is the average equilibrium charge state q ave of ions moving in a used gas. This is because the recoil ion can not be properly transported to the focal plane of the separator, if the q ave of an element of interest in a gas is unknown. We have systematically measured equilibrium charge state distributions of heavy ions ( 169 Tm, 208 Pb, 193,209 Bi, 196 Po, 200 At, 203,204 Fr, 212 Ac, 234 Bk, 245 Fm, 254 No, 255 Lr, and 265 Hs) moving in a helium gas by using the gas-filled recoil separator GARIS at RIKEN. Ana then, the empirical formula on q ave of heavy ions in a helium gas was derived as a function of the velocity and the atomic number of an ion on the basis of the Tomas-Fermi model of the atom. The formula was found to be applicable to search for transactinide nuclides of 271 Ds, 272 Rg, and 277 112 produced by cold fusion reactions. Using the formula on q ave , we searched for a new isotope of element 113 produced by the cold fusion reaction of 209 Bi( 70 Zn,n) 278 113. As a result, a decay chain due to an evaporation residue of 278 113 was observed. Recently, we have successfully observed the 2nd decay chain due to an evaporation residue of 278 113. In this report, we will present experimental results in detail, and will also discuss the average equilibrium charge sate of 278 113 in a helium gas by
Directory of Open Access Journals (Sweden)
Carlos Quispe
2013-04-01
Full Text Available El Niño connects globally climate, ecosystems and socio-economic activities. Since 1980 this event has been tried to be predicted, but until now the statistical and dynamical models are insuffi cient. Thus, the objective of the present work was to explore using an autoregressive moving average model the effect of El Niño over the sea surface temperature (TSM off the Peruvian coast. The work involved 5 stages: identifi cation, estimation, diagnostic checking, forecasting and validation. Simple and partial autocorrelation functions (FAC and FACP were used to identify and reformulate the orders of the model parameters, as well as Akaike information criterium (AIC and Schwarz criterium (SC for the selection of the best models during the diagnostic checking. Among the main results the models ARIMA(12,0,11 were proposed, which simulated monthly conditions in agreement with the observed conditions off the Peruvian coast: cold conditions at the end of 2004, and neutral conditions at the beginning of 2005.
Synchronized moving aperture radiation therapy (SMART): average tumour trajectory for lung patients
International Nuclear Information System (INIS)
Neicu, Toni; Shirato, Hiroki; Seppenwoolde, Yvette; Jiang, Steve B
2003-01-01
Synchronized moving aperture radiation therapy (SMART) is a new technique for treating mobile tumours under development at Massachusetts General Hospital (MGH). The basic idea of SMART is to synchronize the moving radiation beam aperture formed by a dynamic multileaf collimator (DMLC) with the tumour motion induced by respiration. SMART is based on the concept of the average tumour trajectory (ATT) exhibited by a tumour during respiration. During the treatment simulation stage, tumour motion is measured and the ATT is derived. Then, the original IMRT MLC leaf sequence is modified using the ATT to compensate for tumour motion. During treatment, the tumour motion is monitored. The treatment starts when leaf motion and tumour motion are synchronized at a specific breathing phase. The treatment will halt when the tumour drifts away from the ATT and will resume when the synchronization between tumour motion and radiation beam is re-established. In this paper, we present a method to derive the ATT from measured tumour trajectory data. We also investigate the validity of the ATT concept for lung tumours during normal breathing. The lung tumour trajectory data were acquired during actual radiotherapy sessions using a real-time tumour-tracking system. SMART treatment is simulated by assuming that the radiation beam follows the derived ATT and the tumour follows the measured trajectory. In simulation, the treatment starts at exhale phase. The duty cycle of SMART delivery was calculated for various treatment times and gating thresholds, as well as for various exhale phases where the treatment begins. The simulation results show that in the case of free breathing, for 4 out of 11 lung datasets with tumour motion greater than 1 cm from peak to peak, the error in tumour tracking can be controlled to within a couple of millimetres while maintaining a reasonable delivery efficiency. That is to say, without any breath coaching/control, the ATT is a valid concept for some lung
Tienfuan Kerh; Shin-Hung Wu
2017-01-01
Forecasting of a typhoon moving path may help to evaluate the potential negative impacts in the neighbourhood areas along the moving path. This study proposed a work of using both static and dynamic neural network models to link a time series of typhoon track parameters including longitude and latitude of the typhoon central location, cyclonic radius, central wind speed, and typhoon moving speed. Based on the historical records of 100 typhoons, the performances of neural network models are ev...
Energy Technology Data Exchange (ETDEWEB)
Yoon, Jai-Woong; Sawant, Amit; Suh, Yelin; Cho, Byung-Chul; Suh, Tae-Suk; Keall, Paul [Department of Biomedical Engineering, College of Medicine, Catholic University of Korea, Seoul, Korea 131-700 and Research Institute of Biomedical Engineering, Catholic University of Korea, Seoul, 131-700 (Korea, Republic of); Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States); Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States) and Department of Radiation Oncology, Asan Medical Center, Seoul, 138-736 (Korea, Republic of); Department of Biomedical Engineering, College of Medicine, Catholic University of Korea, Seoul, 131-700 and Research Institute of Biomedical Engineering, Catholic University of Korea, Seoul, 131-700 (Korea, Republic of); Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States) and Radiation Physics Laboratory, Sydney Medical School, University of Sydney, 2006 (Australia)
2011-07-15
Purpose: In dynamic multileaf collimator (MLC) motion tracking with complex intensity-modulated radiation therapy (IMRT) fields, target motion perpendicular to the MLC leaf travel direction can cause beam holds, which increase beam delivery time by up to a factor of 4. As a means to balance delivery efficiency and accuracy, a moving average algorithm was incorporated into a dynamic MLC motion tracking system (i.e., moving average tracking) to account for target motion perpendicular to the MLC leaf travel direction. The experimental investigation of the moving average algorithm compared with real-time tracking and no compensation beam delivery is described. Methods: The properties of the moving average algorithm were measured and compared with those of real-time tracking (dynamic MLC motion tracking accounting for both target motion parallel and perpendicular to the leaf travel direction) and no compensation beam delivery. The algorithm was investigated using a synthetic motion trace with a baseline drift and four patient-measured 3D tumor motion traces representing regular and irregular motions with varying baseline drifts. Each motion trace was reproduced by a moving platform. The delivery efficiency, geometric accuracy, and dosimetric accuracy were evaluated for conformal, step-and-shoot IMRT, and dynamic sliding window IMRT treatment plans using the synthetic and patient motion traces. The dosimetric accuracy was quantified via a {gamma}-test with a 3%/3 mm criterion. Results: The delivery efficiency ranged from 89 to 100% for moving average tracking, 26%-100% for real-time tracking, and 100% (by definition) for no compensation. The root-mean-square geometric error ranged from 3.2 to 4.0 mm for moving average tracking, 0.7-1.1 mm for real-time tracking, and 3.7-7.2 mm for no compensation. The percentage of dosimetric points failing the {gamma}-test ranged from 4 to 30% for moving average tracking, 0%-23% for real-time tracking, and 10%-47% for no compensation
Yoon, Jai-Woong; Sawant, Amit; Suh, Yelin; Cho, Byung-Chul; Suh, Tae-Suk; Keall, Paul
2011-07-01
In dynamic multileaf collimator (MLC) motion tracking with complex intensity-modulated radiation therapy (IMRT) fields, target motion perpendicular to the MLC leaf travel direction can cause beam holds, which increase beam delivery time by up to a factor of 4. As a means to balance delivery efficiency and accuracy, a moving average algorithm was incorporated into a dynamic MLC motion tracking system (i.e., moving average tracking) to account for target motion perpendicular to the MLC leaf travel direction. The experimental investigation of the moving average algorithm compared with real-time tracking and no compensation beam delivery is described. The properties of the moving average algorithm were measured and compared with those of real-time tracking (dynamic MLC motion tracking accounting for both target motion parallel and perpendicular to the leaf travel direction) and no compensation beam delivery. The algorithm was investigated using a synthetic motion trace with a baseline drift and four patient-measured 3D tumor motion traces representing regular and irregular motions with varying baseline drifts. Each motion trace was reproduced by a moving platform. The delivery efficiency, geometric accuracy, and dosimetric accuracy were evaluated for conformal, step-and-shoot IMRT, and dynamic sliding window IMRT treatment plans using the synthetic and patient motion traces. The dosimetric accuracy was quantified via a tgamma-test with a 3%/3 mm criterion. The delivery efficiency ranged from 89 to 100% for moving average tracking, 26%-100% for real-time tracking, and 100% (by definition) for no compensation. The root-mean-square geometric error ranged from 3.2 to 4.0 mm for moving average tracking, 0.7-1.1 mm for real-time tracking, and 3.7-7.2 mm for no compensation. The percentage of dosimetric points failing the gamma-test ranged from 4 to 30% for moving average tracking, 0%-23% for real-time tracking, and 10%-47% for no compensation. The delivery efficiency of
Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection
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Heng Liu
2017-01-01
Full Text Available In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Pedersen, Asger Roer; Herskin, Mette S
2010-01-01
in sequences of approximately 20 s for the period of 10 min. Afterwards the cows were stimulated to move/lift the legs while standing in a cubicle. The behaviour was video recorded, and the recordings were analysed second by second for walking and standing behaviour as well as the number of steps taken....... Various algorithms for predicting walking/standing status were compared. The algorithms were all based on a limit of a moving average calculated by using one of two outputs of the accelerometer, either a motion index or a step count, and applied over periods of 3 or 5 s. Furthermore, we investigated...... the effect of additionally applying the rule: a walking period must last at least 5 s. The results indicate that the lowest misclassification rate (10%) of walking and standing was obtained based on the step count with a moving average of 3 s and with the rule applied. However, the rate of misclassification...
On critical cases in limit theory for stationary increments Lévy driven moving averages
DEFF Research Database (Denmark)
Basse-O'Connor, Andreas; Podolskij, Mark
averages. The limit theory heavily depends on the interplay between the given order of the increments, the considered power, the Blumenthal-Getoor index of the driving pure jump Lévy process L and the behavior of the kernel function g at 0. In this work we will study the critical cases, which were...
ANALISIS CURAH HUJAN DAN DEBIT MODEL SWAT DENGAN METODE MOVING AVERAGE DI DAS CILIWUNG HULU
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Defri Satiya Zuma
2017-09-01
Full Text Available Watershed can be regarded as a hydrological system that has a function in transforming rainwater as an input into outputs such as flow and sediment. The transformation of inputs into outputs has specific forms and properties. The transformation involves many processes, including processes occurred on the surface of the land, river basins, in soil and aquifer. This study aimed to apply the SWAT model in Ciliwung Hulu Watershed, asses the effect of average rainfall on 3 days, 5 days, 7 days and 10 days of the hydrological characteristics in Ciliwung Hulu Watershed. The correlation coefficient (r between rainfall and discharge was positive, it indicated that there was an unidirectional relationship between rainfall and discharge in the upstream, midstream and downstream of the watershed. The upper limit ratio of discharge had a downward trend from upstream to downstream, while the lower limit ratio of discharge had an upward trend from upstream to downstream. It showed that the discharge peak in Ciliwung Hulu Watershed from upstream to downstream had a downward trend while the baseflow from upstream to downstream had an upward trend. It showed that the upstream of Ciliwung Hulu Watershed had the highest ratio of discharge peak and baseflow so it needs the soil and water conservations and technical civil measures. The discussion concluded that the SWAT model could be well applied in Ciliwung Hulu Watershed, the most affecting average rainfall on the hydrological characteristics was the average rainfall of 10 days. On average rainfall of 10 days, all components had contributed maximally for river discharge.
Canada’s 2010 Tax Competitiveness Ranking: Moving to the Average but Biased Against Services
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Duanjie Chen
2011-02-01
Full Text Available For the first time since 1975 (the year Canada’s marginal effective tax rates were first measured, Canada has become the most tax-competitive country among G-7 states with respect to taxation of capital investment. Even more remarkably, Canada accomplished this feat within a mere six years, having previously been the least taxcompetitive G-7 member. Even in comparison to strongly growing emerging economies, Canada’s 2010 marginal effective tax rate on capital is still above average. The planned reductions in federal and provincial corporate taxes by 2013 will reduce Canada’s effective tax rate on new investments to 18.4 percent, below the Organization for Economic Co-operation and Development (OECD 2010 average and close to the average of the 50 non-OECD countries studied. This remarkable change in Canada’s tax competitiveness must be maintained in the coming years, as countries are continually reducing their business taxation despite the recent fiscal pressures arising from the 2008-9 downturn in the world economy. Many countries have forged ahead with significant reforms designed to increase tax competitiveness and improve tax neutrality including Greece, Israel, Japan, New Zealand, Taiwan and the United Kingdom. The continuing bias in Canada’s corporate income tax structure favouring manufacturing and processing business warrants close scrutiny. Measured by the difference between the marginal effective tax rate on capital between manufacturing and the broad range of service sectors, Canada has the greatest gap in tax burdens between manufacturing and services among OECD countries. Surprisingly, preferential tax treatment (such as fast write-off and investment tax credits favouring only manufacturing and processing activities has become the norm in Canada, although it does not exist in most developed economies.
Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models
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Jiefang Dong
2016-12-01
Full Text Available With the rapid economic development of the Xinjiang Uygur Autonomous Region, the area’s transport sector has witnessed significant growth, which in turn has led to a large increase in carbon dioxide emissions. As such, calculating of the carbon footprint of Xinjiang’s transportation sector and probing the driving factors of carbon dioxide emissions are of great significance to the region’s energy conservation and environmental protection. This paper provides an account of the growth in the carbon emissions of Xinjiang’s transportation sector during the period from 1989 to 2012. We also analyze the transportation sector’s trends and historical evolution. Combined with the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology model and ridge regression, this study further quantitatively analyzes the factors that influence the carbon emissions of Xinjiang’s transportation sector. The results indicate the following: (1 the total carbon emissions and per capita carbon emissions of Xinjiang’s transportation sector both continued to rise rapidly during this period; their average annual growth rates were 10.8% and 9.1%, respectively; (2 the carbon emissions of the transportation sector come mainly from the consumption of diesel and gasoline, which accounted for an average of 36.2% and 2.6% of carbon emissions, respectively; in addition, the overall carbon emission intensity of the transportation sector showed an “S”-pattern trend within the study period; (3 population density plays a dominant role in increasing carbon dioxide emissions. Population is then followed by per capita GDP and, finally, energy intensity. Cargo turnover has a more significant potential impact on and role in emission reduction than do private vehicles. This is because road freight is the primary form of transportation used across Xinjiang, and this form of transportation has low energy efficiency. These findings have important
Consensus in averager-copier-voter networks of moving dynamical agents
Shang, Yilun
2017-02-01
This paper deals with a hybrid opinion dynamics comprising averager, copier, and voter agents, which ramble as random walkers on a spatial network. Agents exchange information following some deterministic and stochastic protocols if they reside at the same site in the same time. Based on stochastic stability of Markov chains, sufficient conditions guaranteeing consensus in the sense of almost sure convergence have been obtained. The ultimate consensus state is identified in the form of an ergodicity result. Simulation studies are performed to validate the effectiveness and availability of our theoretical results. The existence/non-existence of voters and the proportion of them are unveiled to play key roles during the consensus-reaching process.
Tan, K. L.; Chong, Z. L.; Khoo, M. B. C.; Teoh, W. L.; Teh, S. Y.
2017-09-01
Quality control is crucial in a wide variety of fields, as it can help to satisfy customers’ needs and requirements by enhancing and improving the products and services to a superior quality level. The EWMA median chart was proposed as a useful alternative to the EWMA \\bar{X} chart because the median-type chart is robust against contamination, outliers or small deviation from the normality assumption compared to the traditional \\bar{X}-type chart. To provide a complete understanding of the run-length distribution, the percentiles of the run-length distribution should be investigated rather than depending solely on the average run length (ARL) performance measure. This is because interpretation depending on the ARL alone can be misleading, as the process mean shifts change according to the skewness and shape of the run-length distribution, varying from almost symmetric when the magnitude of the mean shift is large, to highly right-skewed when the process is in-control (IC) or slightly out-of-control (OOC). Before computing the percentiles of the run-length distribution, optimal parameters of the EWMA median chart will be obtained by minimizing the OOC ARL, while retaining the IC ARL at a desired value.
International Nuclear Information System (INIS)
Benth, Fred Espen; Taib, Che Mohd Imran Che
2013-01-01
We extend the concept of half life of an Ornstein–Uhlenbeck process to Lévy-driven continuous-time autoregressive moving average processes with stochastic volatility. The half life becomes state dependent, and we analyze its properties in terms of the characteristics of the process. An empirical example based on daily temperatures observed in Petaling Jaya, Malaysia, is presented, where the proposed model is estimated and the distribution of the half life is simulated. The stationarity of the dynamics yield futures prices which asymptotically tend to constant at an exponential rate when time to maturity goes to infinity. The rate is characterized by the eigenvalues of the dynamics. An alternative description of this convergence can be given in terms of our concept of half life. - Highlights: • The concept of half life is extended to Levy-driven continuous time autoregressive moving average processes • The dynamics of Malaysian temperatures are modeled using a continuous time autoregressive model with stochastic volatility • Forward prices on temperature become constant when time to maturity tends to infinity • Convergence in time to maturity is at an exponential rate given by the eigenvalues of the model temperature model
International Nuclear Information System (INIS)
Pollock, D.A.; Gunst, R.F.; Schucany, W.R.
1994-01-01
SSC Collider Dipole Magnet field quality specifications define limits of variation for the population mean (Systematic) and standard deviation (RMS deviation) of allowed and unallowed multipole coefficients generated by the full collection of dipole magnets throughout the Collider operating cycle. A fundamental Quality Control issue is how to determine the acceptability of individual magnets during production, in other words taken one at a time and compared to the population parameters. Provided that the normal distribution assumptions hold, the random variation of multipoles for individual magnets may be evaluated by comparing the measured results to ± 3 x RMS tolerance, centered on the design nominal. To evaluate the local and cumulative systematic variation of the magnets against the distribution tolerance, individual magnet results need to be combined with others that come before it. This paper demonstrates a Statistical Quality Control method (the Unweighted Moving Average control chart) to evaluate individual magnet performance and process stability against population tolerances. The DESY/HERA Dipole cold skew quadrupole measurements for magnets in production order are used to evaluate non-stationarity of the mean over time for the cumulative set of magnets, as well as for a moving sample
Directory of Open Access Journals (Sweden)
Menxi Xie
2017-06-01
Full Text Available High performance synchronization methord is citical for grid connected power converter. For single-phase system, power based phase-locked loop(pPLL uses a multiplier as phase detector(PD. As single-phase grid voltage is distorted, the phase error information contains ac disturbances oscillating at integer multiples of fundamental frequency which lead to detection error. This paper presents a new scheme based on moving average filter(MAF applied in-loop of pPLL. The signal characteristic of phase error is dissussed in detail. A predictive rule is adopted to compensate the delay induced by MAF, thus achieving fast dynamic response. In the case of frequency deviate from nomimal, estimated frequency is fed back to adjust the filter window length of MAF and buffer size of predictive rule. Simulation and experimental results show that proposed PLL achieves good performance under adverse grid conditions.
Directory of Open Access Journals (Sweden)
Amjad Ali
2015-01-01
Full Text Available A new simple moving voltage average (SMVA technique with fixed step direct control incremental conductance method is introduced to reduce solar photovoltaic voltage (VPV oscillation under nonuniform solar irradiation conditions. To evaluate and validate the performance of the proposed SMVA method in comparison with the conventional fixed step direct control incremental conductance method under extreme conditions, different scenarios were simulated. Simulation results show that in most cases SMVA gives better results with more stability as compared to traditional fixed step direct control INC with faster tracking system along with reduction in sustained oscillations and possesses fast steady state response and robustness. The steady state oscillations are almost eliminated because of extremely small dP/dV around maximum power (MP, which verify that the proposed method is suitable for standalone PV system under extreme weather conditions not only in terms of bus voltage stability but also in overall system efficiency.
International Nuclear Information System (INIS)
Marseguerra, M.; Minoggio, S.; Rossi, A.; Zio, E.
1992-01-01
The correlated noise affecting many industrial plants under stationary or cyclo-stationary conditions - nuclear reactors included -has been successfully modeled by autoregressive moving average (ARMA) due to the versatility of this technique. The relatively recent neural network methods have similar features and much effort is being devoted to exploring their usefulness in forecasting and control. Identifying a signal by means of an ARMA model gives rise to the problem of selecting its correct order. Similar difficulties must be faced when applying neural network methods and, specifically, particular care must be given to the setting up of the appropriate network topology, the data normalization procedure and the learning code. In the present paper the capability of some neural networks of learning ARMA and seasonal ARMA processes is investigated. The results of the tested cases look promising since they indicate that the neural networks learn the underlying process with relative ease so that their forecasting capability may represent a convenient fault diagnosis tool. (Author)
International Nuclear Information System (INIS)
McCall, K C; Jeraj, R
2007-01-01
A new approach to the problem of modelling and predicting respiration motion has been implemented. This is a dual-component model, which describes the respiration motion as a non-periodic time series superimposed onto a periodic waveform. A periodic autoregressive moving average algorithm has been used to define a mathematical model of the periodic and non-periodic components of the respiration motion. The periodic components of the motion were found by projecting multiple inhale-exhale cycles onto a common subspace. The component of the respiration signal that is left after removing this periodicity is a partially autocorrelated time series and was modelled as an autoregressive moving average (ARMA) process. The accuracy of the periodic ARMA model with respect to fluctuation in amplitude and variation in length of cycles has been assessed. A respiration phantom was developed to simulate the inter-cycle variations seen in free-breathing and coached respiration patterns. At ±14% variability in cycle length and maximum amplitude of motion, the prediction errors were 4.8% of the total motion extent for a 0.5 s ahead prediction, and 9.4% at 1.0 s lag. The prediction errors increased to 11.6% at 0.5 s and 21.6% at 1.0 s when the respiration pattern had ±34% variations in both these parameters. Our results have shown that the accuracy of the periodic ARMA model is more strongly dependent on the variations in cycle length than the amplitude of the respiration cycles
The Prediction of Exchange Rates with the Use of Auto-Regressive Integrated Moving-Average Models
Directory of Open Access Journals (Sweden)
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.
Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E
2017-06-14
Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.
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%.
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.
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.
Mohebbi, Mohammadreza; Wolfe, Rory; Forbes, Andrew
2014-01-01
This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference. PMID:24413702
Directory of Open Access Journals (Sweden)
Rodrigo Silva Vidotto
2009-04-01
Full Text Available The increase in the number of investors at Bovespa since 2000 is due to stabilized inflation and falling interest rates. The use of tools that assist investors in selling and buying stocks is very important in a competitive and risky market. The technical analysis of stocks is used to search for trends in the movements of share prices and therefore indicate a suitable moment to buy or sell stocks. Among these technical indicators is the Moving Average Convergence-Divergence [MACD], which uses the concept of moving average in its equation and is considered by financial analysts as a simple tool to operate and analyze. This article aims to assess the effectiveness of the use of the MACD to indicate the moment to purchase and sell stocks in five companies – selected at random – a total of ninety companies in the Bovespa New Market and analyze the profitability gained during 2006, taking as a reference the valorization of the Ibovespa exchange in that year. The results show that the cumulative average return of the five companies was of 26.7% against a cumulative average return of 0.90% for Ibovespa.
International Nuclear Information System (INIS)
Nakamura, Mitsuhiro; Miyabe, Yuki; Matsuo, Yukinori; Kamomae, Takeshi; Nakata, Manabu; Yano, Shinsuke; Sawada, Akira; Mizowaki, Takashi; Hiraoka, Masahiro
2012-01-01
The purpose of this study was to experimentally assess the validity of heterogeneity-corrected dose-volume prescription on respiratory-averaged computed tomography (RACT) images in stereotactic body radiotherapy (SBRT) for moving tumors. Four-dimensional computed tomography (CT) data were acquired while a dynamic anthropomorphic thorax phantom with a solitary target moved. Motion pattern was based on cos (t) with a constant respiration period of 4.0 sec along the longitudinal axis of the CT couch. The extent of motion (A 1 ) was set in the range of 0.0–12.0 mm at 3.0-mm intervals. Treatment planning with the heterogeneity-corrected dose-volume prescription was designed on RACT images. A new commercially available Monte Carlo algorithm of well-commissioned 6-MV photon beam was used for dose calculation. Dosimetric effects of intrafractional tumor motion were then investigated experimentally under the same conditions as 4D CT simulation using the dynamic anthropomorphic thorax phantom, films, and an ionization chamber. The passing rate of γ index was 98.18%, with the criteria of 3 mm/3%. The dose error between the planned and the measured isocenter dose in moving condition was within ± 0.7%. From the dose area histograms on the film, the mean ± standard deviation of the dose covering 100% of the cross section of the target was 102.32 ± 1.20% (range, 100.59–103.49%). By contrast, the irradiated areas receiving more than 95% dose for A 1 = 12 mm were 1.46 and 1.33 times larger than those for A 1 = 0 mm in the coronal and sagittal planes, respectively. This phantom study demonstrated that the cross section of the target received 100% dose under moving conditions in both the coronal and sagittal planes, suggesting that the heterogeneity-corrected dose-volume prescription on RACT images is acceptable in SBRT for moving tumors.
Wilson, Robert M.
2001-01-01
Since 1750, the number of cataclysmic volcanic eruptions (volcanic explosivity index (VEI)>=4) per decade spans 2-11, with 96 percent located in the tropics and extra-tropical Northern Hemisphere. A two-point moving average of the volcanic time series has higher values since the 1860's than before, being 8.00 in the 1910's (the highest value) and 6.50 in the 1980's, the highest since the 1910's peak. Because of the usual behavior of the first difference of the two-point moving averages, one infers that its value for the 1990's will measure approximately 6.50 +/- 1, implying that approximately 7 +/- 4 cataclysmic volcanic eruptions should be expected during the present decade (2000-2009). Because cataclysmic volcanic eruptions (especially those having VEI>=5) nearly always have been associated with short-term episodes of global cooling, the occurrence of even one might confuse our ability to assess the effects of global warming. Poisson probability distributions reveal that the probability of one or more events with a VEI>=4 within the next ten years is >99 percent. It is approximately 49 percent for an event with a VEI>=5, and 18 percent for an event with a VEI>=6. Hence, the likelihood that a climatically significant volcanic eruption will occur within the next ten years appears reasonably high.
Passos, Cláudia P; Cardoso, Susana M; Barros, António S; Silva, Carlos M; Coimbra, Manuel A
2010-02-28
Fourier transform infrared (FTIR) spectroscopy has being emphasised as a widespread technique in the quick assess of food components. In this work, procyanidins were extracted with methanol and acetone/water from the seeds of white and red grape varieties. A fractionation by graded methanol/chloroform precipitations allowed to obtain 26 samples that were characterised using thiolysis as pre-treatment followed by HPLC-UV and MS detection. The average degree of polymerisation (DPn) of the procyanidins in the samples ranged from 2 to 11 flavan-3-ol residues. FTIR spectroscopy within the wavenumbers region of 1800-700 cm(-1) allowed to build a partial least squares (PLS1) regression model with 8 latent variables (LVs) for the estimation of the DPn, giving a RMSECV of 11.7%, with a R(2) of 0.91 and a RMSEP of 2.58. The application of orthogonal projection to latent structures (O-PLS1) clarifies the interpretation of the regression model vectors. Moreover, the O-PLS procedure has removed 88% of non-correlated variations with the DPn, allowing to relate the increase of the absorbance peaks at 1203 and 1099 cm(-1) with the increase of the DPn due to the higher proportion of substitutions in the aromatic ring of the polymerised procyanidin molecules. Copyright 2009 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Kowal Robert
2016-12-01
Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Multiple areas of research function within its scope. One of the many fundamental questions in the model concerns proving the efficiency of the most commonly used OLS estimators and examining their properties. In the literature of the subject one can find taking back to this scope and certain solutions in that regard. Methodically, they are borrowed from the multiple regression model or also from a boundary partial model. Not everything, however, is here complete and consistent. In the paper a completely new scheme is proposed, based on the implementation of the Cauchy-Schwarz inequality in the arrangement of the constraint aggregated from calibrated appropriately secondary constraints of unbiasedness which in a result of choice the appropriate calibrator for each variable directly leads to showing this property. A separate range-is a matter of choice of such a calibrator. These deliberations, on account of the volume and kinds of the calibration, were divided into a few parts. In the one the efficiency of OLS estimators is proven in a mixed scheme of the calibration by averages, that is preliminary, and in the most basic frames of the proposed methodology. In these frames the future outlines and general premises constituting the base of more distant generalizations are being created.
International Nuclear Information System (INIS)
Fehlau, P.E.
1993-01-01
The author compared a recursive digital filter proposed as a detection method for French special nuclear material monitors with the author's detection methods, which employ a moving-average scaler or a sequential probability-ratio test. Each of these nine test subjects repeatedly carried a test source through a walk-through portal monitor that had the same nuisance-alarm rate with each method. He found that the average detection probability for the test source is also the same for each method. However, the recursive digital filter may have on drawback: its exponentially decreasing response to past radiation intensity prolongs the impact of any interference from radiation sources of radiation-producing machinery. He also examined the influence of each test subject on the monitor's operation by measuring individual attenuation factors for background and source radiation, then ranked the subjects' attenuation factors against their individual probabilities for detecting the test source. The one inconsistent ranking was probably caused by that subject's unusually long stride when passing through the portal
International Nuclear Information System (INIS)
Murase, Kenya; Yamazaki, Youichi; Shinohara, Masaaki
2003-01-01
The purpose of this study was to investigate the feasibility of the autoregressive moving average (ARMA) model for quantification of cerebral blood flow (CBF) with dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) in comparison with deconvolution analysis based on singular value decomposition (DA-SVD). Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various CBFs, cerebral blood volumes and signal-to-noise ratios (SNRs) for three different types of residue function (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The ARMA model and DA-SVD were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIFs, and the estimated values were compared with the assumed values. We found that the CBF value estimated by the ARMA model was more sensitive to the SNR and the delay in AIF than that obtained by DA-SVD. Although the ARMA model considerably overestimated CBF at low SNRs, it estimated the CBF more accurately than did DA-SVD at high SNRs for the exponential or triangular residue function. We believe this study will contribute to an understanding of the usefulness and limitations of the ARMA model when applied to quantification of CBF with DSC-MRI. (author)
Akita, Yasuyuki; Chen, Jiu-Chiuan; Serre, Marc L
2012-09-01
Geostatistical methods are widely used in estimating long-term exposures for epidemiological studies on air pollution, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and the uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian maximum entropy (BME) method and applied this framework to estimate fine particulate matter (PM(2.5)) yearly average concentrations over the contiguous US. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingness in the air-monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM(2.5) data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM(2.5). Moreover, the MWBME method further reduces the MSE by 8.4-43.7%, with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM(2.5) across large geographical domains with expected spatial non-stationarity.
Akita, Yasuyuki; Chen, Jiu-Chiuan; Serre, Marc L.
2013-01-01
Geostatistical methods are widely used in estimating long-term exposures for air pollution epidemiological studies, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian Maximum Entropy (BME) method and applied this framework to estimate fine particulate matter (PM2.5) yearly average concentrations over the contiguous U.S. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingnees in the air monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM2.5 data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM2.5. Moreover, the MWBME method further reduces the MSE by 8.4% to 43.7% with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM2.5 across large geographical domains with expected spatial non-stationarity. PMID:22739679
Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing
2017-02-01
UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.
Directory of Open Access Journals (Sweden)
S. P. Arunachalam
2018-01-01
Full Text Available Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR and atrial fibrillation (AF was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.
Directory of Open Access Journals (Sweden)
Agus Supriatna
2017-11-01
Full Text Available The tourism sector is one of the contributors of foreign exchange is quite influential in improving the economy of Indonesia. The development of this sector will have a positive impact, including employment opportunities and opportunities for entrepreneurship in various industries such as adventure tourism, craft or hospitality. The beauty and natural resources owned by Indonesia become a tourist attraction for domestic and foreign tourists. One of the many tourist destination is the island of Bali. The island of Bali is not only famous for its natural, cultural diversity and arts but there are also add the value of tourism. In 2015 the increase in the number of tourist arrivals amounted to 6.24% from the previous year. In improving the quality of services, facing a surge of visitors, or prepare a strategy in attracting tourists need a prediction of arrival so that planning can be more efficient and effective. This research used Holt Winter's method and Seasonal Autoregressive Integrated Moving Average (SARIMA method to predict tourist arrivals. Based on data of foreign tourist arrivals who visited the Bali island in January 2007 until June 2016, the result of Holt Winter's method with parameter values α=0.1 ,β=0.1 ,γ=0.3 has an error MAPE is 6,171873. While the result of SARIMA method with (0,1,1〖(1,0,0〗12 model has an error MAPE is 5,788615 and it can be concluded that SARIMA method is better. Keywords: Foreign Tourist, Prediction, Bali Island, Holt-Winter’s, SARIMA.
Earnest, Arul; Chen, Mark I; Ng, Donald; Sin, Leo Yee
2005-05-11
The main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. We found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.
Directory of Open Access Journals (Sweden)
Earnest Arul
2005-05-01
Full Text Available Abstract Background The main objective of this study is to apply autoregressive integrated moving average (ARIMA models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. Methods This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. Results We found that the ARIMA (1,0,3 model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. Conclusion ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious
Time Series ARIMA Models of Undergraduate Grade Point Average.
Rogers, Bruce G.
The Auto-Regressive Integrated Moving Average (ARIMA) Models, often referred to as Box-Jenkins models, are regression methods for analyzing sequential dependent observations with large amounts of data. The Box-Jenkins approach, a three-stage procedure consisting of identification, estimation and diagnosis, was used to select the most appropriate…
Noftle, Erik E; Fleeson, William
2010-03-01
In 3 intensive cross-sectional studies, age differences in behavior averages and variabilities were examined. Three questions were posed: Does variability differ among age groups? Does the sizable variability in young adulthood persist throughout the life span? Do past conclusions about trait development, based on trait questionnaires, hold up when actual behavior is examined? Three groups participated: young adults (18-23 years), middle-aged adults (35-55 years), and older adults (65-81 years). In 2 experience-sampling studies, participants reported their current behavior multiple times per day for 1- or 2-week spans. In a 3rd study, participants interacted in standardized laboratory activities on 8 occasions. First, results revealed a sizable amount of intraindividual variability in behavior for all adult groups, with average within-person standard deviations ranging from about half a point to well over 1 point on 6-point scales. Second, older adults were most variable in Openness, whereas young adults were most variable in Agreeableness and Emotional Stability. Third, most specific patterns of maturation-related age differences in actual behavior were more greatly pronounced and differently patterned than those revealed by the trait questionnaire method. When participants interacted in standardized situations, personality differences between young adults and middle-aged adults were larger, and older adults exhibited a more positive personality profile than they exhibited in their everyday lives.
Sánchez-Ortuño, M Montserrat; Carney, Colleen E; Edinger, Jack D; Wyatt, James K; Harris, Andrea
2011-04-01
We explored differences between individuals with DSM-IV-TR diagnoses of primary insomnia (PI) and insomnia related to a mental disorder (IMD) by using serial measurements of self-reported sleep variables (sleep onset latency, SOL; wake after sleep onset, WASO; total sleep time, TST; sleep efficiency, SE), and visual analogue scale ratings of 2 forms of bedtime arousal (cognitive and emotional). Furthermore, we sought to examine the relationship between sleep and arousal within each diagnostic subgroup. Between-group and within-group comparisons. Duke and Rush University Medical Centers, USA. One hundred eighty-seven insomnia sufferers (126 women, average age 47.15 years) diagnosed by sleep specialists at 2 sleep centers as PI patients (n=126) and IMD patients (n=61). N/A. Multilevel models for sleep measures indicated that IMD displayed significantly more instability across nights in their TST (i.e., larger changes) than did PI patients. With respect to pre-sleep arousal, IMD patients exhibited higher mean levels of emotional arousal, as well as more instability on the nightly ratings of this measure. Within the PI group, correlational analyses revealed a moderate relationship between the 2 arousal variables and SOL (r values 0.29 and 0.26), whereas the corresponding correlations were negligible and statistically nonsignificant in the IMD group. We found a number of differences on nighttime variables between those diagnosed with primary insomnia and those diagnosed with insomnia related to a mental disorder. These differences imply different perpetuating mechanisms involved in their ongoing sleep difficulties. Additionally, they support the categorical distinctiveness and the concurrent validity of these insomnia subtypes.
Poullis, Michael
2014-11-01
EuroSCORE II, despite improving on the original EuroSCORE system, has not solved all the calibration and predictability issues. Recursive, non-linear and mixed recursive and non-linear regression analysis were assessed with regard to sensitivity, specificity and predictability of the original EuroSCORE and EuroSCORE II systems. The original logistic EuroSCORE, EuroSCORE II and recursive, non-linear and mixed recursive and non-linear regression analyses of these risk models were assessed via receiver operator characteristic curves (ROC) and Hosmer-Lemeshow statistic analysis with regard to the accuracy of predicting in-hospital mortality. Analysis was performed for isolated coronary artery bypass grafts (CABGs) (n = 2913), aortic valve replacement (AVR) (n = 814), mitral valve surgery (n = 340), combined AVR and CABG (n = 517), aortic (n = 350), miscellaneous cases (n = 642), and combinations of the above cases (n = 5576). The original EuroSCORE had an ROC below 0.7 for isolated AVR and combined AVR and CABG. None of the methods described increased the ROC above 0.7. The EuroSCORE II risk model had an ROC below 0.7 for isolated AVR only. Recursive regression, non-linear regression, and mixed recursive and non-linear regression all increased the ROC above 0.7 for isolated AVR. The original EuroSCORE had a Hosmer-Lemeshow statistic that was above 0.05 for all patients and the subgroups analysed. All of the techniques markedly increased the Hosmer-Lemeshow statistic. The EuroSCORE II risk model had a Hosmer-Lemeshow statistic that was significant for all patients (P linear regression failed to improve on the original Hosmer-Lemeshow statistic. The mixed recursive and non-linear regression using the EuroSCORE II risk model was the only model that produced an ROC of 0.7 or above for all patients and procedures and had a Hosmer-Lemeshow statistic that was highly non-significant. The original EuroSCORE and the EuroSCORE II risk models do not have adequate ROC and Hosmer
U.S. Department of Health & Human Services — A list of a variety of averages for each state or territory as well as the national average, including each quality measure, staffing, fine amount and number of...
Wu, Yu-Jie; Lin, Guan-Wei
2017-04-01
Since 1999, Taiwan has experienced a rapid rise in the number of landslides, and the number even reached a peak after the 2009 Typhoon Morakot. Although it is proved that the ground-motion signals induced by slope processes could be recorded by seismograph, it is difficult to be distinguished from continuous seismic records due to the lack of distinct P and S waves. In this study, we combine three common seismic detectors including the short-term average/long-term average (STA/LTA) approach, and two diagnostic functions of moving average and scintillation index. Based on these detectors, we have established an auto-detection algorithm of landslide-quakes and the detection thresholds are defined to distinguish landslide-quake from earthquakes and background noises. To further improve the proposed detection algorithm, we apply it to seismic archives recorded by Broadband Array in Taiwan for Seismology (BATS) during the 2009 Typhoon Morakots and consequently the discrete landslide-quakes detected by the automatic algorithm are located. The detection algorithm show that the landslide-detection results are consistent with that of visual inspection and hence can be used to automatically monitor landslide-quakes.
Forecasting daily meteorological time series using ARIMA and regression models
Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir
2018-04-01
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
and Multinomial Logistic Regression
African Journals Online (AJOL)
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Institute of Scientific and Technical Information of China (English)
Guo Yan
2007-01-01
@@ China has already become the world's largest manufacturer of cement,copper and steel.Chinese producers have moved onto the world stage and dominated the global consumer market from textiles to electronics with amazing speed and efficiency.
Estimation of pure moving average vector models | Usoro ...
African Journals Online (AJOL)
International Journal of Natural and Applied Sciences. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 3, No 3 (2007) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register. DOWNLOAD FULL TEXT ...
on the performance of Autoregressive Moving Average Polynomial
African Journals Online (AJOL)
Timothy Ademakinwa
estimated using least squares and Newton Raphson iterative methods. To determine the order of the ... r is the degree of polynomial while j is the number of lag of the ..... use a real time series dataset, monthly rainfall and temperature series ...
Dynamic Regression Intervention Modeling for the Malaysian Daily Load
Directory of Open Access Journals (Sweden)
Fadhilah Abdrazak
2014-05-01
Full Text Available Malaysia is a unique country due to having both fixed and moving holidays. These moving holidays may overlap with other fixed holidays and therefore, increase the complexity of the load forecasting activities. The errors due to holidays’ effects in the load forecasting are known to be higher than other factors. If these effects can be estimated and removed, the behavior of the series could be better viewed. Thus, the aim of this paper is to improve the forecasting errors by using a dynamic regression model with intervention analysis. Based on the linear transfer function method, a daily load model consists of either peak or average is developed. The developed model outperformed the seasonal ARIMA model in estimating the fixed and moving holidays’ effects and achieved a smaller Mean Absolute Percentage Error (MAPE in load forecast.
Korpi, Martin; Clark, William A W
2017-05-01
By modelling the distribution of percentage income gains for movers in Sweden, using multinomial logistic regression, this paper shows that those receiving large pecuniary returns from migration are primarily those moving to the larger metropolitan areas and those with higher education, and that there is much more variability in income gains than what is often assumed in models of average gains to migration. This suggests that human capital models of internal migration often overemphasize the job and income motive for moving, and fail to explore where and when human capital motivated migration occurs.
Wavelet regression model in forecasting crude oil price
Hamid, Mohd Helmie; Shabri, Ani
2017-05-01
This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.
International Nuclear Information System (INIS)
Chrien, R.E.
1986-10-01
The principles of resonance averaging as applied to neutron capture reactions are described. Several illustrations of resonance averaging to problems of nuclear structure and the distribution of radiative strength in nuclei are provided. 30 refs., 12 figs
Abstract Expression Grammar Symbolic Regression
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
DEFF Research Database (Denmark)
Gramkow, Claus
1999-01-01
In this article two common approaches to averaging rotations are compared to a more advanced approach based on a Riemannian metric. Very offten the barycenter of the quaternions or matrices that represent the rotations are used as an estimate of the mean. These methods neglect that rotations belo...... approximations to the Riemannian metric, and that the subsequent corrections are inherient in the least squares estimation. Keywords: averaging rotations, Riemannian metric, matrix, quaternion......In this article two common approaches to averaging rotations are compared to a more advanced approach based on a Riemannian metric. Very offten the barycenter of the quaternions or matrices that represent the rotations are used as an estimate of the mean. These methods neglect that rotations belong...
International Nuclear Information System (INIS)
Ichiguchi, Katsuji
1998-01-01
A new reduced set of resistive MHD equations is derived by averaging the full MHD equations on specified flux coordinates, which is consistent with 3D equilibria. It is confirmed that the total energy is conserved and the linearized equations for ideal modes are self-adjoint. (author)
Determining average yarding distance.
Roger H. Twito; Charles N. Mann
1979-01-01
Emphasis on environmental and esthetic quality in timber harvesting has brought about increased use of complex boundaries of cutting units and a consequent need for a rapid and accurate method of determining the average yarding distance and area of these units. These values, needed for evaluation of road and landing locations in planning timber harvests, are easily and...
Watson, Jane; Chick, Helen
2012-01-01
This paper analyses the responses of 247 middle school students to items requiring the concept of average in three different contexts: a city's weather reported in maximum daily temperature, the number of children in a family, and the price of houses. The mixed but overall disappointing performance on the six items in the three contexts indicates…
Averaging operations on matrices
Indian Academy of Sciences (India)
2014-07-03
Jul 3, 2014 ... Role of Positive Definite Matrices. • Diffusion Tensor Imaging: 3 × 3 pd matrices model water flow at each voxel of brain scan. • Elasticity: 6 × 6 pd matrices model stress tensors. • Machine Learning: n × n pd matrices occur as kernel matrices. Tanvi Jain. Averaging operations on matrices ...
Directory of Open Access Journals (Sweden)
Patricia Bouyer
2015-09-01
Full Text Available Two-player quantitative zero-sum games provide a natural framework to synthesize controllers with performance guarantees for reactive systems within an uncontrollable environment. Classical settings include mean-payoff games, where the objective is to optimize the long-run average gain per action, and energy games, where the system has to avoid running out of energy. We study average-energy games, where the goal is to optimize the long-run average of the accumulated energy. We show that this objective arises naturally in several applications, and that it yields interesting connections with previous concepts in the literature. We prove that deciding the winner in such games is in NP inter coNP and at least as hard as solving mean-payoff games, and we establish that memoryless strategies suffice to win. We also consider the case where the system has to minimize the average-energy while maintaining the accumulated energy within predefined bounds at all times: this corresponds to operating with a finite-capacity storage for energy. We give results for one-player and two-player games, and establish complexity bounds and memory requirements.
Books average previous decade of economic misery.
Bentley, R Alexander; Acerbi, Alberto; Ormerod, Paul; Lampos, Vasileios
2014-01-01
For the 20(th) century since the Depression, we find a strong correlation between a 'literary misery index' derived from English language books and a moving average of the previous decade of the annual U.S. economic misery index, which is the sum of inflation and unemployment rates. We find a peak in the goodness of fit at 11 years for the moving average. The fit between the two misery indices holds when using different techniques to measure the literary misery index, and this fit is significantly better than other possible correlations with different emotion indices. To check the robustness of the results, we also analysed books written in German language and obtained very similar correlations with the German economic misery index. The results suggest that millions of books published every year average the authors' shared economic experiences over the past decade.
Books Average Previous Decade of Economic Misery
Bentley, R. Alexander; Acerbi, Alberto; Ormerod, Paul; Lampos, Vasileios
2014-01-01
For the 20th century since the Depression, we find a strong correlation between a ‘literary misery index’ derived from English language books and a moving average of the previous decade of the annual U.S. economic misery index, which is the sum of inflation and unemployment rates. We find a peak in the goodness of fit at 11 years for the moving average. The fit between the two misery indices holds when using different techniques to measure the literary misery index, and this fit is significantly better than other possible correlations with different emotion indices. To check the robustness of the results, we also analysed books written in German language and obtained very similar correlations with the German economic misery index. The results suggest that millions of books published every year average the authors' shared economic experiences over the past decade. PMID:24416159
GIS Tools to Estimate Average Annual Daily Traffic
2012-06-01
This project presents five tools that were created for a geographical information system to estimate Annual Average Daily : Traffic using linear regression. Three of the tools can be used to prepare spatial data for linear regression. One tool can be...
DEFF Research Database (Denmark)
Gramkow, Claus
2001-01-01
In this paper two common approaches to averaging rotations are compared to a more advanced approach based on a Riemannian metric. Very often the barycenter of the quaternions or matrices that represent the rotations are used as an estimate of the mean. These methods neglect that rotations belong ...... approximations to the Riemannian metric, and that the subsequent corrections are inherent in the least squares estimation.......In this paper two common approaches to averaging rotations are compared to a more advanced approach based on a Riemannian metric. Very often the barycenter of the quaternions or matrices that represent the rotations are used as an estimate of the mean. These methods neglect that rotations belong...
Moving Horizon Estimation and Control
DEFF Research Database (Denmark)
Jørgensen, John Bagterp
successful and applied methodology beyond PID-control for control of industrial processes. The main contribution of this thesis is introduction and definition of the extended linear quadratic optimal control problem for solution of numerical problems arising in moving horizon estimation and control...... problems. Chapter 1 motivates moving horizon estimation and control as a paradigm for control of industrial processes. It introduces the extended linear quadratic control problem and discusses its central role in moving horizon estimation and control. Introduction, application and efficient solution....... It provides an algorithm for computation of the maximal output admissible set for linear model predictive control. Appendix D provides results concerning linear regression. Appendix E discuss prediction error methods for identification of linear models tailored for model predictive control....
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
Eliazar, Iddo
2018-02-01
The popular perception of statistical distributions is depicted by the iconic bell curve which comprises of a massive bulk of 'middle-class' values, and two thin tails - one of small left-wing values, and one of large right-wing values. The shape of the bell curve is unimodal, and its peak represents both the mode and the mean. Thomas Friedman, the famous New York Times columnist, recently asserted that we have entered a human era in which "Average is Over" . In this paper we present mathematical models for the phenomenon that Friedman highlighted. While the models are derived via different modeling approaches, they share a common foundation. Inherent tipping points cause the models to phase-shift from a 'normal' bell-shape statistical behavior to an 'anomalous' statistical behavior: the unimodal shape changes to an unbounded monotone shape, the mode vanishes, and the mean diverges. Hence: (i) there is an explosion of small values; (ii) large values become super-large; (iii) 'middle-class' values are wiped out, leaving an infinite rift between the small and the super large values; and (iv) "Average is Over" indeed.
An approximate analytical approach to resampling averages
DEFF Research Database (Denmark)
Malzahn, Dorthe; Opper, M.
2004-01-01
Using a novel reformulation, we develop a framework to compute approximate resampling data averages analytically. The method avoids multiple retraining of statistical models on the samples. Our approach uses a combination of the replica "trick" of statistical physics and the TAP approach for appr...... for approximate Bayesian inference. We demonstrate our approach on regression with Gaussian processes. A comparison with averages obtained by Monte-Carlo sampling shows that our method achieves good accuracy....
De Luca, G.; Magnus, J.R.
2011-01-01
In this article, we describe the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals, which implement, respectively, the exact Bayesian model-averaging estimator and the weighted-average least-squares
Average nuclear surface properties
International Nuclear Information System (INIS)
Groote, H. von.
1979-01-01
The definition of the nuclear surface energy is discussed for semi-infinite matter. This definition is extended also for the case that there is a neutron gas instead of vacuum on the one side of the plane surface. The calculations were performed with the Thomas-Fermi Model of Syler and Blanchard. The parameters of the interaction of this model were determined by a least squares fit to experimental masses. The quality of this fit is discussed with respect to nuclear masses and density distributions. The average surface properties were calculated for different particle asymmetry of the nucleon-matter ranging from symmetry beyond the neutron-drip line until the system no longer can maintain the surface boundary and becomes homogeneous. The results of the calculations are incorporated in the nuclear Droplet Model which then was fitted to experimental masses. (orig.)
Americans' Average Radiation Exposure
International Nuclear Information System (INIS)
2000-01-01
We live with radiation every day. We receive radiation exposures from cosmic rays, from outer space, from radon gas, and from other naturally radioactive elements in the earth. This is called natural background radiation. It includes the radiation we get from plants, animals, and from our own bodies. We also are exposed to man-made sources of radiation, including medical and dental treatments, television sets and emission from coal-fired power plants. Generally, radiation exposures from man-made sources are only a fraction of those received from natural sources. One exception is high exposures used by doctors to treat cancer patients. Each year in the United States, the average dose to people from natural and man-made radiation sources is about 360 millirem. A millirem is an extremely tiny amount of energy absorbed by tissues in the body
Job Surfing: Move On to Move Up.
Martin, Justin
1997-01-01
Looks at the process of switching jobs and changing careers. Discusses when to consider options and make the move as well as the need to be flexible and open minded. Provides a test for determining the chances of promotion and when to move on. (JOW)
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Moving and Being Moved: Implications for Practice.
Kretchmar, R. Scott
2000-01-01
Uses philosophical writings, a novel about baseball, and a nonfiction work on rowing to analyze levels of meaning in physical activity, showing why three popular methods for enhancing meaning have not succeeded and may have moved some students away from deeper levels of meaning. The paper suggests that using hints taken from the three books could…
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
Cassie Meador; Mark Twery; Meagan. Leatherbury
2011-01-01
The Moving Field Guides (MFG) project is a creative take on site interpretation. Moving Field Guides provide an example of how scientific and artistic endeavors work in parallel. Both begin with keen observations that produce information that must be analyzed, understood, and interpreted. That interpretation then needs to be communicated to others to complete the...
Mohan, Audrey
2018-01-01
The purpose of this 2-3 day lesson is to introduce students in Grades 2-4 to the idea that people move around the world for a variety of reasons. In this activity, students explore why people move through class discussion, a guided reading, and interviews. The teacher elicits student ideas using the compelling question (Dimension 1 of the C3…
The difference between alternative averages
Directory of Open Access Journals (Sweden)
James Vaupel
2012-09-01
Full Text Available BACKGROUND Demographers have long been interested in how compositional change, e.g., change in age structure, affects population averages. OBJECTIVE We want to deepen understanding of how compositional change affects population averages. RESULTS The difference between two averages of a variable, calculated using alternative weighting functions, equals the covariance between the variable and the ratio of the weighting functions, divided by the average of the ratio. We compare weighted and unweighted averages and also provide examples of use of the relationship in analyses of fertility and mortality. COMMENTS Other uses of covariances in formal demography are worth exploring.
Embodied affectivity: On moving and being moved
Directory of Open Access Journals (Sweden)
Thomas eFuchs
2014-06-01
Full Text Available There is a growing body of research indicating that bodily sensation and behaviour strongly influences one’s emotional reaction towards certain situations or objects. On this background, a framework model of embodied affectivity is suggested: we regard emotions as resulting from the circular interaction between affective qualities or affordances in the environment and the subject’s bodily resonance, be it in the form of sensations, postures, expressive movements or movement tendencies. Motion and emotion are thus intrinsically connected: one is moved by movement (perception; impression; affection and moved to move (action; expression; e-motion. Through its resonance, the body functions as a medium of emotional perception: it colours or charges self-experience and the environment with affective valences while it remains itself in the background of one’s own awareness. This model is then applied to emotional social understanding or interaffectivity which is regarded as an intertwinement of two cycles of embodied affectivity, thus continuously modifying each partner’s affective affordances and bodily resonance. We conclude with considerations of how embodied affectivity is altered in psychopathology and can be addressed in psychotherapy of the embodied self.
Directory of Open Access Journals (Sweden)
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
PARALLEL MOVING MECHANICAL SYSTEMS
Directory of Open Access Journals (Sweden)
Florian Ion Tiberius Petrescu
2014-09-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Moving mechanical systems parallel structures are solid, fast, and accurate. Between parallel systems it is to be noticed Stewart platforms, as the oldest systems, fast, solid and precise. The work outlines a few main elements of Stewart platforms. Begin with the geometry platform, kinematic elements of it, and presented then and a few items of dynamics. Dynamic primary element on it means the determination mechanism kinetic energy of the entire Stewart platforms. It is then in a record tail cinematic mobile by a method dot matrix of rotation. If a structural mottoelement consists of two moving elements which translates relative, drive train and especially dynamic it is more convenient to represent the mottoelement as a single moving components. We have thus seven moving parts (the six motoelements or feet to which is added mobile platform 7 and one fixed.
Reconstruction of missing daily streamflow data using dynamic regression models
Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault
2015-12-01
River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.
DEFF Research Database (Denmark)
Hansen, Lennard Højbjerg
2014-01-01
Every day we are presented with bodily expressions in audiovisual media – by anchors, journalists and characters in films for instance. This article explores how body language in the moving image has been and can be approached in a scholarly manner.......Every day we are presented with bodily expressions in audiovisual media – by anchors, journalists and characters in films for instance. This article explores how body language in the moving image has been and can be approached in a scholarly manner....
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages
DEFF Research Database (Denmark)
Malzahn, Dorthe; Opper, Manfred
2003-01-01
We apply the replica method of Statistical Physics combined with a variational method to the approximate analytical computation of bootstrap averages for estimating the generalization error. We demonstrate our approach on regression with Gaussian processes and compare our results with averages...
How to average logarithmic retrievals?
Directory of Open Access Journals (Sweden)
B. Funke
2012-04-01
Full Text Available Calculation of mean trace gas contributions from profiles obtained by retrievals of the logarithm of the abundance rather than retrievals of the abundance itself are prone to biases. By means of a system simulator, biases of linear versus logarithmic averaging were evaluated for both maximum likelihood and maximum a priori retrievals, for various signal to noise ratios and atmospheric variabilities. These biases can easily reach ten percent or more. As a rule of thumb we found for maximum likelihood retrievals that linear averaging better represents the true mean value in cases of large local natural variability and high signal to noise ratios, while for small local natural variability logarithmic averaging often is superior. In the case of maximum a posteriori retrievals, the mean is dominated by the a priori information used in the retrievals and the method of averaging is of minor concern. For larger natural variabilities, the appropriateness of the one or the other method of averaging depends on the particular case because the various biasing mechanisms partly compensate in an unpredictable manner. This complication arises mainly because of the fact that in logarithmic retrievals the weight of the prior information depends on abundance of the gas itself. No simple rule was found on which kind of averaging is superior, and instead of suggesting simple recipes we cannot do much more than to create awareness of the traps related with averaging of mixing ratios obtained from logarithmic retrievals.
Lagrangian averaging with geodesic mean.
Oliver, Marcel
2017-11-01
This paper revisits the derivation of the Lagrangian averaged Euler (LAE), or Euler- α equations in the light of an intrinsic definition of the averaged flow map as the geodesic mean on the volume-preserving diffeomorphism group. Under the additional assumption that first-order fluctuations are statistically isotropic and transported by the mean flow as a vector field, averaging of the kinetic energy Lagrangian of an ideal fluid yields the LAE Lagrangian. The derivation presented here assumes a Euclidean spatial domain without boundaries.
Moving a House by Moved Participants
DEFF Research Database (Denmark)
Axel, Erik
himself in controlling every detail of the shape of the concrete slaps. He pushed all the other participants of the meetings, asking for details, information, the change of drawings etc. He explained the technical issues he was pursuing, was prepared for problems at the meetings, was well informed, always......? The participant observer believed it was a matter of changing coordinates, but the engineers immediately saw it was an issue of pipes in the ground, could they be moved and still function as planned? To decide the possibility of this suggestion the engineer was given the task of investigating the consequences...... they saw him as a bit pushy. On the other hand they understood why he was so since his firm would be fined if the concrete slabs did not meet specifications. The case will be the basis for a discussion of double motivation of the engineer, his evident interest in his professional work, and the wish...
Averaging in spherically symmetric cosmology
International Nuclear Information System (INIS)
Coley, A. A.; Pelavas, N.
2007-01-01
The averaging problem in cosmology is of fundamental importance. When applied to study cosmological evolution, the theory of macroscopic gravity (MG) can be regarded as a long-distance modification of general relativity. In the MG approach to the averaging problem in cosmology, the Einstein field equations on cosmological scales are modified by appropriate gravitational correlation terms. We study the averaging problem within the class of spherically symmetric cosmological models. That is, we shall take the microscopic equations and effect the averaging procedure to determine the precise form of the correlation tensor in this case. In particular, by working in volume-preserving coordinates, we calculate the form of the correlation tensor under some reasonable assumptions on the form for the inhomogeneous gravitational field and matter distribution. We find that the correlation tensor in a Friedmann-Lemaitre-Robertson-Walker (FLRW) background must be of the form of a spatial curvature. Inhomogeneities and spatial averaging, through this spatial curvature correction term, can have a very significant dynamical effect on the dynamics of the Universe and cosmological observations; in particular, we discuss whether spatial averaging might lead to a more conservative explanation of the observed acceleration of the Universe (without the introduction of exotic dark matter fields). We also find that the correlation tensor for a non-FLRW background can be interpreted as the sum of a spatial curvature and an anisotropic fluid. This may lead to interesting effects of averaging on astrophysical scales. We also discuss the results of averaging an inhomogeneous Lemaitre-Tolman-Bondi solution as well as calculations of linear perturbations (that is, the backreaction) in an FLRW background, which support the main conclusions of the analysis
Averaging models: parameters estimation with the R-Average procedure
Directory of Open Access Journals (Sweden)
S. Noventa
2010-01-01
Full Text Available The Functional Measurement approach, proposed within the theoretical framework of Information Integration Theory (Anderson, 1981, 1982, can be a useful multi-attribute analysis tool. Compared to the majority of statistical models, the averaging model can account for interaction effects without adding complexity. The R-Average method (Vidotto & Vicentini, 2007 can be used to estimate the parameters of these models. By the use of multiple information criteria in the model selection procedure, R-Average allows for the identification of the best subset of parameters that account for the data. After a review of the general method, we present an implementation of the procedure in the framework of R-project, followed by some experiments using a Monte Carlo method.
MOVES regional level sensitivity analysis
2012-01-01
The MOVES Regional Level Sensitivity Analysis was conducted to increase understanding of the operations of the MOVES Model in regional emissions analysis and to highlight the following: : the relative sensitivity of selected MOVES Model input paramet...
Steganalysis using logistic regression
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
International Nuclear Information System (INIS)
Kimura, Shigehiko
1983-01-01
As a ground water flow which is difficult to explain by Darcy's theory, there is stagnant water in strata, which moves by pumping and leads to land subsidence. This is now a major problem in Japan. Such move on an extensive scale has been investigated in detail by means of 3 H such as from rainfall in addition to ordinary measurement. The move of ground water is divided broadly into that in an unsaturated stratum from ground surface to water-table and that in a saturated stratum below the water-table. The course of the analyses made so far by 3 H contained in water, and the future trend of its usage are described. A flow model of regarding water as plastic fluid and its flow as channel assembly may be available for some flow mechanism which is not possible to explain with Darcy's theory. (Mori, K.)
International Nuclear Information System (INIS)
Ikuta, Kazunari; Miyahara, Akira.
1983-06-01
The concept of the limiter-divertor proposed by Mirnov is extended to a toroidal limiter-divertor (which we call moving toroidal limiter) using the stream of ferromagnetic balls coated with a low Z materials such as plastics, graphite and ceramics. An important advantage of the use of the ferromagnetic materials would be possible soft landing of the balls on a catcher, provided that the temperature of the balls is below Curie point. Moreover, moving toroidal limiter would work as a protector of the first wall not only against the vertical movement of plasma ring but also against the violent inward motion driven by major disruption because the orbit of the ball in the case of moving toroidal limiter distributes over the small major radius side of the toroidal plasma. (author)
Dave Maré; Jason Timmins
2003-01-01
This paper examines whether New Zealand residents move from low-growth to high-growth regions, using New Zealand census data from the past three inter-censal periods (covering 1986-2001). We focus on the relationship between employment growth and migration flows to gauge the strength of the relationship and the stability of the relationship over the business cycle. We find that people move to areas of high employment growth, but that the probability of leaving a region is less strongly relate...
Evaluations of average level spacings
International Nuclear Information System (INIS)
Liou, H.I.
1980-01-01
The average level spacing for highly excited nuclei is a key parameter in cross section formulas based on statistical nuclear models, and also plays an important role in determining many physics quantities. Various methods to evaluate average level spacings are reviewed. Because of the finite experimental resolution, to detect a complete sequence of levels without mixing other parities is extremely difficult, if not totally impossible. Most methods derive the average level spacings by applying a fit, with different degrees of generality, to the truncated Porter-Thomas distribution for reduced neutron widths. A method that tests both distributions of level widths and positions is discussed extensivey with an example of 168 Er data. 19 figures, 2 tables
Edgar, Jim; And Others
1986-01-01
Presents papers from Illinois State Library and Shawnee Library System's "Libraries on the MOVE" conference focusing on how libraries can impact economic/cultural climate of an area. Topics addressed included information services of rural libraries; marketing; rural library development; library law; information access; interagency…
DEFF Research Database (Denmark)
Christensen, Mark Schram; Grünbaum, Thor
2017-01-01
In this chapter, we assume the existence of a sense of “movement activity” that arises when a person actively moves a body part. This sense is usually supposed to be part of sense of agency (SoA). The purpose of the chapter is to determine whether the already existing experimental paradigms can...
DEFF Research Database (Denmark)
Agarwal, Pankaj K.; Arge, Lars Allan; Erickson, Jeff
2003-01-01
We propose three indexing schemes for storing a set S of N points in the plane, each moving along a linear trajectory, so that any query of the following form can be answered quickly: Given a rectangle R and a real value t, report all K points of S that lie inside R at time t. We first present an...
Covell, Charlotte
2016-01-23
Charlotte Covell is commercial business manager at Virbac UK, a role that gives her responsibility for the company's sales to corporate practices, some buying groups and internet pharmacies. She began her career as a veterinary nurse, but moved into industry and now has a role in senior business management. British Veterinary Association.
Piwnicki, P.; Leonhardt, U.
2001-01-01
Light experiences a moving medium as an effective gravitational field. In the limit of low medium velocities the medium flow plays the role of a magnetic vector potential. We review the background of our theory [U. Leonhardt and P. Piwnicki, Phys. Rev. A 60, 4301 (1999); Phys. Rev. Lett. 84, 822 (2000)], including our proposal of making optical black holes.
Fawcett, Gay
1996-01-01
New ways of thinking about leadership require that leaders move their big desks and establish environments that encourage trust and open communication. Educational leaders must trust their colleagues to make wise choices. When teachers are treated democratically as leaders, classrooms will also become democratic learning organizations. (SM)
Rennie, Richard
2015-01-01
The history of the moving image (the cinema) is well documented in books and on the Internet. This article offers a number of activities that can easily be carried out in a science class. They make use of the phenomenon of "Persistence of Vision." The activities presented herein demonstrate the functionality of the phenakistoscope, the…
Ainscow, Mel; Hopkins, David
1992-01-01
In many countries, education legislation embodies contradictory pressures for centralization and decentralization. In the United Kingdom, there is growing government control over policy and direction of schools; schools are also being given more responsibility for resource management. "Moving" schools within Improving the Quality of…
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
Ergodic averages via dominating processes
DEFF Research Database (Denmark)
Møller, Jesper; Mengersen, Kerrie
2006-01-01
We show how the mean of a monotone function (defined on a state space equipped with a partial ordering) can be estimated, using ergodic averages calculated from upper and lower dominating processes of a stationary irreducible Markov chain. In particular, we do not need to simulate the stationary...... Markov chain and we eliminate the problem of whether an appropriate burn-in is determined or not. Moreover, when a central limit theorem applies, we show how confidence intervals for the mean can be estimated by bounding the asymptotic variance of the ergodic average based on the equilibrium chain....
Su, Nan-Yao; Lee, Sang-Hee
2008-04-01
Marked termites were released in a linear-connected foraging arena, and the spatial heterogeneity of their capture probabilities was averaged for both directions at distance r from release point to obtain a symmetrical distribution, from which the density function of directionally averaged capture probability P(x) was derived. We hypothesized that as marked termites move into the population and given sufficient time, the directionally averaged capture probability may reach an equilibrium P(e) over the distance r and thus satisfy the equal mixing assumption of the mark-recapture protocol. The equilibrium capture probability P(e) was used to estimate the population size N. The hypothesis was tested in a 50-m extended foraging arena to simulate the distance factor of field colonies of subterranean termites. Over the 42-d test period, the density functions of directionally averaged capture probability P(x) exhibited four phases: exponential decline phase, linear decline phase, equilibrium phase, and postequilibrium phase. The equilibrium capture probability P(e), derived as the intercept of the linear regression during the equilibrium phase, correctly projected N estimates that were not significantly different from the known number of workers in the arena. Because the area beneath the probability density function is a constant (50% in this study), preequilibrium regression parameters and P(e) were used to estimate the population boundary distance 1, which is the distance between the release point and the boundary beyond which the population is absent.
International Nuclear Information System (INIS)
Nickerson, David M.; Madsen, Brooks C.
2005-01-01
Continuous monitoring of precipitation in East Central Florida has occurred since 1978 at a sampling site located on the University of Central Florida (UCF) campus. Monthly volume-weighted average (VWA) concentration for several major analytes that are present in precipitation samples was calculated from samples collected daily. Monthly VWA concentration and wet deposition of H + , NH 4 + , Ca 2+ , Mg 2+ , NO 3 - , Cl - and SO 4 2- were evaluated by a nonlinear regression (NLR) model that considered 10-year data (from 1978 to 1987) and 20-year data (from 1978 to 1997). Little change in the NLR parameter estimates was indicated among the 10-year and 20-year evaluations except for general decreases in the predicted trends from the 10-year to the 20-year fits. Box-Jenkins autoregressive integrated moving average (ARIMA) models with linear trend were considered as an alternative to the NLR models for these data. The NLR and ARIMA model forecasts for 1998 were compared to the actual 1998 data. For monthly VWA concentration values, the two models gave similar results. For the wet deposition values, the ARIMA models performed considerably better. - Autoregressive integrated moving average models of precipitation data are an improvement over nonlinear models for the prediction of precipitation chemistry composition
High average power supercontinuum sources
Indian Academy of Sciences (India)
The physical mechanisms and basic experimental techniques for the creation of high average spectral power supercontinuum sources is briefly reviewed. We focus on the use of high-power ytterbium-doped fibre lasers as pump sources, and the use of highly nonlinear photonic crystal fibres as the nonlinear medium.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
DEFF Research Database (Denmark)
Simonsen, Gunvor
2008-01-01
The article examines the development of African diaspora history during the last fifty years. It outlines the move from a focus on African survivals to a focus on deep rooted cultural principles and back again to a revived interest in concrete cultural transfers from Africa to the Americas....... This circular movement can be explained by a combination of elements characterizing African Atlantic and black Atlantic history. Among them is a lack of attention to questions of periodisation and change. Likewise, it has proven difficult to conceptualize Africa and America at one and the same time...... as characterized by cultural diversity and variation. Moreover, the field has been haunted by a tendency of moving to easily from descriptive evidence to conclusions about African identity in the Americas. A promising way to overcome these problems, it is suggested, is to develop research that focuses on single...
When good = better than average
Directory of Open Access Journals (Sweden)
Don A. Moore
2007-10-01
Full Text Available People report themselves to be above average on simple tasks and below average on difficult tasks. This paper proposes an explanation for this effect that is simpler than prior explanations. The new explanation is that people conflate relative with absolute evaluation, especially on subjective measures. The paper then presents a series of four studies that test this conflation explanation. These tests distinguish conflation from other explanations, such as differential weighting and selecting the wrong referent. The results suggest that conflation occurs at the response stage during which people attempt to disambiguate subjective response scales in order to choose an answer. This is because conflation has little effect on objective measures, which would be equally affected if the conflation occurred at encoding.
Bracey, Andrew
2010-01-01
Electric Moving Shadow Garden is a multi-directional exploration of the links between artists and cinema, with multiple reference and contextual points. it accompanied the exhibition, UnSpooling: Artists & Cinema, curated by Bracey and Dave Griffiths at Corernhouse, Manchester, who also edited the publication. Published to accompany the Cornerhouse exhibition, UnSpooling: Artists & Cinema, curated by artists Andrew Bracey and Dave Griffiths. This illustrated catalogue explores how internat...
Peter Sollander, AB/OP/TI
2005-01-01
The monitoring of CERN's technical infrastructure has moved from the Technical Control Room in building 212 to the Meyrin Control Room (MCR) in building 354 (see map) and from the TS/CSE group to AB/OP. The operation's team as well as the services provided remain the same as before and you can still reach the operator on shift by calling 72201. Peter Sollander, AB/OP/TI
HR Department
2007-01-01
The CERN Pension Fund has moved to new offices on the 5th floor of Building 5. The Benefits Service of the Fund is now located in Offices 5-5-017 - 5-5-021 - 5-5-023. We remind you that the office hours are: Tuesday/Wednesday/Thursday from 10 am to 12 am and from 3 pm to 5 pm. The Fund would like to take this opportunity to warmly thank all the persons involved in the relocation.
2012-01-01
As part of the "Move! Eat better" campaign, Novae’s nutrition adviser, Irène Rolfo, will give a talk on the subject of everyday good nutrition. This will be held in the main building auditorium at 12:30 on Thursday, 20 September 2012. Don’t miss this informative event. For more information, go to http://cern.ch/bpmm
Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.
2018-04-01
The paper investigates the stochastic modelling and forecasting of monthly average maximum and minimum temperature patterns through suitable seasonal auto regressive integrated moving average (SARIMA) model for the period 1981-2015 in India. The variations and distributions of monthly maximum and minimum temperatures are analyzed through Box plots and cumulative distribution functions. The time series plot indicates that the maximum temperature series contain sharp peaks in almost all the years, while it is not true for the minimum temperature series, so both the series are modelled separately. The possible SARIMA model has been chosen based on observing autocorrelation function (ACF), partial autocorrelation function (PACF), and inverse autocorrelation function (IACF) of the logarithmic transformed temperature series. The SARIMA (1, 0, 0) × (0, 1, 1)12 model is selected for monthly average maximum and minimum temperature series based on minimum Bayesian information criteria. The model parameters are obtained using maximum-likelihood method with the help of standard error of residuals. The adequacy of the selected model is determined using correlation diagnostic checking through ACF, PACF, IACF, and p values of Ljung-Box test statistic of residuals and using normal diagnostic checking through the kernel and normal density curves of histogram and Q-Q plot. Finally, the forecasting of monthly maximum and minimum temperature patterns of India for the next 3 years has been noticed with the help of selected model.
2005-01-01
The Transport Service pulled out all the stops and, more specifically, its fleet of moving and lifting equipment for the Discovery Monday on 6 June - a truly moving experience for all the visitors who took part ! Visitors could play at being machine operator, twiddling the controls of a lift truck fitted with a jib to lift a dummy magnet into a wooden mock-up of a beam-line.They had to show even greater dexterity for this game of lucky dip...CERN-style.Those with a head for heights took to the skies 20 m above ground in a telescopic boom lift.Children were allowed to climb up into the operator's cabin - this is one of the cranes used to move the LHC magnets around. Warm thanks to all members of the Transport Service for their participation, especially B. Goicoechea, T. Ilkei, R. Bihery, S. Prodon, S. Pelletier, Y. Bernard, A. Sallot, B. Pigeard, S. Guinchard, B. Bulot, J. Berrez, Y. Grandjean, A. Bouakkaz, M. Bois, F. Stach, T. Mazzarino and S. Fumey.
Averaging Robertson-Walker cosmologies
International Nuclear Information System (INIS)
Brown, Iain A.; Robbers, Georg; Behrend, Juliane
2009-01-01
The cosmological backreaction arises when one directly averages the Einstein equations to recover an effective Robertson-Walker cosmology, rather than assuming a background a priori. While usually discussed in the context of dark energy, strictly speaking any cosmological model should be recovered from such a procedure. We apply the scalar spatial averaging formalism for the first time to linear Robertson-Walker universes containing matter, radiation and dark energy. The formalism employed is general and incorporates systems of multiple fluids with ease, allowing us to consider quantitatively the universe from deep radiation domination up to the present day in a natural, unified manner. Employing modified Boltzmann codes we evaluate numerically the discrepancies between the assumed and the averaged behaviour arising from the quadratic terms, finding the largest deviations for an Einstein-de Sitter universe, increasing rapidly with Hubble rate to a 0.01% effect for h = 0.701. For the ΛCDM concordance model, the backreaction is of the order of Ω eff 0 ≈ 4 × 10 −6 , with those for dark energy models being within a factor of two or three. The impacts at recombination are of the order of 10 −8 and those in deep radiation domination asymptote to a constant value. While the effective equations of state of the backreactions in Einstein-de Sitter, concordance and quintessence models are generally dust-like, a backreaction with an equation of state w eff < −1/3 can be found for strongly phantom models
Directory of Open Access Journals (Sweden)
Zhi-Sai Ma
2017-01-01
Full Text Available Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA. A kernel ridge regression functional series TARMA (FS-TARMA recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a numerical three-degree-of-freedom time-varying structural system and a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudolinear regression FS-TARMA approach via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics in a recursive manner.
Long, Xi; Pauws, S.C.; Pijl, M.; Lacroix, J.; Goris, A.H.C.; Aarts, R.M.
2009-01-01
Results are provided on predicting daily physical activity level (PAL) data from past data of participants of a physical activity lifestyle program aimed at promoting a healthier lifestyle consisting of more physical exercise. The PAL data quantifies the level of a person’s daily physical activity
Limit theorems for stationary increments Lévy driven moving averages
DEFF Research Database (Denmark)
Basse-O'Connor, Andreas; Lachièze-Rey, Raphaël; Podolskij, Mark
of the kernel function g at 0. First order asymptotic theory essentially comprise three cases: stable convergence towards a certain infinitely divisible distribution, an ergodic type limit theorem and convergence in probability towards an integrated random process. We also prove the second order limit theorem...
Score-driven exponentially weighted moving averages and Value-at-Risk forecasting
Lucas, A.; Zhang, X.
2016-01-01
We present a simple methodology for modeling the time variation in volatilities and other higher-order moments using a recursive updating scheme that is similar to the familiar RiskMetrics approach. The parameters are updated using the score of the forecasting distribution, which allows the
A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
C.M. Hafner (Christian); M.J. McAleer (Michael)
2014-01-01
markdownabstract__Abstract__ One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of
Polynomial regression analysis and significance test of the regression function
International Nuclear Information System (INIS)
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Schaake, K.; Burgers, J.; Mulder, C.H.
2010-01-01
We address the influence of both the ethnic composition of the neighborhood and the ethnicity of individual residents on moving out of neighborhoods in the Netherlands. Using the Housing Research Netherlands survey and multinomial logistic regression analyses of moving out versus not moving or
HR Department
2007-01-01
The CERN Pension Fund has moved to new offices at the 5th floor of Building 5. The Benefits Service of the Fund will henceforth receive you in the offices: 5-5-017 - 5-5-021 - 5-5-023. We remind you that the office hours are: Tuesday/Wednesday/Thursday from 10 am to 12 am and from 3 pm to 5 pm. The Fund would like to take this opportunity to warmly thank all the persons involved in the Removal.
Moving related to separation : who moves and to what distance
Mulder, Clara H.; Malmberg, Gunnar
We address the issue of moving from the joint home on the occasion of separation. Our research question is: To what extent can the occurrence of moves related to separation, and the distance moved, be explained by ties to the location, resources, and other factors influencing the likelihood of
Topological quantization of ensemble averages
International Nuclear Information System (INIS)
Prodan, Emil
2009-01-01
We define the current of a quantum observable and, under well-defined conditions, we connect its ensemble average to the index of a Fredholm operator. The present work builds on a formalism developed by Kellendonk and Schulz-Baldes (2004 J. Funct. Anal. 209 388) to study the quantization of edge currents for continuous magnetic Schroedinger operators. The generalization given here may be a useful tool to scientists looking for novel manifestations of the topological quantization. As a new application, we show that the differential conductance of atomic wires is given by the index of a certain operator. We also comment on how the formalism can be used to probe the existence of edge states
Anaïs Schaeffer
2012-01-01
This year, the CERN Medical Service is launching a new public health campaign. Advertised by the catchphrase “Move! & Eat Better”, the particular aim of the campaign is to encourage people at CERN to take more regular exercise, of whatever kind. The CERN annual relay race is scheduled on 24 May this year. The CERN Medical Service will officially launch its “Move! & Eat Better” campaign at this popular sporting event. “We shall be on hand on the day of the race to strongly advocate regular physical activity,” explains Rachid Belkheir, one of the Medical Service doctors. "We really want to pitch our campaign and answer any questions people may have. Above all we want to set an example. So we are going to walk the same circuit as the runners to underline to people that they can easily incorporate movement into their daily routine.” An underlying concern has prompted this campaign: during their first few year...
Flexible time domain averaging technique
Zhao, Ming; Lin, Jing; Lei, Yaguo; Wang, Xiufeng
2013-09-01
Time domain averaging(TDA) is essentially a comb filter, it cannot extract the specified harmonics which may be caused by some faults, such as gear eccentric. Meanwhile, TDA always suffers from period cutting error(PCE) to different extent. Several improved TDA methods have been proposed, however they cannot completely eliminate the waveform reconstruction error caused by PCE. In order to overcome the shortcomings of conventional methods, a flexible time domain averaging(FTDA) technique is established, which adapts to the analyzed signal through adjusting each harmonic of the comb filter. In this technique, the explicit form of FTDA is first constructed by frequency domain sampling. Subsequently, chirp Z-transform(CZT) is employed in the algorithm of FTDA, which can improve the calculating efficiency significantly. Since the signal is reconstructed in the continuous time domain, there is no PCE in the FTDA. To validate the effectiveness of FTDA in the signal de-noising, interpolation and harmonic reconstruction, a simulated multi-components periodic signal that corrupted by noise is processed by FTDA. The simulation results show that the FTDA is capable of recovering the periodic components from the background noise effectively. Moreover, it can improve the signal-to-noise ratio by 7.9 dB compared with conventional ones. Experiments are also carried out on gearbox test rigs with chipped tooth and eccentricity gear, respectively. It is shown that the FTDA can identify the direction and severity of the eccentricity gear, and further enhances the amplitudes of impulses by 35%. The proposed technique not only solves the problem of PCE, but also provides a useful tool for the fault symptom extraction of rotating machinery.
Leonhardt, U.; Piwnicki, P.
2001-06-01
We review the theory of light propagation in moving media with extremely low group velocity. We intend to clarify the most elementary features of monochromatic slow light in a moving medium and, whenever possible, to give an instructive simplified picture.
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
The average Indian female nose.
Patil, Surendra B; Kale, Satish M; Jaiswal, Sumeet; Khare, Nishant; Math, Mahantesh
2011-12-01
This study aimed to delineate the anthropometric measurements of the noses of young women of an Indian population and to compare them with the published ideals and average measurements for white women. This anthropometric survey included a volunteer sample of 100 young Indian women ages 18 to 35 years with Indian parents and no history of previous surgery or trauma to the nose. Standardized frontal, lateral, oblique, and basal photographs of the subjects' noses were taken, and 12 standard anthropometric measurements of the nose were determined. The results were compared with published standards for North American white women. In addition, nine nasal indices were calculated and compared with the standards for North American white women. The nose of Indian women differs significantly from the white nose. All the nasal measurements for the Indian women were found to be significantly different from those for North American white women. Seven of the nine nasal indices also differed significantly. Anthropometric analysis suggests differences between the Indian female nose and the North American white nose. Thus, a single aesthetic ideal is inadequate. Noses of Indian women are smaller and wider, with a less projected and rounded tip than the noses of white women. This study established the nasal anthropometric norms for nasal parameters, which will serve as a guide for cosmetic and reconstructive surgery in Indian women.
"Our federalism" moves indoors.
Ruger, Theodore W
2013-04-01
A great deal of the US Supreme Court's federalism jurisprudence over the past two decades has focused on the outer limits of federal power, suggesting a mutually exclusive division of jurisdiction between the states and the federal government, where subjects are regulated by one sovereign or the other but not both. This is not an accurate picture of American governance as it has operated over the past half century - most important areas of American life are regulated concurrently by both the federal government and the states. The Supreme Court's June 2012 decision clearing the way for the Patient Protection and Affordable Care Act (PPACA) to move forward thus should not be regarded as an affront to state sovereignty but as a realistic embrace of state power in its active, modern form. The PPACA is infused with multiple major roles for the states, and as the statute goes into operation over the next few years, states retain, and are already exercising, substantial policy discretion.
Moving Spatial Keyword Queries
DEFF Research Database (Denmark)
Wu, Dingming; Yiu, Man Lung; Jensen, Christian S.
2013-01-01
propose two algorithms for computing safe zones that guarantee correct results at any time and that aim to optimize the server-side computation as well as the communication between the server and the client. We exploit tight and conservative approximations of safe zones and aggressive computational space...... text data. State-of-the-art solutions for moving queries employ safe zones that guarantee the validity of reported results as long as the user remains within the safe zone associated with a result. However, existing safe-zone methods focus solely on spatial locations and ignore text relevancy. We...... pruning. We present techniques that aim to compute the next safe zone efficiently, and we present two types of conservative safe zones that aim to reduce the communication cost. Empirical studies with real data suggest that the proposals are efficient. To understand the effectiveness of the proposed safe...
2013-01-01
Are you curious to know whether you’re doing enough daily exercise…? Test yourself with a pedometer! Through the Move! Eat better campaign, launched in May 2012, the CERN medical service is aiming to improve the health of members of the personnel by encouraging them to prioritise physical activity in conjunction with a balanced diet. Various successful activities have already taken place: relay race/Nordic walk, Bike2work, Zumba and fitness workshops, two conferences (“Physical activity for health” and “Good nutrition every day”), events in the restaurants, as well as posters and a website. Although everyone has got the message from our various communications that physical activity is good for your health, there is still a relevant question being asked: “What is the minimum amount of exercise recommended?” 10,000 steps per day is the ideal figure, which has been demonstrated as beneficial by scientific studies ...
DEFF Research Database (Denmark)
2015-01-01
Katalog til udstillingen på Museum Jorn - What moves us? Le Corbusier & Asger Jorn - 12. sept. - 13. dec. 2015. Kataloget undersøger Le Corbusiers skifte fra en rationelt funderet tilgang til arkitekturen til en poetisk, materialistisk tilgang i efterkrigstiden. Den viser hans indflydelse på den...... yngre Asger Jorn og beskriver danskerens første beundring, som sidenhen forvandledes til skarp kritik. Kataloget, som er rigt illustreret med billeder af Le Corbusiers og Asger Jorns kunst og arkitektur, indeholder også genoptryk af originale tekster, samt bidrag i ord og billeder fra fremtrædende...... eksperter. Kataloget indeholder en række artikler af internationale skribenter under flg. overskrifter: Le Corbusier - kunstnerarkitekten i efterkrigstidens Europa Le Corbusier og Asger Jorn - David mod Goliat Gensyn med Le Corbusier - spor i dansk arkitektur og byrum...
Banichuk, Nikolay; Neittaanmäki, Pekka; Saksa, Tytti; Tuovinen, Tero
2014-01-01
This book deals with theoretical aspects of modelling the mechanical behaviour of manufacturing, processing, transportation or other systems in which the processed or supporting material is travelling through the system. Examples of such applications include paper making, transmission cables, band saws, printing presses, manufacturing of plastic films and sheets, and extrusion of aluminium foil, textiles and other materials. The work focuses on out-of-plane dynamics and stability analysis for isotropic and orthotropic travelling elastic and viscoelastic materials, with and without fluid-structure interaction, using analytical and semi-analytical approaches. Also topics such as fracturing and fatigue are discussed in the context of moving materials. The last part of the book deals with optimization problems involving physical constraints arising from the stability and fatigue analyses, including uncertainties in the parameters. The book is intended for researchers and specialists in the field, providin...
Plans, Patterns, and Move Categories Guiding a Highly Selective Search
Trippen, Gerhard
In this paper we present our ideas for an Arimaa-playing program (also called a bot) that uses plans and pattern matching to guide a highly selective search. We restrict move generation to moves in certain move categories to reduce the number of moves considered by the bot significantly. Arimaa is a modern board game that can be played with a standard Chess set. However, the rules of the game are not at all like those of Chess. Furthermore, Arimaa was designed to be as simple and intuitive as possible for humans, yet challenging for computers. While all established Arimaa bots use alpha-beta search with a variety of pruning techniques and other heuristics ending in an extensive positional leaf node evaluation, our new bot, Rat, starts with a positional evaluation of the current position. Based on features found in the current position - supported by pattern matching using a directed position graph - our bot Rat decides which of a given set of plans to follow. The plan then dictates what types of moves can be chosen. This is another major difference from bots that generate "all" possible moves for a particular position. Rat is only allowed to generate moves that belong to certain categories. Leaf nodes are evaluated only by a straightforward material evaluation to help avoid moves that lose material. This highly selective search looks, on average, at only 5 moves out of 5,000 to over 40,000 possible moves in a middle game position.
Gamez, Sara I.
2017-01-01
The purpose of this qualitative study was to explore the transitional experiences of foster youth college students. The study explored how foster youth experienced moving into, moving through, and moving out of the college environment and what resources and strategies they used to thrive during their college transitions. In addition, this study…
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
Averaging, not internal noise, limits the development of coherent motion processing
Directory of Open Access Journals (Sweden)
Catherine Manning
2014-10-01
Full Text Available The development of motion processing is a critical part of visual development, allowing children to interact with moving objects and navigate within a dynamic environment. However, global motion processing, which requires pooling motion information across space, develops late, reaching adult-like levels only by mid-to-late childhood. The reasons underlying this protracted development are not yet fully understood. In this study, we sought to determine whether the development of motion coherence sensitivity is limited by internal noise (i.e., imprecision in estimating the directions of individual elements and/or global pooling across local estimates. To this end, we presented equivalent noise direction discrimination tasks and motion coherence tasks at both slow (1.5°/s and fast (6°/s speeds to children aged 5, 7, 9 and 11 years, and adults. We show that, as children get older, their levels of internal noise reduce, and they are able to average across more local motion estimates. Regression analyses indicated, however, that age-related improvements in coherent motion perception are driven solely by improvements in averaging and not by reductions in internal noise. Our results suggest that the development of coherent motion sensitivity is primarily limited by developmental changes within brain regions involved in integrating motion signals (e.g., MT/V5.
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Forecasting systems reliability based on support vector regression with genetic algorithms
International Nuclear Information System (INIS)
Chen, K.-Y.
2007-01-01
This study applies a novel neural-network technique, support vector regression (SVR), to forecast reliability in engine systems. The aim of this study is to examine the feasibility of SVR in systems reliability prediction by comparing it with the existing neural-network approaches and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. A real reliability data for 40 suits of turbochargers were employed as the data set. The experimental results demonstrate that SVR outperforms the existing neural-network approaches and the traditional ARIMA models based on the normalized root mean square error and mean absolute percentage error
Stone, Wesley W.; Gilliom, Robert J.
2011-01-01
Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, can be improved for application to the U.S. Corn Belt region by developing region-specific models that include important watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for predicting annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. All streams used in development of WARP-CB models drain watersheds with atrazine use intensity greater than 17 kilograms per square kilometer (kg/km2). The WARP-CB models accounted for 53 to 62 percent of the variability in the various concentration statistics among the model-development sites.
Role of moving planes and moving spheres following Dupin cyclides
Jia, Xiaohong
2014-03-01
We provide explicit representations of three moving planes that form a μ-basis for a standard Dupin cyclide. We also show how to compute μ-bases for Dupin cyclides in general position and orientation from their implicit equations. In addition, we describe the role of moving planes and moving spheres in bridging between the implicit and rational parametric representations of these cyclides. © 2014 Elsevier B.V.
Role of moving planes and moving spheres following Dupin cyclides
Jia, Xiaohong
2014-01-01
We provide explicit representations of three moving planes that form a μ-basis for a standard Dupin cyclide. We also show how to compute μ-bases for Dupin cyclides in general position and orientation from their implicit equations. In addition, we describe the role of moving planes and moving spheres in bridging between the implicit and rational parametric representations of these cyclides. © 2014 Elsevier B.V.
Energy Technology Data Exchange (ETDEWEB)
Azadeh, A; Seraj, O [Department of Industrial Engineering and Research Institute of Energy Management and Planning, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, University of Tehran, P.O. Box 11365-4563 (Iran); Saberi, M [Department of Industrial Engineering, University of Tafresh (Iran); Institute for Digital Ecosystems and Business Intelligence, Curtin University of Technology, Perth (Australia)
2010-06-15
This study presents an integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression (FR) or time series and the integrated algorithm could be an ideal substitute for such cases. At First, preferred Time series model is selected from linear or nonlinear models. For this, after selecting preferred Auto Regression Moving Average (ARMA) model, Mcleod-Li test is applied to determine nonlinearity condition. When, nonlinearity condition is satisfied, the preferred nonlinear model is selected and defined as preferred time series model. At last, the preferred model from fuzzy regression and time series model is selected by the Granger-Newbold. Also, the impact of data preprocessing on the fuzzy regression performance is considered. Monthly electricity consumption of Iran from March 1994 to January 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with other intelligent tools such as Genetic Algorithm (GA) and Artificial Neural Network (ANN). (author)
2012-01-01
CERN has many traditions, but in a week that’s seen the launch of the Medical Service’s ‘Move & eat better’ campaign, it’s refreshing to note that among the oldest is a sporting one. The CERN relay race dates back to 15 October 1971 when 21 pioneering teams set off to pound the pavements of CERN. Back then, the Focus users group came in first with a time of 12 minutes and 42 seconds. Today’s route is slightly different, and the number of teams has risen to over 100, with a new category of Nordic Walking introduced, as part of the campaign, for the first time. The relay has provided some memorable events, and perhaps one of the longest-standing records in the history of sport, with the UA1 strollers’ 10 minutes and 13 seconds unbeaten for thirty years. In the women’s category, the UN Gazelles set the fastest time of 13 minutes and 16 seconds in 1996, while in the veterans category, you wi...
Della Mussia, S
2004-01-01
The first large active detector component was lowered into the ATLAS cavern on 1st March. It consisted of the 8 modules forming the lower part of the central barrel of the tile hadronic calorimeter. The work of assembling the barrel, which comprises 64 modules, started the following day. Two road trailers each with 64 wheels, positioned side by side. This was the solution chosen to transport the lower part of the central barrel of ATLAS' tile hadronic calorimeter from Building 185 to the PX16 shaft at Point 1 (see Figure 1). The transportation, and then the installation of the component in the experimental cavern, which took place over three days were, to say the least, rather spectacular. On 25 February, the component, consisting of eight 6-metre modules, was loaded on to the trailers. The segment of the barrel was transported on a steel support so that it wouldn't move an inch during the journey. On 26 February, once all the necessary safety checks had been carried out, the convoy was able to leave Buildi...
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
International Nuclear Information System (INIS)
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
Tumor regression patterns in retinoblastoma
International Nuclear Information System (INIS)
Zafar, S.N.; Siddique, S.N.; Zaheer, N.
2016-01-01
To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)
Heterogeneous CPU-GPU moving targets detection for UAV video
Li, Maowen; Tang, Linbo; Han, Yuqi; Yu, Chunlei; Zhang, Chao; Fu, Huiquan
2017-07-01
Moving targets detection is gaining popularity in civilian and military applications. On some monitoring platform of motion detection, some low-resolution stationary cameras are replaced by moving HD camera based on UAVs. The pixels of moving targets in the HD Video taken by UAV are always in a minority, and the background of the frame is usually moving because of the motion of UAVs. The high computational cost of the algorithm prevents running it at higher resolutions the pixels of frame. Hence, to solve the problem of moving targets detection based UAVs video, we propose a heterogeneous CPU-GPU moving target detection algorithm for UAV video. More specifically, we use background registration to eliminate the impact of the moving background and frame difference to detect small moving targets. In order to achieve the effect of real-time processing, we design the solution of heterogeneous CPU-GPU framework for our method. The experimental results show that our method can detect the main moving targets from the HD video taken by UAV, and the average process time is 52.16ms per frame which is fast enough to solve the problem.
Regression to Causality : Regression-style presentation influences causal attribution
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
Inflation, Forecast Intervals and Long Memory Regression Models
C.S. Bos (Charles); Ph.H.B.F. Franses (Philip Hans); M. Ooms (Marius)
2001-01-01
textabstractWe examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading
Inflation, Forecast Intervals and Long Memory Regression Models
Ooms, M.; Bos, C.S.; Franses, P.H.
2003-01-01
We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators
International Nuclear Information System (INIS)
Geloni, Gianluca; Kocharyan, Vitali; Saldin, Evgeni
2017-04-01
It is generally accepted that in order to describe the dynamics of relativistic particles in the laboratory (lab) frame it is sufficient to take into account the relativistic dependence of the particle momenta on the velocity. This solution of the dynamics problem in the lab frame makes no reference to Lorentz transformations. For this reason they are not discussed in particle tracking calculations in accelerator and plasma physics. It is generally believed that the electrodynamics problem can be treated within the same ''single inertial frame'' description without reference to Lorentz transformations. In particular, in order to evaluate radiation fields arising from charged particles in motion we need to know their velocities and positions as a function of the lab frame time t. The relativistic motion of a particle in the lab frame is described by Newton's second law ''corrected'' for the relativistic dependence of momentum on velocity. It is assumed in all standard derivations that one can perform identification of the trajectories in the source part of the usual Maxwell's equations with the trajectories vector x(t) measured (or calculated by using the corrected Newton's second law) in the lab frame. This way of coupling fields and particles is considered since more than a century as the relativistically correct procedure.We argue that this procedure needs to be changed, and we demonstrate the following, completely counterintuitive statement: the results of conventional theory of radiation by relativistically moving charges are not consistent with the principle of relativity. In order to find the trajectory of a particle in the lab frame consistent with the usual Maxwell's equations, one needs to solve the dynamic equation inmanifestly covariant form by using the coordinate-independent proper time τ to parameterize the particle world-line in space-time. We show that there is a difference between ''true'' particle trajectory vector x(t) calculated or measured in
Average monthly and annual climate maps for Bolivia
Vicente-Serrano, Sergio M.
2015-02-24
This study presents monthly and annual climate maps for relevant hydroclimatic variables in Bolivia. We used the most complete network of precipitation and temperature stations available in Bolivia, which passed a careful quality control and temporal homogenization procedure. Monthly average maps at the spatial resolution of 1 km were modeled by means of a regression-based approach using topographic and geographic variables as predictors. The monthly average maximum and minimum temperatures, precipitation and potential exoatmospheric solar radiation under clear sky conditions are used to estimate the monthly average atmospheric evaporative demand by means of the Hargreaves model. Finally, the average water balance is estimated on a monthly and annual scale for each 1 km cell by means of the difference between precipitation and atmospheric evaporative demand. The digital layers used to create the maps are available in the digital repository of the Spanish National Research Council.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a
From Rasch scores to regression
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Testing Heteroscedasticity in Robust Regression
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Averaging of nonlinearity-managed pulses
International Nuclear Information System (INIS)
Zharnitsky, Vadim; Pelinovsky, Dmitry
2005-01-01
We consider the nonlinear Schroedinger equation with the nonlinearity management which describes Bose-Einstein condensates under Feshbach resonance. By using an averaging theory, we derive the Hamiltonian averaged equation and compare it with other averaging methods developed for this problem. The averaged equation is used for analytical approximations of nonlinearity-managed solitons
Energy Technology Data Exchange (ETDEWEB)
Geloni, Gianluca [European XFEL GmbH, Hamburg (Germany); Kocharyan, Vitali; Saldin, Evgeni [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
2017-04-15
It is generally accepted that in order to describe the dynamics of relativistic particles in the laboratory (lab) frame it is sufficient to take into account the relativistic dependence of the particle momenta on the velocity. This solution of the dynamics problem in the lab frame makes no reference to Lorentz transformations. For this reason they are not discussed in particle tracking calculations in accelerator and plasma physics. It is generally believed that the electrodynamics problem can be treated within the same ''single inertial frame'' description without reference to Lorentz transformations. In particular, in order to evaluate radiation fields arising from charged particles in motion we need to know their velocities and positions as a function of the lab frame time t. The relativistic motion of a particle in the lab frame is described by Newton's second law ''corrected'' for the relativistic dependence of momentum on velocity. It is assumed in all standard derivations that one can perform identification of the trajectories in the source part of the usual Maxwell's equations with the trajectories vector x(t) measured (or calculated by using the corrected Newton's second law) in the lab frame. This way of coupling fields and particles is considered since more than a century as the relativistically correct procedure.We argue that this procedure needs to be changed, and we demonstrate the following, completely counterintuitive statement: the results of conventional theory of radiation by relativistically moving charges are not consistent with the principle of relativity. In order to find the trajectory of a particle in the lab frame consistent with the usual Maxwell's equations, one needs to solve the dynamic equation inmanifestly covariant form by using the coordinate-independent proper time τ to parameterize the particle world-line in space-time. We show that there is a difference between &apos
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.
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Complex regression Doppler optical coherence tomography
Elahi, Sahar; Gu, Shi; Thrane, Lars; Rollins, Andrew M.; Jenkins, Michael W.
2018-04-01
We introduce a new method to measure Doppler shifts more accurately and extend the dynamic range of Doppler optical coherence tomography (OCT). The two-point estimate of the conventional Doppler method is replaced with a regression that is applied to high-density B-scans in polar coordinates. We built a high-speed OCT system using a 1.68-MHz Fourier domain mode locked laser to acquire high-density B-scans (16,000 A-lines) at high enough frame rates (˜100 fps) to accurately capture the dynamics of the beating embryonic heart. Flow phantom experiments confirm that the complex regression lowers the minimum detectable velocity from 12.25 mm / s to 374 μm / s, whereas the maximum velocity of 400 mm / s is measured without phase wrapping. Complex regression Doppler OCT also demonstrates higher accuracy and precision compared with the conventional method, particularly when signal-to-noise ratio is low. The extended dynamic range allows monitoring of blood flow over several stages of development in embryos without adjusting the imaging parameters. In addition, applying complex averaging recovers hidden features in structural images.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
Regression filter for signal resolution
International Nuclear Information System (INIS)
Matthes, W.
1975-01-01
The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Minimum Delay Moving Object Detection
Lao, Dong
2017-11-09
We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
Imaging of Moving Ground Vehicles
National Research Council Canada - National Science Library
Rihaczek, A
1996-01-01
... requires that use be made of the complex image. The yaw/pitch/roll/bounce/flex motion of a moving ground vehicle demands that different motion compensations be applied to different parts of the vehicle...
DEFF Research Database (Denmark)
”I believe that all people need to move about. Actually, some have difficulties in doing so. They stay in their home neighbourhoods where they’ve grown up and feel safe. I can understand that, but my wife and I, we didn’t want that. We are more open to new ideas.” This anthology is about seniors...... on the move. In seven chapters, Nordic researchers from various disciplines, by means of ethnographic methods, attempt to comprehend the phenomenon of Nordic seniors who move to leisure areas in their own or in other countries. The number of people involved in this kind of migratory movement has grown...... above gives voice to one of these seniors, stressing the necessity of moving. The anthology contributes to the international body of literature about later life migration, specifically representing experiences made by Nordic seniors. As shown here, mobility and migration in later life have implications...
Minimum Delay Moving Object Detection
Lao, Dong
2017-01-08
We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
Minimum Delay Moving Object Detection
Lao, Dong; Sundaramoorthi, Ganesh
2017-01-01
We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
Autowaves in moving excitable media
Directory of Open Access Journals (Sweden)
V.A.Davydov
2004-01-01
Full Text Available Within the framework of kinematic theory of autowaves we suggest a method for analytic description of stationary autowave structures appearing at the boundary between the moving and fixed excitable media. The front breakdown phenomenon is predicted for such structures. Autowave refraction and, particulary, one-side "total reflection" at the boundary is considered. The obtained analytical results are confirmed by computer simulations. Prospects of the proposed method for further studies of autowave dynamics in the moving excitable media are discussed.
Infinite games with uncertain moves
Directory of Open Access Journals (Sweden)
Nicholas Asher
2013-03-01
Full Text Available We study infinite two-player games where one of the players is unsure about the set of moves available to the other player. In particular, the set of moves of the other player is a strict superset of what she assumes it to be. We explore what happens to sets in various levels of the Borel hierarchy under such a situation. We show that the sets at every alternate level of the hierarchy jump to the next higher level.
The average size of ordered binary subgraphs
van Leeuwen, J.; Hartel, Pieter H.
To analyse the demands made on the garbage collector in a graph reduction system, the change in size of an average graph is studied when an arbitrary edge is removed. In ordered binary trees the average number of deleted nodes as a result of cutting a single edge is equal to the average size of a
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Regression Models for Repairable Systems
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
Short-term load forecasting with increment regression tree
Energy Technology Data Exchange (ETDEWEB)
Yang, Jingfei; Stenzel, Juergen [Darmstadt University of Techonology, Darmstadt 64283 (Germany)
2006-06-15
This paper presents a new regression tree method for short-term load forecasting. Both increment and non-increment tree are built according to the historical data to provide the data space partition and input variable selection. Support vector machine is employed to the samples of regression tree nodes for further fine regression. Results of different tree nodes are integrated through weighted average method to obtain the comprehensive forecasting result. The effectiveness of the proposed method is demonstrated through its application to an actual system. (author)
Failure and reliability prediction by support vector machines regression of time series data
International Nuclear Information System (INIS)
Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique
2011-01-01
Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.
Transport of the moving barrier driven by chiral active particles
Liao, Jing-jing; Huang, Xiao-qun; Ai, Bao-quan
2018-03-01
Transport of a moving V-shaped barrier exposed to a bath of chiral active particles is investigated in a two-dimensional channel. Due to the chirality of active particles and the transversal asymmetry of the barrier position, active particles can power and steer the directed transport of the barrier in the longitudinal direction. The transport of the barrier is determined by the chirality of active particles. The moving barrier and active particles move in the opposite directions. The average velocity of the barrier is much larger than that of active particles. There exist optimal parameters (the chirality, the self-propulsion speed, the packing fraction, and the channel width) at which the average velocity of the barrier takes its maximal value. In particular, tailoring the geometry of the barrier and the active concentration provides novel strategies to control the transport properties of micro-objects or cargoes in an active medium.
Wang, Ling; Abdel-Aty, Mohamed; Wang, Xuesong; Yu, Rongjie
2018-02-01
There have been plenty of traffic safety studies based on average daily traffic (ADT), average hourly traffic (AHT), or microscopic traffic at 5 min intervals. Nevertheless, not enough research has compared the performance of these three types of safety studies, and seldom of previous studies have intended to find whether the results of one type of study is transferable to the other two studies. First, this study built three models: a Bayesian Poisson-lognormal model to estimate the daily crash frequency using ADT, a Bayesian Poisson-lognormal model to estimate the hourly crash frequency using AHT, and a Bayesian logistic regression model for the real-time safety analysis using microscopic traffic. The model results showed that the crash contributing factors found by different models were comparable but not the same. Four variables, i.e., the logarithm of volume, the standard deviation of speed, the logarithm of segment length, and the existence of diverge segment, were positively significant in the three models. Additionally, weaving segments experienced higher daily and hourly crash frequencies than merge and basic segments. Then, each of the ADT-based, AHT-based, and real-time models was used to estimate safety conditions at different levels: daily and hourly, meanwhile, the real-time model was also used in 5 min intervals. The results uncovered that the ADT- and AHT-based safety models performed similar in predicting daily and hourly crash frequencies, and the real-time safety model was able to provide hourly crash frequency. Copyright © 2017 Elsevier Ltd. All rights reserved.
GRID PRICING VERSUS AVERAGE PRICING FOR SLAUGHTER CATTLE: AN EMPIRICAL ANALYSIS
Fausti, Scott W.; Qasmi, Bashir A.
1999-01-01
The paper compares weekly producer revenue under grid pricing and average dressed weight pricing methods for 2560 cattle over a period of 102 weeks. Regression analysis is applied to identify factors affecting the revenue differential.
Moving event and moving participant in aspectual conceptions
Directory of Open Access Journals (Sweden)
Izutsu Katsunobu
2016-06-01
Full Text Available This study advances an analysis of the event conception of aspectual forms in four East Asian languages: Ainu, Japanese, Korean, and Ryukyuan. As earlier studies point out, event conceptions can be divided into two major types: the moving-event type and the moving-participant type, respectively. All aspectual forms in Ainu and Korean, and most forms in Japanese and Ryukyuan are based on that type of event conception. Moving-participant oriented Ainu and movingevent oriented Japanese occupy two extremes, between which Korean and Ryukyuan stand. Notwithstanding the geographical relationships among the four languages, Ryukyuan is closer to Ainu than to Korean, whereas Korean is closer to Ainu than to Japanese.
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Directional quantile regression in R
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
Polylinear regression analysis in radiochemistry
International Nuclear Information System (INIS)
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
Minimum Delay Moving Object Detection
Lao, Dong
2017-05-14
This thesis presents a general framework and method for detection of an object in a video based on apparent motion. The object moves, at some unknown time, differently than the “background” motion, which can be induced from camera motion. The goal of proposed method is to detect and segment the object as soon it moves in an online manner. Since motion estimation can be unreliable between frames, more than two frames are needed to reliably detect the object. Observing more frames before declaring a detection may lead to a more accurate detection and segmentation, since more motion may be observed leading to a stronger motion cue. However, this leads to greater delay. The proposed method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms, defined as declarations of detection before the object moves or incorrect or inaccurate segmentation at the detection time. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Averaging for solitons with nonlinearity management
International Nuclear Information System (INIS)
Pelinovsky, D.E.; Kevrekidis, P.G.; Frantzeskakis, D.J.
2003-01-01
We develop an averaging method for solitons of the nonlinear Schroedinger equation with a periodically varying nonlinearity coefficient, which is used to effectively describe solitons in Bose-Einstein condensates, in the context of the recently proposed technique of Feshbach resonance management. Using the derived local averaged equation, we study matter-wave bright and dark solitons and demonstrate a very good agreement between solutions of the averaged and full equations
DSCOVR Magnetometer Level 2 One Minute Averages
National Oceanic and Atmospheric Administration, Department of Commerce — Interplanetary magnetic field observations collected from magnetometer on DSCOVR satellite - 1-minute average of Level 1 data
DSCOVR Magnetometer Level 2 One Second Averages
National Oceanic and Atmospheric Administration, Department of Commerce — Interplanetary magnetic field observations collected from magnetometer on DSCOVR satellite - 1-second average of Level 1 data
Spacetime averaging of exotic singularity universes
International Nuclear Information System (INIS)
Dabrowski, Mariusz P.
2011-01-01
Taking a spacetime average as a measure of the strength of singularities we show that big-rips (type I) are stronger than big-bangs. The former have infinite spacetime averages while the latter have them equal to zero. The sudden future singularities (type II) and w-singularities (type V) have finite spacetime averages. The finite scale factor (type III) singularities for some values of the parameters may have an infinite average and in that sense they may be considered stronger than big-bangs.
NOAA Average Annual Salinity (3-Zone)
California Natural Resource Agency — The 3-Zone Average Annual Salinity Digital Geography is a digital spatial framework developed using geographic information system (GIS) technology. These salinity...
Yearly, seasonal and monthly daily average diffuse sky radiation models
International Nuclear Information System (INIS)
Kassem, A.S.; Mujahid, A.M.; Turner, D.W.
1993-01-01
A daily average diffuse sky radiation regression model based on daily global radiation was developed utilizing two year data taken near Blytheville, Arkansas (Lat. =35.9 0 N, Long. = 89.9 0 W), U.S.A. The model has a determination coefficient of 0.91 and 0.092 standard error of estimate. The data were also analyzed for a seasonal dependence and four seasonal average daily models were developed for the spring, summer, fall and winter seasons. The coefficient of determination is 0.93, 0.81, 0.94 and 0.93, whereas the standard error of estimate is 0.08, 0.102, 0.042 and 0.075 for spring, summer, fall and winter, respectively. A monthly average daily diffuse sky radiation model was also developed. The coefficient of determination is 0.92 and the standard error of estimate is 0.083. A seasonal monthly average model was also developed which has 0.91 coefficient of determination and 0.085 standard error of estimate. The developed monthly daily average and daily models compare well with a selected number of previously developed models. (author). 11 ref., figs., tabs
Automatic Moving Object Segmentation for Freely Moving Cameras
Directory of Open Access Journals (Sweden)
Yanli Wan
2014-01-01
Full Text Available This paper proposes a new moving object segmentation algorithm for freely moving cameras which is very common for the outdoor surveillance system, the car build-in surveillance system, and the robot navigation system. A two-layer based affine transformation model optimization method is proposed for camera compensation purpose, where the outer layer iteration is used to filter the non-background feature points, and the inner layer iteration is used to estimate a refined affine model based on the RANSAC method. Then the feature points are classified into foreground and background according to the detected motion information. A geodesic based graph cut algorithm is then employed to extract the moving foreground based on the classified features. Unlike the existing global optimization or the long term feature point tracking based method, our algorithm only performs on two successive frames to segment the moving foreground, which makes it suitable for the online video processing applications. The experiment results demonstrate the effectiveness of our algorithm in both of the high accuracy and the fast speed.
Improving consensus structure by eliminating averaging artifacts
Directory of Open Access Journals (Sweden)
KC Dukka B
2009-03-01
Full Text Available Abstract Background Common structural biology methods (i.e., NMR and molecular dynamics often produce ensembles of molecular structures. Consequently, averaging of 3D coordinates of molecular structures (proteins and RNA is a frequent approach to obtain a consensus structure that is representative of the ensemble. However, when the structures are averaged, artifacts can result in unrealistic local geometries, including unphysical bond lengths and angles. Results Herein, we describe a method to derive representative structures while limiting the number of artifacts. Our approach is based on a Monte Carlo simulation technique that drives a starting structure (an extended or a 'close-by' structure towards the 'averaged structure' using a harmonic pseudo energy function. To assess the performance of the algorithm, we applied our approach to Cα models of 1364 proteins generated by the TASSER structure prediction algorithm. The average RMSD of the refined model from the native structure for the set becomes worse by a mere 0.08 Å compared to the average RMSD of the averaged structures from the native structure (3.28 Å for refined structures and 3.36 A for the averaged structures. However, the percentage of atoms involved in clashes is greatly reduced (from 63% to 1%; in fact, the majority of the refined proteins had zero clashes. Moreover, a small number (38 of refined structures resulted in lower RMSD to the native protein versus the averaged structure. Finally, compared to PULCHRA 1, our approach produces representative structure of similar RMSD quality, but with much fewer clashes. Conclusion The benchmarking results demonstrate that our approach for removing averaging artifacts can be very beneficial for the structural biology community. Furthermore, the same approach can be applied to almost any problem where averaging of 3D coordinates is performed. Namely, structure averaging is also commonly performed in RNA secondary prediction 2, which
40 CFR 76.11 - Emissions averaging.
2010-07-01
... 40 Protection of Environment 16 2010-07-01 2010-07-01 false Emissions averaging. 76.11 Section 76.11 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) ACID RAIN NITROGEN OXIDES EMISSION REDUCTION PROGRAM § 76.11 Emissions averaging. (a) General...
Determinants of College Grade Point Averages
Bailey, Paul Dean
2012-01-01
Chapter 2: The Role of Class Difficulty in College Grade Point Averages. Grade Point Averages (GPAs) are widely used as a measure of college students' ability. Low GPAs can remove a students from eligibility for scholarships, and even continued enrollment at a university. However, GPAs are determined not only by student ability but also by the…
Carlson Wagonlit Travel is moving
2013-01-01
The renovation of the Main Building continues! Because of this, Carlson Wagonlit Travel will move from building 62 to building 510 on 4 October and the agency will be closed in the afternoon. An emergency service will be organised for official travels only. Phone: 022 799 75 73 & 022 799 75 78 / e-mail: cern@carlsonwagonlit.ch
Minimum Delay Moving Object Detection
Lao, Dong
2017-01-01
This thesis presents a general framework and method for detection of an object in a video based on apparent motion. The object moves, at some unknown time, differently than the “background” motion, which can be induced from camera motion. The goal
Congestion and residential moving behaviour
DEFF Research Database (Denmark)
Larsen, Morten Marott; Pilegaard, Ninette; Van Ommeren, Jos
2008-01-01
to congestion. We focus on the equilibrium in which some workers currently living in one region accept jobs in the other, with a fraction of them choosing to commute from their current residence to the new job in the other region and the remainder choosing to move to the region in which the new job is located...
International Nuclear Information System (INIS)
1983-12-01
The conceptual design of a moving ring reactor ''Karin-1'' has been carried out to advance fusion system design, to clarify the research and development problems, and to decide their priority. In order to attain these objectives, a D-T reactor with tritium breeding blanket is designed, a commercial reactor with net power output of 500 MWe is designed, the compatibility of plasma physics with fusion engineering is demonstrated, and some other guideline is indicated. A moving ring reactor is composed mainly of three parts. In the first formation section, a plasma ring is formed and heated up to ignition temperature. The plasma ring of compact torus is transported from the formation section through the next burning section to generate fusion power. Then the plasma ring moves into the last recovery section, and the energy and particles of the plasma ring are recovered. The outline of a moving ring reactor ''Karin-1'' is described. As a candidate material for the first wall, SiC was adopted to reduce the MHD effect and to minimize the interaction with neutrons and charged particles. The thin metal lining was applied to the SiC surface to solve the problem of the compatibility with lithium blanket. Plasma physics, the engineering aspect and the items of research and development are described. (Kako, I.)
Minimum Delay Moving Object Detection
Lao, Dong; Sundaramoorthi, Ganesh
2017-01-01
We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon
Spontaneous regression of pulmonary bullae
International Nuclear Information System (INIS)
Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.
2002-01-01
The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd
Quantum algorithm for linear regression
Wang, Guoming
2017-07-01
We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
Hybrid Heat Capacity - Moving Slab Laser Concept
International Nuclear Information System (INIS)
Stappaerts, E A
2002-01-01
A hybrid configuration of a heat capacity laser (HCL) and a moving slab laser (MSL) has been studied. Multiple volumes of solid-state laser material are sequentially diode-pumped and their energy extracted. When a volume reaches a maximum temperature after a ''sub-magazine depth'', it is moved out of the pumping region into a cooling region, and a new volume is introduced. The total magazine depth equals the submagazine depth times the number of volumes. The design parameters are chosen to provide high duty factor operation, resulting in effective use of the diode arrays. The concept significantly reduces diode array cost over conventional heat capacity lasers, and it is considered enabling for many potential applications. A conceptual design study of the hybrid configuration has been carried out. Three concepts were evaluated using CAD tools. The concepts are described and their relative merits discussed. Because of reduced disk size and diode cost, the hybrid concept may allow scaling to average powers on the order of 0.5 MW/module
Moving Magnetic Features Around a Pore
Energy Technology Data Exchange (ETDEWEB)
Kaithakkal, A. J.; Riethmüller, T. L.; Solanki, S. K.; Lagg, A.; Barthol, P.; Gandorfer, A.; Gizon, L.; Hirzberger, J.; VanNoort, M. [Max Planck Institute for Solar System Research, Justus-von-Liebig-Weg 3, Göttingen D-37077 (Germany); Rodríguez, J. Blanco [Grupo de Astronomía y Ciencias del Espacio, Universidad de Valencia, E-46980 Paterna, Valencia (Spain); Iniesta, J. C. Del Toro; Suárez, D. Orozco [Instituto de Astrofísica de Andalucía (CSIC), Apartado de Correos 3004, E-18080 Granada (Spain); Schmidt, W. [Kiepenheuer-Institut für Sonnenphysik, Schöneckstr. 6, D-79104 Freiburg (Germany); Pillet, V. Martínez [National Solar Observatory, 3665 Discovery Drive, Boulder, CO 80303 (United States); Knölker, M., E-mail: anjali@mps.mpg.de [High Altitude Observatory, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000 (United States)
2017-03-01
Spectropolarimetric observations from Sunrise/IMaX, obtained in 2013 June, are used for a statistical analysis to determine the physical properties of moving magnetic features (MMFs) observed near a pore. MMFs of the same and opposite polarity, with respect to the pore, are found to stream from its border at an average speed of 1.3 km s{sup −1} and 1.2 km s{sup −1}, respectively, with mainly same-polarity MMFs found further away from the pore. MMFs of both polarities are found to harbor rather weak, inclined magnetic fields. Opposite-polarity MMFs are blueshifted, whereas same-polarity MMFs do not show any preference for up- or downflows. Most of the MMFs are found to be of sub-arcsecond size and carry a mean flux of ∼1.2 × 10{sup 17} Mx.
The Effect of Direction on Cursor Moving Kinematics
Directory of Open Access Journals (Sweden)
Chiu-Ping Lu
2012-02-01
Full Text Available There have been only few studies to substantiate the kinematic characteristics of cursor movement. In this study, a quantitative experimental research method was used to explore the effect of moving direction on the kinematics of cursor movement in 24 typical young persons using our previously developed computerized measuring program. The results of multiple one way repeated measures ANOVAs and post hoc LSD tests demonstrated that the moving direction had effects on average velocity, movement time, movement unit and peak velocity. Moving leftward showed better efficiency than moving rightward, upward and downward from the kinematic evidences such as velocity, movement unit and time. Moreover, the unique pattern of the power spectral density (PSD of velocity (strategy for power application explained why the smoothness was still maintained while moving leftward even under an unstable situation with larger momentum. Moreover, the information from this cursor moving study can guide us to relocate the toolbars and icons in the window interface, especially for individuals with physical disabilities whose performances are easily interrupted while controlling the cursor in specific directions.
Estimating the exceedance probability of rain rate by logistic regression
Chiu, Long S.; Kedem, Benjamin
1990-01-01
Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.
Radiation regression patterns after cobalt plaque insertion for retinoblastoma
International Nuclear Information System (INIS)
Buys, R.J.; Abramson, D.H.; Ellsworth, R.M.; Haik, B.
1983-01-01
An analysis of 31 eyes of 30 patients who had been treated with cobalt plaques for retinoblastoma disclosed that a type I radiation regression pattern developed in 15 patients; type II, in one patient, and type III, in five patients. Nine patients had a regression pattern characterized by complete destruction of the tumor, the surrounding choroid, and all of the vessels in the area into which the plaque was inserted. This resulting white scar, corresponding to the sclerae only, was classified as a type IV radiation regression pattern. There was no evidence of tumor recurrence in patients with type IV regression patterns, with an average follow-up of 6.5 years, after receiving cobalt plaque therapy. Twenty-nine of these 30 patients had been unsuccessfully treated with at least one other modality (ie, light coagulation, cryotherapy, external beam radiation, or chemotherapy)
Radiation regression patterns after cobalt plaque insertion for retinoblastoma
Energy Technology Data Exchange (ETDEWEB)
Buys, R.J.; Abramson, D.H.; Ellsworth, R.M.; Haik, B.
1983-08-01
An analysis of 31 eyes of 30 patients who had been treated with cobalt plaques for retinoblastoma disclosed that a type I radiation regression pattern developed in 15 patients; type II, in one patient, and type III, in five patients. Nine patients had a regression pattern characterized by complete destruction of the tumor, the surrounding choroid, and all of the vessels in the area into which the plaque was inserted. This resulting white scar, corresponding to the sclerae only, was classified as a type IV radiation regression pattern. There was no evidence of tumor recurrence in patients with type IV regression patterns, with an average follow-up of 6.5 years, after receiving cobalt plaque therapy. Twenty-nine of these 30 patients had been unsuccessfully treated with at least one other modality (ie, light coagulation, cryotherapy, external beam radiation, or chemotherapy).
Computation of the bounce-average code
International Nuclear Information System (INIS)
Cutler, T.A.; Pearlstein, L.D.; Rensink, M.E.
1977-01-01
The bounce-average computer code simulates the two-dimensional velocity transport of ions in a mirror machine. The code evaluates and bounce-averages the collision operator and sources along the field line. A self-consistent equilibrium magnetic field is also computed using the long-thin approximation. Optionally included are terms that maintain μ, J invariance as the magnetic field changes in time. The assumptions and analysis that form the foundation of the bounce-average code are described. When references can be cited, the required results are merely stated and explained briefly. A listing of the code is appended
International Nuclear Information System (INIS)
Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.
2013-01-01
Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability
Prediction, Regression and Critical Realism
DEFF Research Database (Denmark)
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...
On Weighted Support Vector Regression
DEFF Research Database (Denmark)
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Yin, Yip Chee; Hock-Eam, Lim
2012-09-01
This paper investigates the forecasting ability of Mallows Model Averaging (MMA) by conducting an empirical analysis of five Asia countries, Malaysia, Thailand, Philippines, Indonesia and China's GDP growth rate. Results reveal that MMA has no noticeable differences in predictive ability compared to the general autoregressive fractional integrated moving average model (ARFIMA) and its predictive ability is sensitive to the effect of financial crisis. MMA could be an alternative forecasting method for samples without recent outliers such as financial crisis.
Post-model selection inference and model averaging
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2011-07-01
Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.
Rotational averaging of multiphoton absorption cross sections
Energy Technology Data Exchange (ETDEWEB)
Friese, Daniel H., E-mail: daniel.h.friese@uit.no; Beerepoot, Maarten T. P.; Ruud, Kenneth [Centre for Theoretical and Computational Chemistry, University of Tromsø — The Arctic University of Norway, N-9037 Tromsø (Norway)
2014-11-28
Rotational averaging of tensors is a crucial step in the calculation of molecular properties in isotropic media. We present a scheme for the rotational averaging of multiphoton absorption cross sections. We extend existing literature on rotational averaging to even-rank tensors of arbitrary order and derive equations that require only the number of photons as input. In particular, we derive the first explicit expressions for the rotational average of five-, six-, and seven-photon absorption cross sections. This work is one of the required steps in making the calculation of these higher-order absorption properties possible. The results can be applied to any even-rank tensor provided linearly polarized light is used.
Sea Surface Temperature Average_SST_Master
National Oceanic and Atmospheric Administration, Department of Commerce — Sea surface temperature collected via satellite imagery from http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.html and averaged for each region using ArcGIS...
Trajectory averaging for stochastic approximation MCMC algorithms
Liang, Faming
2010-01-01
to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305-320]. The application of the trajectory averaging estimator to other stochastic approximationMCMC algorithms, for example, a stochastic
Should the average tax rate be marginalized?
Czech Academy of Sciences Publication Activity Database
Feldman, N. E.; Katuščák, Peter
-, č. 304 (2006), s. 1-65 ISSN 1211-3298 Institutional research plan: CEZ:MSM0021620846 Keywords : tax * labor supply * average tax Subject RIV: AH - Economics http://www.cerge-ei.cz/pdf/wp/Wp304.pdf
A practical guide to averaging functions
Beliakov, Gleb; Calvo Sánchez, Tomasa
2016-01-01
This book offers an easy-to-use and practice-oriented reference guide to mathematical averages. It presents different ways of aggregating input values given on a numerical scale, and of choosing and/or constructing aggregating functions for specific applications. Building on a previous monograph by Beliakov et al. published by Springer in 2007, it outlines new aggregation methods developed in the interim, with a special focus on the topic of averaging aggregation functions. It examines recent advances in the field, such as aggregation on lattices, penalty-based aggregation and weakly monotone averaging, and extends many of the already existing methods, such as: ordered weighted averaging (OWA), fuzzy integrals and mixture functions. A substantial mathematical background is not called for, as all the relevant mathematical notions are explained here and reported on together with a wealth of graphical illustrations of distinct families of aggregation functions. The authors mainly focus on practical applications ...
MN Temperature Average (1961-1990) - Line
Minnesota Department of Natural Resources — This data set depicts 30-year averages (1961-1990) of monthly and annual temperatures for Minnesota. Isolines and regions were created using kriging and...
MN Temperature Average (1961-1990) - Polygon
Minnesota Department of Natural Resources — This data set depicts 30-year averages (1961-1990) of monthly and annual temperatures for Minnesota. Isolines and regions were created using kriging and...
The Value and Feasibility of Farming Differently Than the Local Average
Morris, Cooper; Dhuyvetter, Kevin; Yeager, Elizabeth A; Regier, Greg
2018-01-01
The purpose of this research is to quantify the value of being different than the local average and feasibility of distinguishing particular parts of an operation from the local average. Kansas crop farms are broken down by their farm characteristics, production practices, and management performances. An ordinary least squares regression model is used to quantify the value of having different than average characteristics, practices, and management performances. The degree farms have distingui...
Average Bandwidth Allocation Model of WFQ
Directory of Open Access Journals (Sweden)
Tomáš Balogh
2012-01-01
Full Text Available We present a new iterative method for the calculation of average bandwidth assignment to traffic flows using a WFQ scheduler in IP based NGN networks. The bandwidth assignment calculation is based on the link speed, assigned weights, arrival rate, and average packet length or input rate of the traffic flows. We prove the model outcome with examples and simulation results using NS2 simulator.
Nonequilibrium statistical averages and thermo field dynamics
International Nuclear Information System (INIS)
Marinaro, A.; Scarpetta, Q.
1984-01-01
An extension of thermo field dynamics is proposed, which permits the computation of nonequilibrium statistical averages. The Brownian motion of a quantum oscillator is treated as an example. In conclusion it is pointed out that the procedure proposed to computation of time-dependent statistical average gives the correct two-point Green function for the damped oscillator. A simple extension can be used to compute two-point Green functions of free particles
Moving Manifolds in Electromagnetic Fields
Directory of Open Access Journals (Sweden)
David V. Svintradze
2017-08-01
Full Text Available We propose dynamic non-linear equations for moving surfaces in an electromagnetic field. The field is induced by a material body with a boundary of the surface. Correspondingly the potential energy, set by the field at the boundary can be written as an addition of four-potential times four-current to a contraction of the electromagnetic tensor. Proper application of the minimal action principle to the system Lagrangian yields dynamic non-linear equations for moving three dimensional manifolds in electromagnetic fields. The equations in different conditions simplify to Maxwell equations for massless three surfaces, to Euler equations for a dynamic fluid, to magneto-hydrodynamic equations and to the Poisson-Boltzmann equation.
de Jong, Johan; Shokoohi, Roya
2017-01-01
Insufficient physical activity or being inactive is one of the leading risk factors for non-communicable diseases worldwide. Globally between 6-10% of premature mortality, caused by non-communicable diseases, could be avoided if people adhered to general physical activity guidelines. Besides that, studies link sitting for prolonged periods of time with many serious health concerns. The solution seems simple: Stand up and move forward. However, human behavior is difficult to change – due to th...
Improved averaging for non-null interferometry
Fleig, Jon F.; Murphy, Paul E.
2013-09-01
Arithmetic averaging of interferometric phase measurements is a well-established method for reducing the effects of time varying disturbances, such as air turbulence and vibration. Calculating a map of the standard deviation for each pixel in the average map can provide a useful estimate of its variability. However, phase maps of complex and/or high density fringe fields frequently contain defects that severely impair the effectiveness of simple phase averaging and bias the variability estimate. These defects include large or small-area phase unwrapping artifacts, large alignment components, and voids that change in number, location, or size. Inclusion of a single phase map with a large area defect into the average is usually sufficient to spoil the entire result. Small-area phase unwrapping and void defects may not render the average map metrologically useless, but they pessimistically bias the variance estimate for the overwhelming majority of the data. We present an algorithm that obtains phase average and variance estimates that are robust against both large and small-area phase defects. It identifies and rejects phase maps containing large area voids or unwrapping artifacts. It also identifies and prunes the unreliable areas of otherwise useful phase maps, and removes the effect of alignment drift from the variance estimate. The algorithm has several run-time adjustable parameters to adjust the rejection criteria for bad data. However, a single nominal setting has been effective over a wide range of conditions. This enhanced averaging algorithm can be efficiently integrated with the phase map acquisition process to minimize the number of phase samples required to approach the practical noise floor of the metrology environment.
Energy Technology Data Exchange (ETDEWEB)
Hourdakis, C J, E-mail: khour@gaec.gr [Ionizing Radiation Calibration Laboratory-Greek Atomic Energy Commission, PO Box 60092, 15310 Agia Paraskevi, Athens, Attiki (Greece)
2011-04-07
The practical peak voltage (PPV) has been adopted as the reference measuring quantity for the x-ray tube voltage. However, the majority of commercial kV-meter models measure the average peak, U-bar{sub P}, the average, U-bar, the effective, U{sub eff} or the maximum peak, U{sub P} tube voltage. This work proposed a method for determination of the PPV from measurements with a kV-meter that measures the average U-bar or the average peak, U-bar{sub p} voltage. The kV-meter reading can be converted to the PPV by applying appropriate calibration coefficients and conversion factors. The average peak k{sub PPV,kVp} and the average k{sub PPV,Uav} conversion factors were calculated from virtual voltage waveforms for conventional diagnostic radiology (50-150 kV) and mammography (22-35 kV) tube voltages and for voltage ripples from 0% to 100%. Regression equation and coefficients provide the appropriate conversion factors at any given tube voltage and ripple. The influence of voltage waveform irregularities, like 'spikes' and pulse amplitude variations, on the conversion factors was investigated and discussed. The proposed method and the conversion factors were tested using six commercial kV-meters at several x-ray units. The deviations between the reference and the calculated - according to the proposed method - PPV values were less than 2%. Practical aspects on the voltage ripple measurement were addressed and discussed. The proposed method provides a rigorous base to determine the PPV with kV-meters from U-bar{sub p} and U-bar measurement. Users can benefit, since all kV-meters, irrespective of their measuring quantity, can be used to determine the PPV, complying with the IEC standard requirements.
DRREP: deep ridge regressed epitope predictor.
Sher, Gene; Zhi, Degui; Zhang, Shaojie
2017-10-03
The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Regularized Label Relaxation Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu
2018-04-01
Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.
Random walk of passive tracers among randomly moving obstacles.
Gori, Matteo; Donato, Irene; Floriani, Elena; Nardecchia, Ilaria; Pettini, Marco
2016-04-14
This study is mainly motivated by the need of understanding how the diffusion behavior of a biomolecule (or even of a larger object) is affected by other moving macromolecules, organelles, and so on, inside a living cell, whence the possibility of understanding whether or not a randomly walking biomolecule is also subject to a long-range force field driving it to its target. By means of the Continuous Time Random Walk (CTRW) technique the topic of random walk in random environment is here considered in the case of a passively diffusing particle among randomly moving and interacting obstacles. The relevant physical quantity which is worked out is the diffusion coefficient of the passive tracer which is computed as a function of the average inter-obstacles distance. The results reported here suggest that if a biomolecule, let us call it a test molecule, moves towards its target in the presence of other independently interacting molecules, its motion can be considerably slowed down.
Asynchronous Gossip for Averaging and Spectral Ranking
Borkar, Vivek S.; Makhijani, Rahul; Sundaresan, Rajesh
2014-08-01
We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.
Benchmarking statistical averaging of spectra with HULLAC
Klapisch, Marcel; Busquet, Michel
2008-11-01
Knowledge of radiative properties of hot plasmas is important for ICF, astrophysics, etc When mid-Z or high-Z elements are present, the spectra are so complex that one commonly uses statistically averaged description of atomic systems [1]. In a recent experiment on Fe[2], performed under controlled conditions, high resolution transmission spectra were obtained. The new version of HULLAC [3] allows the use of the same model with different levels of details/averaging. We will take advantage of this feature to check the effect of averaging with comparison with experiment. [1] A Bar-Shalom, J Oreg, and M Klapisch, J. Quant. Spectros. Rad. Transf. 65, 43 (2000). [2] J. E. Bailey, G. A. Rochau, C. A. Iglesias et al., Phys. Rev. Lett. 99, 265002-4 (2007). [3]. M. Klapisch, M. Busquet, and A. Bar-Shalom, AIP Conference Proceedings 926, 206-15 (2007).
An approach to averaging digitized plantagram curves.
Hawes, M R; Heinemeyer, R; Sovak, D; Tory, B
1994-07-01
The averaging of outline shapes of the human foot for the purposes of determining information concerning foot shape and dimension within the context of comfort of fit of sport shoes is approached as a mathematical problem. An outline of the human footprint is obtained by standard procedures and the curvature is traced with a Hewlett Packard Digitizer. The paper describes the determination of an alignment axis, the identification of two ray centres and the division of the total curve into two overlapping arcs. Each arc is divided by equiangular rays which intersect chords between digitized points describing the arc. The radial distance of each ray is averaged within groups of foot lengths which vary by +/- 2.25 mm (approximately equal to 1/2 shoe size). The method has been used to determine average plantar curves in a study of 1197 North American males (Hawes and Sovak 1993).
Exploiting scale dependence in cosmological averaging
International Nuclear Information System (INIS)
Mattsson, Teppo; Ronkainen, Maria
2008-01-01
We study the role of scale dependence in the Buchert averaging method, using the flat Lemaitre–Tolman–Bondi model as a testing ground. Within this model, a single averaging scale gives predictions that are too coarse, but by replacing it with the distance of the objects R(z) for each redshift z, we find an O(1%) precision at z<2 in the averaged luminosity and angular diameter distances compared to their exact expressions. At low redshifts, we show the improvement for generic inhomogeneity profiles, and our numerical computations further verify it up to redshifts z∼2. At higher redshifts, the method breaks down due to its inability to capture the time evolution of the inhomogeneities. We also demonstrate that the running smoothing scale R(z) can mimic acceleration, suggesting that it could be at least as important as the backreaction in explaining dark energy as an inhomogeneity induced illusion
Stochastic Averaging and Stochastic Extremum Seeking
Liu, Shu-Jun
2012-01-01
Stochastic Averaging and Stochastic Extremum Seeking develops methods of mathematical analysis inspired by the interest in reverse engineering and analysis of bacterial convergence by chemotaxis and to apply similar stochastic optimization techniques in other environments. The first half of the text presents significant advances in stochastic averaging theory, necessitated by the fact that existing theorems are restricted to systems with linear growth, globally exponentially stable average models, vanishing stochastic perturbations, and prevent analysis over infinite time horizon. The second half of the text introduces stochastic extremum seeking algorithms for model-free optimization of systems in real time using stochastic perturbations for estimation of their gradients. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms...
Aperture averaging in strong oceanic turbulence
Gökçe, Muhsin Caner; Baykal, Yahya
2018-04-01
Receiver aperture averaging technique is employed in underwater wireless optical communication (UWOC) systems to mitigate the effects of oceanic turbulence, thus to improve the system performance. The irradiance flux variance is a measure of the intensity fluctuations on a lens of the receiver aperture. Using the modified Rytov theory which uses the small-scale and large-scale spatial filters, and our previously presented expression that shows the atmospheric structure constant in terms of oceanic turbulence parameters, we evaluate the irradiance flux variance and the aperture averaging factor of a spherical wave in strong oceanic turbulence. Irradiance flux variance variations are examined versus the oceanic turbulence parameters and the receiver aperture diameter are examined in strong oceanic turbulence. Also, the effect of the receiver aperture diameter on the aperture averaging factor is presented in strong oceanic turbulence.
Regional averaging and scaling in relativistic cosmology
International Nuclear Information System (INIS)
Buchert, Thomas; Carfora, Mauro
2002-01-01
Averaged inhomogeneous cosmologies lie at the forefront of interest, since cosmological parameters such as the rate of expansion or the mass density are to be considered as volume-averaged quantities and only these can be compared with observations. For this reason the relevant parameters are intrinsically scale-dependent and one wishes to control this dependence without restricting the cosmological model by unphysical assumptions. In the latter respect we contrast our way to approach the averaging problem in relativistic cosmology with shortcomings of averaged Newtonian models. Explicitly, we investigate the scale-dependence of Eulerian volume averages of scalar functions on Riemannian three-manifolds. We propose a complementary view of a Lagrangian smoothing of (tensorial) variables as opposed to their Eulerian averaging on spatial domains. This programme is realized with the help of a global Ricci deformation flow for the metric. We explain rigorously the origin of the Ricci flow which, on heuristic grounds, has already been suggested as a possible candidate for smoothing the initial dataset for cosmological spacetimes. The smoothing of geometry implies a renormalization of averaged spatial variables. We discuss the results in terms of effective cosmological parameters that would be assigned to the smoothed cosmological spacetime. In particular, we find that on the smoothed spatial domain B-bar evaluated cosmological parameters obey Ω-bar B-bar m + Ω-bar B-bar R + Ω-bar B-bar A + Ω-bar B-bar Q 1, where Ω-bar B-bar m , Ω-bar B-bar R and Ω-bar B-bar A correspond to the standard Friedmannian parameters, while Ω-bar B-bar Q is a remnant of cosmic variance of expansion and shear fluctuations on the averaging domain. All these parameters are 'dressed' after smoothing out the geometrical fluctuations, and we give the relations of the 'dressed' to the 'bare' parameters. While the former provide the framework of interpreting observations with a 'Friedmannian bias
Average: the juxtaposition of procedure and context
Watson, Jane; Chick, Helen; Callingham, Rosemary
2014-09-01
This paper presents recent data on the performance of 247 middle school students on questions concerning average in three contexts. Analysis includes considering levels of understanding linking definition and context, performance across contexts, the relative difficulty of tasks, and difference in performance for male and female students. The outcomes lead to a discussion of the expectations of the curriculum and its implementation, as well as assessment, in relation to students' skills in carrying out procedures and their understanding about the meaning of average in context.
Average-case analysis of numerical problems
2000-01-01
The average-case analysis of numerical problems is the counterpart of the more traditional worst-case approach. The analysis of average error and cost leads to new insight on numerical problems as well as to new algorithms. The book provides a survey of results that were mainly obtained during the last 10 years and also contains new results. The problems under consideration include approximation/optimal recovery and numerical integration of univariate and multivariate functions as well as zero-finding and global optimization. Background material, e.g. on reproducing kernel Hilbert spaces and random fields, is provided.
Grassmann Averages for Scalable Robust PCA
DEFF Research Database (Denmark)
Hauberg, Søren; Feragen, Aasa; Black, Michael J.
2014-01-01
As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase—“big data” implies “big outliers”. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can...... to vectors (subspaces) or elements of vectors; we focus on the latter and use a trimmed average. The resulting Trimmed Grassmann Average (TGA) is particularly appropriate for computer vision because it is robust to pixel outliers. The algorithm has low computational complexity and minimal memory requirements...
Directory of Open Access Journals (Sweden)
C. Zerefos
2018-05-01
Full Text Available This paper is focusing on the representativeness of single lidar stations for zonally averaged ozone profile variations over the middle and upper stratosphere. From the lower to the upper stratosphere, ozone profiles from single or grouped lidar stations correlate well with zonal means calculated from the Solar Backscatter Ultraviolet Radiometer (SBUV satellite overpasses. The best representativeness with significant correlation coefficients is found within ±15° of latitude circles north or south of any lidar station. This paper also includes a multivariate linear regression (MLR analysis on the relative importance of proxy time series for explaining variations in the vertical ozone profiles. Studied proxies represent variability due to influences outside of the earth system (solar cycle and within the earth system, i.e. dynamic processes (the Quasi Biennial Oscillation, QBO; the Arctic Oscillation, AO; the Antarctic Oscillation, AAO; the El Niño Southern Oscillation, ENSO, those due to volcanic aerosol (aerosol optical depth, AOD, tropopause height changes (including global warming and those influences due to anthropogenic contributions to atmospheric chemistry (equivalent effective stratospheric chlorine, EESC. Ozone trends are estimated, with and without removal of proxies, from the total available 1980 to 2015 SBUV record. Except for the chemistry related proxy (EESC and its orthogonal function, the removal of the other proxies does not alter the significance of the estimated long-term trends. At heights above 15 hPa an inflection point between 1997 and 1999 marks the end of significant negative ozone trends, followed by a recent period between 1998 and 2015 with positive ozone trends. At heights between 15 and 40 hPa the pre-1998 negative ozone trends tend to become less significant as we move towards 2015, below which the lower stratosphere ozone decline continues in agreement with findings of recent literature.
Zerefos, Christos; Kapsomenakis, John; Eleftheratos, Kostas; Tourpali, Kleareti; Petropavlovskikh, Irina; Hubert, Daan; Godin-Beekmann, Sophie; Steinbrecht, Wolfgang; Frith, Stacey; Sofieva, Viktoria; Hassler, Birgit
2018-05-01
This paper is focusing on the representativeness of single lidar stations for zonally averaged ozone profile variations over the middle and upper stratosphere. From the lower to the upper stratosphere, ozone profiles from single or grouped lidar stations correlate well with zonal means calculated from the Solar Backscatter Ultraviolet Radiometer (SBUV) satellite overpasses. The best representativeness with significant correlation coefficients is found within ±15° of latitude circles north or south of any lidar station. This paper also includes a multivariate linear regression (MLR) analysis on the relative importance of proxy time series for explaining variations in the vertical ozone profiles. Studied proxies represent variability due to influences outside of the earth system (solar cycle) and within the earth system, i.e. dynamic processes (the Quasi Biennial Oscillation, QBO; the Arctic Oscillation, AO; the Antarctic Oscillation, AAO; the El Niño Southern Oscillation, ENSO), those due to volcanic aerosol (aerosol optical depth, AOD), tropopause height changes (including global warming) and those influences due to anthropogenic contributions to atmospheric chemistry (equivalent effective stratospheric chlorine, EESC). Ozone trends are estimated, with and without removal of proxies, from the total available 1980 to 2015 SBUV record. Except for the chemistry related proxy (EESC) and its orthogonal function, the removal of the other proxies does not alter the significance of the estimated long-term trends. At heights above 15 hPa an inflection point between 1997 and 1999 marks the end of significant negative ozone trends, followed by a recent period between 1998 and 2015 with positive ozone trends. At heights between 15 and 40 hPa the pre-1998 negative ozone trends tend to become less significant as we move towards 2015, below which the lower stratosphere ozone decline continues in agreement with findings of recent literature.
Model averaging, optimal inference and habit formation
Directory of Open Access Journals (Sweden)
Thomas H B FitzGerald
2014-06-01
Full Text Available Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour.
Generalized Jackknife Estimators of Weighted Average Derivatives
DEFF Research Database (Denmark)
Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael
With the aim of improving the quality of asymptotic distributional approximations for nonlinear functionals of nonparametric estimators, this paper revisits the large-sample properties of an important member of that class, namely a kernel-based weighted average derivative estimator. Asymptotic...
Average beta measurement in EXTRAP T1
International Nuclear Information System (INIS)
Hedin, E.R.
1988-12-01
Beginning with the ideal MHD pressure balance equation, an expression for the average poloidal beta, Β Θ , is derived. A method for unobtrusively measuring the quantities used to evaluate Β Θ in Extrap T1 is described. The results if a series of measurements yielding Β Θ as a function of externally applied toroidal field are presented. (author)
HIGH AVERAGE POWER OPTICAL FEL AMPLIFIERS
International Nuclear Information System (INIS)
2005-01-01
Historically, the first demonstration of the optical FEL was in an amplifier configuration at Stanford University [l]. There were other notable instances of amplifying a seed laser, such as the LLNL PALADIN amplifier [2] and the BNL ATF High-Gain Harmonic Generation FEL [3]. However, for the most part FELs are operated as oscillators or self amplified spontaneous emission devices. Yet, in wavelength regimes where a conventional laser seed can be used, the FEL can be used as an amplifier. One promising application is for very high average power generation, for instance FEL's with average power of 100 kW or more. The high electron beam power, high brightness and high efficiency that can be achieved with photoinjectors and superconducting Energy Recovery Linacs (ERL) combine well with the high-gain FEL amplifier to produce unprecedented average power FELs. This combination has a number of advantages. In particular, we show that for a given FEL power, an FEL amplifier can introduce lower energy spread in the beam as compared to a traditional oscillator. This properly gives the ERL based FEL amplifier a great wall-plug to optical power efficiency advantage. The optics for an amplifier is simple and compact. In addition to the general features of the high average power FEL amplifier, we will look at a 100 kW class FEL amplifier is being designed to operate on the 0.5 ampere Energy Recovery Linac which is under construction at Brookhaven National Laboratory's Collider-Accelerator Department
Bayesian Averaging is Well-Temperated
DEFF Research Database (Denmark)
Hansen, Lars Kai
2000-01-01
Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a finite sample. While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is l...
Gibbs equilibrium averages and Bogolyubov measure
International Nuclear Information System (INIS)
Sankovich, D.P.
2011-01-01
Application of the functional integration methods in equilibrium statistical mechanics of quantum Bose-systems is considered. We show that Gibbs equilibrium averages of Bose-operators can be represented as path integrals over a special Gauss measure defined in the corresponding space of continuous functions. We consider some problems related to integration with respect to this measure
High average-power induction linacs
International Nuclear Information System (INIS)
Prono, D.S.; Barrett, D.; Bowles, E.; Caporaso, G.J.; Chen, Yu-Jiuan; Clark, J.C.; Coffield, F.; Newton, M.A.; Nexsen, W.; Ravenscroft, D.; Turner, W.C.; Watson, J.A.
1989-01-01
Induction linear accelerators (LIAs) are inherently capable of accelerating several thousand amperes of ∼ 50-ns duration pulses to > 100 MeV. In this paper the authors report progress and status in the areas of duty factor and stray power management. These technologies are vital if LIAs are to attain high average power operation. 13 figs
Function reconstruction from noisy local averages
International Nuclear Information System (INIS)
Chen Yu; Huang Jianguo; Han Weimin
2008-01-01
A regularization method is proposed for the function reconstruction from noisy local averages in any dimension. Error bounds for the approximate solution in L 2 -norm are derived. A number of numerical examples are provided to show computational performance of the method, with the regularization parameters selected by different strategies
A singularity theorem based on spatial averages
Indian Academy of Sciences (India)
journal of. July 2007 physics pp. 31–47. A singularity theorem based on spatial ... In this paper I would like to present a result which confirms – at least partially – ... A detailed analysis of how the model fits in with the .... Further, the statement that the spatial average ...... Financial support under grants FIS2004-01626 and no.
Multiphase averaging of periodic soliton equations
International Nuclear Information System (INIS)
Forest, M.G.
1979-01-01
The multiphase averaging of periodic soliton equations is considered. Particular attention is given to the periodic sine-Gordon and Korteweg-deVries (KdV) equations. The periodic sine-Gordon equation and its associated inverse spectral theory are analyzed, including a discussion of the spectral representations of exact, N-phase sine-Gordon solutions. The emphasis is on physical characteristics of the periodic waves, with a motivation from the well-known whole-line solitons. A canonical Hamiltonian approach for the modulational theory of N-phase waves is prescribed. A concrete illustration of this averaging method is provided with the periodic sine-Gordon equation; explicit averaging results are given only for the N = 1 case, laying a foundation for a more thorough treatment of the general N-phase problem. For the KdV equation, very general results are given for multiphase averaging of the N-phase waves. The single-phase results of Whitham are extended to general N phases, and more importantly, an invariant representation in terms of Abelian differentials on a Riemann surface is provided. Several consequences of this invariant representation are deduced, including strong evidence for the Hamiltonian structure of N-phase modulational equations
Essays on model averaging and political economics
Wang, W.
2013-01-01
This thesis first investigates various issues related with model averaging, and then evaluates two policies, i.e. West Development Drive in China and fiscal decentralization in U.S, using econometric tools. Chapter 2 proposes a hierarchical weighted least squares (HWALS) method to address multiple
2010-01-01
... 7 Agriculture 10 2010-01-01 2010-01-01 false On average. 1209.12 Section 1209.12 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (MARKETING AGREEMENTS... CONSUMER INFORMATION ORDER Mushroom Promotion, Research, and Consumer Information Order Definitions § 1209...
High average-power induction linacs
International Nuclear Information System (INIS)
Prono, D.S.; Barrett, D.; Bowles, E.
1989-01-01
Induction linear accelerators (LIAs) are inherently capable of accelerating several thousand amperes of /approximately/ 50-ns duration pulses to > 100 MeV. In this paper we report progress and status in the areas of duty factor and stray power management. These technologies are vital if LIAs are to attain high average power operation. 13 figs
Average Costs versus Net Present Value
E.A. van der Laan (Erwin); R.H. Teunter (Ruud)
2000-01-01
textabstractWhile the net present value (NPV) approach is widely accepted as the right framework for studying production and inventory control systems, average cost (AC) models are more widely used. For the well known EOQ model it can be verified that (under certain conditions) the AC approach gives
Average beta-beating from random errors
Tomas Garcia, Rogelio; Langner, Andy Sven; Malina, Lukas; Franchi, Andrea; CERN. Geneva. ATS Department
2018-01-01
The impact of random errors on average β-beating is studied via analytical derivations and simulations. A systematic positive β-beating is expected from random errors quadratic with the sources or, equivalently, with the rms β-beating. However, random errors do not have a systematic eﬀect on the tune.
Reliability Estimates for Undergraduate Grade Point Average
Westrick, Paul A.
2017-01-01
Undergraduate grade point average (GPA) is a commonly employed measure in educational research, serving as a criterion or as a predictor depending on the research question. Over the decades, researchers have used a variety of reliability coefficients to estimate the reliability of undergraduate GPA, which suggests that there has been no consensus…
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Moving walls and geometric phases
Energy Technology Data Exchange (ETDEWEB)
Facchi, Paolo, E-mail: paolo.facchi@ba.infn.it [Dipartimento di Fisica and MECENAS, Università di Bari, I-70126 Bari (Italy); INFN, Sezione di Bari, I-70126 Bari (Italy); Garnero, Giancarlo, E-mail: giancarlo.garnero@uniba.it [Dipartimento di Fisica and MECENAS, Università di Bari, I-70126 Bari (Italy); INFN, Sezione di Bari, I-70126 Bari (Italy); Marmo, Giuseppe [Dipartimento di Scienze Fisiche and MECENAS, Università di Napoli “Federico II”, I-80126 Napoli (Italy); INFN, Sezione di Napoli, I-80126 Napoli (Italy); Samuel, Joseph [Raman Research Institute, 560080 Bangalore (India)
2016-09-15
We unveil the existence of a non-trivial Berry phase associated to the dynamics of a quantum particle in a one dimensional box with moving walls. It is shown that a suitable choice of boundary conditions has to be made in order to preserve unitarity. For these boundary conditions we compute explicitly the geometric phase two-form on the parameter space. The unboundedness of the Hamiltonian describing the system leads to a natural prescription of renormalization for divergent contributions arising from the boundary.
Moving Tourism Social Entrepreneurship Forward
DEFF Research Database (Denmark)
Sheldon, Pauline; Dredge, Dianne; Daniele, Roberto
2017-01-01
This chapter concludes the book by considering the role that research and education can play to move the TSE agenda forward. In addition to consolidating the chapter authors’ thoughts about the future of SE and tourism, it also lays out some directions for research tracks in the future....... It considers the changes needed in research approaches, in our universities, our curricula, our learners, and ourselves as academics. These changes we hope will stimulate the dialog on how TSE can mobilize the energy, vision and social spirit of those who seek to change the world for the better through tourism....
Energy Technology Data Exchange (ETDEWEB)
Redondo, Javier [Muenchen Univ. (Germany). Arnold Sommerfeld Center; Max-Planck-Institut fuer Physik, Muenchen (Germany); Doebrich, Babette [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
2013-11-15
This proceedings contribution reports from the workshop Dark Matter - a light move, held at DESY in Hamburg in June 2013. Dark Matter particle candidates span a huge parameter range. In particular, well motivated candidates exist also in the sub-eV mass region, for example the axion. Whilst a plethora of searches for rather heavy Dark Matter particles exists, there are only very few experiments aimed at direct detection of sub-eV Dark Matter to this date. The aim of our workshop was to discuss if and how this could be changed in the near future.
Live histograms in moving windows
International Nuclear Information System (INIS)
Zhil'tsov, V.E.
1989-01-01
Application of computer graphics for specific hardware testing is discussed. The hardware is position sensitive detector (multiwire proportional chamber) which is used in high energy physics experiments, and real-out electronics for it. Testing program is described (XPERT), which utilises multi-window user interface. Data are represented as histograms in windows. The windows on the screen may be moved, reordered, their sizes may be changed. Histograms may be put to any window, and hardcopy may be made. Some program internals are discussed. The computer environment is quite simple: MS-DOS IBM PC/XT, 256 KB RAM, CGA, 5.25'' FD, Epson MX. 4 refs.; 7 figs
International Nuclear Information System (INIS)
Redondo, Javier; Doebrich, Babette
2013-11-01
This proceedings contribution reports from the workshop Dark Matter - a light move, held at DESY in Hamburg in June 2013. Dark Matter particle candidates span a huge parameter range. In particular, well motivated candidates exist also in the sub-eV mass region, for example the axion. Whilst a plethora of searches for rather heavy Dark Matter particles exists, there are only very few experiments aimed at direct detection of sub-eV Dark Matter to this date. The aim of our workshop was to discuss if and how this could be changed in the near future.
Tendon surveillance requirements - average tendon force
International Nuclear Information System (INIS)
Fulton, J.F.
1982-01-01
Proposed Rev. 3 to USNRC Reg. Guide 1.35 discusses the need for comparing, for individual tendons, the measured and predicted lift-off forces. Such a comparison is intended to detect any abnormal tendon force loss which might occur. Recognizing that there are uncertainties in the prediction of tendon losses, proposed Guide 1.35.1 has allowed specific tolerances on the fundamental losses. Thus, the lift-off force acceptance criteria for individual tendons appearing in Reg. Guide 1.35, Proposed Rev. 3, is stated relative to a lower bound predicted tendon force, which is obtained using the 'plus' tolerances on the fundamental losses. There is an additional acceptance criterion for the lift-off forces which is not specifically addressed in these two Reg. Guides; however, it is included in a proposed Subsection IWX to ASME Code Section XI. This criterion is based on the overriding requirement that the magnitude of prestress in the containment structure be sufficeint to meet the minimum prestress design requirements. This design requirement can be expressed as an average tendon force for each group of vertical hoop, or dome tendons. For the purpose of comparing the actual tendon forces with the required average tendon force, the lift-off forces measured for a sample of tendons within each group can be averaged to construct the average force for the entire group. However, the individual lift-off forces must be 'corrected' (normalized) prior to obtaining the sample average. This paper derives the correction factor to be used for this purpose. (orig./RW)
Autonomous Landing on Moving Platforms
Mendoza Chavez, Gilberto
2016-08-01
This thesis investigates autonomous landing of a micro air vehicle (MAV) on a nonstationary ground platform. Unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs) are becoming every day more ubiquitous. Nonetheless, many applications still require specialized human pilots or supervisors. Current research is focusing on augmenting the scope of tasks that these vehicles are able to accomplish autonomously. Precise autonomous landing on moving platforms is essential for self-deployment and recovery of MAVs, but it remains a challenging task for both autonomous and piloted vehicles. Model Predictive Control (MPC) is a widely used and effective scheme to control constrained systems. One of its variants, output-feedback tube-based MPC, ensures robust stability for systems with bounded disturbances under system state reconstruction. This thesis proposes a MAV control strategy based on this variant of MPC to perform rapid and precise autonomous landing on moving targets whose nominal (uncommitted) trajectory and velocity are slowly varying. The proposed approach is demonstrated on an experimental setup.
Moving as an interspecies unit
DEFF Research Database (Denmark)
Mondeme, Chloé
In urban spaces, people are normatively expected to move, to be mobile. In such “evolving ecologies” (Haddington, Nevile and Mondada, 2013), pedestrians, cyclists, and car-drivers are constantly adjusting to one another's mobility. Stillness, conversely, is noticed and somehow disruptive (Hadding......In urban spaces, people are normatively expected to move, to be mobile. In such “evolving ecologies” (Haddington, Nevile and Mondada, 2013), pedestrians, cyclists, and car-drivers are constantly adjusting to one another's mobility. Stillness, conversely, is noticed and somehow disruptive...... (Haddington, 2012 ; Lan Hing Ting & al., 2013). Staying immobile, for instance just before crossing a street, is in that respect a non-dynamic action yet to account for. Will the projected next action of the still participant be to wait and stay for a while, or to cross the street? To put is in other words......, how can immobility be unproblematically bound to a waiting status? What are the embodied resources used to be ‘doing wainting’ (Ayass, 2015)? In this presentation we will look at a collection of street crossings accomplished by blind persons with their guide-dogs. In the absence of visual access...
Stone, Wesley W.; Gilliom, Robert J.
2012-01-01
Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region-specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. The WARP-CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model-development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model-development sites. The WARP-CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine-use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP-CB models. The WARP-CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine-use intensities of 17 kg/km2 of watershed area or greater.
Domestic Multinationals and Foreign-Owned Firms in Italy: Evidence from Quantile Regression
Directory of Open Access Journals (Sweden)
Grasseni, Mara
2010-06-01
Full Text Available This paper investigates the performance differences across and within foreign-owned firms and domestic multinationals in Italy. Used for the empirical analysis are non-parametric tests based on the concept of first order stochastic dominance and quantile regression technique. The firm-level analysis distinguishes between foreign-owned firms of different nationalities and domestic MNEs according to the location of their FDI, and it focuses not only on productivity but also on differences in average wages, capital intensity, and financial and non-financial indicators, namely ROS, ROI and debt leverage. Overall, the results provide evidence of remarkable heterogeneity across and within multinationals. In particular, it seems not possible to identify a clear foreign advantage at least in terms of productivity, because foreign-owned firms do not outperform domestic multinationals. Interesting results are obtained when focusing on ROS and ROI, where the profitability gaps change as one moves from the bottom to the top of the conditional distribution. Domestic multinationals investing only in developed countries present higher ROS and ROI compared with the subgroups of foreign-owned firms, but only at the lower quantiles, while at the upper quantiles the advantage seems to favour foreign firms. Finally, in regard to domestic multinationals, there is strong evidence that those active only in less developed countries persistently exhibit the worst performances
Czech Academy of Sciences Publication Activity Database
Krištoufek, Ladislav
Roč. 406 , č. 1 (2014), s. 169-175 ISSN 0378-4371 R&D Projects: GA ČR(CZ) GP14-11402P Grant - others:GA ČR(CZ) GAP402/11/0948 Program:GA Institutional support: RVO:67985556 Keywords : correlations * econophysics * non-stationarity Subject RIV: AH - Economics Impact factor: 1.732, year: 2014 http://library.utia.cas.cz/separaty/2014/E/kristoufek-0433529.pdf
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2009-01-01
A generalized theory of ARMA modeling, covering a wide range of researches. with model identification, order determination, estimation and diagnostic checking is presented. We evolved standardization of wind data to overcome non-stationarity. With our techniques on generating synthetic values of wind series using MTM, we modeled and simulated autocorrelated function (ACF). MTM is found relatively a better simulator as compared to ARMA. We used twenty year of wind data. MTM required fast computation and suitable algorithm for backward calculations to yield ACF values. We found ARMA (p, q) model suitableble for both large range (1-6 hours) and short range (1-2 hours). This indicates that forecast values can be considered for appropriate wind energy conversion system. (author)
Regression of oral lichenoid lesions after replacement of dental restorations.
Mårell, L; Tillberg, A; Widman, L; Bergdahl, J; Berglund, A
2014-05-01
The aim of the study was to determine the prognosis and to evaluate the regression of lichenoid contact reactions (LCR) and oral lichen planus (OLP) after replacement of dental restorative materials suspected as causing the lesions. Forty-four referred patients with oral lesions participated in a follow-up study that was initiated an average of 6 years after the first examination at the Department of Odontology, i.e. the baseline examination. The patients underwent odontological clinical examination and answered a questionnaire with questions regarding dental health, medical and psychological health, and treatments undertaken from baseline to follow-up. After exchange of dental materials, regression of oral lesions was significantly higher among patients with LCR than with OLP. As no cases with OLP regressed after an exchange of materials, a proper diagnosis has to be made to avoid unnecessary exchanges of intact restorations on patients with OLP.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Semiparametric regression during 2003–2007
Ruppert, David; Wand, M.P.; Carroll, Raymond J.
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Statistics on exponential averaging of periodograms
Energy Technology Data Exchange (ETDEWEB)
Peeters, T.T.J.M. [Netherlands Energy Research Foundation (ECN), Petten (Netherlands); Ciftcioglu, Oe. [Istanbul Technical Univ. (Turkey). Dept. of Electrical Engineering
1994-11-01
The algorithm of exponential averaging applied to subsequent periodograms of a stochastic process is used to estimate the power spectral density (PSD). For an independent process, assuming the periodogram estimates to be distributed according to a {chi}{sup 2} distribution with 2 degrees of freedom, the probability density function (PDF) of the PSD estimate is derived. A closed expression is obtained for the moments of the distribution. Surprisingly, the proof of this expression features some new insights into the partitions and Eulers infinite product. For large values of the time constant of the averaging process, examination of the cumulant generating function shows that the PDF approximates the Gaussian distribution. Although restrictions for the statistics are seemingly tight, simulation of a real process indicates a wider applicability of the theory. (orig.).
Statistics on exponential averaging of periodograms
International Nuclear Information System (INIS)
Peeters, T.T.J.M.; Ciftcioglu, Oe.
1994-11-01
The algorithm of exponential averaging applied to subsequent periodograms of a stochastic process is used to estimate the power spectral density (PSD). For an independent process, assuming the periodogram estimates to be distributed according to a χ 2 distribution with 2 degrees of freedom, the probability density function (PDF) of the PSD estimate is derived. A closed expression is obtained for the moments of the distribution. Surprisingly, the proof of this expression features some new insights into the partitions and Eulers infinite product. For large values of the time constant of the averaging process, examination of the cumulant generating function shows that the PDF approximates the Gaussian distribution. Although restrictions for the statistics are seemingly tight, simulation of a real process indicates a wider applicability of the theory. (orig.)
ANALYSIS OF THE FACTORS AFFECTING THE AVERAGE
Directory of Open Access Journals (Sweden)
Carmen BOGHEAN
2013-12-01
Full Text Available Productivity in agriculture most relevantly and concisely expresses the economic efficiency of using the factors of production. Labour productivity is affected by a considerable number of variables (including the relationship system and interdependence between factors, which differ in each economic sector and influence it, giving rise to a series of technical, economic and organizational idiosyncrasies. The purpose of this paper is to analyse the underlying factors of the average work productivity in agriculture, forestry and fishing. The analysis will take into account the data concerning the economically active population and the gross added value in agriculture, forestry and fishing in Romania during 2008-2011. The distribution of the average work productivity per factors affecting it is conducted by means of the u-substitution method.
Weighted estimates for the averaging integral operator
Czech Academy of Sciences Publication Activity Database
Opic, Bohumír; Rákosník, Jiří
2010-01-01
Roč. 61, č. 3 (2010), s. 253-262 ISSN 0010-0757 R&D Projects: GA ČR GA201/05/2033; GA ČR GA201/08/0383 Institutional research plan: CEZ:AV0Z10190503 Keywords : averaging integral operator * weighted Lebesgue spaces * weights Subject RIV: BA - General Mathematics Impact factor: 0.474, year: 2010 http://link.springer.com/article/10.1007%2FBF03191231
Average Transverse Momentum Quantities Approaching the Lightfront
Boer, Daniel
2015-01-01
In this contribution to Light Cone 2014, three average transverse momentum quantities are discussed: the Sivers shift, the dijet imbalance, and the $p_T$ broadening. The definitions of these quantities involve integrals over all transverse momenta that are overly sensitive to the region of large transverse momenta, which conveys little information about the transverse momentum distributions of quarks and gluons inside hadrons. TMD factorization naturally suggests alternative definitions of su...
Time-averaged MSD of Brownian motion
Andreanov, Alexei; Grebenkov, Denis
2012-01-01
We study the statistical properties of the time-averaged mean-square displacements (TAMSD). This is a standard non-local quadratic functional for inferring the diffusion coefficient from an individual random trajectory of a diffusing tracer in single-particle tracking experiments. For Brownian motion, we derive an exact formula for the Laplace transform of the probability density of the TAMSD by mapping the original problem onto chains of coupled harmonic oscillators. From this formula, we de...
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
A Seemingly Unrelated Poisson Regression Model
King, Gary
1989-01-01
This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.
International Nuclear Information System (INIS)
Fang, Tingting; Lahdelma, Risto
2016-01-01
Highlights: • Social factor is considered for the linear regression models besides weather file. • Simultaneously optimize all the coefficients for linear regression models. • SARIMA combined with linear regression is used to forecast the heat demand. • The accuracy for both linear regression and time series models are evaluated. - Abstract: Forecasting heat demand is necessary for production and operation planning of district heating (DH) systems. In this study we first propose a simple regression model where the hourly outdoor temperature and wind speed forecast the heat demand. Weekly rhythm of heat consumption as a social component is added to the model to significantly improve the accuracy. The other type of model is the seasonal autoregressive integrated moving average (SARIMA) model with exogenous variables as a combination to take weather factors, and the historical heat consumption data as depending variables. One outstanding advantage of the model is that it peruses the high accuracy for both long-term and short-term forecast by considering both exogenous factors and time series. The forecasting performance of both linear regression models and time series model are evaluated based on real-life heat demand data for the city of Espoo in Finland by out-of-sample tests for the last 20 full weeks of the year. The results indicate that the proposed linear regression model (T168h) using 168-h demand pattern with midweek holidays classified as Saturdays or Sundays gives the highest accuracy and strong robustness among all the tested models based on the tested forecasting horizon and corresponding data. Considering the parsimony of the input, the ease of use and the high accuracy, the proposed T168h model is the best in practice. The heat demand forecasting model can also be developed for individual buildings if automated meter reading customer measurements are available. This would allow forecasting the heat demand based on more accurate heat consumption
Average configuration of the geomagnetic tail
International Nuclear Information System (INIS)
Fairfield, D.H.
1979-01-01
Over 3000 hours of Imp 6 magnetic field data obtained between 20 and 33 R/sub E/ in the geomagnetic tail have been used in a statistical study of the tail configuration. A distribution of 2.5-min averages of B/sub z/ as a function of position across the tail reveals that more flux crosses the equatorial plane near the dawn and dusk flanks (B-bar/sub z/=3.γ) than near midnight (B-bar/sub z/=1.8γ). The tail field projected in the solar magnetospheric equatorial plane deviates from the x axis due to flaring and solar wind aberration by an angle α=-0.9 Y/sub SM/-2.7, where Y/sub SM/ is in earth radii and α is in degrees. After removing these effects, the B/sub y/ component of the tail field is found to depend on interplanetary sector structure. During an 'away' sector the B/sub y/ component of the tail field is on average 0.5γ greater than that during a 'toward' sector, a result that is true in both tail lobes and is independent of location across the tail. This effect means the average field reversal between northern and southern lobes of the tail is more often 178 0 rather than the 180 0 that is generally supposed
Unscrambling The "Average User" Of Habbo Hotel
Directory of Open Access Journals (Sweden)
Mikael Johnson
2007-01-01
Full Text Available The “user” is an ambiguous concept in human-computer interaction and information systems. Analyses of users as social actors, participants, or configured users delineate approaches to studying design-use relationships. Here, a developer’s reference to a figure of speech, termed the “average user,” is contrasted with design guidelines. The aim is to create an understanding about categorization practices in design through a case study about the virtual community, Habbo Hotel. A qualitative analysis highlighted not only the meaning of the “average user,” but also the work that both the developer and the category contribute to this meaning. The average user a represents the unknown, b influences the boundaries of the target user groups, c legitimizes the designer to disregard marginal user feedback, and d keeps the design space open, thus allowing for creativity. The analysis shows how design and use are intertwined and highlights the developers’ role in governing different users’ interests.
Changing mortality and average cohort life expectancy
Directory of Open Access Journals (Sweden)
Robert Schoen
2005-10-01
Full Text Available Period life expectancy varies with changes in mortality, and should not be confused with the life expectancy of those alive during that period. Given past and likely future mortality changes, a recent debate has arisen on the usefulness of the period life expectancy as the leading measure of survivorship. An alternative aggregate measure of period mortality which has been seen as less sensitive to period changes, the cross-sectional average length of life (CAL has been proposed as an alternative, but has received only limited empirical or analytical examination. Here, we introduce a new measure, the average cohort life expectancy (ACLE, to provide a precise measure of the average length of life of cohorts alive at a given time. To compare the performance of ACLE with CAL and with period and cohort life expectancy, we first use population models with changing mortality. Then the four aggregate measures of mortality are calculated for England and Wales, Norway, and Switzerland for the years 1880 to 2000. CAL is found to be sensitive to past and present changes in death rates. ACLE requires the most data, but gives the best representation of the survivorship of cohorts present at a given time.
Reasons for Moving in Nonmetro Iowa
Burke, Sandra Charvat; Edelman, Mark
2007-01-01
This study highlights the experiences of people who have recently moved to or from 19 selected nonmetropolitan counties of Iowa. This report highlights work, family, community, and housing reasons for moving. The purpose is to increase understanding about why people move so community leaders and citizens can develop actionable strategies for attracting and retaining population.
Directory of Open Access Journals (Sweden)
Drzewiecki Wojciech
2016-12-01
Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.
Regression with Sparse Approximations of Data
DEFF Research Database (Denmark)
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
Spontaneous regression of a congenital melanocytic nevus
Directory of Open Access Journals (Sweden)
Amiya Kumar Nath
2011-01-01
Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.
International Nuclear Information System (INIS)
Smith, A.C. Jr.; Ashworth, C.P.; Abreu, K.E.
1982-01-01
We have completed a design of the Prototype Moving-Ring Reactor. The fusion fuel is confined in current-carrying rings of magnetically-field-reversed plasma (Compact Toroids). The plasma rings, formed by a coaxial plasma gun, undergo adiabatic magnetic compression to ignition temperature while they are being injected into the reactor's burner section. The cylindrical burner chamber is divided into three burn stations. Separator coils and a slight axial guide field gradient are used to shuttle the ignited toroids rapidly from one burn station to the next, pausing for 1/3 of the total burn time at each station. D-T- 3 He ice pellets refuel the rings at a rate which maintains constant radiated power
AHP 21: Review: Moving Mountains
Directory of Open Access Journals (Sweden)
William B. Noseworthy
2012-12-01
Full Text Available Moving Mountains stands out among recent discussions of the Southeast Asian Highlands, drawing from twelve contributors with extensive field experience living and working in locales closed to nonCommunist academics between 1945 and 1990 (3. The authors' methodologies focus on the anthropological approach of participant observation combined with oral history. Previously, substantial research had been confined to the experience of "hill tribes" in Northern Thailand (11, unless one gained access to the massive collections of French language research under the École Française d'Extrême Orient (EFEO or the Société Asiatique (SA, both in Paris. As such, this volume's contributors are able to ring out the voices of Southeast Asian Massif populations in a way that demonstrates a mindful assembly of research, while carefully narrating a more complex view of the region than that presented by Scott's (2009:22 "zones of refuge." ...
Directory of Open Access Journals (Sweden)
Keith Evan Green
2017-12-01
Full Text Available The next horizons of human-computer interaction promise a whirling world of digital bytes, physical bits, and their hybrids. Are human beings prepared to inhabit such cyber-physical, adaptive environments? Assuming an optimistic view, this chapter offers a reply, drawing from art and art history, environmental design, literature, psychology, and evolutionary anthropology, to identify wide-ranging motivations for the design of such “new places” of human-computer interaction. Moreover, the author makes a plea to researchers focused in the domain of adaptive environments to pause and take a longer, more comprehensive, more self-reflective view to see what we’re doing, to recognize where we are, and to possibly find ourselves and others within our designed artifacts and systems that make the world move.
Directory of Open Access Journals (Sweden)
Michela Moretti
2015-12-01
Full Text Available The paper proposes a reading of the Expo 2015 landscape project through the essay "Moving Forest "by Franco Zagari and Benedetto Selleri; in which the authors trace the design process of the exposition site. It describes the design features of the green spaces that surround and mark the Exposition City. The green project is the connection between innovation, technology and rural landscape, like that surrounds the site. The Expo map represents one of the largest landscape projects in the last years in Europe, with its 300,000 square meters, organized in a sequence of different landscape that improve a gradual transition from the rural and natural landscape outside, to the urban landscape inside the exposition city.
Mobility narratives that move me
DEFF Research Database (Denmark)
Lysgaard, Jonas Andreasen; Kronlid, David
and fiction. By opposing the classic anthropocentric ontological split between human (discourse) and object, fiction and Reality, OOO opens up for a new understanding of the role of narratives as independent objects on the same scale as grains of sand, universities, axolotls and planets. In short...... of speed, acceleration, the rhythm of technogenic moving and mooring, which can be translated into an understanding of our own movements and moorings through life and how we engage with new things, such as mediating new information through a certain pace, rhythm, movement, acceleration, slowing down....... This fictive/Real example of a narrative object illustrates that regardless of the metaphysical status of what/who we encounter in education and throughout life, if this experience becomes meaningful to us, we are navigating around them, towards them, and enmesh with them in the same principle ways...
Alpha detection on moving surfaces
International Nuclear Information System (INIS)
MacArthur, D.; Orr, C.; Luff, C.
1998-01-01
Both environmental restoration (ER) and decontamination and decommissioning (D and D) require characterization of large surface areas (walls, floors, in situ soil, soil and rubble on a conveyor belt, etc.) for radioactive contamination. Many facilities which have processed alpha active material such as plutonium or uranium require effective and efficient characterization for alpha contamination. Traditional methods for alpha surface characterization are limited by the short range and poor penetration of alpha particles. These probes are only sensitive to contamination located directly under the probe. Furthermore, the probe must be held close to the surface to be monitored in order to avoid excessive losses in the ambient air. The combination of proximity and thin detector windows can easily cause instrument damage unless extreme care is taken. The long-range alpha detection (LRAD) system addresses these problems by detecting the ions generated by alpha particles interacting with ambient air rather than the alpha particle directly. Thus, detectors based on LRAD overcome the limitations due to alpha particle range (the ions can travel many meters as opposed to the several-centimeter alpha particle range) and penetrating ability (an LRAD-based detector has no window). Unfortunately, all LRAD-based detectors described previously are static devices, i.e., these detectors cannot be used over surfaces which are continuously moving. In this paper, the authors report on the first tests of two techniques (the electrostatic ion seal and the gridded electrostatic LRAD detector) which extend the capabilities of LRAD surface monitors to use over moving surfaces. This dynamic surface monitoring system was developed jointly by Los Alamos National Laboratory and at BNFL Instruments. All testing was performed at the BNFL Instruments facility in the UK
Li, Guang; Wei, Jie; Huang, Hailiang; Gaebler, Carl Philipp; Yuan, Amy; Deasy, Joseph O
2015-12-01
To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients ( f v ) to account for most of the spectrum energy (Σ f v 2 ). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves ( R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors
Liu, Jiakai; Tan, Chin Hon; Badrick, Tony; Loh, Tze Ping
2018-02-01
An increase in analytical imprecision (expressed as CV a ) can introduce additional variability (i.e. noise) to the patient results, which poses a challenge to the optimal management of patients. Relatively little work has been done to address the need for continuous monitoring of analytical imprecision. Through numerical simulations, we describe the use of moving standard deviation (movSD) and a recently described moving sum of outlier (movSO) patient results as means for detecting increased analytical imprecision, and compare their performances against internal quality control (QC) and the average of normal (AoN) approaches. The power of detecting an increase in CV a is suboptimal under routine internal QC procedures. The AoN technique almost always had the highest average number of patient results affected before error detection (ANPed), indicating that it had generally the worst capability for detecting an increased CV a . On the other hand, the movSD and movSO approaches were able to detect an increased CV a at significantly lower ANPed, particularly for measurands that displayed a relatively small ratio of biological variation to CV a. CONCLUSION: The movSD and movSO approaches are effective in detecting an increase in CV a for high-risk measurands with small biological variation. Their performance is relatively poor when the biological variation is large. However, the clinical risks of an increase in analytical imprecision is attenuated for these measurands as an increased analytical imprecision will only add marginally to the total variation and less likely to impact on the clinical care. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.
Operator product expansion and its thermal average
Energy Technology Data Exchange (ETDEWEB)
Mallik, S [Saha Inst. of Nuclear Physics, Calcutta (India)
1998-05-01
QCD sum rules at finite temperature, like the ones at zero temperature, require the coefficients of local operators, which arise in the short distance expansion of the thermal average of two-point functions of currents. We extend the configuration space method, applied earlier at zero temperature, to the case at finite temperature. We find that, upto dimension four, two new operators arise, in addition to the two appearing already in the vacuum correlation functions. It is argued that the new operators would contribute substantially to the sum rules, when the temperature is not too low. (orig.) 7 refs.
Fluctuations of wavefunctions about their classical average
International Nuclear Information System (INIS)
Benet, L; Flores, J; Hernandez-Saldana, H; Izrailev, F M; Leyvraz, F; Seligman, T H
2003-01-01
Quantum-classical correspondence for the average shape of eigenfunctions and the local spectral density of states are well-known facts. In this paper, the fluctuations of the quantum wavefunctions around the classical value are discussed. A simple random matrix model leads to a Gaussian distribution of the amplitudes whose width is determined by the classical shape of the eigenfunction. To compare this prediction with numerical calculations in chaotic models of coupled quartic oscillators, we develop a rescaling method for the components. The expectations are broadly confirmed, but deviations due to scars are observed. This effect is much reduced when both Hamiltonians have chaotic dynamics
Phase-averaged transport for quasiperiodic Hamiltonians
Bellissard, J; Schulz-Baldes, H
2002-01-01
For a class of discrete quasi-periodic Schroedinger operators defined by covariant re- presentations of the rotation algebra, a lower bound on phase-averaged transport in terms of the multifractal dimensions of the density of states is proven. This result is established under a Diophantine condition on the incommensuration parameter. The relevant class of operators is distinguished by invariance with respect to symmetry automorphisms of the rotation algebra. It includes the critical Harper (almost-Mathieu) operator. As a by-product, a new solution of the frame problem associated with Weyl-Heisenberg-Gabor lattices of coherent states is given.
Baseline-dependent averaging in radio interferometry
Wijnholds, S. J.; Willis, A. G.; Salvini, S.
2018-05-01
This paper presents a detailed analysis of the applicability and benefits of baseline-dependent averaging (BDA) in modern radio interferometers and in particular the Square Kilometre Array. We demonstrate that BDA does not affect the information content of the data other than a well-defined decorrelation loss for which closed form expressions are readily available. We verify these theoretical findings using simulations. We therefore conclude that BDA can be used reliably in modern radio interferometry allowing a reduction of visibility data volume (and hence processing costs for handling visibility data) by more than 80 per cent.
Multistage parallel-serial time averaging filters
International Nuclear Information System (INIS)
Theodosiou, G.E.
1980-01-01
Here, a new time averaging circuit design, the 'parallel filter' is presented, which can reduce the time jitter, introduced in time measurements using counters of large dimensions. This parallel filter could be considered as a single stage unit circuit which can be repeated an arbitrary number of times in series, thus providing a parallel-serial filter type as a result. The main advantages of such a filter over a serial one are much less electronic gate jitter and time delay for the same amount of total time uncertainty reduction. (orig.)
Time-averaged MSD of Brownian motion
International Nuclear Information System (INIS)
Andreanov, Alexei; Grebenkov, Denis S
2012-01-01
We study the statistical properties of the time-averaged mean-square displacements (TAMSD). This is a standard non-local quadratic functional for inferring the diffusion coefficient from an individual random trajectory of a diffusing tracer in single-particle tracking experiments. For Brownian motion, we derive an exact formula for the Laplace transform of the probability density of the TAMSD by mapping the original problem onto chains of coupled harmonic oscillators. From this formula, we deduce the first four cumulant moments of the TAMSD, the asymptotic behavior of the probability density and its accurate approximation by a generalized Gamma distribution
Time-dependent angularly averaged inverse transport
International Nuclear Information System (INIS)
Bal, Guillaume; Jollivet, Alexandre
2009-01-01
This paper concerns the reconstruction of the absorption and scattering parameters in a time-dependent linear transport equation from knowledge of angularly averaged measurements performed at the boundary of a domain of interest. Such measurement settings find applications in medical and geophysical imaging. We show that the absorption coefficient and the spatial component of the scattering coefficient are uniquely determined by such measurements. We obtain stability results on the reconstruction of the absorption and scattering parameters with respect to the measured albedo operator. The stability results are obtained by a precise decomposition of the measurements into components with different singular behavior in the time domain
Independence, Odd Girth, and Average Degree
DEFF Research Database (Denmark)
Löwenstein, Christian; Pedersen, Anders Sune; Rautenbach, Dieter
2011-01-01
We prove several tight lower bounds in terms of the order and the average degree for the independence number of graphs that are connected and/or satisfy some odd girth condition. Our main result is the extension of a lower bound for the independence number of triangle-free graphs of maximum...... degree at most three due to Heckman and Thomas [Discrete Math 233 (2001), 233–237] to arbitrary triangle-free graphs. For connected triangle-free graphs of order n and size m, our result implies the existence of an independent set of order at least (4n−m−1) / 7. ...
Bootstrapping Density-Weighted Average Derivatives
DEFF Research Database (Denmark)
Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael
Employing the "small bandwidth" asymptotic framework of Cattaneo, Crump, and Jansson (2009), this paper studies the properties of a variety of bootstrap-based inference procedures associated with the kernel-based density-weighted averaged derivative estimator proposed by Powell, Stock, and Stoker...... (1989). In many cases validity of bootstrap-based inference procedures is found to depend crucially on whether the bandwidth sequence satisfies a particular (asymptotic linearity) condition. An exception to this rule occurs for inference procedures involving a studentized estimator employing a "robust...
Average Nuclear properties based on statistical model
International Nuclear Information System (INIS)
El-Jaick, L.J.
1974-01-01
The rough properties of nuclei were investigated by statistical model, in systems with the same and different number of protons and neutrons, separately, considering the Coulomb energy in the last system. Some average nuclear properties were calculated based on the energy density of nuclear matter, from Weizsscker-Beth mass semiempiric formulae, generalized for compressible nuclei. In the study of a s surface energy coefficient, the great influence exercised by Coulomb energy and nuclear compressibility was verified. For a good adjust of beta stability lines and mass excess, the surface symmetry energy were established. (M.C.K.) [pt
Time-averaged MSD of Brownian motion
Andreanov, Alexei; Grebenkov, Denis S.
2012-07-01
We study the statistical properties of the time-averaged mean-square displacements (TAMSD). This is a standard non-local quadratic functional for inferring the diffusion coefficient from an individual random trajectory of a diffusing tracer in single-particle tracking experiments. For Brownian motion, we derive an exact formula for the Laplace transform of the probability density of the TAMSD by mapping the original problem onto chains of coupled harmonic oscillators. From this formula, we deduce the first four cumulant moments of the TAMSD, the asymptotic behavior of the probability density and its accurate approximation by a generalized Gamma distribution.
Parents' Reactions to Finding Out That Their Children Have Average or above Average IQ Scores.
Dirks, Jean; And Others
1983-01-01
Parents of 41 children who had been given an individually-administered intelligence test were contacted 19 months after testing. Parents of average IQ children were less accurate in their memory of test results. Children with above average IQ experienced extremely low frequencies of sibling rivalry, conceit or pressure. (Author/HLM)
Dina Verdín; Allison Godwin; Adam Kirn; Lisa Benson; Geoff Potvin
2018-01-01
Women’s participation in engineering remains well below that of men at all degree levels. However, despite the low enrollment of women in engineering as a whole, some engineering disciplines report above average female enrollment. We used multiple linear regression to examine the attitudes, beliefs, career outcome expectations, and career choice of first-year female engineering students enrolled in below average, average, and above average female representation disciplines in engineering. Our...
Trajectory averaging for stochastic approximation MCMC algorithms
Liang, Faming
2010-10-01
The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400-407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305-320]. The application of the trajectory averaging estimator to other stochastic approximationMCMC algorithms, for example, a stochastic approximation MLE algorithm for missing data problems, is also considered in the paper. © Institute of Mathematical Statistics, 2010.
Averaged null energy condition from causality
Hartman, Thomas; Kundu, Sandipan; Tajdini, Amirhossein
2017-07-01
Unitary, Lorentz-invariant quantum field theories in flat spacetime obey mi-crocausality: commutators vanish at spacelike separation. For interacting theories in more than two dimensions, we show that this implies that the averaged null energy, ∫ duT uu , must be non-negative. This non-local operator appears in the operator product expansion of local operators in the lightcone limit, and therefore contributes to n-point functions. We derive a sum rule that isolates this contribution and is manifestly positive. The argument also applies to certain higher spin operators other than the stress tensor, generating an infinite family of new constraints of the form ∫ duX uuu··· u ≥ 0. These lead to new inequalities for the coupling constants of spinning operators in conformal field theory, which include as special cases (but are generally stronger than) the existing constraints from the lightcone bootstrap, deep inelastic scattering, conformal collider methods, and relative entropy. We also comment on the relation to the recent derivation of the averaged null energy condition from relative entropy, and suggest a more general connection between causality and information-theoretic inequalities in QFT.
Beta-energy averaging and beta spectra
International Nuclear Information System (INIS)
Stamatelatos, M.G.; England, T.R.
1976-07-01
A simple yet highly accurate method for approximately calculating spectrum-averaged beta energies and beta spectra for radioactive nuclei is presented. This method should prove useful for users who wish to obtain accurate answers without complicated calculations of Fermi functions, complex gamma functions, and time-consuming numerical integrations as required by the more exact theoretical expressions. Therefore, this method should be a good time-saving alternative for investigators who need to make calculations involving large numbers of nuclei (e.g., fission products) as well as for occasional users interested in restricted number of nuclides. The average beta-energy values calculated by this method differ from those calculated by ''exact'' methods by no more than 1 percent for nuclides with atomic numbers in the 20 to 100 range and which emit betas of energies up to approximately 8 MeV. These include all fission products and the actinides. The beta-energy spectra calculated by the present method are also of the same quality
Asymptotic Time Averages and Frequency Distributions
Directory of Open Access Journals (Sweden)
Muhammad El-Taha
2016-01-01
Full Text Available Consider an arbitrary nonnegative deterministic process (in a stochastic setting {X(t, t≥0} is a fixed realization, i.e., sample-path of the underlying stochastic process with state space S=(-∞,∞. Using a sample-path approach, we give necessary and sufficient conditions for the long-run time average of a measurable function of process to be equal to the expectation taken with respect to the same measurable function of its long-run frequency distribution. The results are further extended to allow unrestricted parameter (time space. Examples are provided to show that our condition is not superfluous and that it is weaker than uniform integrability. The case of discrete-time processes is also considered. The relationship to previously known sufficient conditions, usually given in stochastic settings, will also be discussed. Our approach is applied to regenerative processes and an extension of a well-known result is given. For researchers interested in sample-path analysis, our results will give them the choice to work with the time average of a process or its frequency distribution function and go back and forth between the two under a mild condition.
Chaotic Universe, Friedmannian on the average 2
Energy Technology Data Exchange (ETDEWEB)
Marochnik, L S [AN SSSR, Moscow. Inst. Kosmicheskikh Issledovanij
1980-11-01
The cosmological solutions are found for the equations for correlators, describing a statistically chaotic Universe, Friedmannian on the average in which delta-correlated fluctuations with amplitudes h >> 1 are excited. For the equation of state of matter p = n epsilon, the kind of solutions depends on the position of maximum of the spectrum of the metric disturbances. The expansion of the Universe, in which long-wave potential and vortical motions and gravitational waves (modes diverging at t ..-->.. 0) had been excited, tends asymptotically to the Friedmannian one at t ..-->.. identity and depends critically on n: at n < 0.26, the solution for the scalefactor is situated higher than the Friedmannian one, and lower at n > 0.26. The influence of finite at t ..-->.. 0 long-wave fluctuation modes leads to an averaged quasiisotropic solution. The contribution of quantum fluctuations and of short-wave parts of the spectrum of classical fluctuations to the expansion law is considered. Their influence is equivalent to the contribution from an ultrarelativistic gas with corresponding energy density and pressure. The restrictions are obtained for the degree of chaos (the spectrum characteristics) compatible with the observed helium abundance, which could have been retained by a completely chaotic Universe during its expansion up to the nucleosynthesis epoch.
Averaging in the presence of sliding errors
International Nuclear Information System (INIS)
Yost, G.P.
1991-08-01
In many cases the precision with which an experiment can measure a physical quantity depends on the value of that quantity. Not having access to the true value, experimental groups are forced to assign their errors based on their own measured value. Procedures which attempt to derive an improved estimate of the true value by a suitable average of such measurements usually weight each experiment's measurement according to the reported variance. However, one is in a position to derive improved error estimates for each experiment from the average itself, provided an approximate idea of the functional dependence of the error on the central value is known. Failing to do so can lead to substantial biases. Techniques which avoid these biases without loss of precision are proposed and their performance is analyzed with examples. These techniques are quite general and can bring about an improvement even when the behavior of the errors is not well understood. Perhaps the most important application of the technique is in fitting curves to histograms
Advanced colorectal neoplasia risk stratification by penalized logistic regression.
Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F
2016-08-01
Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.
Directory of Open Access Journals (Sweden)
Siti Choirun Nisak
2016-06-01
Full Text Available Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS assumes the variance-covariance matrix has a constant error ~(, but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR approach is used to accommodate the weakness of the OLS. SUR assumption is ~(, for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS. Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1(1,12,36,(0,(1-SUR with determination coefficient generated with the average of 57.726%.
Method and apparatus for a combination moving bed thermal treatment reactor and moving bed filter
Energy Technology Data Exchange (ETDEWEB)
Badger, Phillip C.; Dunn, Jr., Kenneth J.
2015-09-01
A moving bed gasification/thermal treatment reactor includes a geometry in which moving bed reactor particles serve as both a moving bed filter and a heat carrier to provide thermal energy for thermal treatment reactions, such that the moving bed filter and the heat carrier are one and the same to remove solid particulates or droplets generated by thermal treatment processes or injected into the moving bed filter from other sources.
High average power linear induction accelerator development
International Nuclear Information System (INIS)
Bayless, J.R.; Adler, R.J.
1987-07-01
There is increasing interest in linear induction accelerators (LIAs) for applications including free electron lasers, high power microwave generators and other types of radiation sources. Lawrence Livermore National Laboratory has developed LIA technology in combination with magnetic pulse compression techniques to achieve very impressive performance levels. In this paper we will briefly discuss the LIA concept and describe our development program. Our goals are to improve the reliability and reduce the cost of LIA systems. An accelerator is presently under construction to demonstrate these improvements at an energy of 1.6 MeV in 2 kA, 65 ns beam pulses at an average beam power of approximately 30 kW. The unique features of this system are a low cost accelerator design and an SCR-switched, magnetically compressed, pulse power system. 4 refs., 7 figs
FEL system with homogeneous average output
Energy Technology Data Exchange (ETDEWEB)
Douglas, David R.; Legg, Robert; Whitney, R. Roy; Neil, George; Powers, Thomas Joseph
2018-01-16
A method of varying the output of a free electron laser (FEL) on very short time scales to produce a slightly broader, but smooth, time-averaged wavelength spectrum. The method includes injecting into an accelerator a sequence of bunch trains at phase offsets from crest. Accelerating the particles to full energy to result in distinct and independently controlled, by the choice of phase offset, phase-energy correlations or chirps on each bunch train. The earlier trains will be more strongly chirped, the later trains less chirped. For an energy recovered linac (ERL), the beam may be recirculated using a transport system with linear and nonlinear momentum compactions M.sub.56, which are selected to compress all three bunch trains at the FEL with higher order terms managed.
Quetelet, the average man and medical knowledge.
Caponi, Sandra
2013-01-01
Using two books by Adolphe Quetelet, I analyze his theory of the 'average man', which associates biological and social normality with the frequency with which certain characteristics appear in a population. The books are Sur l'homme et le développement de ses facultés and Du systeme social et des lois qui le régissent. Both reveal that Quetelet's ideas are permeated by explanatory strategies drawn from physics and astronomy, and also by discursive strategies drawn from theology and religion. The stability of the mean as opposed to the dispersion of individual characteristics and events provided the basis for the use of statistics in social sciences and medicine.
[Quetelet, the average man and medical knowledge].
Caponi, Sandra
2013-01-01
Using two books by Adolphe Quetelet, I analyze his theory of the 'average man', which associates biological and social normality with the frequency with which certain characteristics appear in a population. The books are Sur l'homme et le développement de ses facultés and Du systeme social et des lois qui le régissent. Both reveal that Quetelet's ideas are permeated by explanatory strategies drawn from physics and astronomy, and also by discursive strategies drawn from theology and religion. The stability of the mean as opposed to the dispersion of individual characteristics and events provided the basis for the use of statistics in social sciences and medicine.
Asymmetric network connectivity using weighted harmonic averages
Morrison, Greg; Mahadevan, L.
2011-02-01
We propose a non-metric measure of the "closeness" felt between two nodes in an undirected, weighted graph using a simple weighted harmonic average of connectivity, that is a real-valued Generalized Erdös Number (GEN). While our measure is developed with a collaborative network in mind, the approach can be of use in a variety of artificial and real-world networks. We are able to distinguish between network topologies that standard distance metrics view as identical, and use our measure to study some simple analytically tractable networks. We show how this might be used to look at asymmetry in authorship networks such as those that inspired the integer Erdös numbers in mathematical coauthorships. We also show the utility of our approach to devise a ratings scheme that we apply to the data from the NetFlix prize, and find a significant improvement using our method over a baseline.
Angle-averaged Compton cross sections
International Nuclear Information System (INIS)
Nickel, G.H.
1983-01-01
The scattering of a photon by an individual free electron is characterized by six quantities: α = initial photon energy in units of m 0 c 2 ; α/sub s/ = scattered photon energy in units of m 0 c 2 ; β = initial electron velocity in units of c; phi = angle between photon direction and electron direction in the laboratory frame (LF); theta = polar angle change due to Compton scattering, measured in the electron rest frame (ERF); and tau = azimuthal angle change in the ERF. We present an analytic expression for the average of the Compton cross section over phi, theta, and tau. The lowest order approximation to this equation is reasonably accurate for photons and electrons with energies of many keV
Average Gait Differential Image Based Human Recognition
Directory of Open Access Journals (Sweden)
Jinyan Chen
2014-01-01
Full Text Available The difference between adjacent frames of human walking contains useful information for human gait identification. Based on the previous idea a silhouettes difference based human gait recognition method named as average gait differential image (AGDI is proposed in this paper. The AGDI is generated by the accumulation of the silhouettes difference between adjacent frames. The advantage of this method lies in that as a feature image it can preserve both the kinetic and static information of walking. Comparing to gait energy image (GEI, AGDI is more fit to representation the variation of silhouettes during walking. Two-dimensional principal component analysis (2DPCA is used to extract features from the AGDI. Experiments on CASIA dataset show that AGDI has better identification and verification performance than GEI. Comparing to PCA, 2DPCA is a more efficient and less memory storage consumption feature extraction method in gait based recognition.
Reynolds averaged simulation of unsteady separated flow
International Nuclear Information System (INIS)
Iaccarino, G.; Ooi, A.; Durbin, P.A.; Behnia, M.
2003-01-01
The accuracy of Reynolds averaged Navier-Stokes (RANS) turbulence models in predicting complex flows with separation is examined. The unsteady flow around square cylinder and over a wall-mounted cube are simulated and compared with experimental data. For the cube case, none of the previously published numerical predictions obtained by steady-state RANS produced a good match with experimental data. However, evidence exists that coherent vortex shedding occurs in this flow. Its presence demands unsteady RANS computation because the flow is not statistically stationary. The present study demonstrates that unsteady RANS does indeed predict periodic shedding, and leads to much better concurrence with available experimental data than has been achieved with steady computation
Angle-averaged Compton cross sections
Energy Technology Data Exchange (ETDEWEB)
Nickel, G.H.
1983-01-01
The scattering of a photon by an individual free electron is characterized by six quantities: ..cap alpha.. = initial photon energy in units of m/sub 0/c/sup 2/; ..cap alpha../sub s/ = scattered photon energy in units of m/sub 0/c/sup 2/; ..beta.. = initial electron velocity in units of c; phi = angle between photon direction and electron direction in the laboratory frame (LF); theta = polar angle change due to Compton scattering, measured in the electron rest frame (ERF); and tau = azimuthal angle change in the ERF. We present an analytic expression for the average of the Compton cross section over phi, theta, and tau. The lowest order approximation to this equation is reasonably accurate for photons and electrons with energies of many keV.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
The balanced survivor average causal effect.
Greene, Tom; Joffe, Marshall; Hu, Bo; Li, Liang; Boucher, Ken
2013-05-07
Statistical analysis of longitudinal outcomes is often complicated by the absence of observable values in patients who die prior to their scheduled measurement. In such cases, the longitudinal data are said to be "truncated by death" to emphasize that the longitudinal measurements are not simply missing, but are undefined after death. Recently, the truncation by death problem has been investigated using the framework of principal stratification to define the target estimand as the survivor average causal effect (SACE), which in the context of a two-group randomized clinical trial is the mean difference in the longitudinal outcome between the treatment and control groups for the principal stratum of always-survivors. The SACE is not identified without untestable assumptions. These assumptions have often been formulated in terms of a monotonicity constraint requiring that the treatment does not reduce survival in any patient, in conjunction with assumed values for mean differences in the longitudinal outcome between certain principal strata. In this paper, we introduce an alternative estimand, the balanced-SACE, which is defined as the average causal effect on the longitudinal outcome in a particular subset of the always-survivors that is balanced with respect to the potential survival times under the treatment and control. We propose a simple estimator of the balanced-SACE that compares the longitudinal outcomes between equivalent fractions of the longest surviving patients between the treatment and control groups and does not require a monotonicity assumption. We provide expressions for the large sample bias of the estimator, along with sensitivity analyses and strategies to minimize this bias. We consider statistical inference under a bootstrap resampling procedure.
Optimization of Moving Coil Actuators for Digital Displacement Machines
DEFF Research Database (Denmark)
Nørgård, Christian; Bech, Michael Møller; Roemer, Daniel Beck
2016-01-01
This paper focuses on deriving an optimal moving coil actuator design, used as force pro-ducing element in hydraulic on/off valves for Digital Displacement machines. Different moving coil actuator geometry topologies (permanent magnet placement and magnetiza-tion direction) are optimized for actu......This paper focuses on deriving an optimal moving coil actuator design, used as force pro-ducing element in hydraulic on/off valves for Digital Displacement machines. Different moving coil actuator geometry topologies (permanent magnet placement and magnetiza-tion direction) are optimized...... for actuating annular seat valves in a digital displacement machine. The optimization objectives are to the minimize the actuator power, the valve flow losses and the height of the actuator. Evaluation of the objective function involves static finite element simulation and simulation of an entire operation...... designs requires approximately 20 W on average and may be realized in 20 mm × Ø 22.5 mm (height × diameter) for a 20 kW pressure chamber. The optimization is carried out using the multi-objective Generalized Differential Evolu-tion optimization algorithm GDE3 which successfully handles constrained multi-objective...
Cosmology with moving bimetric fluids
Energy Technology Data Exchange (ETDEWEB)
García-García, Carlos; Maroto, Antonio L.; Martín-Moruno, Prado, E-mail: cargar08@ucm.es, E-mail: maroto@ucm.es, E-mail: pradomm@ucm.es [Departamento de Física Teórica I, Universidad Complutense de Madrid, E-28040 Madrid (Spain)
2016-12-01
We study cosmological implications of bigravity and massive gravity solutions with non-simultaneously diagonal metrics by considering the generalized Gordon and Kerr-Schild ansatzes. The scenario that we obtain is equivalent to that of General Relativity with additional non-comoving perfect fluids. We show that the most general ghost-free bimetric theory generates three kinds of effective fluids whose equations of state are fixed by a function of the ansatz. Different choices of such function allow to reproduce the behaviour of different dark fluids. In particular, the Gordon ansatz is suitable for the description of various kinds of slowly-moving fluids, whereas the Kerr-Schild one is shown to describe a null dark energy component. The motion of those dark fluids with respect to the CMB is shown to generate, in turn, a relative motion of baryonic matter with respect to radition which contributes to the CMB anisotropies. CMB dipole observations are able to set stringent limits on the dark sector described by the effective bimetric fluid.
Computations of slowly moving shocks
International Nuclear Information System (INIS)
Karni, S.; Canic, S.
1997-01-01
Computations of slowly moving shocks by shock capturing schemes may generate oscillations are generated already by first-order schemes, but become more pronounced in higher-order schemes which seem to exhibit different behaviors: (i) the first-order upwind (UW) scheme which generates strong oscillations and (ii) the Lax-Friedrichs scheme which appears not to generate any disturbances at all. A key observation is that in the UW case, the numerical viscosity in the shock family vanishes inside the slow shock layer. Simple scaling arguments show the third-order effects on the solution may no longer be neglected. We derive the third-order modified equation for the UW scheme and regard the oscillatory solution as a traveling wave solution of the parabolic modified equation for the perturbation. We then look at the governing equation for the perturbation, which points to a plausible mechanism by which postshock oscillations are generated. It contains a third-order source term that becomes significant inside the shock layer, and a nonlinear coupling term which projects the perturbation on all characteristic fields, including those not associated with the shock family. 5 refs., 8 figs
Pipelines : moving biomass and energy
Energy Technology Data Exchange (ETDEWEB)
Kumar, A. [Alberta Univ., Edmonton, AB (Canada). Dept. of Mechanical Engineering
2006-07-01
Moving biomass and energy through pipelines was presented. Field sourced biomass utilization for fuel was discussed in terms of competing cost factors; economies of scale; and differing fuel plant sizes. The cost versus scale in a bioenergy facility was illustrated in chart format. The transportation cost of biomass was presented as it is a major component of total biomass processing cost and is in the typical range of 25-45 per cent of total processing costs for truck transport of biomass. Issues in large scale biomass utilization, scale effects in transportation, and components of transport cost were identified. Other topics related to transportation issues included approaches to pipeline transport; cost of wood chips in pipeline transport; and distance variable cost of transporting wood chips by pipeline. Practical applications were also offered. In addition, the presentation provided and illustrated a model for an ethanol plant supplied by truck transport as well as a sample configuration for 19 truck based ethanol plants versus one large facility supplied by truck plus 18 pipelines. Last, pipeline transport of bio-oil and pipeline transport of syngas was discussed. It was concluded that pipeline transport can help in reducing congestion issues in large scale biomass utilization and that it can offer a means to achieve large plant size. Some current research at the University of Alberta on pipeline transport of raw biomass, bio-oil and hydrogen production from biomass for oil sands and pipeline transport was also presented. tabs., figs.
Leadership in moving human groups.
Directory of Open Access Journals (Sweden)
Margarete Boos
2014-04-01
Full Text Available How is movement of individuals coordinated as a group? This is a fundamental question of social behaviour, encompassing phenomena such as bird flocking, fish schooling, and the innumerable activities in human groups that require people to synchronise their actions. We have developed an experimental paradigm, the HoneyComb computer-based multi-client game, to empirically investigate human movement coordination and leadership. Using economic games as a model, we set monetary incentives to motivate players on a virtual playfield to reach goals via players' movements. We asked whether (I humans coordinate their movements when information is limited to an individual group member's observation of adjacent group member motion, (II whether an informed group minority can lead an uninformed group majority to the minority's goal, and if so, (III how this minority exerts its influence. We showed that in a human group--on the basis of movement alone--a minority can successfully lead a majority. Minorities lead successfully when (a their members choose similar initial steps towards their goal field and (b they are among the first in the whole group to make a move. Using our approach, we empirically demonstrate that the rules of swarming behaviour apply to humans. Even complex human behaviour, such as leadership and directed group movement, follow simple rules that are based on visual perception of local movement.
IT Department
2009-01-01
As of 2nd March 2009, the Telecom Lab will move to Building 58 R-017. The Telecom Lab is the central point for all support questions regarding CERN mobile phone services (provision of SIM cards, requests for modifications of subscriptions, diagnostics for mobile phone problems, etc.). The opening hours as well as the contact details for the Telecom Lab remain unchanged: New location: Building 58 R-017 Opening hours: Every week day, from 11 a.m. to 12 a.m. Phone number: 72480 Email address: labo.telecom@cern.ch This change has no impact on support requests for mobile services. Users can still submit their requests concerning mobile phone subscriptions using the usual EDH form (https://edh.cern.ch/Document/GSM). The automatic message sent to inform users of their SIM card availability will be updated to indicate the new Telecom Lab location. You can find all information related to CERN mobile phone services at the following link: http://cern.ch/gsm CS Section - IT/CS group
Regression models of reactor diagnostic signals
International Nuclear Information System (INIS)
Vavrin, J.
1989-01-01
The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Sedimentological regimes for turbidity currents: Depth-averaged theory
Halsey, Thomas C.; Kumar, Amit; Perillo, Mauricio M.
2017-07-01
Turbidity currents are one of the most significant means by which sediment is moved from the continents into the deep ocean; their properties are interesting both as elements of the global sediment cycle and due to their role in contributing to the formation of deep water oil and gas reservoirs. One of the simplest models of the dynamics of turbidity current flow was introduced three decades ago, and is based on depth-averaging of the fluid mechanical equations governing the turbulent gravity-driven flow of relatively dilute turbidity currents. We examine the sedimentological regimes of a simplified version of this model, focusing on the role of the Richardson number Ri [dimensionless inertia] and Rouse number Ro [dimensionless sedimentation velocity] in determining whether a current is net depositional or net erosional. We find that for large Rouse numbers, the currents are strongly net depositional due to the disappearance of local equilibria between erosion and deposition. At lower Rouse numbers, the Richardson number also plays a role in determining the degree of erosion versus deposition. The currents become more erosive at lower values of the product Ro × Ri, due to the effect of clear water entrainment. At higher values of this product, the turbulence becomes insufficient to maintain the sediment in suspension, as first pointed out by Knapp and Bagnold. We speculate on the potential for two-layer solutions in this insufficiently turbulent regime, which would comprise substantial bedload flow with an overlying turbidity current.
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.
Internal migration: why do Filipinos move?
Jolipa, N
1980-01-01
The rapid movement of Filipinos from 1 part of the Philippines to another is not a new phenomenon, but mobility has been increasing. A study conducted by Peter C. Smith revealed that interprovincial lifetime mobility of the national population increased from 15.8% in 1960 to 17.6% in 1970, while interregional mobility increased from 12.7% to 13.4%. People still disagree as to whether the size and rate of growth of the population are excessive, but there seems to be total consensus as regards its spatial imbalance. Because internal migration appears to be an important factor in national development, a need exists to examine different aspects of internal migration, such as the directions taken by migration flows, the migrants' reasons for moving, the migrants' characteristics, the migrants' success or lack of success at their places of destination, the social problems accompanying internal migration, effforts to deal with the problems caused by internal migration, and the implications of migration trends for policy and for the country's development programs. The most dominant migration trend in the Philippines in recent years has been toward the urban, or more accurately the suburban, areas adjacent to Metropolitan Manila. The city of Manila itself suffered a net outflow, further pointing to the trend toward suburbanization. Migration flows are primarily caused by economic reasons. About one half the sample of a Filipinas Foundation Study moved to provinces other than the province of birth in the pursuit of employment and other economic opportunities. A study of the country's migrant population age 15 and older showed that 53% of migrants were female. For male migrants, age ranges from 20-40; it ranges from 15-35 for females. Where cash income is concerned, migrants in Pernia's study of rural urban migration were better off than nonmigrants. Migrants were, on the average, as well off as native urbanites or metropolitanites. Among the more significant points raised by
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Lee, Jennifer
2012-01-01
The intent of this study was to examine the relationship between media multitasking orientation and grade point average. The study utilized a mixed-methods approach to investigate the research questions. In the quantitative section of the study, the primary method of statistical analyses was multiple regression. The independent variables for the…
Saltonstall, Margot
2013-01-01
This study seeks to advance and expand research on college student success. Using multinomial logistic regression analysis, the study investigates the contribution of psychosocial variables above and beyond traditional achievement and demographic measures to predicting first-semester college grade point average (GPA). It also investigates if…
Do Nondomestic Undergraduates Choose a Major Field in Order to Maximize Grade Point Averages?
Bergman, Matthew E.; Fass-Holmes, Barry
2016-01-01
The authors investigated whether undergraduates attending an American West Coast public university who were not U.S. citizens (nondomestic) maximized their grade point averages (GPA) through their choice of major field. Multiple regression hierarchical linear modeling analyses showed that major field's effect size was small for these…
Can math beat gamers in Quantum Moves?
Sels, Dries
2017-01-01
Abstract: In a recent work on quantum state preparation, Sørensen and co-workers [Nature (London) 532, 210 (2016)] explore the possibility of using video games to help design quantum control protocols. The authors present a game called Quantum Moves (https://www.scienceathome.org/games/quantum-moves/) in which gamers have to move an atom from A to B by means of optical tweezers. They report that, players succeed where purely numerical optimization fails. Moreover, by harnessing the player str...
Trading Fees and Slow-Moving Capital
Buss, Adrian; Dumas, Bernard J
2015-01-01
In some situations, investment capital seems to move slowly towards profitable trades. We develop a model of a financial market in which capital moves slowly simply because there is a proportional cost to moving capital. We incorporate trading fees in an infinite-horizon dynamic general-equilibrium model in which investors optimally and endogenously decide when and how much to trade. We determine the steady-state equilibrium no-trade zone, study the dynamics of equilibrium trades and prices a...
Industrial Applications of High Average Power FELS
Shinn, Michelle D
2005-01-01
The use of lasers for material processing continues to expand, and the annual sales of such lasers exceeds $1 B (US). Large scale (many m2) processing of materials require the economical production of laser powers of the tens of kilowatts, and therefore are not yet commercial processes, although they have been demonstrated. The development of FELs based on superconducting RF (SRF) linac technology provides a scaleable path to laser outputs above 50 kW in the IR, rendering these applications economically viable, since the cost/photon drops as the output power increases. This approach also enables high average power ~ 1 kW output in the UV spectrum. Such FELs will provide quasi-cw (PRFs in the tens of MHz), of ultrafast (pulsewidth ~ 1 ps) output with very high beam quality. This talk will provide an overview of applications tests by our facility's users such as pulsed laser deposition, laser ablation, and laser surface modification, as well as present plans that will be tested with our upgraded FELs. These upg...
Calculating Free Energies Using Average Force
Darve, Eric; Pohorille, Andrew; DeVincenzi, Donald L. (Technical Monitor)
2001-01-01
A new, general formula that connects the derivatives of the free energy along the selected, generalized coordinates of the system with the instantaneous force acting on these coordinates is derived. The instantaneous force is defined as the force acting on the coordinate of interest so that when it is subtracted from the equations of motion the acceleration along this coordinate is zero. The formula applies to simulations in which the selected coordinates are either unconstrained or constrained to fixed values. It is shown that in the latter case the formula reduces to the expression previously derived by den Otter and Briels. If simulations are carried out without constraining the coordinates of interest, the formula leads to a new method for calculating the free energy changes along these coordinates. This method is tested in two examples - rotation around the C-C bond of 1,2-dichloroethane immersed in water and transfer of fluoromethane across the water-hexane interface. The calculated free energies are compared with those obtained by two commonly used methods. One of them relies on determining the probability density function of finding the system at different values of the selected coordinate and the other requires calculating the average force at discrete locations along this coordinate in a series of constrained simulations. The free energies calculated by these three methods are in excellent agreement. The relative advantages of each method are discussed.
Geographic Gossip: Efficient Averaging for Sensor Networks
Dimakis, Alexandros D. G.; Sarwate, Anand D.; Wainwright, Martin J.
Gossip algorithms for distributed computation are attractive due to their simplicity, distributed nature, and robustness in noisy and uncertain environments. However, using standard gossip algorithms can lead to a significant waste in energy by repeatedly recirculating redundant information. For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is related to the slow mixing times of random walks on the communication graph. We propose and analyze an alternative gossiping scheme that exploits geographic information. By utilizing geographic routing combined with a simple resampling method, we demonstrate substantial gains over previously proposed gossip protocols. For regular graphs such as the ring or grid, our algorithm improves standard gossip by factors of $n$ and $\\sqrt{n}$ respectively. For the more challenging case of random geometric graphs, our algorithm computes the true average to accuracy $\\epsilon$ using $O(\\frac{n^{1.5}}{\\sqrt{\\log n}} \\log \\epsilon^{-1})$ radio transmissions, which yields a $\\sqrt{\\frac{n}{\\log n}}$ factor improvement over standard gossip algorithms. We illustrate these theoretical results with experimental comparisons between our algorithm and standard methods as applied to various classes of random fields.
High-average-power solid state lasers
International Nuclear Information System (INIS)
Summers, M.A.
1989-01-01
In 1987, a broad-based, aggressive R ampersand D program aimed at developing the technologies necessary to make possible the use of solid state lasers that are capable of delivering medium- to high-average power in new and demanding applications. Efforts were focused along the following major lines: development of laser and nonlinear optical materials, and of coatings for parasitic suppression and evanescent wave control; development of computational design tools; verification of computational models on thoroughly instrumented test beds; and applications of selected aspects of this technology to specific missions. In the laser materials areas, efforts were directed towards producing strong, low-loss laser glasses and large, high quality garnet crystals. The crystal program consisted of computational and experimental efforts aimed at understanding the physics, thermodynamics, and chemistry of large garnet crystal growth. The laser experimental efforts were directed at understanding thermally induced wave front aberrations in zig-zag slabs, understanding fluid mechanics, heat transfer, and optical interactions in gas-cooled slabs, and conducting critical test-bed experiments with various electro-optic switch geometries. 113 refs., 99 figs., 18 tabs
The concept of average LET values determination
International Nuclear Information System (INIS)
Makarewicz, M.
1981-01-01
The concept of average LET (linear energy transfer) values determination, i.e. ordinary moments of LET in absorbed dose distribution vs. LET of ionizing radiation of any kind and any spectrum (even the unknown ones) has been presented. The method is based on measurement of ionization current with several values of voltage supplying an ionization chamber operating in conditions of columnar recombination of ions or ion recombination in clusters while the chamber is placed in the radiation field at the point of interest. By fitting a suitable algebraic expression to the measured current values one can obtain coefficients of the expression which can be interpreted as values of LET moments. One of the advantages of the method is its experimental and computational simplicity. It has been shown that for numerical estimation of certain effects dependent on LET of radiation it is not necessary to know the dose distribution but only a number of parameters of the distribution, i.e. the LET moments. (author)
On spectral averages in nuclear spectroscopy
International Nuclear Information System (INIS)
Verbaarschot, J.J.M.
1982-01-01
In nuclear spectroscopy one tries to obtain a description of systems of bound nucleons. By means of theoretical models one attemps to reproduce the eigenenergies and the corresponding wave functions which then enable the computation of, for example, the electromagnetic moments and the transition amplitudes. Statistical spectroscopy can be used for studying nuclear systems in large model spaces. In this thesis, methods are developed and applied which enable the determination of quantities in a finite part of the Hilbert space, which is defined by specific quantum values. In the case of averages in a space defined by a partition of the nucleons over the single-particle orbits, the propagation coefficients reduce to Legendre interpolation polynomials. In chapter 1 these polynomials are derived with the help of a generating function and a generalization of Wick's theorem. One can then deduce the centroid and the variance of the eigenvalue distribution in a straightforward way. The results are used to calculate the systematic energy difference between states of even and odd parity for nuclei in the mass region A=10-40. In chapter 2 an efficient method for transforming fixed angular momentum projection traces into fixed angular momentum for the configuration space traces is developed. In chapter 3 it is shown that the secular behaviour can be represented by a Gaussian function of the energies. (Auth.)
Lattice Boltzmann methods for moving boundary flows
International Nuclear Information System (INIS)
Inamuro, Takaji
2012-01-01
The lattice Boltzmann methods (LBMs) for moving boundary flows are presented. The LBM for two-phase fluid flows with the same density and the LBM combined with the immersed boundary method are described. In addition, the LBM on a moving multi-block grid is explained. Three numerical examples (a droplet moving in a constricted tube, the lift generation of a flapping wing and the sedimentation of an elliptical cylinder) are shown in order to demonstrate the applicability of the LBMs to moving boundary problems. (invited review)
Lattice Boltzmann methods for moving boundary flows
Energy Technology Data Exchange (ETDEWEB)
Inamuro, Takaji, E-mail: inamuro@kuaero.kyoto-u.ac.jp [Department of Aeronautics and Astronautics, and Advanced Research Institute of Fluid Science and Engineering, Graduate School of Engineering, Kyoto University, Kyoto 606-8501 (Japan)
2012-04-01
The lattice Boltzmann methods (LBMs) for moving boundary flows are presented. The LBM for two-phase fluid flows with the same density and the LBM combined with the immersed boundary method are described. In addition, the LBM on a moving multi-block grid is explained. Three numerical examples (a droplet moving in a constricted tube, the lift generation of a flapping wing and the sedimentation of an elliptical cylinder) are shown in order to demonstrate the applicability of the LBMs to moving boundary problems. (invited review)
Intermediate and advanced topics in multilevel logistic regression analysis.
Austin, Peter C; Merlo, Juan
2017-09-10
Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,
This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Categorical regression dose-response modeling
The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
2007-01-01
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…
Suppression Situations in Multiple Linear Regression
Shieh, Gwowen
2006-01-01
This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…
Gibrat’s law and quantile regressions
DEFF Research Database (Denmark)
Distante, Roberta; Petrella, Ivan; Santoro, Emiliano
2017-01-01
The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...