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Sample records for linear models glm

  1. Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs).

    Hernández Suárez, Marcos; Astray Dopazo, Gonzalo; Larios López, Dina; Espinosa, Francisco

    2015-01-01

    There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA) have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID) algorithm and Artificial Neural Network (ANN) models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain). Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID) tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit's goodness between 44 and 100

  2. Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID and Artificial Neural Network Models (ANNs.

    Marcos Hernández Suárez

    Full Text Available There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA or Factor Analysis (FA have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID algorithm and Artificial Neural Network (ANN models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain. Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit's goodness

  3. Modeling diarrhea disease in children less than 5 years old: a GAM and GLM approach

    sharif mahmood

    2012-11-01

    Full Text Available Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This paper presents the application of generalized additive model (GAM and generalized linear model (GLM as an exploratory tool for analyzing the factors that affect the occurrence of diarrhea of Bangladeshi child. The relation between the factors that are related with occurrence of diarrhea can be obtained by modeling parametric approach (GLM. But in practice the relation is not straight forward and we require elaborate explanations which incline semiparametric regression (GAM. We present a unified approach for analyzing factors affecting diarrhea via GLM and GAM. We applied Akaike's information criterion to select the best model for our data. Our study analyzes nonlinear resolution of covariate not available with traditional parametric models and the results provide some evidence on how to reduce occurrence of diarrhea by improving socio-economic and public health conditions.

  4. ANOVA and ANCOVA A GLM Approach

    Rutherford, Andrew

    2012-01-01

    Provides an in-depth treatment of ANOVA and ANCOVA techniques from a linear model perspective ANOVA and ANCOVA: A GLM Approach provides a contemporary look at the general linear model (GLM) approach to the analysis of variance (ANOVA) of one- and two-factor psychological experiments. With its organized and comprehensive presentation, the book successfully guides readers through conventional statistical concepts and how to interpret them in GLM terms, treating the main single- and multi-factor designs as they relate to ANOVA and ANCOVA. The book begins with a brief history of the separate dev

  5. Evaluating the double Poisson generalized linear model.

    Zou, Yaotian; Geedipally, Srinivas Reddy; Lord, Dominique

    2013-10-01

    The objectives of this study are to: (1) examine the applicability of the double Poisson (DP) generalized linear model (GLM) for analyzing motor vehicle crash data characterized by over- and under-dispersion and (2) compare the performance of the DP GLM with the Conway-Maxwell-Poisson (COM-Poisson) GLM in terms of goodness-of-fit and theoretical soundness. The DP distribution has seldom been investigated and applied since its first introduction two decades ago. The hurdle for applying the DP is related to its normalizing constant (or multiplicative constant) which is not available in closed form. This study proposed a new method to approximate the normalizing constant of the DP with high accuracy and reliability. The DP GLM and COM-Poisson GLM were developed using two observed over-dispersed datasets and one observed under-dispersed dataset. The modeling results indicate that the DP GLM with its normalizing constant approximated by the new method can handle crash data characterized by over- and under-dispersion. Its performance is comparable to the COM-Poisson GLM in terms of goodness-of-fit (GOF), although COM-Poisson GLM provides a slightly better fit. For the over-dispersed data, the DP GLM performs similar to the NB GLM. Considering the fact that the DP GLM can be easily estimated with inexpensive computation and that it is simpler to interpret coefficients, it offers a flexible and efficient alternative for researchers to model count data. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs.

    Äijö, Tarmo; Yue, Xiaojing; Rao, Anjana; Lähdesmäki, Harri

    2016-09-01

    5-methylcytosine (5mC) is a widely studied epigenetic modification of DNA. The ten-eleven translocation (TET) dioxygenases oxidize 5mC into oxidized methylcytosines (oxi-mCs): 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). DNA methylation modifications have multiple functions. For example, 5mC is shown to be associated with diseases and oxi-mC species are reported to have a role in active DNA demethylation through 5mC oxidation and DNA repair, among others, but the detailed mechanisms are poorly understood. Bisulphite sequencing and its various derivatives can be used to gain information about all methylation modifications at single nucleotide resolution. Analysis of bisulphite based sequencing data is complicated due to the convoluted read-outs and experiment-specific variation in biochemistry. Moreover, statistical analysis is often complicated by various confounding effects. How to analyse 5mC and oxi-mC data sets with arbitrary and complex experimental designs is an open and important problem. We propose the first method to quantify oxi-mC species with arbitrary covariate structures from bisulphite based sequencing data. Our probabilistic modeling framework combines a previously proposed hierarchical generative model for oxi-mC-seq data and a general linear model component to account for confounding effects. We show that our method provides accurate methylation level estimates and accurate detection of differential methylation when compared with existing methods. Analysis of novel and published data gave insights into to the demethylation of the forkhead box P3 (Foxp3) locus during the induced T regulatory cell differentiation. We also demonstrate how our covariate model accurately predicts methylation levels of the Foxp3 locus. Collectively, LuxGLM method improves the analysis of DNA methylation modifications, particularly for oxi-mC species. An implementation of the proposed method is available under MIT license at https://github.org/tare/LuxGLM

  7. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods

    Pourghasemi, Hamid Reza; Rossi, Mauro

    2017-10-01

    Landslides are identified as one of the most important natural hazards in many areas throughout the world. The essential purpose of this study is to compare general linear model (GLM), general additive model (GAM), multivariate adaptive regression spline (MARS), and modified analytical hierarchy process (M-AHP) models and assessment of their performances for landslide susceptibility modeling in the west of Mazandaran Province, Iran. First, landslides were identified by interpreting aerial photographs, and extensive field works. In total, 153 landslides were identified in the study area. Among these, 105 landslides were randomly selected as training data (i.e. used in the models training) and the remaining 48 (30 %) cases were used for the validation (i.e. used in the models validation). Afterward, based on a deep literature review on 220 scientific papers (period between 2005 and 2012), eleven conditioning factors including lithology, land use, distance from rivers, distance from roads, distance from faults, slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, and profile curvature were selected. The Certainty Factor (CF) model was used for managing uncertainty in rule-based systems and evaluation of the correlation between the dependent (landslides) and independent variables. Finally, the landslide susceptibility zonation was produced using GLM, GAM, MARS, and M-AHP models. For evaluation of the models, the area under the curve (AUC) method was used and both success and prediction rate curves were calculated. The evaluation of models for GLM, GAM, and MARS showed 90.50, 88.90, and 82.10 % for training data and 77.52, 70.49, and 78.17 % for validation data, respectively. Furthermore, The AUC value of the produced landslide susceptibility map using M-AHP showed a training value of 77.82 % and validation value of 82.77 % accuracy. Based on the overall assessments, the proposed approaches showed reasonable results for landslide

  8. Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.

    Vidal, Sherry

    Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…

  9. Linear Models

    Searle, Shayle R

    2012-01-01

    This 1971 classic on linear models is once again available--as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.

  10. Capturing spike variability in noisy Izhikevich neurons using point process generalized linear models

    Østergaard, Jacob; Kramer, Mark A.; Eden, Uri T.

    2018-01-01

    current. We then fit these spike train datawith a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven...... by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured....... are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input...

  11. Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

    Faraway, Julian J

    2005-01-01

    Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

  12. Characterizing the performance of the Conway-Maxwell Poisson generalized linear model.

    Francis, Royce A; Geedipally, Srinivas Reddy; Guikema, Seth D; Dhavala, Soma Sekhar; Lord, Dominique; LaRocca, Sarah

    2012-01-01

    Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway-Maxwell Poisson (COM-Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM-Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM-Poisson GLM, and (2) estimate the prediction accuracy of the COM-Poisson GLM using simulated data sets. The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values. The results also show that the COM-Poisson GLM yields accurate parameter estimates. The COM-Poisson GLM provides a promising and flexible approach for performing count data regression. © 2011 Society for Risk Analysis.

  13. Generalized Linear Models in Vehicle Insurance

    Silvie Kafková

    2014-01-01

    Full Text Available Actuaries in insurance companies try to find the best model for an estimation of insurance premium. It depends on many risk factors, e.g. the car characteristics and the profile of the driver. In this paper, an analysis of the portfolio of vehicle insurance data using a generalized linear model (GLM is performed. The main advantage of the approach presented in this article is that the GLMs are not limited by inflexible preconditions. Our aim is to predict the relation of annual claim frequency on given risk factors. Based on a large real-world sample of data from 57 410 vehicles, the present study proposed a classification analysis approach that addresses the selection of predictor variables. The models with different predictor variables are compared by analysis of deviance and Akaike information criterion (AIC. Based on this comparison, the model for the best estimate of annual claim frequency is chosen. All statistical calculations are computed in R environment, which contains stats package with the function for the estimation of parameters of GLM and the function for analysis of deviation.

  14. linear-quadratic-linear model

    Tanwiwat Jaikuna

    2017-02-01

    Full Text Available Purpose: To develop an in-house software program that is able to calculate and generate the biological dose distribution and biological dose volume histogram by physical dose conversion using the linear-quadratic-linear (LQL model. Material and methods : The Isobio software was developed using MATLAB version 2014b to calculate and generate the biological dose distribution and biological dose volume histograms. The physical dose from each voxel in treatment planning was extracted through Computational Environment for Radiotherapy Research (CERR, and the accuracy was verified by the differentiation between the dose volume histogram from CERR and the treatment planning system. An equivalent dose in 2 Gy fraction (EQD2 was calculated using biological effective dose (BED based on the LQL model. The software calculation and the manual calculation were compared for EQD2 verification with pair t-test statistical analysis using IBM SPSS Statistics version 22 (64-bit. Results: Two and three-dimensional biological dose distribution and biological dose volume histogram were displayed correctly by the Isobio software. Different physical doses were found between CERR and treatment planning system (TPS in Oncentra, with 3.33% in high-risk clinical target volume (HR-CTV determined by D90%, 0.56% in the bladder, 1.74% in the rectum when determined by D2cc, and less than 1% in Pinnacle. The difference in the EQD2 between the software calculation and the manual calculation was not significantly different with 0.00% at p-values 0.820, 0.095, and 0.593 for external beam radiation therapy (EBRT and 0.240, 0.320, and 0.849 for brachytherapy (BT in HR-CTV, bladder, and rectum, respectively. Conclusions : The Isobio software is a feasible tool to generate the biological dose distribution and biological dose volume histogram for treatment plan evaluation in both EBRT and BT.

  15. EVALUATING PREDICTIVE ERRORS OF A COMPLEX ENVIRONMENTAL MODEL USING A GENERAL LINEAR MODEL AND LEAST SQUARE MEANS

    A General Linear Model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the Regional Mercury Cycling Model (R-MCM) to simulate epilimnetic total mer...

  16. Linear models with R

    Faraway, Julian J

    2014-01-01

    A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.New to the Second EditionReorganiz

  17. Foundations of linear and generalized linear models

    Agresti, Alan

    2015-01-01

    A valuable overview of the most important ideas and results in statistical analysis Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building. The book begins by illustrating the fundamentals of linear models,

  18. Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences

    Pekár, S.; Brabec, Marek

    2018-01-01

    Roč. 124, č. 2 (2018), s. 86-93 ISSN 0179-1613 Institutional support: RVO:67985807 Keywords : correlated data * generalized estimating equations * marginal model * regression models * statistical analysis Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.398, year: 2016

  19. Dimension of linear models

    Høskuldsson, Agnar

    1996-01-01

    Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four of these cri......Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four...... the basic problems in determining the dimension of linear models. Then each of the eight measures are treated. The results are illustrated by examples....

  20. Dimension of linear models

    Høskuldsson, Agnar

    1996-01-01

    Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four...... the basic problems in determining the dimension of linear models. Then each of the eight measures are treated. The results are illustrated by examples....... of these criteria are widely used ones, while the remaining four are ones derived from the H-principle of mathematical modeling. Many examples from practice show that the criteria derived from the H-principle function better than the known and popular criteria for the number of components. We shall briefly review...

  1. Non linear viscoelastic models

    Agerkvist, Finn T.

    2011-01-01

    Viscoelastic eects are often present in loudspeaker suspensions, this can be seen in the displacement transfer function which often shows a frequency dependent value below the resonance frequency. In this paper nonlinear versions of the standard linear solid model (SLS) are investigated....... The simulations show that the nonlinear version of the Maxwell SLS model can result in a time dependent small signal stiness while the Kelvin Voight version does not....

  2. Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach

    Martin M Monti

    2011-03-01

    Full Text Available Functional Magnetic Resonance Imaging (fMRI is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a General Linear Model (GLM approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.

  3. A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.

    Ana Calabrese

    2011-01-01

    Full Text Available In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF, a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM. In this model, each cell's input is described by: 1 a stimulus filter (STRF; and 2 a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs and modulation limited (ml noise. We compare this model to normalized reverse correlation (NRC, the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons.

  4. A primer on linear models

    Monahan, John F

    2008-01-01

    Preface Examples of the General Linear Model Introduction One-Sample Problem Simple Linear Regression Multiple Regression One-Way ANOVA First Discussion The Two-Way Nested Model Two-Way Crossed Model Analysis of Covariance Autoregression Discussion The Linear Least Squares Problem The Normal Equations The Geometry of Least Squares Reparameterization Gram-Schmidt Orthonormalization Estimability and Least Squares Estimators Assumptions for the Linear Mean Model Confounding, Identifiability, and Estimability Estimability and Least Squares Estimators F

  5. Population decoding of motor cortical activity using a generalized linear model with hidden states.

    Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas; Paninski, Liam

    2010-06-15

    Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

  6. Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States

    Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam

    2010-01-01

    Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500

  7. Dynamic Linear Models with R

    Campagnoli, Patrizia; Petris, Giovanni

    2009-01-01

    State space models have gained tremendous popularity in as disparate fields as engineering, economics, genetics and ecology. Introducing general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. It illustrates the fundamental steps needed to use dynamic linear models in practice, using R package.

  8. Introduction to generalized linear models

    Dobson, Annette J

    2008-01-01

    Introduction Background Scope Notation Distributions Related to the Normal Distribution Quadratic Forms Estimation Model Fitting Introduction Examples Some Principles of Statistical Modeling Notation and Coding for Explanatory Variables Exponential Family and Generalized Linear Models Introduction Exponential Family of Distributions Properties of Distributions in the Exponential Family Generalized Linear Models Examples Estimation Introduction Example: Failure Times for Pressure Vessels Maximum Likelihood Estimation Poisson Regression Example Inference Introduction Sampling Distribution for Score Statistics Taylor Series Approximations Sampling Distribution for MLEs Log-Likelihood Ratio Statistic Sampling Distribution for the Deviance Hypothesis Testing Normal Linear Models Introduction Basic Results Multiple Linear Regression Analysis of Variance Analysis of Covariance General Linear Models Binary Variables and Logistic Regression Probability Distributions ...

  9. Analysis of dental caries using generalized linear and count regression models

    Javali M. Phil

    2013-11-01

    Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.

  10. (Non) linear regression modelling

    Cizek, P.; Gentle, J.E.; Hardle, W.K.; Mori, Y.

    2012-01-01

    We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or

  11. Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms.

    Vilar, Lara; Gómez, Israel; Martínez-Vega, Javier; Echavarría, Pilar; Riaño, David; Martín, M Pilar

    2016-01-01

    The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment.

  12. Explorative methods in linear models

    Høskuldsson, Agnar

    2004-01-01

    The author has developed the H-method of mathematical modeling that builds up the model by parts, where each part is optimized with respect to prediction. Besides providing with better predictions than traditional methods, these methods provide with graphic procedures for analyzing different feat...... features in data. These graphic methods extend the well-known methods and results of Principal Component Analysis to any linear model. Here the graphic procedures are applied to linear regression and Ridge Regression....

  13. Comparisons of GLM and LMA Observations

    Thomas, R. J.; Krehbiel, P. R.; Rison, W.; Stanley, M. A.; Attanasio, A.

    2017-12-01

    Observations from 3-dimensional VHF lightning mapping arrays (LMAs) provide a valuable basis for evaluating the spatial accuracy and detection efficiencies of observations from the recently launched, optical-based Geosynchronous Lightning Mapper (GLM). In this presentation, we describe results of comparing the LMA and GLM observations. First, the observations are compared spatially and temporally at the individual event (pixel) level for sets of individual discharges. For LMA networks in Florida, Colorado, and Oklahoma, the GLM observations are well correlated time-wise with LMA observations but are systematically offset by one- to two pixels ( 10 to 15 or 20 km) in a southwesterly direction from the actual lightning activity. The graphical comparisons show a similar location uncertainty depending on the altitude at which the scattered light is emitted from the parent cloud, due to being observed at slant ranges. Detection efficiencies (DEs) can be accurately determined graphically for intervals where individual flashes in a storm are resolved time-wise, and DEs and false alarm rates can be automated using flash sorting algorithms for overall and/or larger storms. This can be done as a function of flash size and duration, and generally shows high detection rates for larger flashes. Preliminary results during the May 1 2017 ER-2 overflight of Colorado storms indicate decreased detection efficiency if the storm is obscured by an overlying cloud layer.

  14. A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

    Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

    2008-01-01

    As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

  15. Generalized, Linear, and Mixed Models

    McCulloch, Charles E; Neuhaus, John M

    2011-01-01

    An accessible and self-contained introduction to statistical models-now in a modernized new editionGeneralized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed m

  16. Sparse Linear Identifiable Multivariate Modeling

    Henao, Ricardo; Winther, Ole

    2011-01-01

    and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...

  17. Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

    Daniel T. L. Shek

    2011-01-01

    Full Text Available Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes in Hong Kong are presented.

  18. Longitudinal data analyses using linear mixed models in SPSS: concepts, procedures and illustrations.

    Shek, Daniel T L; Ma, Cecilia M S

    2011-01-05

    Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.

  19. Parameterized Linear Longitudinal Airship Model

    Kulczycki, Eric; Elfes, Alberto; Bayard, David; Quadrelli, Marco; Johnson, Joseph

    2010-01-01

    A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamics

  20. Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

    Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J

    2016-05-01

    Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.

  1. Decomposable log-linear models

    Eriksen, Poul Svante

    can be characterized by a structured set of conditional independencies between some variables given some other variables. We term the new model class decomposable log-linear models, which is illustrated to be a much richer class than decomposable graphical models.It covers a wide range of non...... The present paper considers discrete probability models with exact computational properties. In relation to contingency tables this means closed form expressions of the maksimum likelihood estimate and its distribution. The model class includes what is known as decomposable graphicalmodels, which......-hierarchical models, models with structural zeroes, models described by quasi independence and models for level merging. Also, they have a very natural interpretation as they may be formulated by a structured set of conditional independencies between two events given some other event. In relation to contingency...

  2. Linear and Generalized Linear Mixed Models and Their Applications

    Jiang, Jiming

    2007-01-01

    This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners who are interested

  3. The GOES-R Geostationary Lightning Mapper (GLM) and the Global Observing System for Total Lightning

    Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.; Buechler, D.; Carey, L.; Chronis, T.; Mach, D.; Bateman, M.; Peterson, H.; McCaul, E. W., Jr.; hide

    2014-01-01

    for the existing GOES system currently operating over the Western Hemisphere. New and improved instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved temporal, spatial, and spectral resolution for the next generation Advanced Baseline Imager (ABI). The GLM will map total lightning continuously day and night with near-uniform spatial resolution of 8 km with a product latency of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. The GLM will help address the National Weather Service requirement for total lightning observations globally to support warning decision-making and forecast services. Science and application development along with pre-operational product demonstrations and evaluations at NWS national centers, forecast offices, and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and check-out of GOES-R in 2016. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.

  4. Multicollinearity in hierarchical linear models.

    Yu, Han; Jiang, Shanhe; Land, Kenneth C

    2015-09-01

    This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Modelling Loudspeaker Non-Linearities

    Agerkvist, Finn T.

    2007-01-01

    This paper investigates different techniques for modelling the non-linear parameters of the electrodynamic loudspeaker. The methods are tested not only for their accuracy within the range of original data, but also for the ability to work reasonable outside that range, and it is demonstrated...... that polynomial expansions are rather poor at this, whereas an inverse polynomial expansion or localized fitting functions such as the gaussian are better suited for modelling the Bl-factor and compliance. For the inductance the sigmoid function is shown to give very good results. Finally the time varying...

  6. Multivariate covariance generalized linear models

    Bonat, W. H.; Jørgensen, Bent

    2016-01-01

    are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...

  7. Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model

    Lin, Zi-Jing; Li, Lin; Cazzell, Marry; Liu, Hanli

    2013-03-01

    Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique which measures the hemodynamic changes that reflect the brain activity. Diffuse optical tomography (DOT), a variant of fNIRS with multi-channel NIRS measurements, has demonstrated capability of three dimensional (3D) reconstructions of hemodynamic changes due to the brain activity. Conventional method of DOT image analysis to define the brain activation is based upon the paired t-test between two different states, such as resting-state versus task-state. However, it has limitation because the selection of activation and post-activation period is relatively subjective. General linear model (GLM) based analysis can overcome this limitation. In this study, we combine the 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with the risk-decision making process. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The balloon analogue risk task (BART) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks. We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making. Voxel-wise GLM analysis was performed on 18human participants (10 males and 8females).In this work, we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT. Results have shown significant changes in the dorsal lateral prefrontal cortex (DLPFC) during the active choice mode and a different hemodynamic pattern between genders, which are in good agreements with published literatures in functional magnetic resonance imaging (fMRI) and fNIRS studies.

  8. Matrix algebra for linear models

    Gruber, Marvin H J

    2013-01-01

    Matrix methods have evolved from a tool for expressing statistical problems to an indispensable part of the development, understanding, and use of various types of complex statistical analyses. This evolution has made matrix methods a vital part of statistical education. Traditionally, matrix methods are taught in courses on everything from regression analysis to stochastic processes, thus creating a fractured view of the topic. Matrix Algebra for Linear Models offers readers a unique, unified view of matrix analysis theory (where and when necessary), methods, and their applications. Written f

  9. Nonabelian Gauged Linear Sigma Model

    Yongbin RUAN

    2017-01-01

    The gauged linear sigma model (GLSM for short) is a 2d quantum field theory introduced by Witten twenty years ago.Since then,it has been investigated extensively in physics by Hori and others.Recently,an algebro-geometric theory (for both abelian and nonabelian GLSMs) was developed by the author and his collaborators so that he can start to rigorously compute its invariants and check against physical predications.The abelian GLSM was relatively better understood and is the focus of current mathematical investigation.In this article,the author would like to look over the horizon and consider the nonabelian GLSM.The nonabelian case possesses some new features unavailable to the abelian GLSM.To aid the future mathematical development,the author surveys some of the key problems inspired by physics in the nonabelian GLSM.

  10. Linear mixed-effects modeling approach to FMRI group analysis.

    Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W

    2013-06-01

    Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity

  11. Multivariate generalized linear mixed models using R

    Berridge, Damon Mark

    2011-01-01

    Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...

  12. Image navigation and registration for the geostationary lightning mapper (GLM)

    van Bezooijen, Roel W. H.; Demroff, Howard; Burton, Gregory; Chu, Donald; Yang, Shu S.

    2016-10-01

    The Geostationary Lightning Mappers (GLM) for the Geostationary Operational Environmental Satellite (GOES) GOES-R series will, for the first time, provide hemispherical lightning information 24 hours a day from longitudes of 75 and 137 degrees west. The first GLM of a series of four is planned for launch in November, 2016. Observation of lightning patterns by GLM holds promise to improve tornado warning lead times to greater than 20 minutes while halving the present false alarm rates. In addition, GLM will improve airline traffic flow management, and provide climatology data allowing us to understand the Earth's evolving climate. The paper describes the method used for translating the pixel position of a lightning event to its corresponding geodetic longitude and latitude, using the J2000 attitude of the GLM mount frame reported by the spacecraft, the position of the spacecraft, and the alignment of the GLM coordinate frame relative to its mount frame. Because the latter alignment will experience seasonal variation, this alignment is determined daily using GLM background images collected over the previous 7 days. The process involves identification of coastlines in the background images and determination of the alignment change necessary to match the detected coastline with the coastline predicted using the GSHHS database. Registration is achieved using a variation of the Lucas-Kanade algorithm where we added a dither and average technique to improve performance significantly. An innovative water mask technique was conceived to enable self-contained detection of clear coastline sections usable for registration. Extensive simulations using accurate visible images from GOES13 and GOES15 have been used to demonstrate the performance of the coastline registration method, the results of which are presented in the paper.

  13. Nonlinear Modeling by Assembling Piecewise Linear Models

    Yao, Weigang; Liou, Meng-Sing

    2013-01-01

    To preserve nonlinearity of a full order system over a parameters range of interest, we propose a simple modeling approach by assembling a set of piecewise local solutions, including the first-order Taylor series terms expanded about some sampling states. The work by Rewienski and White inspired our use of piecewise linear local solutions. The assembly of these local approximations is accomplished by assigning nonlinear weights, through radial basis functions in this study. The efficacy of the proposed procedure is validated for a two-dimensional airfoil moving at different Mach numbers and pitching motions, under which the flow exhibits prominent nonlinear behaviors. All results confirm that our nonlinear model is accurate and stable for predicting not only aerodynamic forces but also detailed flowfields. Moreover, the model is robustness-accurate for inputs considerably different from the base trajectory in form and magnitude. This modeling preserves nonlinearity of the problems considered in a rather simple and accurate manner.

  14. Linear Logistic Test Modeling with R

    Baghaei, Purya; Kubinger, Klaus D.

    2015-01-01

    The present paper gives a general introduction to the linear logistic test model (Fischer, 1973), an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and interpret its parameters. The…

  15. Assessing the economic impact of paternal involvement: a comparison of the generalized linear model versus decision analysis trees.

    Salihu, Hamisu M; Salemi, Jason L; Nash, Michelle C; Chandler, Kristen; Mbah, Alfred K; Alio, Amina P

    2014-08-01

    Lack of paternal involvement has been shown to be associated with adverse pregnancy outcomes, including infant morbidity and mortality, but the impact on health care costs is unknown. Various methodological approaches have been used in cost minimization and cost effectiveness analyses and it remains unclear how cost estimates vary according to the analytic strategy adopted. We illustrate a methodological comparison of decision analysis modeling and generalized linear modeling (GLM) techniques using a case study that assesses the cost-effectiveness of potential father involvement interventions. We conducted a 12-year retrospective cohort study using a statewide enhanced maternal-infant database that contains both clinical and nonclinical information. A missing name for the father on the infant's birth certificate was used as a proxy for lack of paternal involvement, the main exposure of this study. Using decision analysis modeling and GLM, we compared all infant inpatient hospitalization costs over the first year of life. Costs were calculated from hospital charges using department-level cost-to-charge ratios and were adjusted for inflation. In our cohort of 2,243,891 infants, 9.2% had a father uninvolved during pregnancy. Lack of paternal involvement was associated with higher rates of preterm birth, small-for-gestational age, and infant morbidity and mortality. Both analytic approaches estimate significantly higher per-infant costs for father uninvolved pregnancies (decision analysis model: $1,827, GLM: $1,139). This paper provides sufficient evidence that healthcare costs could be significantly reduced through enhanced father involvement during pregnancy, and buttresses the call for a national program to involve fathers in antenatal care.

  16. The glmS ribozyme cofactor is a general acid-base catalyst.

    Viladoms, Júlia; Fedor, Martha J

    2012-11-21

    The glmS ribozyme is the first natural self-cleaving ribozyme known to require a cofactor. The d-glucosamine-6-phosphate (GlcN6P) cofactor has been proposed to serve as a general acid, but its role in the catalytic mechanism has not been established conclusively. We surveyed GlcN6P-like molecules for their ability to support self-cleavage of the glmS ribozyme and found a strong correlation between the pH dependence of the cleavage reaction and the intrinsic acidity of the cofactors. For cofactors with low binding affinities, the contribution to rate enhancement was proportional to their intrinsic acidity. This linear free-energy relationship between cofactor efficiency and acid dissociation constants is consistent with a mechanism in which the cofactors participate directly in the reaction as general acid-base catalysts. A high value for the Brønsted coefficient (β ~ 0.7) indicates that a significant amount of proton transfer has already occurred in the transition state. The glmS ribozyme is the first self-cleaving RNA to use an exogenous acid-base catalyst.

  17. Core seismic behaviour: linear and non-linear models

    Bernard, M.; Van Dorsselaere, M.; Gauvain, M.; Jenapierre-Gantenbein, M.

    1981-08-01

    The usual methodology for the core seismic behaviour analysis leads to a double complementary approach: to define a core model to be included in the reactor-block seismic response analysis, simple enough but representative of basic movements (diagrid or slab), to define a finer core model, with basic data issued from the first model. This paper presents the history of the different models of both kinds. The inert mass model (IMM) yielded a first rough diagrid movement. The direct linear model (DLM), without shocks and with sodium as an added mass, let to two different ones: DLM 1 with independent movements of the fuel and radial blanket subassemblies, and DLM 2 with a core combined movement. The non-linear (NLM) ''CORALIE'' uses the same basic modelization (Finite Element Beams) but accounts for shocks. It studies the response of a diameter on flats and takes into account the fluid coupling and the wrapper tube flexibility at the pad level. Damping consists of one modal part of 2% and one part due to shocks. Finally, ''CORALIE'' yields the time-history of the displacements and efforts on the supports, but damping (probably greater than 2%) and fluid-structures interaction are still to be precised. The validation experiments were performed on a RAPSODIE core mock-up on scale 1, in similitude of 1/3 as to SPX 1. The equivalent linear model (ELM) was developed for the SPX 1 reactor-block response analysis and a specified seismic level (SB or SM). It is composed of several oscillators fixed to the diagrid and yields the same maximum displacements and efforts than the NLM. The SPX 1 core seismic analysis with a diagrid input spectrum which corresponds to a 0,1 g group acceleration, has been carried out with these models: some aspects of these calculations are presented here

  18. Composite Linear Models | Division of Cancer Prevention

    By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty

  19. Actuarial statistics with generalized linear mixed models

    Antonio, K.; Beirlant, J.

    2007-01-01

    Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics

  20. Continuum limit of gl(M vertical stroke N) spin chains

    Candu, Constantin

    2011-03-01

    We study the spectrum of an integrable antiferromagnetic Hamiltonian of the gl(M vertical stroke N) spin chain of alternating fundamental and dual representations. After extensive numerical analysis, we identify the vacuum and low lying excitations and with this knowledge perform the continuum limit, while keeping a finite gap. All antiferromagnetic gl(n+N vertical stroke N) spin chains with n>0 and N≠0 are shown to possess in the continuum limit 2n-2 multiplets of massive particles which scatter with gl(n) Gross-Neveu like S-matrices, namely their eigenvalues do not depend on N. We argue that the continuum theory is the gl(M vertical stroke N) Gross-Neveu model, that is the massive deformation of the gl(M vertical stroke N) 1 Wess-Zumino-Witten model. As we can see ion the example of gl(2m vertical stroke 1) spin chains, the full particle spectrum is much richer. Our analysis suggests that for a complete characterization of the latter it is not enough to restrict to large volume calculations, as we do in this work. (orig.)

  1. Continuum limit of gl(M vertical stroke N) spin chains

    Candu, Constantin [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany). Gruppe Theorie

    2011-03-15

    We study the spectrum of an integrable antiferromagnetic Hamiltonian of the gl(M vertical stroke N) spin chain of alternating fundamental and dual representations. After extensive numerical analysis, we identify the vacuum and low lying excitations and with this knowledge perform the continuum limit, while keeping a finite gap. All antiferromagnetic gl(n+N vertical stroke N) spin chains with n>0 and N{ne}0 are shown to possess in the continuum limit 2n-2 multiplets of massive particles which scatter with gl(n) Gross-Neveu like S-matrices, namely their eigenvalues do not depend on N. We argue that the continuum theory is the gl(M vertical stroke N) Gross-Neveu model, that is the massive deformation of the gl(M vertical stroke N){sub 1} Wess-Zumino-Witten model. As we can see ion the example of gl(2m vertical stroke 1) spin chains, the full particle spectrum is much richer. Our analysis suggests that for a complete characterization of the latter it is not enough to restrict to large volume calculations, as we do in this work. (orig.)

  2. Comparing linear probability model coefficients across groups

    Holm, Anders; Ejrnæs, Mette; Karlson, Kristian Bernt

    2015-01-01

    of the following three components: outcome truncation, scale parameters and distributional shape of the predictor variable. These results point to limitations in using linear probability model coefficients for group comparisons. We also provide Monte Carlo simulations and real examples to illustrate......This article offers a formal identification analysis of the problem in comparing coefficients from linear probability models between groups. We show that differences in coefficients from these models can result not only from genuine differences in effects, but also from differences in one or more...... these limitations, and we suggest a restricted approach to using linear probability model coefficients in group comparisons....

  3. A first look at lightning energy determined from GLM

    Bitzer, P. M.; Burchfield, J. C.; Brunner, K. N.

    2017-12-01

    The Geostationary Lightning Mapper (GLM) was launched in November 2016 onboard GOES-16 has been undergoing post launch and product post launch testing. While these have typically focused on lightning metrics such as detection efficiency, false alarm rate, and location accuracy, there are other attributes of the lightning discharge that are provided by GLM data. Namely, the optical energy radiated by lightning may provide information useful for lightning physics and the relationship of lightning energy to severe weather development. This work presents initial estimates of the lightning optical energy detected by GLM during this initial testing, with a focus on observations during field campaign during spring 2017 in Huntsville. This region is advantageous for the comparison due to the proliferation of ground-based lightning instrumentation, including a lightning mapping array, interferometer, HAMMA (an array of electric field change meters), high speed video cameras, and several long range VLF networks. In addition, the field campaign included airborne observations of the optical emission and electric field changes. The initial estimates will be compared with previous observations using TRMM-LIS. In addition, a comparison between the operational and scientific GLM data sets will also be discussed.

  4. Installation and Commissioning of the new GLM Implantation Chamber

    Pohl, Christoph

    2016-01-01

    Summer student report about the work with the new implantation chamber for the GLM branch of ISOLDE. In the context of this project an API for the vacuum system of the new chamber was developed and implemented in web application that will be used to control the new implantation chamber at some point.

  5. Spaghetti Bridges: Modeling Linear Relationships

    Kroon, Cindy D.

    2016-01-01

    Mathematics and science are natural partners. One of many examples of this partnership occurs when scientific observations are made, thus providing data that can be used for mathematical modeling. Developing mathematical relationships elucidates such scientific principles. This activity describes a data-collection activity in which students employ…

  6. Non-linear finite element modeling

    Mikkelsen, Lars Pilgaard

    The note is written for courses in "Non-linear finite element method". The note has been used by the author teaching non-linear finite element modeling at Civil Engineering at Aalborg University, Computational Mechanics at Aalborg University Esbjerg, Structural Engineering at the University...

  7. Correlations and Non-Linear Probability Models

    Breen, Richard; Holm, Anders; Karlson, Kristian Bernt

    2014-01-01

    the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models....

  8. Extended Linear Models with Gaussian Priors

    Quinonero, Joaquin

    2002-01-01

    In extended linear models the input space is projected onto a feature space by means of an arbitrary non-linear transformation. A linear model is then applied to the feature space to construct the model output. The dimension of the feature space can be very large, or even infinite, giving the model...... a very big flexibility. Support Vector Machines (SVM's) and Gaussian processes are two examples of such models. In this technical report I present a model in which the dimension of the feature space remains finite, and where a Bayesian approach is used to train the model with Gaussian priors...... on the parameters. The Relevance Vector Machine, introduced by Tipping, is a particular case of such a model. I give the detailed derivations of the expectation-maximisation (EM) algorithm used in the training. These derivations are not found in the literature, and might be helpful for newcomers....

  9. Linear mixed models for longitudinal data

    Molenberghs, Geert

    2000-01-01

    This paperback edition is a reprint of the 2000 edition. This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, conditional linear mixed models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. However, some other commerc...

  10. Linear mixed models in sensometrics

    Kuznetsova, Alexandra

    quality of decision making in Danish as well as international food companies and other companies using the same methods. The two open-source R packages lmerTest and SensMixed implement and support the methodological developments in the research papers as well as the ANOVA modelling part of the Consumer...... an open-source software tool ConsumerCheck was developed in this project and now is available for everyone. will represent a major step forward when concerns this important problem in modern consumer driven product development. Standard statistical software packages can be used for some of the purposes......Today’s companies and researchers gather large amounts of data of different kind. In consumer studies the objective is the collection of the data to better understand consumer acceptance of products. In such studies a number of persons (generally not trained) are selected in order to score products...

  11. Linear causal modeling with structural equations

    Mulaik, Stanley A

    2009-01-01

    Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal

  12. Statistical Tests for Mixed Linear Models

    Khuri, André I; Sinha, Bimal K

    2011-01-01

    An advanced discussion of linear models with mixed or random effects. In recent years a breakthrough has occurred in our ability to draw inferences from exact and optimum tests of variance component models, generating much research activity that relies on linear models with mixed and random effects. This volume covers the most important research of the past decade as well as the latest developments in hypothesis testing. It compiles all currently available results in the area of exact and optimum tests for variance component models and offers the only comprehensive treatment for these models a

  13. Matrix Tricks for Linear Statistical Models

    Puntanen, Simo; Styan, George PH

    2011-01-01

    In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple "tricks" which simplify and clarify the treatment of a problem - both for the student and

  14. Modeling digital switching circuits with linear algebra

    Thornton, Mitchell A

    2014-01-01

    Modeling Digital Switching Circuits with Linear Algebra describes an approach for modeling digital information and circuitry that is an alternative to Boolean algebra. While the Boolean algebraic model has been wildly successful and is responsible for many advances in modern information technology, the approach described in this book offers new insight and different ways of solving problems. Modeling the bit as a vector instead of a scalar value in the set {0, 1} allows digital circuits to be characterized with transfer functions in the form of a linear transformation matrix. The use of transf

  15. Updating Linear Schedules with Lowest Cost: a Linear Programming Model

    Biruk, Sławomir; Jaśkowski, Piotr; Czarnigowska, Agata

    2017-10-01

    Many civil engineering projects involve sets of tasks repeated in a predefined sequence in a number of work areas along a particular route. A useful graphical representation of schedules of such projects is time-distance diagrams that clearly show what process is conducted at a particular point of time and in particular location. With repetitive tasks, the quality of project performance is conditioned by the ability of the planner to optimize workflow by synchronizing the works and resources, which usually means that resources are planned to be continuously utilized. However, construction processes are prone to risks, and a fully synchronized schedule may expire if a disturbance (bad weather, machine failure etc.) affects even one task. In such cases, works need to be rescheduled, and another optimal schedule should be built for the changed circumstances. This typically means that, to meet the fixed completion date, durations of operations have to be reduced. A number of measures are possible to achieve such reduction: working overtime, employing more resources or relocating resources from less to more critical tasks, but they all come at a considerable cost and affect the whole project. The paper investigates the problem of selecting the measures that reduce durations of tasks of a linear project so that the cost of these measures is kept to the minimum and proposes an algorithm that could be applied to find optimal solutions as the need to reschedule arises. Considering that civil engineering projects, such as road building, usually involve less process types than construction projects, the complexity of scheduling problems is lower, and precise optimization algorithms can be applied. Therefore, the authors put forward a linear programming model of the problem and illustrate its principle of operation with an example.

  16. GLM Post Launch Testing and Airborne Science Field Campaign

    Goodman, S. J.; Padula, F.; Koshak, W. J.; Blakeslee, R. J.

    2017-12-01

    The Geostationary Operational Environmental Satellite (GOES-R) series provides the continuity for the existing GOES system currently operating over the Western Hemisphere. The Geostationary Lightning Mapper (GLM) is a wholly new instrument that provides a capability for total lightning detection (cloud and cloud-to-ground flashes). The first satellite in the GOES-R series, now GOES-16, was launched in November 2016 followed by in-orbit post launch testing for approximately 12 months before being placed into operations replacing the GOES-E satellite in December. The GLM will map total lightning continuously throughout day and night with near-uniform spatial resolution of 8 km with a product latency of less than 20 sec over the Americas and adjacent oceanic regions. The total lightning is very useful for identifying hazardous and severe thunderstorms, monitoring storm intensification and tracking evolution. Used in tandem with radar, satellite imagery, and surface observations, total lightning data has great potential to increase lead time for severe storm warnings, improve aviation safety and efficiency, and increase public safety. In this paper we present initial results from the post-launch in-orbit performance testing, airborne science field campaign conducted March-May, 2017 and assessments of the GLM instrument and science products.

  17. A linear model of ductile plastic damage

    Lemaitre, J.

    1983-01-01

    A three-dimensional model of isotropic ductile plastic damage based on a continuum damage variable on the effective stress concept and on thermodynamics is derived. As shown by experiments on several metals and alloys, the model, integrated in the case of proportional loading, is linear with respect to the accumulated plastic strain and shows a large influence of stress triaxiality [fr

  18. Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.

    Kyle A McQuisten

    2009-10-01

    Full Text Available Exogenous short interfering RNAs (siRNAs induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs, General Linear Models (GLMs and Support Vector Machines (SVMs. Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are

  19. Application of Hierarchical Linear Models/Linear Mixed-Effects Models in School Effectiveness Research

    Ker, H. W.

    2014-01-01

    Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

  20. Ground Motion Models for Future Linear Colliders

    Seryi, Andrei

    2000-01-01

    Optimization of the parameters of a future linear collider requires comprehensive models of ground motion. Both general models of ground motion and specific models of the particular site and local conditions are essential. Existing models are not completely adequate, either because they are too general, or because they omit important peculiarities of ground motion. The model considered in this paper is based on recent ground motion measurements performed at SLAC and at other accelerator laboratories, as well as on historical data. The issues to be studied for the models to become more predictive are also discussed

  1. Modelling female fertility traits in beef cattle using linear and non-linear models.

    Naya, H; Peñagaricano, F; Urioste, J I

    2017-06-01

    Female fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h 2  linear models; h 2  > 0.23 and r > 0.24, for non-linear models). While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. © 2017 Blackwell Verlag GmbH.

  2. Modelling point patterns with linear structures

    Møller, Jesper; Rasmussen, Jakob Gulddahl

    2009-01-01

    processes whose realizations contain such linear structures. Such a point process is constructed sequentially by placing one point at a time. The points are placed in such a way that new points are often placed close to previously placed points, and the points form roughly line shaped structures. We...... consider simulations of this model and compare with real data....

  3. Modelling point patterns with linear structures

    Møller, Jesper; Rasmussen, Jakob Gulddahl

    processes whose realizations contain such linear structures. Such a point process is constructed sequentially by placing one point at a time. The points are placed in such a way that new points are often placed close to previously placed points, and the points form roughly line shaped structures. We...... consider simulations of this model and compare with real data....

  4. Optimal designs for linear mixture models

    Mendieta, E.J.; Linssen, H.N.; Doornbos, R.

    1975-01-01

    In a recent paper Snee and Marquardt [8] considered designs for linear mixture models, where the components are subject to individual lower and/or upper bounds. When the number of components is large their algorithm XVERT yields designs far too extensive for practical purposes. The purpose of this

  5. Optimal designs for linear mixture models

    Mendieta, E.J.; Linssen, H.N.; Doornbos, R.

    1975-01-01

    In a recent paper Snee and Marquardt (1974) considered designs for linear mixture models, where the components are subject to individual lower and/or upper bounds. When the number of components is large their algorithm XVERT yields designs far too extensive for practical purposes. The purpose of

  6. Linear factor copula models and their properties

    Krupskii, Pavel; Genton, Marc G.

    2018-01-01

    We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme‐value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme‐value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

  7. Linear factor copula models and their properties

    Krupskii, Pavel

    2018-04-25

    We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme‐value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme‐value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

  8. Diagnostics for Linear Models With Functional Responses

    Xu, Hongquan; Shen, Qing

    2005-01-01

    Linear models where the response is a function and the predictors are vectors are useful in analyzing data from designed experiments and other situations with functional observations. Residual analysis and diagnostics are considered for such models. Studentized residuals are defined and their properties are studied. Chi-square quantile-quantile plots are proposed to check the assumption of Gaussian error process and outliers. Jackknife residuals and an associated test are proposed to det...

  9. Non-linear Loudspeaker Unit Modelling

    Pedersen, Bo Rohde; Agerkvist, Finn T.

    2008-01-01

    Simulations of a 6½-inch loudspeaker unit are performed and compared with a displacement measurement. The non-linear loudspeaker model is based on the major nonlinear functions and expanded with time-varying suspension behaviour and flux modulation. The results are presented with FFT plots of thr...... frequencies and different displacement levels. The model errors are discussed and analysed including a test with loudspeaker unit where the diaphragm is removed....

  10. [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.

  11. Testing Parametric versus Semiparametric Modelling in Generalized Linear Models

    Härdle, W.K.; Mammen, E.; Müller, M.D.

    1996-01-01

    We consider a generalized partially linear model E(Y|X,T) = G{X'b + m(T)} where G is a known function, b is an unknown parameter vector, and m is an unknown function.The paper introduces a test statistic which allows to decide between a parametric and a semiparametric model: (i) m is linear, i.e.

  12. Modeling of Volatility with Non-linear Time Series Model

    Kim Song Yon; Kim Mun Chol

    2013-01-01

    In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.

  13. Thresholding projection estimators in functional linear models

    Cardot, Hervé; Johannes, Jan

    2010-01-01

    We consider the problem of estimating the regression function in functional linear regression models by proposing a new type of projection estimators which combine dimension reduction and thresholding. The introduction of a threshold rule allows to get consistency under broad assumptions as well as minimax rates of convergence under additional regularity hypotheses. We also consider the particular case of Sobolev spaces generated by the trigonometric basis which permits to get easily mean squ...

  14. Decomposed Implicit Models of Piecewise - Linear Networks

    J. Brzobohaty

    1992-05-01

    Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.

  15. From spiking neuron models to linear-nonlinear models.

    Ostojic, Srdjan; Brunel, Nicolas

    2011-01-20

    Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

  16. Stochastic linear programming models, theory, and computation

    Kall, Peter

    2011-01-01

    This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup. To facilitate use as a text, exercises are included throughout the book, and web access is provided to a student version of the authors’ SLP-IOR software. Additionally, the authors have updated the Guide to Available Software, and they have included newer algorithms and modeling systems for SLP. The book is thus suitable as a text for advanced courses in stochastic optimization, and as a reference to the field. From Reviews of the First Edition: "The book presents a comprehensive study of stochastic linear optimization problems and their applications. … T...

  17. Assessment of Brown Bear\\'s (Ursus arctos syriacus Winter Habitat Using Geographically Weighted Regression and Generalized Linear Model in South of Iran

    A. A. Zarei

    2016-03-01

    Full Text Available Winter dens are one of the important components of brown bear's (Ursus arctos syriacus habitat, affecting their reproduction and survival. Therefore identification of factors affecting the habitat selection and suitable denning areas in the conservation of our largest carnivore is necessary. We used Geographically Weighted Logistic Regression (GWLR and Generalized Linear Model (GLM for modeling suitability of denning habitat in Kouhkhom region in Fars province. In the present research, 20 dens (presence locations and 20 caves where signs of bear were not found (absence locations were used as dependent variables and six environmental factors were used for each location as independent variables. The results of GLM showed that variables of distance to settlements, altitude, and distance to water were the most important parameters affecting suitability of the brown bear's denning habitat. The results of GWLR showed the significant local variations in the relationship between occurrence of brown bear dens and the variable of distance to settlements. Based on the results of both models, suitable habitats for denning of the species are impassable areas in the mountains and inaccessible for humans.

  18. Linear accelerator modeling: development and application

    Jameson, R.A.; Jule, W.D.

    1977-01-01

    Most of the parameters of a modern linear accelerator can be selected by simulating the desired machine characteristics in a computer code and observing how the parameters affect the beam dynamics. The code PARMILA is used at LAMPF for the low-energy portion of linacs. Collections of particles can be traced with a free choice of input distributions in six-dimensional phase space. Random errors are often included in order to study the tolerances which should be imposed during manufacture or in operation. An outline is given of the modifications made to the model, the results of experiments which indicate the validity of the model, and the use of the model to optimize the longitudinal tuning of the Alvarez linac

  19. Running vacuum cosmological models: linear scalar perturbations

    Perico, E.L.D. [Instituto de Física, Universidade de São Paulo, Rua do Matão 1371, CEP 05508-090, São Paulo, SP (Brazil); Tamayo, D.A., E-mail: elduartep@usp.br, E-mail: tamayo@if.usp.br [Departamento de Astronomia, Universidade de São Paulo, Rua do Matão 1226, CEP 05508-900, São Paulo, SP (Brazil)

    2017-08-01

    In cosmology, phenomenologically motivated expressions for running vacuum are commonly parameterized as linear functions typically denoted by Λ( H {sup 2}) or Λ( R ). Such models assume an equation of state for the vacuum given by P-bar {sub Λ} = - ρ-bar {sub Λ}, relating its background pressure P-bar {sub Λ} with its mean energy density ρ-bar {sub Λ} ≡ Λ/8π G . This equation of state suggests that the vacuum dynamics is due to an interaction with the matter content of the universe. Most of the approaches studying the observational impact of these models only consider the interaction between the vacuum and the transient dominant matter component of the universe. We extend such models by assuming that the running vacuum is the sum of independent contributions, namely ρ-bar {sub Λ} = Σ {sub i} ρ-bar {sub Λ} {sub i} . Each Λ i vacuum component is associated and interacting with one of the i matter components in both the background and perturbation levels. We derive the evolution equations for the linear scalar vacuum and matter perturbations in those two scenarios, and identify the running vacuum imprints on the cosmic microwave background anisotropies as well as on the matter power spectrum. In the Λ( H {sup 2}) scenario the vacuum is coupled with every matter component, whereas the Λ( R ) description only leads to a coupling between vacuum and non-relativistic matter, producing different effects on the matter power spectrum.

  20. Linear Parametric Model Checking of Timed Automata

    Hune, Tohmas Seidelin; Romijn, Judi; Stoelinga, Mariëlle

    2001-01-01

    We present an extension of the model checker Uppaal capable of synthesize linear parameter constraints for the correctness of parametric timed automata. The symbolic representation of the (parametric) state-space is shown to be correct. A second contribution of this paper is the identication...... of a subclass of parametric timed automata (L/U automata), for which the emptiness problem is decidable, contrary to the full class where it is know to be undecidable. Also we present a number of lemmas enabling the verication eort to be reduced for L/U automata in some cases. We illustrate our approach...

  1. Accounting for misclassification in electronic health records-derived exposures using generalized linear finite mixture models.

    Hubbard, Rebecca A; Johnson, Eric; Chubak, Jessica; Wernli, Karen J; Kamineni, Aruna; Bogart, Andy; Rutter, Carolyn M

    2017-06-01

    Exposures derived from electronic health records (EHR) may be misclassified, leading to biased estimates of their association with outcomes of interest. An example of this problem arises in the context of cancer screening where test indication, the purpose for which a test was performed, is often unavailable. This poses a challenge to understanding the effectiveness of screening tests because estimates of screening test effectiveness are biased if some diagnostic tests are misclassified as screening. Prediction models have been developed for a variety of exposure variables that can be derived from EHR, but no previous research has investigated appropriate methods for obtaining unbiased association estimates using these predicted probabilities. The full likelihood incorporating information on both the predicted probability of exposure-class membership and the association between the exposure and outcome of interest can be expressed using a finite mixture model. When the regression model of interest is a generalized linear model (GLM), the expectation-maximization algorithm can be used to estimate the parameters using standard software for GLMs. Using simulation studies, we compared the bias and efficiency of this mixture model approach to alternative approaches including multiple imputation and dichotomization of the predicted probabilities to create a proxy for the missing predictor. The mixture model was the only approach that was unbiased across all scenarios investigated. Finally, we explored the performance of these alternatives in a study of colorectal cancer screening with colonoscopy. These findings have broad applicability in studies using EHR data where gold-standard exposures are unavailable and prediction models have been developed for estimating proxies.

  2. Aspects of general linear modelling of migration.

    Congdon, P

    1992-01-01

    "This paper investigates the application of general linear modelling principles to analysing migration flows between areas. Particular attention is paid to specifying the form of the regression and error components, and the nature of departures from Poisson randomness. Extensions to take account of spatial and temporal correlation are discussed as well as constrained estimation. The issue of specification bears on the testing of migration theories, and assessing the role migration plays in job and housing markets: the direction and significance of the effects of economic variates on migration depends on the specification of the statistical model. The application is in the context of migration in London and South East England in the 1970s and 1980s." excerpt

  3. Model Selection with the Linear Mixed Model for Longitudinal Data

    Ryoo, Ji Hoon

    2011-01-01

    Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…

  4. Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.

    de Jesus, Karla; Ayala, Helon V H; de Jesus, Kelly; Coelho, Leandro Dos S; Medeiros, Alexandre I A; Abraldes, José A; Vaz, Mário A P; Fernandes, Ricardo J; Vilas-Boas, João Paulo

    2018-03-01

    Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

  5. Modeling patterns in data using linear and related models

    Engelhardt, M.E.

    1996-06-01

    This report considers the use of linear models for analyzing data related to reliability and safety issues of the type usually associated with nuclear power plants. The report discusses some of the general results of linear regression analysis, such as the model assumptions and properties of the estimators of the parameters. The results are motivated with examples of operational data. Results about the important case of a linear regression model with one covariate are covered in detail. This case includes analysis of time trends. The analysis is applied with two different sets of time trend data. Diagnostic procedures and tests for the adequacy of the model are discussed. Some related methods such as weighted regression and nonlinear models are also considered. A discussion of the general linear model is also included. Appendix A gives some basic SAS programs and outputs for some of the analyses discussed in the body of the report. Appendix B is a review of some of the matrix theoretic results which are useful in the development of linear models

  6. Electron Model of Linear-Field FFAG

    Koscielniak, Shane R

    2005-01-01

    A fixed-field alternating-gradient accelerator (FFAG) that employs only linear-field elements ushers in a new regime in accelerator design and dynamics. The linear-field machine has the ability to compact an unprecedented range in momenta within a small component aperture. With a tune variation which results from the natural chromaticity, the beam crosses many strong, uncorrec-table, betatron resonances during acceleration. Further, relativistic particles in this machine exhibit a quasi-parabolic time-of-flight that cannot be addressed with a fixed-frequency rf system. This leads to a new concept of bucketless acceleration within a rotation manifold. With a large energy jump per cell, there is possibly strong synchro-betatron coupling. A few-MeV electron model has been proposed to demonstrate the feasibility of these untested acceleration features and to investigate them at length under a wide range of operating conditions. This paper presents a lattice optimized for a 1.3 GHz rf, initial technology choices f...

  7. Linear models in the mathematics of uncertainty

    Mordeson, John N; Clark, Terry D; Pham, Alex; Redmond, Michael A

    2013-01-01

    The purpose of this book is to present new mathematical techniques for modeling global issues. These mathematical techniques are used to determine linear equations between a dependent variable and one or more independent variables in cases where standard techniques such as linear regression are not suitable. In this book, we examine cases where the number of data points is small (effects of nuclear warfare), where the experiment is not repeatable (the breakup of the former Soviet Union), and where the data is derived from expert opinion (how conservative is a political party). In all these cases the data  is difficult to measure and an assumption of randomness and/or statistical validity is questionable.  We apply our methods to real world issues in international relations such as  nuclear deterrence, smart power, and cooperative threat reduction. We next apply our methods to issues in comparative politics such as successful democratization, quality of life, economic freedom, political stability, and fail...

  8. Nonlinear price impact from linear models

    Patzelt, Felix; Bouchaud, Jean-Philippe

    2017-12-01

    The impact of trades on asset prices is a crucial aspect of market dynamics for academics, regulators, and practitioners alike. Recently, universal and highly nonlinear master curves were observed for price impacts aggregated on all intra-day scales (Patzelt and Bouchaud 2017 arXiv:1706.04163). Here we investigate how well these curves, their scaling, and the underlying return dynamics are captured by linear ‘propagator’ models. We find that the classification of trades as price-changing versus non-price-changing can explain the price impact nonlinearities and short-term return dynamics to a very high degree. The explanatory power provided by the change indicator in addition to the order sign history increases with increasing tick size. To obtain these results, several long-standing technical issues for model calibration and testing are addressed. We present new spectral estimators for two- and three-point cross-correlations, removing the need for previously used approximations. We also show when calibration is unbiased and how to accurately reveal previously overlooked biases. Therefore, our results contribute significantly to understanding both recent empirical results and the properties of a popular class of impact models.

  9. Linear Equating for the NEAT Design: Parameter Substitution Models and Chained Linear Relationship Models

    Kane, Michael T.; Mroch, Andrew A.; Suh, Youngsuk; Ripkey, Douglas R.

    2009-01-01

    This paper analyzes five linear equating models for the "nonequivalent groups with anchor test" (NEAT) design with internal anchors (i.e., the anchor test is part of the full test). The analysis employs a two-dimensional framework. The first dimension contrasts two general approaches to developing the equating relationship. Under a "parameter…

  10. Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

    Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.

    2013-01-01

    Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…

  11. From linear to generalized linear mixed models: A case study in repeated measures

    Compared to traditional linear mixed models, generalized linear mixed models (GLMMs) can offer better correspondence between response variables and explanatory models, yielding more efficient estimates and tests in the analysis of data from designed experiments. Using proportion data from a designed...

  12. The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

    Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

    2017-01-01

    Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

  13. Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

    Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

    2017-02-01

    This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh

  14. Petri Nets as Models of Linear Logic

    Engberg, Uffe Henrik; Winskel, Glynn

    1990-01-01

    The chief purpose of this paper is to appraise the feasibility of Girad's linear logic as a specification language for parallel processes. To this end we propose an interpretation of linear logic in Petri nets, with respect to which we investigate the expressive power of the logic...

  15. Linear approximation model network and its formation via ...

    To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...

  16. Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information.

    Tang, Zaixiang; Shen, Yueping; Li, Yan; Zhang, Xinyan; Wen, Jia; Qian, Chen'ao; Zhuang, Wenzhuo; Shi, Xinghua; Yi, Nengjun

    2018-03-15

    Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information. We propose new Bayesian hierarchical generalized linear models, called group spike-and-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific parameters. We have developed a fast and stable deterministic algorithm to fit the proposed hierarchal GLMs, which can perform variable selection within groups. We assess the performance of the proposed method on several simulated scenarios, by varying the overlap among groups, group size, number of non-null groups, and the correlation within group. Compared with existing methods, the proposed method provides not only more accurate estimates of the parameters but also better prediction. We further demonstrate the application of the proposed procedure on three cancer datasets by utilizing pathway structures of genes. Our results show that the proposed method generates powerful models for predicting disease outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). nyi@uab.edu. Supplementary data are available at Bioinformatics online.

  17. Linear regression crash prediction models : issues and proposed solutions.

    2010-05-01

    The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

  18. Game Theory and its Relationship with Linear Programming Models ...

    Game Theory and its Relationship with Linear Programming Models. ... This paper shows that game theory and linear programming problem are closely related subjects since any computing method devised for ... AJOL African Journals Online.

  19. A Note on the Identifiability of Generalized Linear Mixed Models

    Labouriau, Rodrigo

    2014-01-01

    I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity...... conditions, and, therefore, is extensible to quasi-likelihood based generalized linear models. In particular, binomial and Poisson mixed models with dispersion parameter are identifiable when equipped with the standard parametrization...

  20. Linear control theory for gene network modeling.

    Shin, Yong-Jun; Bleris, Leonidas

    2010-09-16

    Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  1. LINEAR MODEL FOR NON ISOSCELES ABSORBERS.

    BERG,J.S.

    2003-05-12

    Previous analyses have assumed that wedge absorbers are triangularly shaped with equal angles for the two faces. In this case, to linear order, the energy loss depends only on the position in the direction of the face tilt, and is independent of the incoming angle. One can instead construct an absorber with entrance and exit faces facing rather general directions. In this case, the energy loss can depend on both the position and the angle of the particle in question. This paper demonstrates that and computes the effect to linear order.

  2. Equivalent linear damping characterization in linear and nonlinear force-stiffness muscle models.

    Ovesy, Marzieh; Nazari, Mohammad Ali; Mahdavian, Mohammad

    2016-02-01

    In the current research, the muscle equivalent linear damping coefficient which is introduced as the force-velocity relation in a muscle model and the corresponding time constant are investigated. In order to reach this goal, a 1D skeletal muscle model was used. Two characterizations of this model using a linear force-stiffness relationship (Hill-type model) and a nonlinear one have been implemented. The OpenSim platform was used for verification of the model. The isometric activation has been used for the simulation. The equivalent linear damping and the time constant of each model were extracted by using the results obtained from the simulation. The results provide a better insight into the characteristics of each model. It is found that the nonlinear models had a response rate closer to the reality compared to the Hill-type models.

  3. An online re-linearization scheme suited for Model Predictive and Linear Quadratic Control

    Henriksen, Lars Christian; Poulsen, Niels Kjølstad

    This technical note documents the equations for primal-dual interior-point quadratic programming problem solver used for MPC. The algorithm exploits the special structure of the MPC problem and is able to reduce the computational burden such that the computational burden scales with prediction...... horizon length in a linear way rather than cubic, which would be the case if the structure was not exploited. It is also shown how models used for design of model-based controllers, e.g. linear quadratic and model predictive, can be linearized both at equilibrium and non-equilibrium points, making...

  4. Tried and True: Springing into Linear Models

    Darling, Gerald

    2012-01-01

    In eighth grade, students usually learn about forces in science class and linear relationships in math class, crucial topics that form the foundation for further study in science and engineering. An activity that links these two fundamental concepts involves measuring the distance a spring stretches as a function of how much weight is suspended…

  5. Model Predictive Control for Linear Complementarity and Extended Linear Complementarity Systems

    Bambang Riyanto

    2005-11-01

    Full Text Available In this paper, we propose model predictive control method for linear complementarity and extended linear complementarity systems by formulating optimization along prediction horizon as mixed integer quadratic program. Such systems contain interaction between continuous dynamics and discrete event systems, and therefore, can be categorized as hybrid systems. As linear complementarity and extended linear complementarity systems finds applications in different research areas, such as impact mechanical systems, traffic control and process control, this work will contribute to the development of control design method for those areas as well, as shown by three given examples.

  6. Ordinal Log-Linear Models for Contingency Tables

    Brzezińska Justyna

    2016-12-01

    Full Text Available A log-linear analysis is a method providing a comprehensive scheme to describe the association for categorical variables in a contingency table. The log-linear model specifies how the expected counts depend on the levels of the categorical variables for these cells and provide detailed information on the associations. The aim of this paper is to present theoretical, as well as empirical, aspects of ordinal log-linear models used for contingency tables with ordinal variables. We introduce log-linear models for ordinal variables: linear-by-linear association, row effect model, column effect model and RC Goodman’s model. Algorithm, advantages and disadvantages will be discussed in the paper. An empirical analysis will be conducted with the use of R.

  7. Recent Updates to the GEOS-5 Linear Model

    Holdaway, Dan; Kim, Jong G.; Errico, Ron; Gelaro, Ronald; Mahajan, Rahul

    2014-01-01

    Global Modeling and Assimilation Office (GMAO) is close to having a working 4DVAR system and has developed a linearized version of GEOS-5.This talk outlines a series of improvements made to the linearized dynamics, physics and trajectory.Of particular interest is the development of linearized cloud microphysics, which provides the framework for 'all-sky' data assimilation.

  8. Double generalized linear compound poisson models to insurance claims data

    Andersen, Daniel Arnfeldt; Bonat, Wagner Hugo

    2017-01-01

    This paper describes the specification, estimation and comparison of double generalized linear compound Poisson models based on the likelihood paradigm. The models are motivated by insurance applications, where the distribution of the response variable is composed by a degenerate distribution...... implementation and illustrate the application of double generalized linear compound Poisson models using a data set about car insurances....

  9. Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

    Luo, Wen; Azen, Razia

    2013-01-01

    Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

  10. Thurstonian models for sensory discrimination tests as generalized linear models

    Brockhoff, Per B.; Christensen, Rune Haubo Bojesen

    2010-01-01

    as a so-called generalized linear model. The underlying sensory difference 6 becomes directly a parameter of the statistical model and the estimate d' and it's standard error becomes the "usual" output of the statistical analysis. The d' for the monadic A-NOT A method is shown to appear as a standard......Sensory discrimination tests such as the triangle, duo-trio, 2-AFC and 3-AFC tests produce binary data and the Thurstonian decision rule links the underlying sensory difference 6 to the observed number of correct responses. In this paper it is shown how each of these four situations can be viewed...

  11. Linear control theory for gene network modeling.

    Yong-Jun Shin

    Full Text Available Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain and linear state-space (time domain can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  12. Forecasting Volatility of Dhaka Stock Exchange: Linear Vs Non-linear models

    Masudul Islam

    2012-10-01

    Full Text Available Prior information about a financial market is very essential for investor to invest money on parches share from the stock market which can strengthen the economy. The study examines the relative ability of various models to forecast daily stock indexes future volatility. The forecasting models that employed from simple to relatively complex ARCH-class models. It is found that among linear models of stock indexes volatility, the moving average model ranks first using root mean square error, mean absolute percent error, Theil-U and Linex loss function  criteria. We also examine five nonlinear models. These models are ARCH, GARCH, EGARCH, TGARCH and restricted GARCH models. We find that nonlinear models failed to dominate linear models utilizing different error measurement criteria and moving average model appears to be the best. Then we forecast the next two months future stock index price volatility by the best (moving average model.

  13. Generalised linear models for correlated pseudo-observations, with applications to multi-state models

    Andersen, Per Kragh; Klein, John P.; Rosthøj, Susanne

    2003-01-01

    Generalised estimating equation; Generalised linear model; Jackknife pseudo-value; Logistic regression; Markov Model; Multi-state model......Generalised estimating equation; Generalised linear model; Jackknife pseudo-value; Logistic regression; Markov Model; Multi-state model...

  14. Linear and non-linear autoregressive models for short-term wind speed forecasting

    Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.

    2016-01-01

    Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.

  15. Applicability of linear and non-linear potential flow models on a Wavestar float

    Bozonnet, Pauline; Dupin, Victor; Tona, Paolino

    2017-01-01

    as a model based on non-linear potential flow theory and weakscatterer hypothesis are successively considered. Simple tests, such as dip tests, decay tests and captive tests enable to highlight the improvements obtained with the introduction of nonlinearities. Float motion under wave actions and without...... control action, limited to small amplitude motion with a single float, is well predicted by the numerical models, including the linear one. Still, float velocity is better predicted by accounting for non-linear hydrostatic and Froude-Krylov forces.......Numerical models based on potential flow theory, including different types of nonlinearities are compared and validated against experimental data for the Wavestar wave energy converter technology. Exact resolution of the rotational motion, non-linear hydrostatic and Froude-Krylov forces as well...

  16. A linear model of population dynamics

    Lushnikov, A. A.; Kagan, A. I.

    2016-08-01

    The Malthus process of population growth is reformulated in terms of the probability w(n,t) to find exactly n individuals at time t assuming that both the birth and the death rates are linear functions of the population size. The master equation for w(n,t) is solved exactly. It is shown that w(n,t) strongly deviates from the Poisson distribution and is expressed in terms either of Laguerre’s polynomials or a modified Bessel function. The latter expression allows for considerable simplifications of the asymptotic analysis of w(n,t).

  17. Generalized bi-additive modelling for categorical data

    P.J.F. Groenen (Patrick); A.J. Koning (Alex)

    2004-01-01

    textabstractGeneralized linear modelling (GLM) is a versatile technique, which may be viewed as a generalization of well-known techniques such as least squares regression, analysis of variance, loglinear modelling, and logistic regression. In may applications, low-order interaction (such as

  18. A test for the parameters of multiple linear regression models ...

    A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...

  19. Modeling Non-Linear Material Properties in Composite Materials

    2016-06-28

    Technical Report ARWSB-TR-16013 MODELING NON-LINEAR MATERIAL PROPERTIES IN COMPOSITE MATERIALS Michael F. Macri Andrew G...REPORT TYPE Technical 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE MODELING NON-LINEAR MATERIAL PROPERTIES IN COMPOSITE MATERIALS ...systems are increasingly incorporating composite materials into their design. Many of these systems subject the composites to environmental conditions

  20. Reliability modelling and simulation of switched linear system ...

    Reliability modelling and simulation of switched linear system control using temporal databases. ... design of fault-tolerant real-time switching systems control and modelling embedded micro-schedulers for complex systems maintenance.

  1. Multivariate statistical modelling based on generalized linear models

    Fahrmeir, Ludwig

    1994-01-01

    This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...

  2. Approximating chiral quark models with linear σ-models

    Broniowski, Wojciech; Golli, Bojan

    2003-01-01

    We study the approximation of chiral quark models with simpler models, obtained via gradient expansion. The resulting Lagrangian of the type of the linear σ-model contains, at the lowest level of the gradient-expanded meson action, an additional term of the form ((1)/(2))A(σ∂ μ σ+π∂ μ π) 2 . We investigate the dynamical consequences of this term and its relevance to the phenomenology of the soliton models of the nucleon. It is found that the inclusion of the new term allows for a more efficient approximation of the underlying quark theory, especially in those cases where dynamics allows for a large deviation of the chiral fields from the chiral circle, such as in quark models with non-local regulators. This is of practical importance, since the σ-models with valence quarks only are technically much easier to treat and simpler to solve than the quark models with the full-fledged Dirac sea

  3. Latent log-linear models for handwritten digit classification.

    Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

    2012-06-01

    We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

  4. Linear Regression Models for Estimating True Subsurface ...

    47

    The objective is to minimize the processing time and computer memory required. 10 to carry out inversion .... to the mainland by two long bridges. .... term. In this approach, the model converges when the squared sum of the differences. 143.

  5. Numerical modelling in non linear fracture mechanics

    Viggo Tvergaard

    2007-07-01

    Full Text Available Some numerical studies of crack propagation are based on using constitutive models that accountfor damage evolution in the material. When a critical damage value has been reached in a materialpoint, it is natural to assume that this point has no more carrying capacity, as is done numerically in the elementvanish technique. In the present review this procedure is illustrated for micromechanically based materialmodels, such as a ductile failure model that accounts for the nucleation and growth of voids to coalescence, and a model for intergranular creep failure with diffusive growth of grain boundary cavities leading to micro-crack formation. The procedure is also illustrated for low cycle fatigue, based on continuum damage mechanics. In addition, the possibility of crack growth predictions for elastic-plastic solids using cohesive zone models to represent the fracture process is discussed.

  6. Random effect selection in generalised linear models

    Denwood, Matt; Houe, Hans; Forkman, Björn

    We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...

  7. Model Order Reduction for Non Linear Mechanics

    Pinillo, Rubén

    2017-01-01

    Context: Automotive industry is moving towards a new generation of cars. Main idea: Cars are furnished with radars, cameras, sensors, etc… providing useful information about the environment surrounding the car. Goals: Provide an efficient model for the radar input/output. Reducing computational costs by means of big data techniques.

  8. Identification of Influential Points in a Linear Regression Model

    Jan Grosz

    2011-03-01

    Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

  9. Heterotic sigma models and non-linear strings

    Hull, C.M.

    1986-01-01

    The two-dimensional supersymmetric non-linear sigma models are examined with respect to the heterotic string. The paper was presented at the workshop on :Supersymmetry and its applications', Cambridge, United Kingdom, 1985. The non-linear sigma model with Wess-Zumino-type term, the coupling of the fermionic superfields to the sigma model, super-conformal invariance, and the supersymmetric string, are all discussed. (U.K.)

  10. Linear latent variable models: the lava-package

    Holst, Klaus Kähler; Budtz-Jørgensen, Esben

    2013-01-01

    are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation......An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features...

  11. On-line control models for the Stanford Linear Collider

    Sheppard, J.C.; Helm, R.H.; Lee, M.J.; Woodley, M.D.

    1983-03-01

    Models for computer control of the SLAC three-kilometer linear accelerator and damping rings have been developed as part of the control system for the Stanford Linear Collider. Some of these models have been tested experimentally and implemented in the control program for routine linac operations. This paper will describe the development and implementation of these models, as well as some of the operational results

  12. Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

    Liang, Faming

    2013-06-01

    This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  13. Generalized Linear Models with Applications in Engineering and the Sciences

    Myers, Raymond H; Vining, G Geoffrey; Robinson, Timothy J

    2012-01-01

    Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities."-Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Ma

  14. Modelling a linear PM motor including magnetic saturation

    Polinder, H.; Slootweg, J.G.; Compter, J.C.; Hoeijmakers, M.J.

    2002-01-01

    The use of linear permanent-magnet (PM) actuators increases in a wide variety of applications because of the high force density, robustness and accuracy. The paper describes the modelling of a linear PM motor applied in, for example, wafer steppers, including magnetic saturation. This is important

  15. Application of the simplex method of linear programming model to ...

    This work discussed how the simplex method of linear programming could be used to maximize the profit of any business firm using Saclux Paint Company as a case study. It equally elucidated the effect variation in the optimal result obtained from linear programming model, will have on any given firm. It was demonstrated ...

  16. Genetic parameters for racing records in trotters using linear and generalized linear models.

    Suontama, M; van der Werf, J H J; Juga, J; Ojala, M

    2012-09-01

    Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success.

  17. Modelling and economic evaluation of forest biome shifts under climate change in Southwest Germany

    Marc Hanewinkel; Susan Hummel; Dominik. Cullmann

    2010-01-01

    We evaluated the economic effects of a predicted shift from Norway spruce (Picea abies) to European beech (Fagus sylvatica) for a forest area of 1.3 million ha in southwest Germany. The shift was modelled with a generalized linear model (GLM) by using presence/absence data from the National Forest Inventory in Baden-Wurttemberg...

  18. Linear approximation model network and its formation via ...

    niques, an alternative `linear approximation model' (LAM) network approach is .... network is LPV, existing LTI theory is difficult to apply (Kailath 1980). ..... Beck J V, Arnold K J 1977 Parameter estimation in engineering and science (New York: ...

  19. Sphaleron in a non-linear sigma model

    Sogo, Kiyoshi; Fujimoto, Yasushi.

    1989-08-01

    We present an exact classical saddle point solution in a non-linear sigma model. It has a topological charge 1/2 and mediates the vacuum transition. The quantum fluctuations and the transition rate are also examined. (author)

  20. On D-branes from gauged linear sigma models

    Govindarajan, S.; Jayaraman, T.; Sarkar, T.

    2001-01-01

    We study both A-type and B-type D-branes in the gauged linear sigma model by considering worldsheets with boundary. The boundary conditions on the matter and vector multiplet fields are first considered in the large-volume phase/non-linear sigma model limit of the corresponding Calabi-Yau manifold, where we find that we need to add a contact term on the boundary. These considerations enable to us to derive the boundary conditions in the full gauged linear sigma model, including the addition of the appropriate boundary contact terms, such that these boundary conditions have the correct non-linear sigma model limit. Most of the analysis is for the case of Calabi-Yau manifolds with one Kaehler modulus (including those corresponding to hypersurfaces in weighted projective space), though we comment on possible generalisations

  1. Optimization for decision making linear and quadratic models

    Murty, Katta G

    2010-01-01

    While maintaining the rigorous linear programming instruction required, Murty's new book is unique in its focus on developing modeling skills to support valid decision-making for complex real world problems, and includes solutions to brand new algorithms.

  2. Study of linear induction motor characteristics : the Mosebach model

    1976-05-31

    This report covers the Mosebach theory of the double-sided linear induction motor, starting with the ideallized model and accompanying assumptions, and ending with relations for thrust, airgap power, and motor efficiency. Solutions of the magnetic in...

  3. Study of linear induction motor characteristics : the Oberretl model

    1975-05-30

    The Oberretl theory of the double-sided linear induction motor (LIM) is examined, starting with the idealized model and accompanying assumptions, and ending with relations for predicted thrust, airgap power, and motor efficiency. The effect of varyin...

  4. Optimization Research of Generation Investment Based on Linear Programming Model

    Wu, Juan; Ge, Xueqian

    Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

  5. Generalized linear mixed models modern concepts, methods and applications

    Stroup, Walter W

    2012-01-01

    PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data

  6. A comparison of linear tyre models for analysing shimmy

    Besselink, I.J.M.; Maas, J.W.L.H.; Nijmeijer, H.

    2011-01-01

    A comparison is made between three linear, dynamic tyre models using low speed step responses and yaw oscillation tests. The match with the measurements improves with increasing complexity of the tyre model. Application of the different tyre models to a two degree of freedom trailing arm suspension

  7. Unification of three linear models for the transient visual system

    Brinker, den A.C.

    1989-01-01

    Three different linear filters are considered as a model describing the experimentally determined triphasic impulse responses of discs. These impulse responses arc associated with the transient visual system. Each model reveals a different feature of the system. Unification of the models is

  8. A BEHAVIORAL-APPROACH TO LINEAR EXACT MODELING

    ANTOULAS, AC; WILLEMS, JC

    1993-01-01

    The behavioral approach to system theory provides a parameter-free framework for the study of the general problem of linear exact modeling and recursive modeling. The main contribution of this paper is the solution of the (continuous-time) polynomial-exponential time series modeling problem. Both

  9. Linearized models for a new magnetic control in MAST

    Artaserse, G., E-mail: giovanni.artaserse@enea.it [Associazione Euratom-ENEA sulla Fusione, Via Enrico Fermi 45, I-00044 Frascati (RM) (Italy); Maviglia, F.; Albanese, R. [Associazione Euratom-ENEA-CREATE sulla Fusione, Via Claudio 21, I-80125 Napoli (Italy); McArdle, G.J.; Pangione, L. [EURATOM/CCFE Fusion Association, Culham Science Centre, Abingdon, Oxon, OX14 3DB (United Kingdom)

    2013-10-15

    Highlights: ► We applied linearized models for a new magnetic control on MAST tokamak. ► A suite of procedures, conceived to be machine independent, have been used. ► We carried out model-based simulations, taking into account eddy currents effects. ► Comparison with the EFIT flux maps and the experimental magnetic signals are shown. ► A current driven model for the dynamic simulations of the experimental data have been performed. -- Abstract: The aim of this work is to provide reliable linearized models for the design and assessment of a new magnetic control system for MAST (Mega Ampère Spherical Tokamak) using rtEFIT, which can easily be exported to MAST Upgrade. Linearized models for magnetic control have been obtained using the 2D axisymmetric finite element code CREATE L. MAST linearized models include equivalent 2D axisymmetric schematization of poloidal field (PF) coils, vacuum vessel, and other conducting structures. A plasmaless and a double null configuration have been chosen as benchmark cases for the comparison with experimental data and EFIT reconstructions. Good agreement has been found with the EFIT flux map and the experimental signals coming from magnetic probes with only few mismatches probably due to broken sensors. A suite of procedures (equipped with a user friendly interface to be run even remotely) to provide linearized models for magnetic control is now available on the MAST linux machines. A new current driven model has been used to obtain a state space model having the PF coil currents as inputs. Dynamic simulations of experimental data have been carried out using linearized models, including modelling of the effects of the passive structures, showing a fair agreement. The modelling activity has been useful also to reproduce accurately the interaction between plasma current and radial position control loops.

  10. Linearized models for a new magnetic control in MAST

    Artaserse, G.; Maviglia, F.; Albanese, R.; McArdle, G.J.; Pangione, L.

    2013-01-01

    Highlights: ► We applied linearized models for a new magnetic control on MAST tokamak. ► A suite of procedures, conceived to be machine independent, have been used. ► We carried out model-based simulations, taking into account eddy currents effects. ► Comparison with the EFIT flux maps and the experimental magnetic signals are shown. ► A current driven model for the dynamic simulations of the experimental data have been performed. -- Abstract: The aim of this work is to provide reliable linearized models for the design and assessment of a new magnetic control system for MAST (Mega Ampère Spherical Tokamak) using rtEFIT, which can easily be exported to MAST Upgrade. Linearized models for magnetic control have been obtained using the 2D axisymmetric finite element code CREATE L. MAST linearized models include equivalent 2D axisymmetric schematization of poloidal field (PF) coils, vacuum vessel, and other conducting structures. A plasmaless and a double null configuration have been chosen as benchmark cases for the comparison with experimental data and EFIT reconstructions. Good agreement has been found with the EFIT flux map and the experimental signals coming from magnetic probes with only few mismatches probably due to broken sensors. A suite of procedures (equipped with a user friendly interface to be run even remotely) to provide linearized models for magnetic control is now available on the MAST linux machines. A new current driven model has been used to obtain a state space model having the PF coil currents as inputs. Dynamic simulations of experimental data have been carried out using linearized models, including modelling of the effects of the passive structures, showing a fair agreement. The modelling activity has been useful also to reproduce accurately the interaction between plasma current and radial position control loops

  11. H∞ /H2 model reduction through dilated linear matrix inequalities

    Adegas, Fabiano Daher; Stoustrup, Jakob

    2012-01-01

    This paper presents sufficient dilated linear matrix inequalities (LMI) conditions to the $H_{infty}$ and $H_{2}$ model reduction problem. A special structure of the auxiliary (slack) variables allows the original model of order $n$ to be reduced to an order $r=n/s$ where $n,r,s in field{N}$. Arb......This paper presents sufficient dilated linear matrix inequalities (LMI) conditions to the $H_{infty}$ and $H_{2}$ model reduction problem. A special structure of the auxiliary (slack) variables allows the original model of order $n$ to be reduced to an order $r=n/s$ where $n,r,s in field...

  12. Non-linear Growth Models in Mplus and SAS

    Grimm, Kevin J.; Ram, Nilam

    2013-01-01

    Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data collected as part of a study examining the effects of preschool instruction on academic gain we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included. PMID:23882134

  13. Variance Function Partially Linear Single-Index Models1.

    Lian, Heng; Liang, Hua; Carroll, Raymond J

    2015-01-01

    We consider heteroscedastic regression models where the mean function is a partially linear single index model and the variance function depends upon a generalized partially linear single index model. We do not insist that the variance function depend only upon the mean function, as happens in the classical generalized partially linear single index model. We develop efficient and practical estimation methods for the variance function and for the mean function. Asymptotic theory for the parametric and nonparametric parts of the model is developed. Simulations illustrate the results. An empirical example involving ozone levels is used to further illustrate the results, and is shown to be a case where the variance function does not depend upon the mean function.

  14. Comparison between linear quadratic and early time dose models

    Chougule, A.A.; Supe, S.J.

    1993-01-01

    During the 70s, much interest was focused on fractionation in radiotherapy with the aim of improving tumor control rate without producing unacceptable normal tissue damage. To compare the radiobiological effectiveness of various fractionation schedules, empirical formulae such as Nominal Standard Dose, Time Dose Factor, Cumulative Radiation Effect and Tumour Significant Dose, were introduced and were used despite many shortcomings. It has been claimed that a recent linear quadratic model is able to predict the radiobiological responses of tumours as well as normal tissues more accurately. We compared Time Dose Factor and Tumour Significant Dose models with the linear quadratic model for tumour regression in patients with carcinomas of the cervix. It was observed that the prediction of tumour regression estimated by the Tumour Significant Dose and Time Dose factor concepts varied by 1.6% from that of the linear quadratic model prediction. In view of the lack of knowledge of the precise values of the parameters of the linear quadratic model, it should be applied with caution. One can continue to use the Time Dose Factor concept which has been in use for more than a decade as its results are within ±2% as compared to that predicted by the linear quadratic model. (author). 11 refs., 3 figs., 4 tabs

  15. Phylogenetic mixtures and linear invariants for equal input models.

    Casanellas, Marta; Steel, Mike

    2017-04-01

    The reconstruction of phylogenetic trees from molecular sequence data relies on modelling site substitutions by a Markov process, or a mixture of such processes. In general, allowing mixed processes can result in different tree topologies becoming indistinguishable from the data, even for infinitely long sequences. However, when the underlying Markov process supports linear phylogenetic invariants, then provided these are sufficiently informative, the identifiability of the tree topology can be restored. In this paper, we investigate a class of processes that support linear invariants once the stationary distribution is fixed, the 'equal input model'. This model generalizes the 'Felsenstein 1981' model (and thereby the Jukes-Cantor model) from four states to an arbitrary number of states (finite or infinite), and it can also be described by a 'random cluster' process. We describe the structure and dimension of the vector spaces of phylogenetic mixtures and of linear invariants for any fixed phylogenetic tree (and for all trees-the so called 'model invariants'), on any number n of leaves. We also provide a precise description of the space of mixtures and linear invariants for the special case of [Formula: see text] leaves. By combining techniques from discrete random processes and (multi-) linear algebra, our results build on a classic result that was first established by James Lake (Mol Biol Evol 4:167-191, 1987).

  16. Non-linear calibration models for near infrared spectroscopy

    Ni, Wangdong; Nørgaard, Lars; Mørup, Morten

    2014-01-01

    by ridge regression (RR). The performance of the different methods is demonstrated by their practical applications using three real-life near infrared (NIR) data sets. Different aspects of the various approaches including computational time, model interpretability, potential over-fitting using the non-linear...... models on linear problems, robustness to small or medium sample sets, and robustness to pre-processing, are discussed. The results suggest that GPR and BANN are powerful and promising methods for handling linear as well as nonlinear systems, even when the data sets are moderately small. The LS......-SVM), relevance vector machines (RVM), Gaussian process regression (GPR), artificial neural network (ANN), and Bayesian ANN (BANN). In this comparison, partial least squares (PLS) regression is used as a linear benchmark, while the relationship of the methods is considered in terms of traditional calibration...

  17. Estimation and variable selection for generalized additive partial linear models

    Wang, Li

    2011-08-01

    We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.

  18. Matrix model and time-like linear dila ton matter

    Takayanagi, Tadashi

    2004-01-01

    We consider a matrix model description of the 2d string theory whose matter part is given by a time-like linear dilaton CFT. This is equivalent to the c=1 matrix model with a deformed, but very simple Fermi surface. Indeed, after a Lorentz transformation, the corresponding 2d spacetime is a conventional linear dila ton background with a time-dependent tachyon field. We show that the tree level scattering amplitudes in the matrix model perfectly agree with those computed in the world-sheet theory. The classical trajectories of fermions correspond to the decaying D-boranes in the time-like linear dilaton CFT. We also discuss the ground ring structure. Furthermore, we study the properties of the time-like Liouville theory by applying this matrix model description. We find that its ground ring structure is very similar to that of the minimal string. (author)

  19. Vortices, semi-local vortices in gauged linear sigma model

    Kim, Namkwon

    1998-11-01

    We consider the static (2+1)D gauged linear sigma model. By analyzing the governing system of partial differential equations, we investigate various aspects of the model. We show the existence of energy finite vortices under a partially broken symmetry on R 2 with the necessary condition suggested by Y. Yang. We also introduce generalized semi-local vortices and show the existence of energy finite semi-local vortices under a certain condition. The vacuum manifold for the semi-local vortices turns out to be graded. Besides, with a special choice of a representation, we show that the O(3) sigma model of which target space is nonlinear is a singular limit of the gauged linear sigma model of which target space is linear. (author)

  20. TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS

    Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B.

    2017-01-01

    Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions. PMID:29332971

  1. Linear mixed models a practical guide using statistical software

    West, Brady T; Galecki, Andrzej T

    2006-01-01

    Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on bo

  2. Inverse Modelling Problems in Linear Algebra Undergraduate Courses

    Martinez-Luaces, Victor E.

    2013-01-01

    This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…

  3. Optical linear algebra processors - Noise and error-source modeling

    Casasent, D.; Ghosh, A.

    1985-01-01

    The modeling of system and component noise and error sources in optical linear algebra processors (OLAPs) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.

  4. Optical linear algebra processors: noise and error-source modeling.

    Casasent, D; Ghosh, A

    1985-06-01

    The modeling of system and component noise and error sources in optical linear algebra processors (OLAP's) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.

  5. CONTRIBUTIONS TO THE FINITE ELEMENT MODELING OF LINEAR ULTRASONIC MOTORS

    Oana CHIVU

    2013-05-01

    Full Text Available The present paper is concerned with the main modeling elements as produced by means of thefinite element method of linear ultrasonic motors. Hence, first the model is designed and then a modaland harmonic analysis are carried out in view of outlining the main outcomes

  6. Linear and Nonlinear Career Models: Metaphors, Paradigms, and Ideologies.

    Buzzanell, Patrice M.; Goldzwig, Steven R.

    1991-01-01

    Examines the linear or bureaucratic career models (dominant in career research, metaphors, paradigms, and ideologies) which maintain career myths of flexibility and individualized routes to success in organizations incapable of offering such versatility. Describes nonlinear career models which offer suggestive metaphors for re-visioning careers…

  7. Low-energy limit of the extended Linear Sigma Model

    Divotgey, Florian [Johann Wolfgang Goethe-Universitaet, Institut fuer Theoretische Physik, Frankfurt am Main (Germany); Kovacs, Peter [Wigner Research Center for Physics, Hungarian Academy of Sciences, Institute for Particle and Nuclear Physics, Budapest (Hungary); GSI Helmholtzzentrum fuer Schwerionenforschung, ExtreMe Matter Institute, Darmstadt (Germany); Giacosa, Francesco [Johann Wolfgang Goethe-Universitaet, Institut fuer Theoretische Physik, Frankfurt am Main (Germany); Jan-Kochanowski University, Institute of Physics, Kielce (Poland); Rischke, Dirk H. [Johann Wolfgang Goethe-Universitaet, Institut fuer Theoretische Physik, Frankfurt am Main (Germany); University of Science and Technology of China, Interdisciplinary Center for Theoretical Study and Department of Modern Physics, Hefei, Anhui (China)

    2018-01-15

    The extended Linear Sigma Model is an effective hadronic model based on the linear realization of chiral symmetry SU(N{sub f}){sub L} x SU(N{sub f}){sub R}, with (pseudo)scalar and (axial-)vector mesons as degrees of freedom. In this paper, we study the low-energy limit of the extended Linear Sigma Model (eLSM) for N{sub f} = flavors by integrating out all fields except for the pions, the (pseudo-)Nambu-Goldstone bosons of chiral symmetry breaking. The resulting low-energy effective action is identical to Chiral Perturbation Theory (ChPT) after choosing a representative for the coset space generated by chiral symmetry breaking and expanding it in powers of (derivatives of) the pion fields. The tree-level values of the coupling constants of the effective low-energy action agree remarkably well with those of ChPT. (orig.)

  8. Linear Power-Flow Models in Multiphase Distribution Networks: Preprint

    Bernstein, Andrey; Dall' Anese, Emiliano

    2017-05-26

    This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- from advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.

  9. Modelling and measurement of a moving magnet linear compressor performance

    Liang, Kun; Stone, Richard; Davies, Gareth; Dadd, Mike; Bailey, Paul

    2014-01-01

    A novel moving magnet linear compressor with clearance seals and flexure bearings has been designed and constructed. It is suitable for a refrigeration system with a compact heat exchanger, such as would be needed for CPU cooling. The performance of the compressor has been experimentally evaluated with nitrogen and a mathematical model has been developed to evaluate the performance of the linear compressor. The results from the compressor model and the measurements have been compared in terms of cylinder pressure, the ‘P–V’ loop, stroke, mass flow rate and shaft power. The cylinder pressure was not measured directly but was derived from the compressor dynamics and the motor magnetic force characteristics. The comparisons indicate that the compressor model is well validated and can be used to study the performance of this type of compressor, to help with design optimization and the identification of key parameters affecting the system transients. The electrical and thermodynamic losses were also investigated, particularly for the design point (stroke of 13 mm and pressure ratio of 3.0), since a full understanding of these can lead to an increase in compressor efficiency. - Highlights: • Model predictions of the performance of a novel moving magnet linear compressor. • Prototype linear compressor performance measurements using nitrogen. • Reconstruction of P–V loops using a model of the dynamics and electromagnetics. • Close agreement between the model and measurements for the P–V loops. • The design point motor efficiency was 74%, with potential improvements identified

  10. The minimal linear σ model for the Goldstone Higgs

    Feruglio, F.; Gavela, M.B.; Kanshin, K.; Machado, P.A.N.; Rigolin, S.; Saa, S.

    2016-01-01

    In the context of the minimal SO(5) linear σ-model, a complete renormalizable Lagrangian -including gauge bosons and fermions- is considered, with the symmetry softly broken to SO(4). The scalar sector describes both the electroweak Higgs doublet and the singlet σ. Varying the σ mass would allow to sweep from the regime of perturbative ultraviolet completion to the non-linear one assumed in models in which the Higgs particle is a low-energy remnant of some strong dynamics. We analyze the phenomenological implications and constraints from precision observables and LHC data. Furthermore, we derive the d≤6 effective Lagrangian in the limit of heavy exotic fermions.

  11. A variational formulation for linear models in coupled dynamic thermoelasticity

    Feijoo, R.A.; Moura, C.A. de.

    1981-07-01

    A variational formulation for linear models in coupled dynamic thermoelasticity which quite naturally motivates the design of a numerical scheme for the problem, is studied. When linked to regularization or penalization techniques, this algorithm may be applied to more general models, namely, the ones that consider non-linear constraints associated to variational inequalities. The basic postulates of Mechanics and Thermodynamics as well as some well-known mathematical techniques are described. A thorough description of the algorithm implementation with the finite-element method is also provided. Proofs for existence and uniqueness of solutions and for convergence of the approximations are presented, and some numerical results are exhibited. (Author) [pt

  12. Comparison of linear, mixed integer and non-linear programming methods in energy system dispatch modelling

    Ommen, Torben Schmidt; Markussen, Wiebke Brix; Elmegaard, Brian

    2014-01-01

    In the paper, three frequently used operation optimisation methods are examined with respect to their impact on operation management of the combined utility technologies for electric power and DH (district heating) of eastern Denmark. The investigation focusses on individual plant operation...... differences and differences between the solution found by each optimisation method. One of the investigated approaches utilises LP (linear programming) for optimisation, one uses LP with binary operation constraints, while the third approach uses NLP (non-linear programming). The LP model is used...... as a benchmark, as this type is frequently used, and has the lowest amount of constraints of the three. A comparison of the optimised operation of a number of units shows significant differences between the three methods. Compared to the reference, the use of binary integer variables, increases operation...

  13. Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

    Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

    2009-01-01

    This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

  14. Functional linear models for association analysis of quantitative traits.

    Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

    2013-11-01

    Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY

  15. Practical likelihood analysis for spatial generalized linear mixed models

    Bonat, W. H.; Ribeiro, Paulo Justiniano

    2016-01-01

    We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...

  16. Stochastic modeling of mode interactions via linear parabolized stability equations

    Ran, Wei; Zare, Armin; Hack, M. J. Philipp; Jovanovic, Mihailo

    2017-11-01

    Low-complexity approximations of the Navier-Stokes equations have been widely used in the analysis of wall-bounded shear flows. In particular, the parabolized stability equations (PSE) and Floquet theory have been employed to capture the evolution of primary and secondary instabilities in spatially-evolving flows. We augment linear PSE with Floquet analysis to formally treat modal interactions and the evolution of secondary instabilities in the transitional boundary layer via a linear progression. To this end, we leverage Floquet theory by incorporating the primary instability into the base flow and accounting for different harmonics in the flow state. A stochastic forcing is introduced into the resulting linear dynamics to model the effect of nonlinear interactions on the evolution of modes. We examine the H-type transition scenario to demonstrate how our approach can be used to model nonlinear effects and capture the growth of the fundamental and subharmonic modes observed in direct numerical simulations and experiments.

  17. Linear modeling of possible mechanisms for parkinson tremor generation

    Lohnberg, P.

    1978-01-01

    The power of Parkinson tremor is expressed in terms of possibly changed frequency response functions between relevant variables in the neuromuscular system. The derivation starts out from a linear loopless equivalent model of mechanisms for general tremor generation. Hypothetical changes in this

  18. Current algebra of classical non-linear sigma models

    Forger, M.; Laartz, J.; Schaeper, U.

    1992-01-01

    The current algebra of classical non-linear sigma models on arbitrary Riemannian manifolds is analyzed. It is found that introducing, in addition to the Noether current j μ associated with the global symmetry of the theory, a composite scalar field j, the algebra closes under Poisson brackets. (orig.)

  19. Mathematical modelling and linear stability analysis of laser fusion cutting

    Hermanns, Torsten; Schulz, Wolfgang; Vossen, Georg; Thombansen, Ulrich

    2016-01-01

    A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process’ amount of dynamic behavior.

  20. Non Linear signa models probing the string structure

    Abdalla, E.

    1987-01-01

    The introduction of a term depending on the extrinsic curvature to the string action, and related non linear sigma models defined on a symmetric space SO(D)/SO(2) x SO(d-2) is descussed . Coupling to fermions are also treated. (author) [pt

  1. Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models

    Wagler, Amy E.

    2014-01-01

    Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for…

  2. Penalized Estimation in Large-Scale Generalized Linear Array Models

    Lund, Adam; Vincent, Martin; Hansen, Niels Richard

    2017-01-01

    Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension...

  3. Expressions for linearized perturbations in ideal-fluid cosmological models

    Ratra, B.

    1988-01-01

    We present closed-form solutions of the relativistic linear perturbation equations (in synchronous gauge) that govern the evolution of inhomogeneities in homogeneous, spatially flat, ideal-fluid, cosmological models. These expressions, which are valid for irregularities on any scale, allow one to analytically interpolate between the known approximate solutions which are valid at early times and at late times

  4. S-AMP for non-linear observation models

    Cakmak, Burak; Winther, Ole; Fleury, Bernard H.

    2015-01-01

    Recently we presented the S-AMP approach, an extension of approximate message passing (AMP), to be able to handle general invariant matrix ensembles. In this contribution we extend S-AMP to non-linear observation models. We obtain generalized AMP (GAMP) as the special case when the measurement...

  5. Plane answers to complex questions the theory of linear models

    Christensen, Ronald

    1987-01-01

    This book was written to rigorously illustrate the practical application of the projective approach to linear models. To some, this may seem contradictory. I contend that it is possible to be both rigorous and illustrative and that it is possible to use the projective approach in practical applications. Therefore, unlike many other books on linear models, the use of projections and sub­ spaces does not stop after the general theory. They are used wherever I could figure out how to do it. Solving normal equations and using calculus (outside of maximum likelihood theory) are anathema to me. This is because I do not believe that they contribute to the understanding of linear models. I have similar feelings about the use of side conditions. Such topics are mentioned when appropriate and thenceforward avoided like the plague. On the other side of the coin, I just as strenuously reject teaching linear models with a coordinate free approach. Although Joe Eaton assures me that the issues in complicated problems freq...

  6. A simulation model of a coordinated decentralized linear supply chain

    Ashayeri, Jalal; Cannella, S.; Lopez Campos, M.; Miranda, P.A.

    2015-01-01

    This paper presents a simulation-based study of a coordinated, decentralized linear supply chain (SC) system. In the proposed model, any supply tier considers its successors as part of its inventory system and generates replenishment orders on the basis of its partners’ operational information. We

  7. Mathematical modelling and linear stability analysis of laser fusion cutting

    Hermanns, Torsten; Schulz, Wolfgang [RWTH Aachen University, Chair for Nonlinear Dynamics, Steinbachstr. 15, 52047 Aachen (Germany); Vossen, Georg [Niederrhein University of Applied Sciences, Chair for Applied Mathematics and Numerical Simulations, Reinarzstr.. 49, 47805 Krefeld (Germany); Thombansen, Ulrich [RWTH Aachen University, Chair for Laser Technology, Steinbachstr. 15, 52047 Aachen (Germany)

    2016-06-08

    A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process’ amount of dynamic behavior.

  8. Performances Of Estimators Of Linear Models With Autocorrelated ...

    The performances of five estimators of linear models with Autocorrelated error terms are compared when the independent variable is autoregressive. The results reveal that the properties of the estimators when the sample size is finite is quite similar to the properties of the estimators when the sample size is infinite although ...

  9. Performances of estimators of linear auto-correlated error model ...

    The performances of five estimators of linear models with autocorrelated disturbance terms are compared when the independent variable is exponential. The results reveal that for both small and large samples, the Ordinary Least Squares (OLS) compares favourably with the Generalized least Squares (GLS) estimators in ...

  10. A non-linear dissipative model of magnetism

    Durand, P.; Paidarová, Ivana

    2010-01-01

    Roč. 89, č. 6 (2010), s. 67004 ISSN 1286-4854 R&D Projects: GA AV ČR IAA100400501 Institutional research plan: CEZ:AV0Z40400503 Keywords : non-linear dissipative model of magnetism * thermodynamics * physical chemistry Subject RIV: CF - Physical ; Theoretical Chemistry http://epljournal.edpsciences.org/

  11. Modeling and verifying non-linearities in heterodyne displacement interferometry

    Cosijns, S.J.A.G.; Haitjema, H.; Schellekens, P.H.J.

    2002-01-01

    The non-linearities in a heterodyne laser interferometer system occurring from the phase measurement system of the interferometer andfrom non-ideal polarization effects of the optics are modeled into one analytical expression which includes the initial polarization state ofthe laser source, the

  12. Generalized linear longitudinal mixed models with linear covariance structure and multiplicative random effects

    Holst, René; Jørgensen, Bent

    2015-01-01

    The paper proposes a versatile class of multiplicative generalized linear longitudinal mixed models (GLLMM) with additive dispersion components, based on explicit modelling of the covariance structure. The class incorporates a longitudinal structure into the random effects models and retains...... a marginal as well as a conditional interpretation. The estimation procedure is based on a computationally efficient quasi-score method for the regression parameters combined with a REML-like bias-corrected Pearson estimating function for the dispersion and correlation parameters. This avoids...... the multidimensional integral of the conventional GLMM likelihood and allows an extension of the robust empirical sandwich estimator for use with both association and regression parameters. The method is applied to a set of otholit data, used for age determination of fish....

  13. Identifiability Results for Several Classes of Linear Compartment Models.

    Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

    2015-08-01

    Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

  14. Finite element modeling of nanotube structures linear and non-linear models

    Awang, Mokhtar; Muhammad, Ibrahim Dauda

    2016-01-01

    This book presents a new approach to modeling carbon structures such as graphene and carbon nanotubes using finite element methods, and addresses the latest advances in numerical studies for these materials. Based on the available findings, the book develops an effective finite element approach for modeling the structure and the deformation of grapheme-based materials. Further, modeling processing for single-walled and multi-walled carbon nanotubes is demonstrated in detail.

  15. Linear Dynamics Model for Steam Cooled Fast Power Reactors

    Vollmer, H

    1968-04-15

    A linear analytical dynamic model is developed for steam cooled fast power reactors. All main components of such a plant are investigated on a general though relatively simple basis. The model is distributed in those parts concerning the core but lumped as to the external plant components. Coolant is considered as compressible and treated by the actual steam law. Combined use of analogue and digital computer seems most attractive.

  16. Deterministic operations research models and methods in linear optimization

    Rader, David J

    2013-01-01

    Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear

  17. One-loop dimensional reduction of the linear σ model

    Malbouisson, A.P.C.; Silva-Neto, M.B.; Svaiter, N.F.

    1997-05-01

    We perform the dimensional reduction of the linear σ model at one-loop level. The effective of the reduced theory obtained from the integration over the nonzero Matsubara frequencies is exhibited. Thermal mass and coupling constant renormalization constants are given, as well as the thermal renormalization group which controls the dependence of the counterterms on the temperature. We also recover, for the reduced theory, the vacuum instability of the model for large N. (author)

  18. Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

    Yousif, Adil; Elfaki, Faiz

    2017-12-01

    The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

  19. A linearized dispersion relation for orthorhombic pseudo-acoustic modeling

    Song, Xiaolei; Alkhalifah, Tariq Ali

    2012-01-01

    Wavefield extrapolation in acoustic orthorhombic anisotropic media suffers from wave-mode coupling and stability limitations in the parameter range. We introduce a linearized form of the dispersion relation for acoustic orthorhombic media to model acoustic wavefields. We apply the lowrank approximation approach to handle the corresponding space-wavenumber mixed-domain operator. Numerical experiments show that the proposed wavefield extrapolator is accurate and practically free of dispersions. Further, there is no coupling of qSv and qP waves, because we use the analytical dispersion relation. No constraints on Thomsen's parameters are required for stability. The linearized expression may provide useful application for parameter estimation in orthorhombic media.

  20. Non-linear sigma model on the fuzzy supersphere

    Kurkcuoglu, Seckin

    2004-01-01

    In this note we develop fuzzy versions of the supersymmetric non-linear sigma model on the supersphere S (2,2) . In hep-th/0212133 Bott projectors have been used to obtain the fuzzy C P 1 model. Our approach utilizes the use of supersymmetric extensions of these projectors. Here we obtain these (super)-projectors and quantize them in a fashion similar to the one given in hep-th/0212133. We discuss the interpretation of the resulting model as a finite dimensional matrix model. (author)

  1. Optimal difference-based estimation for partially linear models

    Zhou, Yuejin; Cheng, Yebin; Dai, Wenlin; Tong, Tiejun

    2017-01-01

    Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.

  2. Modeling and analysis of linear hyperbolic systems of balance laws

    Bartecki, Krzysztof

    2016-01-01

    This monograph focuses on the mathematical modeling of distributed parameter systems in which mass/energy transport or wave propagation phenomena occur and which are described by partial differential equations of hyperbolic type. The case of linear (or linearized) 2 x 2 hyperbolic systems of balance laws is considered, i.e., systems described by two coupled linear partial differential equations with two variables representing physical quantities, depending on both time and one-dimensional spatial variable. Based on practical examples of a double-pipe heat exchanger and a transportation pipeline, two typical configurations of boundary input signals are analyzed: collocated, wherein both signals affect the system at the same spatial point, and anti-collocated, in which the input signals are applied to the two different end points of the system. The results of this book emerge from the practical experience of the author gained during his studies conducted in the experimental installation of a heat exchange cente...

  3. Optimal difference-based estimation for partially linear models

    Zhou, Yuejin

    2017-12-16

    Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.

  4. A penalized framework for distributed lag non-linear models.

    Gasparrini, Antonio; Scheipl, Fabian; Armstrong, Ben; Kenward, Michael G

    2017-09-01

    Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis. © 2017 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

  5. General mirror pairs for gauged linear sigma models

    Aspinwall, Paul S.; Plesser, M. Ronen [Departments of Mathematics and Physics, Duke University,Box 90320, Durham, NC 27708-0320 (United States)

    2015-11-05

    We carefully analyze the conditions for an abelian gauged linear σ-model to exhibit nontrivial IR behavior described by a nonsingular superconformal field theory determining a superstring vacuum. This is done without reference to a geometric phase, by associating singular behavior to a noncompact space of (semi-)classical vacua. We find that models determined by reflexive combinatorial data are nonsingular for generic values of their parameters. This condition has the pleasant feature that the mirror of a nonsingular gauged linear σ-model is another such model, but it is clearly too strong and we provide an example of a non-reflexive mirror pair. We discuss a weaker condition inspired by considering extremal transitions, which is also mirror symmetric and which we conjecture to be sufficient. We apply these ideas to extremal transitions and to understanding the way in which both Berglund-Hübsch mirror symmetry and the Vafa-Witten mirror orbifold with discrete torsion can be seen as special cases of the general combinatorial duality of gauged linear σ-models. In the former case we encounter an example showing that our weaker condition is still not necessary.

  6. General mirror pairs for gauged linear sigma models

    Aspinwall, Paul S.; Plesser, M. Ronen

    2015-01-01

    We carefully analyze the conditions for an abelian gauged linear σ-model to exhibit nontrivial IR behavior described by a nonsingular superconformal field theory determining a superstring vacuum. This is done without reference to a geometric phase, by associating singular behavior to a noncompact space of (semi-)classical vacua. We find that models determined by reflexive combinatorial data are nonsingular for generic values of their parameters. This condition has the pleasant feature that the mirror of a nonsingular gauged linear σ-model is another such model, but it is clearly too strong and we provide an example of a non-reflexive mirror pair. We discuss a weaker condition inspired by considering extremal transitions, which is also mirror symmetric and which we conjecture to be sufficient. We apply these ideas to extremal transitions and to understanding the way in which both Berglund-Hübsch mirror symmetry and the Vafa-Witten mirror orbifold with discrete torsion can be seen as special cases of the general combinatorial duality of gauged linear σ-models. In the former case we encounter an example showing that our weaker condition is still not necessary.

  7. Robust Linear Models for Cis-eQTL Analysis.

    Rantalainen, Mattias; Lindgren, Cecilia M; Holmes, Christopher C

    2015-01-01

    Expression Quantitative Trait Loci (eQTL) analysis enables characterisation of functional genetic variation influencing expression levels of individual genes. In outbread populations, including humans, eQTLs are commonly analysed using the conventional linear model, adjusting for relevant covariates, assuming an allelic dosage model and a Gaussian error term. However, gene expression data generally have noise that induces heavy-tailed errors relative to the Gaussian distribution and often include atypical observations, or outliers. Such departures from modelling assumptions can lead to an increased rate of type II errors (false negatives), and to some extent also type I errors (false positives). Careful model checking can reduce the risk of type-I errors but often not type II errors, since it is generally too time-consuming to carefully check all models with a non-significant effect in large-scale and genome-wide studies. Here we propose the application of a robust linear model for eQTL analysis to reduce adverse effects of deviations from the assumption of Gaussian residuals. We present results from a simulation study as well as results from the analysis of real eQTL data sets. Our findings suggest that in many situations robust models have the potential to provide more reliable eQTL results compared to conventional linear models, particularly in respect to reducing type II errors due to non-Gaussian noise. Post-genomic data, such as that generated in genome-wide eQTL studies, are often noisy and frequently contain atypical observations. Robust statistical models have the potential to provide more reliable results and increased statistical power under non-Gaussian conditions. The results presented here suggest that robust models should be considered routinely alongside other commonly used methodologies for eQTL analysis.

  8. Linear models for joint association and linkage QTL mapping

    Fernando Rohan L

    2009-09-01

    Full Text Available Abstract Background Populational linkage disequilibrium and within-family linkage are commonly used for QTL mapping and marker assisted selection. The combination of both results in more robust and accurate locations of the QTL, but models proposed so far have been either single marker, complex in practice or well fit to a particular family structure. Results We herein present linear model theory to come up with additive effects of the QTL alleles in any member of a general pedigree, conditional to observed markers and pedigree, accounting for possible linkage disequilibrium among QTLs and markers. The model is based on association analysis in the founders; further, the additive effect of the QTLs transmitted to the descendants is a weighted (by the probabilities of transmission average of the substitution effects of founders' haplotypes. The model allows for non-complete linkage disequilibrium QTL-markers in the founders. Two submodels are presented: a simple and easy to implement Haley-Knott type regression for half-sib families, and a general mixed (variance component model for general pedigrees. The model can use information from all markers. The performance of the regression method is compared by simulation with a more complex IBD method by Meuwissen and Goddard. Numerical examples are provided. Conclusion The linear model theory provides a useful framework for QTL mapping with dense marker maps. Results show similar accuracies but a bias of the IBD method towards the center of the region. Computations for the linear regression model are extremely simple, in contrast with IBD methods. Extensions of the model to genomic selection and multi-QTL mapping are straightforward.

  9. Perceived Resources as a Predictor of Satisfaction with Food-Related Life among Chilean Elderly: An Approach with Generalized Linear Models.

    Lobos, G; Schnettler, B; Grunert, K G; Adasme, C

    2017-01-01

    The main objective of this study is to show why perceived resources are a strong predictor of satisfaction with food-related life in Chilean older adults. Design, sampling and participants: A survey was conducted in rural and urban areas in 30 communes of the Maule Region with 785 participants over 60 years of age who live in their own homes. The Satisfaction with Food-related Life (SWFL) scale was used. Generalized linear models (GLM) were used for the regression analysis. The results led to different considerations: First, older adults' perceived levels of resources are a good reflection of their actual levels of resources. Second, the individuals rated the sum of the perceived resources as 'highly important' to explain older adults' satisfaction with food-related life. Third, SWFL was predicted by satisfaction with economic situation, family importance, quantity of domestic household goods and a relative health indicator. Fourth, older adults who believe they have more resources compared to others are more satisfied with their food-related life. Finally, Poisson and binomial logistic models showed that the sum of perceived resources significantly increased the prediction of SWFL. The main conclusion is that perceived personal resources are a strong predictor of SWFL in Chilean older adults.

  10. A Graphical User Interface to Generalized Linear Models in MATLAB

    Peter Dunn

    1999-07-01

    Full Text Available Generalized linear models unite a wide variety of statistical models in a common theoretical framework. This paper discusses GLMLAB-software that enables such models to be fitted in the popular mathematical package MATLAB. It provides a graphical user interface to the powerful MATLAB computational engine to produce a program that is easy to use but with many features, including offsets, prior weights and user-defined distributions and link functions. MATLAB's graphical capacities are also utilized in providing a number of simple residual diagnostic plots.

  11. MAGDM linear-programming models with distinct uncertain preference structures.

    Xu, Zeshui S; Chen, Jian

    2008-10-01

    Group decision making with preference information on alternatives is an interesting and important research topic which has been receiving more and more attention in recent years. The purpose of this paper is to investigate multiple-attribute group decision-making (MAGDM) problems with distinct uncertain preference structures. We develop some linear-programming models for dealing with the MAGDM problems, where the information about attribute weights is incomplete, and the decision makers have their preferences on alternatives. The provided preference information can be represented in the following three distinct uncertain preference structures: 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first establish some linear-programming models based on decision matrix and each of the distinct uncertain preference structures and, then, develop some linear-programming models to integrate all three structures of subjective uncertain preference information provided by the decision makers and the objective information depicted in the decision matrix. Furthermore, we propose a simple and straightforward approach in ranking and selecting the given alternatives. It is worth pointing out that the developed models can also be used to deal with the situations where the three distinct uncertain preference structures are reduced to the traditional ones, i.e., utility values, fuzzy preference relations, and multiplicative preference relations. Finally, we use a practical example to illustrate in detail the calculation process of the developed approach.

  12. Forecasting the EMU inflation rate: Linear econometric vs. non-linear computational models using genetic neural fuzzy systems

    Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric

    2004-01-01

    This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according...

  13. Modelling of Asphalt Concrete Stiffness in the Linear Viscoelastic Region

    Mazurek, Grzegorz; Iwański, Marek

    2017-10-01

    Stiffness modulus is a fundamental parameter used in the modelling of the viscoelastic behaviour of bituminous mixtures. On the basis of the master curve in the linear viscoelasticity range, the mechanical properties of asphalt concrete at different loading times and temperatures can be predicted. This paper discusses the construction of master curves under rheological mathematical models i.e. the sigmoidal function model (MEPDG), the fractional model, and Bahia and co-workers’ model in comparison to the results from mechanistic rheological models i.e. the generalized Huet-Sayegh model, the generalized Maxwell model and the Burgers model. For the purposes of this analysis, the reference asphalt concrete mix (denoted as AC16W) intended for the binder coarse layer and for traffic category KR3 (5×105 controlled strain mode. The fixed strain level was set at 25με to guarantee that the stiffness modulus of the asphalt concrete would be tested in a linear viscoelasticity range. The master curve was formed using the time-temperature superposition principle (TTSP). The stiffness modulus of asphalt concrete was determined at temperatures 10°C, 20°C and 40°C and at loading times (frequency) of 0.1, 0.3, 1, 3, 10, 20 Hz. The model parameters were fitted to the rheological models using the original programs based on the nonlinear least squares sum method. All the rheological models under analysis were found to be capable of predicting changes in the stiffness modulus of the reference asphalt concrete to satisfactory accuracy. In the cases of the fractional model and the generalized Maxwell model, their accuracy depends on a number of elements in series. The best fit was registered for Bahia and co-workers model, generalized Maxwell model and fractional model. As for predicting the phase angle parameter, the largest discrepancies between experimental and modelled results were obtained using the fractional model. Except the Burgers model, the model matching quality was

  14. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    Irincheeva, Irina

    2012-08-03

    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  15. Linear Model for Optimal Distributed Generation Size Predication

    Ahmed Al Ameri

    2017-01-01

    Full Text Available This article presents a linear model predicting optimal size of Distributed Generation (DG that addresses the minimum power loss. This method is based fundamentally on strong coupling between active power and voltage angle as well as between reactive power and voltage magnitudes. This paper proposes simplified method to calculate the total power losses in electrical grid for different distributed generation sizes and locations. The method has been implemented and tested on several IEEE bus test systems. The results show that the proposed method is capable of predicting approximate optimal size of DG when compared with precision calculations. The method that linearizes a complex model showed a good result, which can actually reduce processing time required. The acceptable accuracy with less time and memory required can help the grid operator to assess power system integrated within large-scale distribution generation.

  16. A non-linear model of economic production processes

    Ponzi, A.; Yasutomi, A.; Kaneko, K.

    2003-06-01

    We present a new two phase model of economic production processes which is a non-linear dynamical version of von Neumann's neoclassical model of production, including a market price-setting phase as well as a production phase. The rate of an economic production process is observed, for the first time, to depend on the minimum of its input supplies. This creates highly non-linear supply and demand dynamics. By numerical simulation, production networks are shown to become unstable when the ratio of different products to total processes increases. This provides some insight into observed stability of competitive capitalist economies in comparison to monopolistic economies. Capitalist economies are also shown to have low unemployment.

  17. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.

    2012-01-01

    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  18. NON-LINEAR FINITE ELEMENT MODELING OF DEEP DRAWING PROCESS

    Hasan YILDIZ

    2004-03-01

    Full Text Available Deep drawing process is one of the main procedures used in different branches of industry. Finding numerical solutions for determination of the mechanical behaviour of this process will save time and money. In die surfaces, which have complex geometries, it is hard to determine the effects of parameters of sheet metal forming. Some of these parameters are wrinkling, tearing, and determination of the flow of the thin sheet metal in the die and thickness change. However, the most difficult one is determination of material properties during plastic deformation. In this study, the effects of all these parameters are analyzed before producing the dies. The explicit non-linear finite element method is chosen to be used in the analysis. The numerical results obtained for non-linear material and contact models are also compared with the experiments. A good agreement between the numerical and the experimental results is obtained. The results obtained for the models are given in detail.

  19. Dynamic generalized linear models for monitoring endemic diseases

    Lopes Antunes, Ana Carolina; Jensen, Dan; Hisham Beshara Halasa, Tariq

    2016-01-01

    The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control...... and eradication programmes based on changes in PRRS sero-prevalence was explored. Results showed a declining trend in PRRS sero-prevalence between 2007 and 2014 suggesting that Danish herds are slowly eradicating PRRS. The simulation study demonstrated the flexibility of DGLMs in adapting to changes intrends...... in sero-prevalence. Based on this, it was possible to detect variations in the growth model component. This study is a proof-of-concept, demonstrating the use of DGLMs for monitoring endemic diseases. In addition, the principles stated might be useful in general research on monitoring and surveillance...

  20. Estimation and Inference for Very Large Linear Mixed Effects Models

    Gao, K.; Owen, A. B.

    2016-01-01

    Linear mixed models with large imbalanced crossed random effects structures pose severe computational problems for maximum likelihood estimation and for Bayesian analysis. The costs can grow as fast as $N^{3/2}$ when there are N observations. Such problems arise in any setting where the underlying factors satisfy a many to many relationship (instead of a nested one) and in electronic commerce applications, the N can be quite large. Methods that do not account for the correlation structure can...

  1. Using Quartile-Quartile Lines as Linear Models

    Gordon, Sheldon P.

    2015-01-01

    This article introduces the notion of the quartile-quartile line as an alternative to the regression line and the median-median line to produce a linear model based on a set of data. It is based on using the first and third quartiles of a set of (x, y) data. Dynamic spreadsheets are used as exploratory tools to compare the different approaches and…

  2. NON-LINEAR MODELING OF THE RHIC INTERACTION REGIONS

    TOMAS, R.; FISCHER, W.; JAIN, A.; LUO, Y.; PILAT, F.

    2004-01-01

    For RHIC's collision lattices the dominant sources of transverse non-linearities are located in the interaction regions. The field quality is available for most of the magnets in the interaction regions from the magnetic measurements, or from extrapolations of these measurements. We discuss the implementation of these measurements in the MADX models of the Blue and the Yellow rings and their impact on beam stability

  3. Electromagnetic axial anomaly in a generalized linear sigma model

    Fariborz, Amir H.; Jora, Renata

    2017-06-01

    We construct the electromagnetic anomaly effective term for a generalized linear sigma model with two chiral nonets, one with a quark-antiquark structure, the other one with a four-quark content. We compute in the leading order of this framework the decays into two photons of six pseudoscalars: π0(137 ), π0(1300 ), η (547 ), η (958 ), η (1295 ) and η (1760 ). Our results agree well with the available experimental data.

  4. Comparison of Linear Prediction Models for Audio Signals

    2009-03-01

    Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.

  5. A quasi-linear gyrokinetic transport model for tokamak plasmas

    Casati, A.

    2009-10-01

    After a presentation of some basics around nuclear fusion, this research thesis introduces the framework of the tokamak strategy to deal with confinement, hence the main plasma instabilities which are responsible for turbulent transport of energy and matter in such a system. The author also briefly introduces the two principal plasma representations, the fluid and the kinetic ones. He explains why the gyro-kinetic approach has been preferred. A tokamak relevant case is presented in order to highlight the relevance of a correct accounting of the kinetic wave-particle resonance. He discusses the issue of the quasi-linear response. Firstly, the derivation of the model, called QuaLiKiz, and its underlying hypotheses to get the energy and the particle turbulent flux are presented. Secondly, the validity of the quasi-linear response is verified against the nonlinear gyro-kinetic simulations. The saturation model that is assumed in QuaLiKiz, is presented and discussed. Then, the author qualifies the global outcomes of QuaLiKiz. Both the quasi-linear energy and the particle flux are compared to the expectations from the nonlinear simulations, across a wide scan of tokamak relevant parameters. Therefore, the coupling of QuaLiKiz within the integrated transport solver CRONOS is presented: this procedure allows the time-dependent transport problem to be solved, hence the direct application of the model to the experiment. The first preliminary results regarding the experimental analysis are finally discussed

  6. Linear theory for filtering nonlinear multiscale systems with model error.

    Berry, Tyrus; Harlim, John

    2014-07-08

    In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online , as part of a filtering

  7. Technical note: A linear model for predicting δ13 Cprotein.

    Pestle, William J; Hubbe, Mark; Smith, Erin K; Stevenson, Joseph M

    2015-08-01

    Development of a model for the prediction of δ(13) Cprotein from δ(13) Ccollagen and Δ(13) Cap-co . Model-generated values could, in turn, serve as "consumer" inputs for multisource mixture modeling of paleodiet. Linear regression analysis of previously published controlled diet data facilitated the development of a mathematical model for predicting δ(13) Cprotein (and an experimentally generated error term) from isotopic data routinely generated during the analysis of osseous remains (δ(13) Cco and Δ(13) Cap-co ). Regression analysis resulted in a two-term linear model (δ(13) Cprotein (%) = (0.78 × δ(13) Cco ) - (0.58× Δ(13) Cap-co ) - 4.7), possessing a high R-value of 0.93 (r(2)  = 0.86, P analysis of human osseous remains. These predicted values are ideal for use in multisource mixture modeling of dietary protein source contribution. © 2015 Wiley Periodicals, Inc.

  8. GlmS and NagB regulate amino sugar metabolism in opposing directions and affect Streptococcus mutans virulence.

    Miki Kawada-Matsuo

    Full Text Available Streptococcus mutans is a cariogenic pathogen that produces an extracellular polysaccharide (glucan from dietary sugars, which allows it to establish a reproductive niche and secrete acids that degrade tooth enamel. While two enzymes (GlmS and NagB are known to be key factors affecting the entrance of amino sugars into glycolysis and cell wall synthesis in several other bacteria, their roles in S. mutans remain unclear. Therefore, we investigated the roles of GlmS and NagB in S. mutans sugar metabolism and determined whether they have an effect on virulence. NagB expression increased in the presence of GlcNAc while GlmS expression decreased, suggesting that the regulation of these enzymes, which functionally oppose one another, is dependent on the concentration of environmental GlcNAc. A glmS-inactivated mutant could not grow in the absence of GlcNAc, while nagB-inactivated mutant growth was decreased in the presence of GlcNAc. Also, nagB inactivation was found to decrease the expression of virulence factors, including cell-surface protein antigen and glucosyltransferase, and to decrease biofilm formation and saliva-induced S. mutans aggregation, while glmS inactivation had the opposite effects on virulence factor expression and bacterial aggregation. Our results suggest that GlmS and NagB function in sugar metabolism in opposing directions, increasing and decreasing S. mutans virulence, respectively.

  9. Neutron stars in non-linear coupling models

    Taurines, Andre R.; Vasconcellos, Cesar A.Z.; Malheiro, Manuel; Chiapparini, Marcelo

    2001-01-01

    We present a class of relativistic models for nuclear matter and neutron stars which exhibits a parameterization, through mathematical constants, of the non-linear meson-baryon couplings. For appropriate choices of the parameters, it recovers current QHD models found in the literature: Walecka, ZM and ZM3 models. We have found that the ZM3 model predicts a very small maximum neutron star mass, ∼ 0.72M s un. A strong similarity between the results of ZM-like models and those with exponential couplings is noted. Finally, we discuss the very intense scalar condensates found in the interior of neutron stars which may lead to negative effective masses. (author)

  10. Neutron stars in non-linear coupling models

    Taurines, Andre R.; Vasconcellos, Cesar A.Z. [Rio Grande do Sul Univ., Porto Alegre, RS (Brazil); Malheiro, Manuel [Universidade Federal Fluminense, Niteroi, RJ (Brazil); Chiapparini, Marcelo [Universidade do Estado, Rio de Janeiro, RJ (Brazil)

    2001-07-01

    We present a class of relativistic models for nuclear matter and neutron stars which exhibits a parameterization, through mathematical constants, of the non-linear meson-baryon couplings. For appropriate choices of the parameters, it recovers current QHD models found in the literature: Walecka, ZM and ZM3 models. We have found that the ZM3 model predicts a very small maximum neutron star mass, {approx} 0.72M{sub s}un. A strong similarity between the results of ZM-like models and those with exponential couplings is noted. Finally, we discuss the very intense scalar condensates found in the interior of neutron stars which may lead to negative effective masses. (author)

  11. Modelling of Rotational Capacity in Reinforced Linear Elements

    Hestbech, Lars; Hagsten, Lars German; Fisker, Jakob

    2011-01-01

    on the rotational capacity of the plastic hinges. The documentation of ductility can be a difficult task as modelling of rotational capacity in plastic hinges of frames is not fully developed. On the basis of the Theory of Plasticity a model is developed to determine rotational capacity in plastic hinges in linear......The Capacity Design Method forms the basis of several seismic design codes. This design philosophy allows plastic deformations in order to decrease seismic demands in structures. However, these plastic deformations must be localized in certain zones where ductility requirements can be documented...... reinforced concrete elements. The model is taking several important parameters into account. Empirical values is avoided which is considered an advantage compared to previous models. Furthermore, the model includes force variations in the reinforcement due to moment distributions and shear as well...

  12. Comparison of linear and non-linear models for the adsorption of fluoride onto geo-material: limonite.

    Sahin, Rubina; Tapadia, Kavita

    2015-01-01

    The three widely used isotherms Langmuir, Freundlich and Temkin were examined in an experiment using fluoride (F⁻) ion adsorption on a geo-material (limonite) at four different temperatures by linear and non-linear models. Comparison of linear and non-linear regression models were given in selecting the optimum isotherm for the experimental results. The coefficient of determination, r², was used to select the best theoretical isotherm. The four Langmuir linear equations (1, 2, 3, and 4) are discussed. Langmuir isotherm parameters obtained from the four Langmuir linear equations using the linear model differed but they were the same when using the nonlinear model. Langmuir-2 isotherm is one of the linear forms, and it had the highest coefficient of determination (r² = 0.99) compared to the other Langmuir linear equations (1, 3 and 4) in linear form, whereas, for non-linear, Langmuir-4 fitted best among all the isotherms because it had the highest coefficient of determination (r² = 0.99). The results showed that the non-linear model may be a better way to obtain the parameters. In the present work, the thermodynamic parameters show that the absorption of fluoride onto limonite is both spontaneous (ΔG 0). Scanning electron microscope and X-ray diffraction images also confirm the adsorption of F⁻ ion onto limonite. The isotherm and kinetic study reveals that limonite can be used as an adsorbent for fluoride removal. In future we can develop new technology for fluoride removal in large scale by using limonite which is cost-effective, eco-friendly and is easily available in the study area.

  13. Network Traffic Monitoring Using Poisson Dynamic Linear Models

    Merl, D. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2011-05-09

    In this article, we discuss an approach for network forensics using a class of nonstationary Poisson processes with embedded dynamic linear models. As a modeling strategy, the Poisson DLM (PoDLM) provides a very flexible framework for specifying structured effects that may influence the evolution of the underlying Poisson rate parameter, including diurnal and weekly usage patterns. We develop a novel particle learning algorithm for online smoothing and prediction for the PoDLM, and demonstrate the suitability of the approach to real-time deployment settings via a new application to computer network traffic monitoring.

  14. On the chiral phase transition in the linear sigma model

    Tran Huu Phat; Nguyen Tuan Anh; Le Viet Hoa

    2003-01-01

    The Cornwall- Jackiw-Tomboulis (CJT) effective action for composite operators at finite temperature is used to investigate the chiral phase transition within the framework of the linear sigma model as the low-energy effective model of quantum chromodynamics (QCD). A new renormalization prescription for the CJT effective action in the Hartree-Fock (HF) approximation is proposed. A numerical study, which incorporates both thermal and quantum effect, shows that in this approximation the phase transition is of first order. However, taking into account the higher-loop diagrams contribution the order of phase transition is unchanged. (author)

  15. Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

    Liang, Faming; Song, Qifan; Yu, Kai

    2013-01-01

    criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening

  16. Application of linearized model to the stability analysis of the pressurized water reactor

    Li Haipeng; Huang Xiaojin; Zhang Liangju

    2008-01-01

    A Linear Time-Invariant model of the Pressurized Water Reactor is formulated through the linearization of the nonlinear model. The model simulation results show that the linearized model agrees well with the nonlinear model under small perturbation. Based upon the Lyapunov's First Method, the linearized model is applied to the stability analysis of the Pressurized Water Reactor. The calculation results show that the methodology of linearization to stability analysis is conveniently feasible. (authors)

  17. The Overgeneralization of Linear Models among University Students' Mathematical Productions: A Long-Term Study

    Esteley, Cristina B.; Villarreal, Monica E.; Alagia, Humberto R.

    2010-01-01

    Over the past several years, we have been exploring and researching a phenomenon that occurs among undergraduate students that we called extension of linear models to non-linear contexts or overgeneralization of linear models. This phenomenon appears when some students use linear representations in situations that are non-linear. In a first phase,…

  18. A Linear Viscoelastic Model Calibration of Sylgard 184.

    Long, Kevin Nicholas; Brown, Judith Alice

    2017-04-01

    We calibrate a linear thermoviscoelastic model for solid Sylgard 184 (90-10 formulation), a lightly cross-linked, highly flexible isotropic elastomer for use both in Sierra / Solid Mechanics via the Universal Polymer Model as well as in Sierra / Structural Dynamics (Salinas) for use as an isotropic viscoelastic material. Material inputs for the calibration in both codes are provided. The frequency domain master curve of oscillatory shear was obtained from a report from Los Alamos National Laboratory (LANL). However, because the form of that data is different from the constitutive models in Sierra, we also present the mapping of the LANL data onto Sandia’s constitutive models. Finally, blind predictions of cyclic tension and compression out to moderate strains of 40 and 20% respectively are compared with Sandia’s legacy cure schedule material. Although the strain rate of the data is unknown, the linear thermoviscoelastic model accurately predicts the experiments out to moderate strains for the slower strain rates, which is consistent with the expectation that quasistatic test procedures were likely followed. This good agreement comes despite the different cure schedules between the Sandia and LANL data.

  19. Predicting Madura cattle growth curve using non-linear model

    Widyas, N.; Prastowo, S.; Widi, T. S. M.; Baliarti, E.

    2018-03-01

    Madura cattle is Indonesian native. It is a composite breed that has undergone hundreds of years of selection and domestication to reach nowadays remarkable uniformity. Crossbreeding has reached the isle of Madura and the Madrasin, a cross between Madura cows and Limousine semen emerged. This paper aimed to compare the growth curve between Madrasin and one type of pure Madura cows, the common Madura cattle (Madura) using non-linear models. Madura cattles are kept traditionally thus reliable records are hardly available. Data were collected from small holder farmers in Madura. Cows from different age classes (5years) were observed, and body measurements (chest girth, body length and wither height) were taken. In total 63 Madura and 120 Madrasin records obtained. Linear model was built with cattle sub-populations and age as explanatory variables. Body weights were estimated based on the chest girth. Growth curves were built using logistic regression. Results showed that within the same age, Madrasin has significantly larger body compared to Madura (plogistic models fit better for Madura and Madrasin cattle data; with the estimated MSE for these models were 39.09 and 759.28 with prediction accuracy of 99 and 92% for Madura and Madrasin, respectively. Prediction of growth curve using logistic regression model performed well in both types of Madura cattle. However, attempts to administer accurate data on Madura cattle are necessary to better characterize and study these cattle.

  20. A non-linear model of information seeking behaviour

    Allen E. Foster

    2005-01-01

    Full Text Available The results of a qualitative, naturalistic, study of information seeking behaviour are reported in this paper. The study applied the methods recommended by Lincoln and Guba for maximising credibility, transferability, dependability, and confirmability in data collection and analysis. Sampling combined purposive and snowball methods, and led to a final sample of 45 inter-disciplinary researchers from the University of Sheffield. In-depth semi-structured interviews were used to elicit detailed examples of information seeking. Coding of interview transcripts took place in multiple iterations over time and used Atlas-ti software to support the process. The results of the study are represented in a non-linear Model of Information Seeking Behaviour. The model describes three core processes (Opening, Orientation, and Consolidation and three levels of contextual interaction (Internal Context, External Context, and Cognitive Approach, each composed of several individual activities and attributes. The interactivity and shifts described by the model show information seeking to be non-linear, dynamic, holistic, and flowing. The paper concludes by describing the whole model of behaviours as analogous to an artist's palette, in which activities remain available throughout information seeking. A summary of key implications of the model and directions for further research are included.

  1. Effect Displays in R for Generalised Linear Models

    John Fox

    2003-07-01

    Full Text Available This paper describes the implementation in R of a method for tabular or graphical display of terms in a complex generalised linear model. By complex, I mean a model that contains terms related by marginality or hierarchy, such as polynomial terms, or main effects and interactions. I call these tables or graphs effect displays. Effect displays are constructed by identifying high-order terms in a generalised linear model. Fitted values under the model are computed for each such term. The lower-order "relatives" of a high-order term (e.g., main effects marginal to an interaction are absorbed into the term, allowing the predictors appearing in the high-order term to range over their values. The values of other predictors are fixed at typical values: for example, a covariate could be fixed at its mean or median, a factor at its proportional distribution in the data, or to equal proportions in its several levels. Variations of effect displays are also described, including representation of terms higher-order to any appearing in the model.

  2. Global numerical modeling of magnetized plasma in a linear device

    Magnussen, Michael Løiten

    Understanding the turbulent transport in the plasma-edge in fusion devices is of utmost importance in order to make precise predictions for future fusion devices. The plasma turbulence observed in linear devices shares many important features with the turbulence observed in the edge of fusion dev...... with simulations performed at different ionization levels, using a simple model for plasma interaction with neutrals. It is found that the steady state and the saturated state of the system bifurcates when the neutral interaction dominates the electron-ion collisions.......Understanding the turbulent transport in the plasma-edge in fusion devices is of utmost importance in order to make precise predictions for future fusion devices. The plasma turbulence observed in linear devices shares many important features with the turbulence observed in the edge of fusion...... devices, and are easier to diagnose due to lower temperatures and a better access to the plasma. In order to gain greater insight into this complex turbulent behavior, numerical simulations of plasma in a linear device are performed in this thesis. Here, a three-dimensional drift-fluid model is derived...

  3. Predicting birth weight with conditionally linear transformation models.

    Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

    2016-12-01

    Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

  4. Wavefront Sensing for WFIRST with a Linear Optical Model

    Jurling, Alden S.; Content, David A.

    2012-01-01

    In this paper we develop methods to use a linear optical model to capture the field dependence of wavefront aberrations in a nonlinear optimization-based phase retrieval algorithm for image-based wavefront sensing. The linear optical model is generated from a ray trace model of the system and allows the system state to be described in terms of mechanical alignment parameters rather than wavefront coefficients. This approach allows joint optimization over images taken at different field points and does not require separate convergence of phase retrieval at individual field points. Because the algorithm exploits field diversity, multiple defocused images per field point are not required for robustness. Furthermore, because it is possible to simultaneously fit images of many stars over the field, it is not necessary to use a fixed defocus to achieve adequate signal-to-noise ratio despite having images with high dynamic range. This allows high performance wavefront sensing using in-focus science data. We applied this technique in a simulation model based on the Wide Field Infrared Survey Telescope (WFIRST) Intermediate Design Reference Mission (IDRM) imager using a linear optical model with 25 field points. We demonstrate sub-thousandth-wave wavefront sensing accuracy in the presence of noise and moderate undersampling for both monochromatic and polychromatic images using 25 high-SNR target stars. Using these high-quality wavefront sensing results, we are able to generate upsampled point-spread functions (PSFs) and use them to determine PSF ellipticity to high accuracy in order to reduce the systematic impact of aberrations on the accuracy of galactic ellipticity determination for weak-lensing science.

  5. A linearized dispersion relation for orthorhombic pseudo-acoustic modeling

    Song, Xiaolei

    2012-11-04

    Wavefield extrapolation in acoustic orthorhombic anisotropic media suffers from wave-mode coupling and stability limitations in the parameter range. We introduce a linearized form of the dispersion relation for acoustic orthorhombic media to model acoustic wavefields. We apply the lowrank approximation approach to handle the corresponding space-wavenumber mixed-domain operator. Numerical experiments show that the proposed wavefield extrapolator is accurate and practically free of dispersions. Further, there is no coupling of qSv and qP waves, because we use the analytical dispersion relation. No constraints on Thomsen\\'s parameters are required for stability. The linearized expression may provide useful application for parameter estimation in orthorhombic media.

  6. Linearized vector radiative transfer model MCC++ for a spherical atmosphere

    Postylyakov, O.V.

    2004-01-01

    Application of radiative transfer models has shown that optical remote sensing requires extra characteristics of radiance field in addition to the radiance intensity itself. Simulation of spectral measurements, analysis of retrieval errors and development of retrieval algorithms are in need of derivatives of radiance with respect to atmospheric constituents under investigation. The presented vector spherical radiative transfer model MCC++ was linearized, which allows the calculation of derivatives of all elements of the Stokes vector with respect to the volume absorption coefficient simultaneously with radiance calculation. The model MCC++ employs Monte Carlo algorithm for radiative transfer simulation and takes into account aerosol and molecular scattering, gas and aerosol absorption, and Lambertian surface albedo. The model treats a spherically symmetrical atmosphere. Relation of the estimated derivatives with other forms of radiance derivatives: the weighting functions used in gas retrieval and the air mass factors used in the DOAS retrieval algorithms, is obtained. Validation of the model against other radiative models is overviewed. The computing time of the intensity for the MCC++ model is about that for radiative models treating sphericity of the atmosphere approximately and is significantly shorter than that for the full spherical models used in the comparisons. The simultaneous calculation of all derivatives (i.e. with respect to absorption in all model atmosphere layers) and the intensity is only 1.2-2 times longer than the calculation of the intensity only

  7. Effective connectivity between superior temporal gyrus and Heschl's gyrus during white noise listening: linear versus non-linear models.

    Hamid, Ka; Yusoff, An; Rahman, Mza; Mohamad, M; Hamid, Aia

    2012-04-01

    This fMRI study is about modelling the effective connectivity between Heschl's gyrus (HG) and the superior temporal gyrus (STG) in human primary auditory cortices. MATERIALS #ENTITYSTARTX00026; Ten healthy male participants were required to listen to white noise stimuli during functional magnetic resonance imaging (fMRI) scans. Statistical parametric mapping (SPM) was used to generate individual and group brain activation maps. For input region determination, two intrinsic connectivity models comprising bilateral HG and STG were constructed using dynamic causal modelling (DCM). The models were estimated and inferred using DCM while Bayesian Model Selection (BMS) for group studies was used for model comparison and selection. Based on the winning model, six linear and six non-linear causal models were derived and were again estimated, inferred, and compared to obtain a model that best represents the effective connectivity between HG and the STG, balancing accuracy and complexity. Group results indicated significant asymmetrical activation (p(uncorr) Model comparison results showed strong evidence of STG as the input centre. The winning model is preferred by 6 out of 10 participants. The results were supported by BMS results for group studies with the expected posterior probability, r = 0.7830 and exceedance probability, ϕ = 0.9823. One-sample t-tests performed on connection values obtained from the winning model indicated that the valid connections for the winning model are the unidirectional parallel connections from STG to bilateral HG (p model comparison between linear and non-linear models using BMS prefers non-linear connection (r = 0.9160, ϕ = 1.000) from which the connectivity between STG and the ipsi- and contralateral HG is gated by the activity in STG itself. We are able to demonstrate that the effective connectivity between HG and STG while listening to white noise for the respective participants can be explained by a non-linear dynamic causal model with

  8. Exactly soluble two-state quantum models with linear couplings

    Torosov, B T; Vitanov, N V

    2008-01-01

    A class of exact analytic solutions of the time-dependent Schroedinger equation is presented for a two-state quantum system coherently driven by a nonresonant external field. The coupling is a linear function of time with a finite duration and the detuning is constant. Four special models are considered in detail, namely the shark, double-shark, tent and zigzag models. The exact solution is derived by rotation of the Landau-Zener propagator at an angle of π/4 and is expressed in terms of Weber's parabolic cylinder function. Approximations for the transition probabilities are derived for all four models by using the asymptotics of the Weber function; these approximations demonstrate various effects of physical interest for each model

  9. Parametric Linear Hybrid Automata for Complex Environmental Systems Modeling

    Samar Hayat Khan Tareen

    2015-07-01

    Full Text Available Environmental systems, whether they be weather patterns or predator-prey relationships, are dependent on a number of different variables, each directly or indirectly affecting the system at large. Since not all of these factors are known, these systems take on non-linear dynamics, making it difficult to accurately predict meaningful behavioral trends far into the future. However, such dynamics do not warrant complete ignorance of different efforts to understand and model close approximations of these systems. Towards this end, we have applied a logical modeling approach to model and analyze the behavioral trends and systematic trajectories that these systems exhibit without delving into their quantification. This approach, formalized by René Thomas for discrete logical modeling of Biological Regulatory Networks (BRNs and further extended in our previous studies as parametric biological linear hybrid automata (Bio-LHA, has been previously employed for the analyses of different molecular regulatory interactions occurring across various cells and microbial species. As relationships between different interacting components of a system can be simplified as positive or negative influences, we can employ the Bio-LHA framework to represent different components of the environmental system as positive or negative feedbacks. In the present study, we highlight the benefits of hybrid (discrete/continuous modeling which lead to refinements among the fore-casted behaviors in order to find out which ones are actually possible. We have taken two case studies: an interaction of three microbial species in a freshwater pond, and a more complex atmospheric system, to show the applications of the Bio-LHA methodology for the timed hybrid modeling of environmental systems. Results show that the approach using the Bio-LHA is a viable method for behavioral modeling of complex environmental systems by finding timing constraints while keeping the complexity of the model

  10. Linear models for multivariate, time series, and spatial data

    Christensen, Ronald

    1991-01-01

    This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It consists of six additional chapters written in the same spirit as the last six chapters of the earlier book. Brief introductions are given to topics related to linear model theory. No attempt is made to give a comprehensive treatment of the topics. Such an effort would be futile. Each chapter is on a topic so broad that an in depth discussion would require a book-Iength treatment. People need to impose structure on the world in order to understand it. There is a limit to the number of unrelated facts that anyone can remem­ ber. If ideas can be put within a broad, sophisticatedly simple structure, not only are they easier to remember but often new insights become avail­ able. In fact, sophisticatedly simple models of the world may be the only ones that work. I have often heard Arnold Zellner say that, to the best of his knowledge, this is true in econometrics. The process of modeling is fundamental to understand...

  11. Linear mixed models a practical guide using statistical software

    West, Brady T; Galecki, Andrzej T

    2014-01-01

    Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM.New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggest...

  12. Tip-tilt disturbance model identification based on non-linear least squares fitting for Linear Quadratic Gaussian control

    Yang, Kangjian; Yang, Ping; Wang, Shuai; Dong, Lizhi; Xu, Bing

    2018-05-01

    We propose a method to identify tip-tilt disturbance model for Linear Quadratic Gaussian control. This identification method based on Levenberg-Marquardt method conducts with a little prior information and no auxiliary system and it is convenient to identify the tip-tilt disturbance model on-line for real-time control. This identification method makes it easy that Linear Quadratic Gaussian control runs efficiently in different adaptive optics systems for vibration mitigation. The validity of the Linear Quadratic Gaussian control associated with this tip-tilt disturbance model identification method is verified by experimental data, which is conducted in replay mode by simulation.

  13. Bayesian uncertainty quantification in linear models for diffusion MRI.

    Sjölund, Jens; Eklund, Anders; Özarslan, Evren; Herberthson, Magnus; Bånkestad, Maria; Knutsson, Hans

    2018-03-29

    Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. Modelling non-linear effects of dark energy

    Bose, Benjamin; Baldi, Marco; Pourtsidou, Alkistis

    2018-04-01

    We investigate the capabilities of perturbation theory in capturing non-linear effects of dark energy. We test constant and evolving w models, as well as models involving momentum exchange between dark energy and dark matter. Specifically, we compare perturbative predictions at 1-loop level against N-body results for four non-standard equations of state as well as varying degrees of momentum exchange between dark energy and dark matter. The interaction is modelled phenomenologically using a time dependent drag term in the Euler equation. We make comparisons at the level of the matter power spectrum and the redshift space monopole and quadrupole. The multipoles are modelled using the Taruya, Nishimichi and Saito (TNS) redshift space spectrum. We find perturbation theory does very well in capturing non-linear effects coming from dark sector interaction. We isolate and quantify the 1-loop contribution coming from the interaction and from the non-standard equation of state. We find the interaction parameter ξ amplifies scale dependent signatures in the range of scales considered. Non-standard equations of state also give scale dependent signatures within this same regime. In redshift space the match with N-body is improved at smaller scales by the addition of the TNS free parameter σv. To quantify the importance of modelling the interaction, we create mock data sets for varying values of ξ using perturbation theory. This data is given errors typical of Stage IV surveys. We then perform a likelihood analysis using the first two multipoles on these sets and a ξ=0 modelling, ignoring the interaction. We find the fiducial growth parameter f is generally recovered even for very large values of ξ both at z=0.5 and z=1. The ξ=0 modelling is most biased in its estimation of f for the phantom w=‑1.1 case.

  15. Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and Other Meteorological Measurements

    Schultz, Christopher J.; Carey, Lawrence D.; Cecil, Daniel J.; Bateman, Monte

    2012-01-01

    The lightning jump algorithm has a robust history in correlating upward trends in lightning to severe and hazardous weather occurrence. The algorithm uses the correlation between the physical principles that govern an updraft's ability to produce microphysical and kinematic conditions conducive for electrification and its role in the development of severe weather conditions. Recent work has demonstrated that the lightning jump algorithm concept holds significant promise in the operational realm, aiding in the identification of thunderstorms that have potential to produce severe or hazardous weather. However, a large amount of work still needs to be completed in spite of these positive results. The total lightning jump algorithm is not a stand-alone concept that can be used independent of other meteorological measurements, parameters, and techniques. For example, the algorithm is highly dependent upon thunderstorm tracking to build lightning histories on convective cells. Current tracking methods show that thunderstorm cell tracking is most reliable and cell histories are most accurate when radar information is incorporated with lightning data. In the absence of radar data, the cell tracking is a bit less reliable but the value added by the lightning information is much greater. For optimal application, the algorithm should be integrated with other measurements that assess storm scale properties (e.g., satellite, radar). Therefore, the recent focus of this research effort has been assessing the lightning jump's relation to thunderstorm tracking, meteorological parameters, and its potential uses in operational meteorology. Furthermore, the algorithm must be tailored for the optically-based GOES-R Geostationary Lightning Mapper (GLM), as what has been observed using Very High Frequency Lightning Mapping Array (VHF LMA) measurements will not exactly translate to what will be observed by GLM due to resolution and other instrument differences. Herein, we present some of

  16. The Extratropical Transition of Tropical Storm Cindy From a GLM, ISS LIS and GPM Perspective

    Heuscher, Lena; Gatlin, Patrick; Petersen, Walt; Liu, Chuntao; Cecil, Daniel J.

    2017-01-01

    The distribution of lightning with respect to tropical convective precipitation systems has been well established in previous studies and more recently by the successful Tropical Rainfall Measuring Mission (TRMM). However, TRMM did not provide information about precipitation features poleward of +/-38 deg latitude. Hence we focus on the evolution of lightning within extra-tropical cyclones traversing the mid-latitudes, especially its oceans. To facilitate such studies, lightning data from the Geostationary Lightning Mapper (GLM) onboard GOES-16 was combined with precipitation features obtained from the Global Precipitation Measurement (GPM) mission constellation of satellites.

  17. Spatial generalised linear mixed models based on distances.

    Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

    2016-10-01

    Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

  18. Linear system identification via backward-time observer models

    Juang, Jer-Nan; Phan, Minh

    1993-01-01

    This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.

  19. Linear mixing model applied to AVHRR LAC data

    Holben, Brent N.; Shimabukuro, Yosio E.

    1993-01-01

    A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse resolution data for global studies.

  20. Accelerating transient simulation of linear reduced order models.

    Thornquist, Heidi K.; Mei, Ting; Keiter, Eric Richard; Bond, Brad

    2011-10-01

    Model order reduction (MOR) techniques have been used to facilitate the analysis of dynamical systems for many years. Although existing model reduction techniques are capable of providing huge speedups in the frequency domain analysis (i.e. AC response) of linear systems, such speedups are often not obtained when performing transient analysis on the systems, particularly when coupled with other circuit components. Reduced system size, which is the ostensible goal of MOR methods, is often insufficient to improve transient simulation speed on realistic circuit problems. It can be shown that making the correct reduced order model (ROM) implementation choices is crucial to the practical application of MOR methods. In this report we investigate methods for accelerating the simulation of circuits containing ROM blocks using the circuit simulator Xyce.

  1. Behavioral modeling of the dominant dynamics in input-output transfer of linear(ized) circuits

    Beelen, T.G.J.; Maten, ter E.J.W.; Sihaloho, H.J.; Eijndhoven, van S.J.L.

    2010-01-01

    We present a powerful procedure for determining both the dominant dynamics of the inputoutput transfer and the corresponding most influential circuit parameters of a linear(ized) circuit. The procedure consists of several steps in which a specific (sub)problem is solved and its solution is used in

  2. Non Linear Modelling and Control of Hydraulic Actuators

    B. Šulc

    2002-01-01

    Full Text Available This paper deals with non-linear modelling and control of a differential hydraulic actuator. The nonlinear state space equations are derived from basic physical laws. They are more powerful than the transfer function in the case of linear models, and they allow the application of an object oriented approach in simulation programs. The effects of all friction forces (static, Coulomb and viscous have been modelled, and many phenomena that are usually neglected are taken into account, e.g., the static term of friction, the leakage between the two chambers and external space. Proportional Differential (PD and Fuzzy Logic Controllers (FLC have been applied in order to make a comparison by means of simulation. Simulation is performed using Matlab/Simulink, and some of the results are compared graphically. FLC is tuned in a such way that it produces a constant control signal close to its maximum (or minimum, where possible. In the case of PD control the occurrence of peaks cannot be avoided. These peaks produce a very high velocity that oversteps the allowed values.

  3. Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

    Almedeij, Jaber

    2012-01-01

    Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984

  4. A linear model for flow over complex terrain

    Frank, H P [Risoe National Lab., Wind Energy and Atmospheric Physics Dept., Roskilde (Denmark)

    1999-03-01

    A linear flow model similar to WA{sup s}P or LINCOM has been developed. Major differences are an isentropic temperature equation which allows internal gravity waves, and vertical advection of the shear of the mean flow. The importance of these effects are illustrated by examples. Resource maps are calculated from a distribution of geostrophic winds and stratification for Pyhaetunturi Fell in northern Finland and Acqua Spruzza in Italy. Stratification becomes important if the inverse Froude number formulated with the width of the hill becomes of order one or greater. (au) EU-JOULE-3. 16 refs.

  5. Linear-quadratic model predictions for tumor control probability

    Yaes, R.J.

    1987-01-01

    Sigmoid dose-response curves for tumor control are calculated from the linear-quadratic model parameters α and Β, obtained from human epidermoid carcinoma cell lines, and are much steeper than the clinical dose-response curves for head and neck cancers. One possible explanation is the presence of small radiation-resistant clones arising from mutations in an initially homogeneous tumor. Using the mutation theory of Delbruck and Luria and of Goldie and Coldman, the authors discuss the implications of such radiation-resistant clones for clinical radiation therapy

  6. Inventory model using bayesian dynamic linear model for demand forecasting

    Marisol Valencia-Cárdenas

    2014-12-01

    Full Text Available An important factor of manufacturing process is the inventory management of terminated product. Constantly, industry is looking for better alternatives to establish an adequate plan of production and stored quantities, with optimal cost, getting quantities in a time horizon, which permits to define resources and logistics with anticipation, needed to distribute products on time. Total absence of historical data, required by many statistical models to forecast, demands the search for other kind of accurate techniques. This work presents an alternative that not only permits to forecast, in an adjusted way, but also, to provide optimal quantities to produce and store with an optimal cost, using Bayesian statistics. The proposal is illustrated with real data. Palabras clave: estadística bayesiana, optimización, modelo de inventarios, modelo lineal dinámico bayesiano. Keywords: Bayesian statistics, opti

  7. Phenomenology of non-minimal supersymmetric models at linear colliders

    Porto, Stefano

    2015-06-01

    The focus of this thesis is on the phenomenology of several non-minimal supersymmetric models in the context of future linear colliders (LCs). Extensions of the minimal supersymmetric Standard Model (MSSM) may accommodate the observed Higgs boson mass at about 125 GeV in a more natural way than the MSSM, with a richer phenomenology. We consider both F-term extensions of the MSSM, as for instance the non-minimal supersymmetric Standard Model (NMSSM), as well as D-terms extensions arising at low energies from gauge extended supersymmetric models. The NMSSM offers a solution to the μ-problem with an additional gauge singlet supermultiplet. The enlarged neutralino sector of the NMSSM can be accurately studied at a LC and used to distinguish the model from the MSSM. We show that exploiting the power of the polarised beams of a LC can be used to reconstruct the neutralino and chargino sector and eventually distinguish the NMSSM even considering challenging scenarios that resemble the MSSM. Non-decoupling D-terms extensions of the MSSM can raise the tree-level Higgs mass with respect to the MSSM. This is done through additional contributions to the Higgs quartic potential, effectively generated by an extended gauge group. We study how this can happen and we show how these additional non-decoupling D-terms affect the SM-like Higgs boson couplings to fermions and gauge bosons. We estimate how the deviations from the SM couplings can be spotted at the Large Hadron Collider (LHC) and at the International Linear Collider (ILC), showing how the ILC would be suitable for the model identication. Since our results prove that a linear collider is a fundamental machine for studying supersymmetry phenomenology at a high level of precision, we argue that also a thorough comprehension of the physics at the interaction point (IP) of a LC is needed. Therefore, we finally consider the possibility of observing intense electromagnetic field effects and nonlinear quantum electrodynamics

  8. Non-Linear Slosh Damping Model Development and Validation

    Yang, H. Q.; West, Jeff

    2015-01-01

    Propellant tank slosh dynamics are typically represented by a mechanical model of spring mass damper. This mechanical model is then included in the equation of motion of the entire vehicle for Guidance, Navigation and Control (GN&C) analysis. For a partially-filled smooth wall propellant tank, the critical damping based on classical empirical correlation is as low as 0.05%. Due to this low value of damping, propellant slosh is potential sources of disturbance critical to the stability of launch and space vehicles. It is postulated that the commonly quoted slosh damping is valid only under the linear regime where the slosh amplitude is small. With the increase of slosh amplitude, the critical damping value should also increase. If this nonlinearity can be verified and validated, the slosh stability margin can be significantly improved, and the level of conservatism maintained in the GN&C analysis can be lessened. The purpose of this study is to explore and to quantify the dependence of slosh damping with slosh amplitude. Accurately predicting the extremely low damping value of a smooth wall tank is very challenging for any Computational Fluid Dynamics (CFD) tool. One must resolve thin boundary layers near the wall and limit numerical damping to minimum. This computational study demonstrates that with proper grid resolution, CFD can indeed accurately predict the low damping physics from smooth walls under the linear regime. Comparisons of extracted damping values with experimental data for different tank sizes show very good agreements. Numerical simulations confirm that slosh damping is indeed a function of slosh amplitude. When slosh amplitude is low, the damping ratio is essentially constant, which is consistent with the empirical correlation. Once the amplitude reaches a critical value, the damping ratio becomes a linearly increasing function of the slosh amplitude. A follow-on experiment validated the developed nonlinear damping relationship. This discovery can

  9. Non linear permanent magnets modelling with the finite element method

    Chavanne, J.; Meunier, G.; Sabonnadiere, J.C.

    1989-01-01

    In order to perform the calculation of permanent magnets with the finite element method, it is necessary to take into account the anisotropic behaviour of hard magnetic materials (Ferrites, NdFeB, SmCo5). In linear cases, the permeability of permanent magnets is a tensor. This one is fully described with the permeabilities parallel and perpendicular to the easy axis of the magnet. In non linear cases, the model uses a texture function which represents the distribution of the local easy axis of the cristallytes of the magnet. This function allows a good representation of the angular dependance of the coercitive field of the magnet. As a result, it is possible to express the magnetic induction B and the tensor as functions of the field and the texture parameter. This model has been implemented in the software FLUX3D where the tensor is used for the Newton-Raphson procedure. 3D demagnetization of a ferrite magnet by a NdFeB magnet is a suitable representative example. They analyze the results obtained for an ideally oriented ferrite magnet and a real one using a measured texture parameter

  10. Linear collider signal of anomaly mediated supersymmetry breaking model

    Ghosh Dilip Kumar; Kundu, Anirban; Roy, Probir; Roy, Sourov

    2001-01-01

    Though the minimal model of anomaly mediated supersymmetry breaking has been significantly constrained by recent experimental and theoretical work, there are still allowed regions of the parameter space for moderate to large values of tan β. We show that these regions will be comprehensively probed in a √s = 1 TeV e + e - linear collider. Diagnostic signals to this end are studied by zeroing in on a unique and distinct feature of a large class of models in this genre: a neutral winolike Lightest Supersymmetric Particle closely degenerate in mass with a winolike chargino. The pair production processes e + e - → e tilde L ± e tilde L ± , e tilde R ± e tilde R ± , e tilde L ± e tilde R ± , ν tilde anti ν tilde, χ tilde 1 0 χ tilde 2 0 , χ tilde 2 0 χ tilde 2 0 are all considered at √s = 1 TeV corresponding to the proposed TESLA linear collider in two natural categories of mass ordering in the sparticle spectra. The signals analysed comprise multiple combinations of fast charged leptons (any of which can act as the trigger) plus displaced vertices X D (any of which can be identified by a heavy ionizing track terminating in the detector) and/or associated soft pions with characteristic momentum distributions. (author)

  11. Linear versus quadratic portfolio optimization model with transaction cost

    Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah

    2014-06-01

    Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.

  12. Probabilistic model of ligaments and tendons: Quasistatic linear stretching

    Bontempi, M.

    2009-03-01

    Ligaments and tendons have a significant role in the musculoskeletal system and are frequently subjected to injury. This study presents a model of collagen fibers, based on the study of a statistical distribution of fibers when they are subjected to quasistatic linear stretching. With respect to other methodologies, this model is able to describe the behavior of the bundle using less ad hoc hypotheses and is able to describe all the quasistatic stretch-load responses of the bundle, including the yield and failure regions described in the literature. It has two other important results: the first is that it is able to correlate the mechanical behavior of the bundle with its internal structure, and it suggests a methodology to deduce the fibers population distribution directly from the tensile-test data. The second is that it can follow fibers’ structure evolution during the stretching and it is possible to study the internal adaptation of fibers in physiological and pathological conditions.

  13. Linear mixing model applied to coarse resolution satellite data

    Holben, Brent N.; Shimabukuro, Yosio E.

    1992-01-01

    A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies.

  14. Relating Cohesive Zone Model to Linear Elastic Fracture Mechanics

    Wang, John T.

    2010-01-01

    The conditions required for a cohesive zone model (CZM) to predict a failure load of a cracked structure similar to that obtained by a linear elastic fracture mechanics (LEFM) analysis are investigated in this paper. This study clarifies why many different phenomenological cohesive laws can produce similar fracture predictions. Analytical results for five cohesive zone models are obtained, using five different cohesive laws that have the same cohesive work rate (CWR-area under the traction-separation curve) but different maximum tractions. The effect of the maximum traction on the predicted cohesive zone length and the remote applied load at fracture is presented. Similar to the small scale yielding condition for an LEFM analysis to be valid. the cohesive zone length also needs to be much smaller than the crack length. This is a necessary condition for a CZM to obtain a fracture prediction equivalent to an LEFM result.

  15. Locally supersymmetric D=3 non-linear sigma models

    Wit, B. de; Tollsten, A.K.; Nicolai, H.

    1993-01-01

    We study non-linear sigma models with N local supersymmetries in three space-time dimensions. For N=1 and 2 the target space of these models is riemannian or Kaehler, respectively. All N>2 theories are associated with Einstein spaces. For N=3 the target space is quaternionic, while for N=4 it generally decomposes, into two separate quaternionic spaces, associated with inequivalent supermultiplets. For N=5, 6, 8 there is a unique (symmetric) space for any given number of supermultiplets. Beyond that there are only theories based on a single supermultiplet for N=9, 10, 12 and 16, associated with coset spaces with the exceptional isometry groups F 4(-20) , E 6(-14) , E 7(-5) and E 8(+8) , respectively. For N=3 and N ≥ 5 the D=2 theories obtained by dimensional reduction are two-loop finite. (orig.)

  16. Explicit estimating equations for semiparametric generalized linear latent variable models

    Ma, Yanyuan

    2010-07-05

    We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.

  17. Synthetic Domain Theory and Models of Linear Abadi & Plotkin Logic

    Møgelberg, Rasmus Ejlers; Birkedal, Lars; Rosolini, Guiseppe

    2008-01-01

    Plotkin suggested using a polymorphic dual intuitionistic/linear type theory (PILLY) as a metalanguage for parametric polymorphism and recursion. In recent work the first two authors and R.L. Petersen have defined a notion of parametric LAPL-structure, which are models of PILLY, in which one can...... reason using parametricity and, for example, solve a large class of domain equations, as suggested by Plotkin.In this paper, we show how an interpretation of a strict version of Bierman, Pitts and Russo's language Lily into synthetic domain theory presented by Simpson and Rosolini gives rise...... to a parametric LAPL-structure. This adds to the evidence that the notion of LAPL-structure is a general notion, suitable for treating many different parametric models, and it provides formal proofs of consequences of parametricity expected to hold for the interpretation. Finally, we show how these results...

  18. Solving large mixed linear models using preconditioned conjugate gradient iteration.

    Strandén, I; Lidauer, M

    1999-12-01

    Continuous evaluation of dairy cattle with a random regression test-day model requires a fast solving method and algorithm. A new computing technique feasible in Jacobi and conjugate gradient based iterative methods using iteration on data is presented. In the new computing technique, the calculations in multiplication of a vector by a matrix were recorded to three steps instead of the commonly used two steps. The three-step method was implemented in a general mixed linear model program that used preconditioned conjugate gradient iteration. Performance of this program in comparison to other general solving programs was assessed via estimation of breeding values using univariate, multivariate, and random regression test-day models. Central processing unit time per iteration with the new three-step technique was, at best, one-third that needed with the old technique. Performance was best with the test-day model, which was the largest and most complex model used. The new program did well in comparison to other general software. Programs keeping the mixed model equations in random access memory required at least 20 and 435% more time to solve the univariate and multivariate animal models, respectively. Computations of the second best iteration on data took approximately three and five times longer for the animal and test-day models, respectively, than did the new program. Good performance was due to fast computing time per iteration and quick convergence to the final solutions. Use of preconditioned conjugate gradient based methods in solving large breeding value problems is supported by our findings.

  19. Direction of Effects in Multiple Linear Regression Models.

    Wiedermann, Wolfgang; von Eye, Alexander

    2015-01-01

    Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

  20. Linear model applied to the evaluation of pharmaceutical stability data

    Renato Cesar Souza

    2013-09-01

    Full Text Available The expiry date on the packaging of a product gives the consumer the confidence that the product will retain its identity, content, quality and purity throughout the period of validity of the drug. The definition of this term in the pharmaceutical industry is based on stability data obtained during the product registration. By the above, this work aims to apply the linear regression according to the guideline ICH Q1E, 2003, to evaluate some aspects of a product undergoing in a registration phase in Brazil. With this propose, the evaluation was realized with the development center of a multinational company in Brazil, with samples of three different batches composed by two active principal ingredients in two different packages. Based on the preliminary results obtained, it was possible to observe the difference of degradation tendency of the product in two different packages and the relationship between the variables studied, added knowledge so new models of linear equations can be applied and developed for other products.

  1. Fourth standard model family neutrino at future linear colliders

    Ciftci, A.K.; Ciftci, R.; Sultansoy, S.

    2005-01-01

    It is known that flavor democracy favors the existence of the fourth standard model (SM) family. In order to give nonzero masses for the first three-family fermions flavor democracy has to be slightly broken. A parametrization for democracy breaking, which gives the correct values for fundamental fermion masses and, at the same time, predicts quark and lepton Cabibbo-Kobayashi-Maskawa (CKM) matrices in a good agreement with the experimental data, is proposed. The pair productions of the fourth SM family Dirac (ν 4 ) and Majorana (N 1 ) neutrinos at future linear colliders with √(s)=500 GeV, 1 TeV, and 3 TeV are considered. The cross section for the process e + e - →ν 4 ν 4 (N 1 N 1 ) and the branching ratios for possible decay modes of the both neutrinos are determined. The decays of the fourth family neutrinos into muon channels (ν 4 (N 1 )→μ ± W ± ) provide cleanest signature at e + e - colliders. Meanwhile, in our parametrization this channel is dominant. W bosons produced in decays of the fourth family neutrinos will be seen in detector as either di-jets or isolated leptons. As an example, we consider the production of 200 GeV mass fourth family neutrinos at √(s)=500 GeV linear colliders by taking into account di-muon plus four jet events as signatures

  2. Influence of the void fraction in the linear reactivity model

    Castillo, J.A.; Ramirez, J.R.; Alonso, G.

    2003-01-01

    The linear reactivity model allows the multicycle analysis in pressurized water reactors in a simple and quick way. In the case of the Boiling water reactors the void fraction it varies axially from 0% of voids in the inferior part of the fuel assemblies until approximately 70% of voids to the exit of the same ones. Due to this it is very important the determination of the average void fraction during different stages of the reactor operation to predict the burnt one appropriately of the same ones to inclination of the pattern of linear reactivity. In this work a pursuit is made of the profile of power for different steps of burnt of a typical operation cycle of a Boiling water reactor. Starting from these profiles it builds an algorithm that allows to determine the voids profile and this way to obtain the average value of the same one. The results are compared against those reported by the CM-PRESTO code that uses another method to carry out this calculation. Finally, the range in which is the average value of the void fraction during a typical cycle is determined and an estimate of the impact that it would have the use of this value in the prediction of the reactivity produced by the fuel assemblies is made. (Author)

  3. Characteristics and Properties of a Simple Linear Regression Model

    Kowal Robert

    2016-12-01

    Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Despite the passage of time, it continues to raise interest both from the theoretical side as well as from the application side. One of the many fundamental questions in the model concerns determining derivative characteristics and studying the properties existing in their scope, referring to the first of these aspects. The literature of the subject provides several classic solutions in that regard. In the paper, a completely new design is proposed, based on the direct application of variance and its properties, resulting from the non-correlation of certain estimators with the mean, within the scope of which some fundamental dependencies of the model characteristics are obtained in a much more compact manner. The apparatus allows for a simple and uniform demonstration of multiple dependencies and fundamental properties in the model, and it does it in an intuitive manner. The results were obtained in a classic, traditional area, where everything, as it might seem, has already been thoroughly studied and discovered.

  4. A simple non-linear model of immune response

    Gutnikov, Sergei; Melnikov, Yuri

    2003-01-01

    It is still unknown why the adaptive immune response in the natural immune system based on clonal proliferation of lymphocytes requires interaction of at least two different cell types with the same antigen. We present a simple mathematical model illustrating that the system with separate types of cells for antigen recognition and patogen destruction provides more robust adaptive immunity than the system where just one cell type is responsible for both recognition and destruction. The model is over-simplified as we did not have an intention of describing the natural immune system. However, our model provides a tool for testing the proposed approach through qualitative analysis of the immune system dynamics in order to construct more sophisticated models of the immune systems that exist in the living nature. It also opens a possibility to explore specific features of highly non-linear dynamics in nature-inspired computational paradigms like artificial immune systems and immunocomputing . We expect this paper to be of interest not only for mathematicians but also for biologists; therefore we made effort to explain mathematics in sufficient detail for readers without professional mathematical background

  5. Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models.

    Nolte, Daniel; Tsang, Chui Kit; Zhang, Kai Yu; Ding, Ziyun; Kedgley, Angela E; Bull, Anthony M J

    2016-10-03

    Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.

  6. A Non-linear Stochastic Model for an Office Building with Air Infiltration

    Thavlov, Anders; Madsen, Henrik

    2015-01-01

    This paper presents a non-linear heat dynamic model for a multi-room office building with air infiltration. Several linear and non-linear models, with and without air infiltration, are investigated and compared. The models are formulated using stochastic differential equations and the model...

  7. Distributing Correlation Coefficients of Linear Structure-Activity/Property Models

    Sorana D. BOLBOACA

    2011-12-01

    Full Text Available Quantitative structure-activity/property relationships are mathematical relationships linking chemical structure and activity/property in a quantitative manner. These in silico approaches are frequently used to reduce animal testing and risk-assessment, as well as to increase time- and cost-effectiveness in characterization and identification of active compounds. The aim of our study was to investigate the pattern of correlation coefficients distribution associated to simple linear relationships linking the compounds structure with their activities. A set of the most common ordnance compounds found at naval facilities with a limited data set with a range of toxicities on aquatic ecosystem and a set of seven properties was studied. Statistically significant models were selected and investigated. The probability density function of the correlation coefficients was investigated using a series of possible continuous distribution laws. Almost 48% of the correlation coefficients proved fit Beta distribution, 40% fit Generalized Pareto distribution, and 12% fit Pert distribution.

  8. Modeling and analysis of linearized wheel-rail contact dynamics

    Soomro, Z.

    2014-01-01

    The dynamics of the railway vehicles are nonlinear and depend upon several factors including vehicle speed, normal load and adhesion level. The presence of contaminants on the railway track makes them unpredictable too. Therefore in order to develop an effective control strategy it is important to analyze the effect of each factor on dynamic response thoroughly. In this paper a linearized model of a railway wheel-set is developed and is later analyzed by varying the speed and adhesion level by keeping the normal load constant. A wheel-set is the wheel-axle assembly of a railroad car. Patch contact is the study of the deformation of solids that touch each other at one or more points. (author)

  9. Human visual modeling and image deconvolution by linear filtering

    Larminat, P. de; Barba, D.; Gerber, R.; Ronsin, J.

    1978-01-01

    The problem is the numerical restoration of images degraded by passing through a known and spatially invariant linear system, and by the addition of a stationary noise. We propose an improvement of the Wiener's filter to allow the restoration of such images. This improvement allows to reduce the important drawbacks of classical Wiener's filter: the voluminous data processing, the lack of consideration of the vision's characteristivs which condition the perception by the observer of the restored image. In a first paragraph, we describe the structure of the visual detection system and a modelling method of this system. In the second paragraph we explain a restoration method by Wiener filtering that takes the visual properties into account and that can be adapted to the local properties of the image. Then the results obtained on TV images or scintigrams (images obtained by a gamma-camera) are commented [fr

  10. Convergence diagnostics for Eigenvalue problems with linear regression model

    Shi, Bo; Petrovic, Bojan

    2011-01-01

    Although the Monte Carlo method has been extensively used for criticality/Eigenvalue problems, a reliable, robust, and efficient convergence diagnostics method is still desired. Most methods are based on integral parameters (multiplication factor, entropy) and either condense the local distribution information into a single value (e.g., entropy) or even disregard it. We propose to employ the detailed cycle-by-cycle local flux evolution obtained by using mesh tally mechanism to assess the source and flux convergence. By applying a linear regression model to each individual mesh in a mesh tally for convergence diagnostics, a global convergence criterion can be obtained. We exemplify this method on two problems and obtain promising diagnostics results. (author)

  11. A Dynamic Linear Modeling Approach to Public Policy Change

    Loftis, Matthew; Mortensen, Peter Bjerre

    2017-01-01

    Theories of public policy change, despite their differences, converge on one point of strong agreement. The relationship between policy and its causes can and does change over time. This consensus yields numerous empirical implications, but our standard analytical tools are inadequate for testing...... them. As a result, the dynamic and transformative relationships predicted by policy theories have been left largely unexplored in time-series analysis of public policy. This paper introduces dynamic linear modeling (DLM) as a useful statistical tool for exploring time-varying relationships in public...... policy. The paper offers a detailed exposition of the DLM approach and illustrates its usefulness with a time series analysis of U.S. defense policy from 1957-2010. The results point the way for a new attention to dynamics in the policy process and the paper concludes with a discussion of how...

  12. Baryon and meson phenomenology in the extended Linear Sigma Model

    Giacosa, Francesco; Habersetzer, Anja; Teilab, Khaled; Eshraim, Walaa; Divotgey, Florian; Olbrich, Lisa; Gallas, Susanna; Wolkanowski, Thomas; Janowski, Stanislaus; Heinz, Achim; Deinet, Werner; Rischke, Dirk H. [Institute for Theoretical Physics, J. W. Goethe University, Max-von-Laue-Str. 1, 60438 Frankfurt am Main (Germany); Kovacs, Peter; Wolf, Gyuri [Institute for Particle and Nuclear Physics, Wigner Research Center for Physics, Hungarian Academy of Sciences, H-1525 Budapest (Hungary); Parganlija, Denis [Institute for Theoretical Physics, Vienna University of Technology, Wiedner Hauptstr. 8-10, A-1040 Vienna (Austria)

    2014-07-01

    The vacuum phenomenology obtained within the so-called extended Linear Sigma Model (eLSM) is presented. The eLSM Lagrangian is constructed by including from the very beginning vector and axial-vector d.o.f., and by requiring dilatation invariance and chiral symmetry. After a general introduction of the approach, particular attention is devoted to the latest results. In the mesonic sector the strong decays of the scalar and the pseudoscalar glueballs, the weak decays of the tau lepton into vector and axial-vector mesons, and the description of masses and decays of charmed mesons are shown. In the baryonic sector the omega production in proton-proton scattering and the inclusion of baryons with strangeness are described.

  13. Non Abelian T-duality in Gauged Linear Sigma Models

    Bizet, Nana Cabo; Martínez-Merino, Aldo; Zayas, Leopoldo A. Pando; Santos-Silva, Roberto

    2018-04-01

    Abelian T-duality in Gauged Linear Sigma Models (GLSM) forms the basis of the physical understanding of Mirror Symmetry as presented by Hori and Vafa. We consider an alternative formulation of Abelian T-duality on GLSM's as a gauging of a global U(1) symmetry with the addition of appropriate Lagrange multipliers. For GLSMs with Abelian gauge groups and without superpotential we reproduce the dual models introduced by Hori and Vafa. We extend the construction to formulate non-Abelian T-duality on GLSMs with global non-Abelian symmetries. The equations of motion that lead to the dual model are obtained for a general group, they depend in general on semi-chiral superfields; for cases such as SU(2) they depend on twisted chiral superfields. We solve the equations of motion for an SU(2) gauged group with a choice of a particular Lie algebra direction of the vector superfield. This direction covers a non-Abelian sector that can be described by a family of Abelian dualities. The dual model Lagrangian depends on twisted chiral superfields and a twisted superpotential is generated. We explore some non-perturbative aspects by making an Ansatz for the instanton corrections in the dual theories. We verify that the effective potential for the U(1) field strength in a fixed configuration on the original theory matches the one of the dual theory. Imposing restrictions on the vector superfield, more general non-Abelian dual models are obtained. We analyze the dual models via the geometry of their susy vacua.

  14. A comparison of linear interpolation models for iterative CT reconstruction.

    Hahn, Katharina; Schöndube, Harald; Stierstorfer, Karl; Hornegger, Joachim; Noo, Frédéric

    2016-12-01

    Recent reports indicate that model-based iterative reconstruction methods may improve image quality in computed tomography (CT). One difficulty with these methods is the number of options available to implement them, including the selection of the forward projection model and the penalty term. Currently, the literature is fairly scarce in terms of guidance regarding this selection step, whereas these options impact image quality. Here, the authors investigate the merits of three forward projection models that rely on linear interpolation: the distance-driven method, Joseph's method, and the bilinear method. The authors' selection is motivated by three factors: (1) in CT, linear interpolation is often seen as a suitable trade-off between discretization errors and computational cost, (2) the first two methods are popular with manufacturers, and (3) the third method enables assessing the importance of a key assumption in the other methods. One approach to evaluate forward projection models is to inspect their effect on discretized images, as well as the effect of their transpose on data sets, but significance of such studies is unclear since the matrix and its transpose are always jointly used in iterative reconstruction. Another approach is to investigate the models in the context they are used, i.e., together with statistical weights and a penalty term. Unfortunately, this approach requires the selection of a preferred objective function and does not provide clear information on features that are intrinsic to the model. The authors adopted the following two-stage methodology. First, the authors analyze images that progressively include components of the singular value decomposition of the model in a reconstructed image without statistical weights and penalty term. Next, the authors examine the impact of weights and penalty on observed differences. Image quality metrics were investigated for 16 different fan-beam imaging scenarios that enabled probing various aspects

  15. Optimizing Biorefinery Design and Operations via Linear Programming Models

    Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric

    2017-03-28

    The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for

  16. A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments.

    Pinti, Paola; Merla, Arcangelo; Aichelburg, Clarisse; Lind, Frida; Power, Sarah; Swingler, Elizabeth; Hamilton, Antonia; Gilbert, Sam; Burgess, Paul W; Tachtsidis, Ilias

    2017-07-15

    Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that "brain-first" rather than "behaviour-first" analysis is

  17. Linear models for sound from supersonic reacting mixing layers

    Chary, P. Shivakanth; Samanta, Arnab

    2016-12-01

    We perform a linearized reduced-order modeling of the aeroacoustic sound sources in supersonic reacting mixing layers to explore their sensitivities to some of the flow parameters in radiating sound. Specifically, we investigate the role of outer modes as the effective flow compressibility is raised, when some of these are expected to dominate over the traditional Kelvin-Helmholtz (K-H) -type central mode. Although the outer modes are known to be of lesser importance in the near-field mixing, how these radiate to the far-field is uncertain, on which we focus. On keeping the flow compressibility fixed, the outer modes are realized via biasing the respective mean densities of the fast (oxidizer) or slow (fuel) side. Here the mean flows are laminar solutions of two-dimensional compressible boundary layers with an imposed composite (turbulent) spreading rate, which we show to significantly alter the growth of instability waves by saturating them earlier, similar to in nonlinear calculations, achieved here via solving the linear parabolized stability equations. As the flow parameters are varied, instability of the slow modes is shown to be more sensitive to heat release, potentially exceeding equivalent central modes, as these modes yield relatively compact sound sources with lesser spreading of the mixing layer, when compared to the corresponding fast modes. In contrast, the radiated sound seems to be relatively unaffected when the mixture equivalence ratio is varied, except for a lean mixture which is shown to yield a pronounced effect on the slow mode radiation by reducing its modal growth.

  18. Linear programming model can explain respiration of fermentation products

    Möller, Philip; Liu, Xiaochen; Schuster, Stefan

    2018-01-01

    Many differentiated cells rely primarily on mitochondrial oxidative phosphorylation for generating energy in the form of ATP needed for cellular metabolism. In contrast most tumor cells instead rely on aerobic glycolysis leading to lactate to about the same extent as on respiration. Warburg found that cancer cells to support oxidative phosphorylation, tend to ferment glucose or other energy source into lactate even in the presence of sufficient oxygen, which is an inefficient way to generate ATP. This effect also occurs in striated muscle cells, activated lymphocytes and microglia, endothelial cells and several mammalian cell types, a phenomenon termed the “Warburg effect”. The effect is paradoxical at first glance because the ATP production rate of aerobic glycolysis is much slower than that of respiration and the energy demands are better to be met by pure oxidative phosphorylation. We tackle this question by building a minimal model including three combined reactions. The new aspect in extension to earlier models is that we take into account the possible uptake and oxidation of the fermentation products. We examine the case where the cell can allocate protein on several enzymes in a varying distribution and model this by a linear programming problem in which the objective is to maximize the ATP production rate under different combinations of constraints on enzymes. Depending on the cost of reactions and limitation of the substrates, this leads to pure respiration, pure fermentation, and a mixture of respiration and fermentation. The model predicts that fermentation products are only oxidized when glucose is scarce or its uptake is severely limited. PMID:29415045

  19. Linear programming model can explain respiration of fermentation products.

    Möller, Philip; Liu, Xiaochen; Schuster, Stefan; Boley, Daniel

    2018-01-01

    Many differentiated cells rely primarily on mitochondrial oxidative phosphorylation for generating energy in the form of ATP needed for cellular metabolism. In contrast most tumor cells instead rely on aerobic glycolysis leading to lactate to about the same extent as on respiration. Warburg found that cancer cells to support oxidative phosphorylation, tend to ferment glucose or other energy source into lactate even in the presence of sufficient oxygen, which is an inefficient way to generate ATP. This effect also occurs in striated muscle cells, activated lymphocytes and microglia, endothelial cells and several mammalian cell types, a phenomenon termed the "Warburg effect". The effect is paradoxical at first glance because the ATP production rate of aerobic glycolysis is much slower than that of respiration and the energy demands are better to be met by pure oxidative phosphorylation. We tackle this question by building a minimal model including three combined reactions. The new aspect in extension to earlier models is that we take into account the possible uptake and oxidation of the fermentation products. We examine the case where the cell can allocate protein on several enzymes in a varying distribution and model this by a linear programming problem in which the objective is to maximize the ATP production rate under different combinations of constraints on enzymes. Depending on the cost of reactions and limitation of the substrates, this leads to pure respiration, pure fermentation, and a mixture of respiration and fermentation. The model predicts that fermentation products are only oxidized when glucose is scarce or its uptake is severely limited.

  20. Transport coefficients from SU(3) Polyakov linearmodel

    Tawfik, A.; Diab, A.

    2015-01-01

    In the mean field approximation, the grand potential of SU(3) Polyakov linearmodel (PLSM) is analyzed for the order parameter of the light and strange chiral phase-transitions, σ l and σ s , respectively, and for the deconfinement order parameters φ and φ*. Furthermore, the subtracted condensate Δ l,s and the chiral order-parameters M b are compared with lattice QCD calculations. By using the dynamical quasiparticle model (DQPM), which can be considered as a system of noninteracting massive quasiparticles, we have evaluated the decay width and the relaxation time of quarks and gluons. In the framework of LSM and with Polyakov loop corrections included, the interaction measure Δ/T 4 , the specific heat c v and speed of sound squared c s 2 have been determined, as well as the temperature dependence of the normalized quark number density n q /T 3 and the quark number susceptibilities χ q /T 2 at various values of the baryon chemical potential. The electric and heat conductivity, σ e and κ, and the bulk and shear viscosities normalized to the thermal entropy, ζ/s and η/s, are compared with available results of lattice QCD calculations.

  1. Generalized Functional Linear Models With Semiparametric Single-Index Interactions

    Li, Yehua

    2010-06-01

    We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.

  2. Sparse linear models: Variational approximate inference and Bayesian experimental design

    Seeger, Matthias W

    2009-01-01

    A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

  3. Sparse linear models: Variational approximate inference and Bayesian experimental design

    Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)

    2009-12-01

    A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

  4. Generalized Functional Linear Models With Semiparametric Single-Index Interactions

    Li, Yehua; Wang, Naisyin; Carroll, Raymond J.

    2010-01-01

    We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.

  5. Modeling containment of large wildfires using generalized linear mixed-model analysis

    Mark Finney; Isaac C. Grenfell; Charles W. McHugh

    2009-01-01

    Billions of dollars are spent annually in the United States to contain large wildland fires, but the factors contributing to suppression success remain poorly understood. We used a regression model (generalized linear mixed-model) to model containment probability of individual fires, assuming that containment was a repeated-measures problem (fixed effect) and...

  6. Decomposing interactions using GLM in combination with the COMPARE, LMATRIX and MMATRIX subcommands in SPSS

    Glen T. Howell

    2012-02-01

    Full Text Available In this tutorial, we provide researchers who use SPSS step-by-step instructions for decomposing interactions when a three-way ANOVA is conducted using the GLM procedure. We start with a demonstration of how a two-way interaction can be decomposed using the COMPARE subcommand in combination with syntax. Then, we provide instructions with examples for conducting simple interaction and second-order simple effects analyses for three-way ANOVAs with between-subjects, within-subjects, and mixed between- and within-subjects variables using the LMATRIX or MMATRIX subcommands. Provided in Appendices are general rules that can be used to derive design-specific LMATRIX and MMATRIX subcommands.

  7. Stochastic linear hybrid systems: Modeling, estimation, and application

    Seah, Chze Eng

    Hybrid systems are dynamical systems which have interacting continuous state and discrete state (or mode). Accurate modeling and state estimation of hybrid systems are important in many applications. We propose a hybrid system model, known as the Stochastic Linear Hybrid System (SLHS), to describe hybrid systems with stochastic linear system dynamics in each mode and stochastic continuous-state-dependent mode transitions. We then develop a hybrid estimation algorithm, called the State-Dependent-Transition Hybrid Estimation (SDTHE) algorithm, to estimate the continuous state and discrete state of the SLHS from noisy measurements. It is shown that the SDTHE algorithm is more accurate or more computationally efficient than existing hybrid estimation algorithms. Next, we develop a performance analysis algorithm to evaluate the performance of the SDTHE algorithm in a given operating scenario. We also investigate sufficient conditions for the stability of the SDTHE algorithm. The proposed SLHS model and SDTHE algorithm are illustrated to be useful in several applications. In Air Traffic Control (ATC), to facilitate implementations of new efficient operational concepts, accurate modeling and estimation of aircraft trajectories are needed. In ATC, an aircraft's trajectory can be divided into a number of flight modes. Furthermore, as the aircraft is required to follow a given flight plan or clearance, its flight mode transitions are dependent of its continuous state. However, the flight mode transitions are also stochastic due to navigation uncertainties or unknown pilot intents. Thus, we develop an aircraft dynamics model in ATC based on the SLHS. The SDTHE algorithm is then used in aircraft tracking applications to estimate the positions/velocities of aircraft and their flight modes accurately. Next, we develop an aircraft conformance monitoring algorithm to detect any deviations of aircraft trajectories in ATC that might compromise safety. In this application, the SLHS

  8. Identification of an Equivalent Linear Model for a Non-Linear Time-Variant RC-Structure

    Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

    are investigated and compared with ARMAX models used on a running window. The techniques are evaluated using simulated data generated by the non-linear finite element program SARCOF modeling a 10-storey 3-bay concrete structure subjected to amplitude modulated Gaussian white noise filtered through a Kanai......This paper considers estimation of the maximum softening for a RC-structure subjected to earthquake excitation. The so-called Maximum Softening damage indicator relates the global damage state of the RC-structure to the relative decrease of the fundamental eigenfrequency in an equivalent linear...

  9. Second-order kinetic model for the sorption of cadmium onto tree fern: a comparison of linear and non-linear methods.

    Ho, Yuh-Shan

    2006-01-01

    A comparison was made of the linear least-squares method and a trial-and-error non-linear method of the widely used pseudo-second-order kinetic model for the sorption of cadmium onto ground-up tree fern. Four pseudo-second-order kinetic linear equations are discussed. Kinetic parameters obtained from the four kinetic linear equations using the linear method differed but they were the same when using the non-linear method. A type 1 pseudo-second-order linear kinetic model has the highest coefficient of determination. Results show that the non-linear method may be a better way to obtain the desired parameters.

  10. Comparison of linear, skewed-linear, and proportional hazard models for the analysis of lambing interval in Ripollesa ewes.

    Casellas, J; Bach, R

    2012-06-01

    Lambing interval is a relevant reproductive indicator for sheep populations under continuous mating systems, although there is a shortage of selection programs accounting for this trait in the sheep industry. Both the historical assumption of small genetic background and its unorthodox distribution pattern have limited its implementation as a breeding objective. In this manuscript, statistical performances of 3 alternative parametrizations [i.e., symmetric Gaussian mixed linear (GML) model, skew-Gaussian mixed linear (SGML) model, and piecewise Weibull proportional hazard (PWPH) model] have been compared to elucidate the preferred methodology to handle lambing interval data. More specifically, flock-by-flock analyses were performed on 31,986 lambing interval records (257.3 ± 0.2 d) from 6 purebred Ripollesa flocks. Model performances were compared in terms of deviance information criterion (DIC) and Bayes factor (BF). For all flocks, PWPH models were clearly preferred; they generated a reduction of 1,900 or more DIC units and provided BF estimates larger than 100 (i.e., PWPH models against linear models). These differences were reduced when comparing PWPH models with different number of change points for the baseline hazard function. In 4 flocks, only 2 change points were required to minimize the DIC, whereas 4 and 6 change points were needed for the 2 remaining flocks. These differences demonstrated a remarkable degree of heterogeneity across sheep flocks that must be properly accounted for in genetic evaluation models to avoid statistical biases and suboptimal genetic trends. Within this context, all 6 Ripollesa flocks revealed substantial genetic background for lambing interval with heritabilities ranging between 0.13 and 0.19. This study provides the first evidence of the suitability of PWPH models for lambing interval analysis, clearly discarding previous parametrizations focused on mixed linear models.

  11. Behavioral and macro modeling using piecewise linear techniques

    Kruiskamp, M.W.; Leenaerts, D.M.W.; Antao, B.

    1998-01-01

    In this paper we will demonstrate that most digital, analog as well as behavioral components can be described using piecewise linear approximations of their real behavior. This leads to several advantages from the viewpoint of simulation. We will also give a method to store the resulting linear

  12. Simultaneous Balancing and Model Reduction of Switched Linear Systems

    Monshizadeh, Nima; Trentelman, Hendrikus; Camlibel, M.K.

    2011-01-01

    In this paper, first, balanced truncation of linear systems is revisited. Then, simultaneous balancing of multiple linear systems is investigated. Necessary and sufficient conditions are introduced to identify the case where simultaneous balancing is possible. The validity of these conditions is not

  13. Genomic prediction based on data from three layer lines using non-linear regression models.

    Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

    2014-11-06

    Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional

  14. Sampled-data models for linear and nonlinear systems

    Yuz, Juan I

    2014-01-01

    Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar. Rather than emphasising the differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates being as high as they are, it is becoming more appropriate to emphasise connections and similarities. The text is driven by three motives: ·      the ubiquity of computers in modern control and signal-processing equipment means that sampling of systems that really evolve continuously is unavoidable; ·      although superficially straightforward, sampling can easily produce erroneous results when not treated properly; and ·      the need for a thorough understanding of many aspects of sampling among researchers and engineers dealing with applications to which they are central. The authors tackle many misconceptions which, although appearing reasonable at first sight, are in fact either p...

  15. Dynamics of edge currents in a linearly quenched Haldane model

    Mardanya, Sougata; Bhattacharya, Utso; Agarwal, Amit; Dutta, Amit

    2018-03-01

    In a finite-time quantum quench of the Haldane model, the Chern number determining the topology of the bulk remains invariant, as long as the dynamics is unitary. Nonetheless, the corresponding boundary attribute, the edge current, displays interesting dynamics. For the case of sudden and adiabatic quenches the postquench edge current is solely determined by the initial and the final Hamiltonians, respectively. However for a finite-time (τ ) linear quench in a Haldane nanoribbon, we show that the evolution of the edge current from the sudden to the adiabatic limit is not monotonic in τ and has a turning point at a characteristic time scale τ =τ0 . For small τ , the excited states lead to a huge unidirectional surge in the edge current of both edges. On the other hand, in the limit of large τ , the edge current saturates to its expected equilibrium ground-state value. This competition between the two limits lead to the observed nonmonotonic behavior. Interestingly, τ0 seems to depend only on the Semenoff mass and the Haldane flux. A similar dynamics for the edge current is also expected in other systems with topological phases.

  16. Parameter estimation and hypothesis testing in linear models

    Koch, Karl-Rudolf

    1999-01-01

    The necessity to publish the second edition of this book arose when its third German edition had just been published. This second English edition is there­ fore a translation of the third German edition of Parameter Estimation and Hypothesis Testing in Linear Models, published in 1997. It differs from the first English edition by the addition of a new chapter on robust estimation of parameters and the deletion of the section on discriminant analysis, which has been more completely dealt with by the author in the book Bayesian In­ ference with Geodetic Applications, Springer-Verlag, Berlin Heidelberg New York, 1990. Smaller additions and deletions have been incorporated, to im­ prove the text, to point out new developments or to eliminate errors which became apparent. A few examples have been also added. I thank Springer-Verlag for publishing this second edition and for the assistance in checking the translation, although the responsibility of errors remains with the author. I also want to express my thanks...

  17. Linear multivariate evaluation models for spatial perception of soundscape.

    Deng, Zhiyong; Kang, Jian; Wang, Daiwei; Liu, Aili; Kang, Joe Zhengyu

    2015-11-01

    Soundscape is a sound environment that emphasizes the awareness of auditory perception and social or cultural understandings. The case of spatial perception is significant to soundscape. However, previous studies on the auditory spatial perception of the soundscape environment have been limited. Based on 21 native binaural-recorded soundscape samples and a set of auditory experiments for subjective spatial perception (SSP), a study of the analysis among semantic parameters, the inter-aural-cross-correlation coefficient (IACC), A-weighted-equal sound-pressure-level (L(eq)), dynamic (D), and SSP is introduced to verify the independent effect of each parameter and to re-determine some of their possible relationships. The results show that the more noisiness the audience perceived, the worse spatial awareness they received, while the closer and more directional the sound source image variations, dynamics, and numbers of sound sources in the soundscape are, the better the spatial awareness would be. Thus, the sensations of roughness, sound intensity, transient dynamic, and the values of Leq and IACC have a suitable range for better spatial perception. A better spatial awareness seems to promote the preference slightly for the audience. Finally, setting SSPs as functions of the semantic parameters and Leq-D-IACC, two linear multivariate evaluation models of subjective spatial perception are proposed.

  18. Form factors in the projected linear chiral sigma model

    Alberto, P.; Coimbra Univ.; Bochum Univ.; Ruiz Arriola, E.; Fiolhais, M.; Urbano, J.N.; Coimbra Univ.; Goeke, K.; Gruemmer, F.; Bochum Univ.

    1990-01-01

    Several nucleon form factors are computed within the framework of the linear chiral soliton model. To this end variational means and projection techniques applied to generalized hedgehog quark-boson Fock states are used. In this procedure the Goldberger-Treiman relation and a virial theorem for the pion-nucleon form factor are well fulfilled demonstrating the consistency of the treatment. Both proton and neutron charge form factors are correctly reproduced, as well as the proton magnetic one. The shapes of the neutron magnetic and of the axial form factors are good but their absolute values at the origin are too large. The slopes of all the form factors at zero momentum transfer are in good agreement with the experimental data. The pion-nucleon form factor exhibits to great extent a monopole shape with a cut-off mass of Λ=690 MeV. Electromagnetic form factors for the vertex γNΔ and the nucleon spin distribution are also evaluated and discussed. (orig.)

  19. Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

    R. Barbiero

    2007-05-01

    Full Text Available Model Output Statistics (MOS refers to a method of post-processing the direct outputs of numerical weather prediction (NWP models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511–L60 and LAMI-3. Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2°C, close to the natural variability inside the area itself.

  20. Modelling and Inverse-Modelling: Experiences with O.D.E. Linear Systems in Engineering Courses

    Martinez-Luaces, Victor

    2009-01-01

    In engineering careers courses, differential equations are widely used to solve problems concerned with modelling. In particular, ordinary differential equations (O.D.E.) linear systems appear regularly in Chemical Engineering, Food Technology Engineering and Environmental Engineering courses, due to the usefulness in modelling chemical kinetics,…

  1. An improved robust model predictive control for linear parameter-varying input-output models

    Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.

    2018-01-01

    This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal

  2. A non-linear state space approach to model groundwater fluctuations

    Berendrecht, W.L.; Heemink, A.W.; Geer, F.C. van; Gehrels, J.C.

    2006-01-01

    A non-linear state space model is developed for describing groundwater fluctuations. Non-linearity is introduced by modeling the (unobserved) degree of water saturation of the root zone. The non-linear relations are based on physical concepts describing the dependence of both the actual

  3. Half-trek criterion for generic identifiability of linear structural equation models

    Foygel, R.; Draisma, J.; Drton, M.

    2012-01-01

    A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a mixed graph in which directed edges encode the linear equations, and bidirected edges indicate possible correlations

  4. Half-trek criterion for generic identifiability of linear structural equation models

    Foygel, R.; Draisma, J.; Drton, M.

    2011-01-01

    A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a mixed graph in which directed edges encode the linear equations, and bidirected edges indicate possible correlations

  5. On-line validation of linear process models using generalized likelihood ratios

    Tylee, J.L.

    1981-12-01

    A real-time method for testing the validity of linear models of nonlinear processes is described and evaluated. Using generalized likelihood ratios, the model dynamics are continually monitored to see if the process has moved far enough away from the nominal linear model operating point to justify generation of a new linear model. The method is demonstrated using a seventh-order model of a natural circulation steam generator

  6. Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

    Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

    2016-01-01

    Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19

  7. Simultaneous Balancing and Model Reduction of Switched Linear Systems

    Monshizadeh, Nima; Trentelman, Hendrikus; Camlibel, M.K.

    2011-01-01

    In this paper, first, balanced truncation of linear systems is revisited. Then, simultaneous balancing of multiple linear systems is investigated. Necessary and sufficient conditions are introduced to identify the case where simultaneous balancing is possible. The validity of these conditions is not limited to a certain type of balancing, and they are applicable for different types of balancing corresponding to different equations, like Lyapunov or Riccati equations. The results obtained are ...

  8. Developing ontological model of computational linear algebra - preliminary considerations

    Wasielewska, K.; Ganzha, M.; Paprzycki, M.; Lirkov, I.

    2013-10-01

    The aim of this paper is to propose a method for application of ontologically represented domain knowledge to support Grid users. The work is presented in the context provided by the Agents in Grid system, which aims at development of an agent-semantic infrastructure for efficient resource management in the Grid. Decision support within the system should provide functionality beyond the existing Grid middleware, specifically, help the user to choose optimal algorithm and/or resource to solve a problem from a given domain. The system assists the user in at least two situations. First, for users without in-depth knowledge about the domain, it should help them to select the method and the resource that (together) would best fit the problem to be solved (and match the available resources). Second, if the user explicitly indicates the method and the resource configuration, it should "verify" if her choice is consistent with the expert recommendations (encapsulated in the knowledge base). Furthermore, one of the goals is to simplify the use of the selected resource to execute the job; i.e., provide a user-friendly method of submitting jobs, without required technical knowledge about the Grid middleware. To achieve the mentioned goals, an adaptable method of expert knowledge representation for the decision support system has to be implemented. The selected approach is to utilize ontologies and semantic data processing, supported by multicriterial decision making. As a starting point, an area of computational linear algebra was selected to be modeled, however, the paper presents a general approach that shall be easily extendable to other domains.

  9. Symmetry conservation in the linear chiral soliton model

    Goeke, K.

    1988-01-01

    The linear chiral soliton model with quark fields and elementary pion- and sigma-fields is solved in order to describe static properties of the nucleon and the delta resonance. To this end a Fock-state of the system is constructed consisting out of three valence quarks in a first orbit with a generalized hedgehog spin-flavour configuration. Coherent states are used to provide a quantum description for the mesonic parts of the total wave function. The corresponding classical pion field also exhibit a generalized hedgehog structure. In a pure mean field approximation the variation of the total energy results in the ordinary hedgehog form. In a quantized approach the generalized hedgehog-baryon is projected onto states with good spin and isospin and then noticeable deviations from the simple hedgehog form, if the relevant degrees of freedom of the wave function are varied after the projection. Various nucleon properties are calculated. These include proton and neutron charge radii, and the magnetic moment of the proton for which good agreement with experiment is obtained. The absolute value of the neutron magnetic moment comes out too large, similarly as the axial vector coupling constant and the pion-nucleon-nucleon coupling constant.To the generalization of the hedgehog the Goldberger-Treiman relation and a corresponding virial theorem are fulfilled. Variation of the quark-meson coupling parameter g and the sigma mass m σ shows that the g A is always at least 40 % too large compared to experiment. Hence it is concluded that either the inclusion of the polarization of the Dirac sea and/or further mesons with may be vector character or the consideration of intrinsic deformation is necessary. The concepts and results of the projections are compared with the semiclassical collective quantization method. 6 tabs., 14 figs., 43 refs

  10. Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

    Mingwu Jin

    2012-01-01

    Full Text Available Local canonical correlation analysis (CCA is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM, a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

  11. Log-normal frailty models fitted as Poisson generalized linear mixed models.

    Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver

    2016-12-01

    The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  12. The Linearity of Optical Tomography: Sensor Model and Experimental Verification

    Siti Zarina MOHD. MUJI

    2011-09-01

    Full Text Available The aim of this paper is to show the linearization of optical sensor. Linearity of the sensor response is a must in optical tomography application, which affects the tomogram result. Two types of testing are used namely, testing using voltage parameter and testing with time unit parameter. For the former, the testing is by measuring the voltage when the obstacle is placed between transmitter and receiver. The obstacle diameters are between 0.5 until 3 mm. The latter is also the same testing but the obstacle is bigger than the former which is 59.24 mm and the testing purpose is to measure the time unit spend for the ball when it cut the area of sensing circuit. Both results show a linear relation that proves the optical sensors is suitable for process tomography application.

  13. Robust Comparison of the Linear Model Structures in Self-tuning Adaptive Control

    Zhou, Jianjun; Conrad, Finn

    1989-01-01

    The Generalized Predictive Controller (GPC) is extended to the systems with a generalized linear model structure which contains a number of choices of linear model structures. The Recursive Prediction Error Method (RPEM) is used to estimate the unknown parameters of the linear model structures...... to constitute a GPC self-tuner. Different linear model structures commonly used are compared and evaluated by applying them to the extended GPC self-tuner as well as to the special cases of the GPC, the GMV and MV self-tuners. The simulation results show how the choice of model structure affects the input......-output behaviour of self-tuning controllers....

  14. Efficient Estimation of Non-Linear Dynamic Panel Data Models with Application to Smooth Transition Models

    Gørgens, Tue; Skeels, Christopher L.; Wurtz, Allan

    This paper explores estimation of a class of non-linear dynamic panel data models with additive unobserved individual-specific effects. The models are specified by moment restrictions. The class includes the panel data AR(p) model and panel smooth transition models. We derive an efficient set...... of moment restrictions for estimation and apply the results to estimation of panel smooth transition models with fixed effects, where the transition may be determined endogenously. The performance of the GMM estimator, both in terms of estimation precision and forecasting performance, is examined in a Monte...

  15. A wild model of linear arithmetic and discretely ordered modules

    Glivický, Petr; Pudlák, Pavel

    2017-01-01

    Roč. 63, č. 6 (2017), s. 501-508 ISSN 0942-5616 EU Projects: European Commission(XE) 339691 - FEALORA Institutional support: RVO:67985840 Keywords : linear arithmetics Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.250, year: 2016

  16. Evaluation of linear induction motor characteristics : the Yamamura model

    1975-04-30

    The Yamamura theory of the double-sided linear induction motor (LIM) excited by a constant current source is discussed in some detail. The report begins with a derivation of thrust and airgap power using the method of vector potentials and theorem of...

  17. Model structure learning: A support vector machine approach for LPV linear-regression models

    Toth, R.; Laurain, V.; Zheng, W-X.; Poolla, K.

    2011-01-01

    Accurate parametric identification of Linear Parameter-Varying (LPV) systems requires an optimal prior selection of a set of functional dependencies for the parametrization of the model coefficients. Inaccurate selection leads to structural bias while over-parametrization results in a variance

  18. Effect of linear and non-linear blade modelling techniques on simulated fatigue and extreme loads using Bladed

    Beardsell, Alec; Collier, William; Han, Tao

    2016-09-01

    There is a trend in the wind industry towards ever larger and more flexible turbine blades. Blade tip deflections in modern blades now commonly exceed 10% of blade length. Historically, the dynamic response of wind turbine blades has been analysed using linear models of blade deflection which include the assumption of small deflections. For modern flexible blades, this assumption is becoming less valid. In order to continue to simulate dynamic turbine performance accurately, routine use of non-linear models of blade deflection may be required. This can be achieved by representing the blade as a connected series of individual flexible linear bodies - referred to in this paper as the multi-part approach. In this paper, Bladed is used to compare load predictions using single-part and multi-part blade models for several turbines. The study examines the impact on fatigue and extreme loads and blade deflection through reduced sets of load calculations based on IEC 61400-1 ed. 3. Damage equivalent load changes of up to 16% and extreme load changes of up to 29% are observed at some turbine load locations. It is found that there is no general pattern in the loading differences observed between single-part and multi-part blade models. Rather, changes in fatigue and extreme loads with a multi-part blade model depend on the characteristics of the individual turbine and blade. Key underlying causes of damage equivalent load change are identified as differences in edgewise- torsional coupling between the multi-part and single-part models, and increased edgewise rotor mode damping in the multi-part model. Similarly, a causal link is identified between torsional blade dynamics and changes in ultimate load results.

  19. Admissible Estimators in the General Multivariate Linear Model with Respect to Inequality Restricted Parameter Set

    Shangli Zhang

    2009-01-01

    Full Text Available By using the methods of linear algebra and matrix inequality theory, we obtain the characterization of admissible estimators in the general multivariate linear model with respect to inequality restricted parameter set. In the classes of homogeneous and general linear estimators, the necessary and suffcient conditions that the estimators of regression coeffcient function are admissible are established.

  20. Preisach hysteresis model for non-linear 2D heat diffusion

    Jancskar, Ildiko; Ivanyi, Amalia

    2006-01-01

    This paper analyzes a non-linear heat diffusion process when the thermal diffusivity behaviour is a hysteretic function of the temperature. Modelling this temperature dependence, the discrete Preisach algorithm as general hysteresis model has been integrated into a non-linear multigrid solver. The hysteretic diffusion shows a heating-cooling asymmetry in character. The presented type of hysteresis speeds up the thermal processes in the modelled systems by a very interesting non-linear way

  1. Feature-space-based FMRI analysis using the optimal linear transformation.

    Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

    2010-09-01

    The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.

  2. Study of the critical behavior of the O(N) linear and nonlinear sigma models

    Graziani, F.R.

    1983-01-01

    A study of the large N behavior of both the O(N) linear and nonlinear sigma models is presented. The purpose is to investigate the relationship between the disordered (ordered) phase of the linear and nonlinear sigma models. Utilizing operator product expansions and stability analyses, it is shown that for 2 - (lambda/sub R/(M) is the dimensionless renormalized quartic coupling and lambda* is the IR fixed point) limit of the linear sigma model which yields the nonlinear sigma model. It is also shown that stable large N linear sigma models with lambda 0) and nonlinear models are trivial. This result (i.e., triviality) is well known but only for one and two component models. Interestingly enough, the lambda< d = 4 linear sigma model remains nontrivial and tachyonic free

  3. Non-linear nuclear engineering models as genetic programming application

    Domingos, Roberto P.; Schirru, Roberto; Martinez, Aquilino S.

    1997-01-01

    This work presents a Genetic Programming paradigm and a nuclear application. A field of Artificial Intelligence, based on the concepts of Species Evolution and Natural Selection, can be understood as a self-programming process where the computer is the main agent responsible for the discovery of a program able to solve a given problem. In the present case, the problem was to find a mathematical expression in symbolic form, able to express the existent relation between equivalent ratio of a fuel cell, the enrichment of fuel elements and the multiplication factor. Such expression would avoid repeatedly reactor physics codes execution for core optimization. The results were compared with those obtained by different techniques such as Neural Networks and Linear Multiple Regression. Genetic Programming has shown to present a performance as good as, and under some features superior to Neural Network and Linear Multiple Regression. (author). 10 refs., 8 figs., 1 tabs

  4. AN ADA LINEAR ALGEBRA PACKAGE MODELED AFTER HAL/S

    Klumpp, A. R.

    1994-01-01

    This package extends the Ada programming language to include linear algebra capabilities similar to those of the HAL/S programming language. The package is designed for avionics applications such as Space Station flight software. In addition to the HAL/S built-in functions, the package incorporates the quaternion functions used in the Shuttle and Galileo projects, and routines from LINPAK that solve systems of equations involving general square matrices. Language conventions in this package follow those of HAL/S to the maximum extent practical and minimize the effort required for writing new avionics software and translating existent software into Ada. Valid numeric types in this package include scalar, vector, matrix, and quaternion declarations. (Quaternions are fourcomponent vectors used in representing motion between two coordinate frames). Single precision and double precision floating point arithmetic is available in addition to the standard double precision integer manipulation. Infix operators are used instead of function calls to define dot products, cross products, quaternion products, and mixed scalar-vector, scalar-matrix, and vector-matrix products. The package contains two generic programs: one for floating point, and one for integer. The actual component type is passed as a formal parameter to the generic linear algebra package. The procedures for solving systems of linear equations defined by general matrices include GEFA, GECO, GESL, and GIDI. The HAL/S functions include ABVAL, UNIT, TRACE, DET, INVERSE, TRANSPOSE, GET, PUT, FETCH, PLACE, and IDENTITY. This package is written in Ada (Version 1.2) for batch execution and is machine independent. The linear algebra software depends on nothing outside the Ada language except for a call to a square root function for floating point scalars (such as SQRT in the DEC VAX MATHLIB library). This program was developed in 1989, and is a copyrighted work with all copyright vested in NASA.

  5. Generation companies decision-making modeling by linear control theory

    Gutierrez-Alcaraz, G.; Sheble, Gerald B.

    2010-01-01

    This paper proposes four decision-making procedures to be employed by electric generating companies as part of their bidding strategies when competing in an oligopolistic market: naive, forward, adaptive, and moving average expectations. Decision-making is formulated in a dynamic framework by using linear control theory. The results reveal that interactions among all GENCOs affect market dynamics. Several numerical examples are reported, and conclusions are presented. (author)

  6. A Comparison of Alternative Estimators of Linearly Aggregated Macro Models

    Fikri Akdeniz

    2012-07-01

    Full Text Available Normal 0 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Times New Roman","serif"; mso-ansi-language:TR; mso-fareast-language:TR;} This paper deals with the linear aggregation problem. For the true underlying micro relations, which explain the micro behavior of the individuals, no restrictive rank conditions are assumed. Thus the analysis is presented in a framework utilizing generalized inverses of singular matrices. We investigate several estimators for certain linear transformations of the systematic part of the corresponding macro relations. Homogeneity of micro parameters is discussed. Best linear unbiased estimation for micro parameters is described.

  7. Mixed models, linear dependency, and identification in age-period-cohort models.

    O'Brien, Robert M

    2017-07-20

    This paper examines the identification problem in age-period-cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  8. Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

    Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

    2016-03-01

    In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor

    Muayad Al-Qaisy

    2015-02-01

    Full Text Available In this article, multi-input multi-output (MIMO linear model predictive controller (LMPC based on state space model and nonlinear model predictive controller based on neural network (NNMPC are applied on a continuous stirred tank reactor (CSTR. The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA and reactor temperature (T. NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.

  10. Utility of low-order linear nuclear-power-plant models in plant diagnostics and control

    Tylee, J.L.

    1981-01-01

    A low-order, linear model of a pressurized water reactor (PWR) plant is described and evaluated. The model consists of 23 linear, first-order difference equations and simulates all subsystems of both the primary and secondary sides of the plant. Comparisons between the calculated model response and available test data show the model to be an adequate representation of the actual plant dynamics. Suggested use for the model in an on-line digital plant diagnostics and control system are presented

  11. Some computer simulations based on the linear relative risk model

    Gilbert, E.S.

    1991-10-01

    This report presents the results of computer simulations designed to evaluate and compare the performance of the likelihood ratio statistic and the score statistic for making inferences about the linear relative risk mode. The work was motivated by data on workers exposed to low doses of radiation, and the report includes illustration of several procedures for obtaining confidence limits for the excess relative risk coefficient based on data from three studies of nuclear workers. The computer simulations indicate that with small sample sizes and highly skewed dose distributions, asymptotic approximations to the score statistic or to the likelihood ratio statistic may not be adequate. For testing the null hypothesis that the excess relative risk is equal to zero, the asymptotic approximation to the likelihood ratio statistic was adequate, but use of the asymptotic approximation to the score statistic rejected the null hypothesis too often. Frequently the likelihood was maximized at the lower constraint, and when this occurred, the asymptotic approximations for the likelihood ratio and score statistics did not perform well in obtaining upper confidence limits. The score statistic and likelihood ratio statistics were found to perform comparably in terms of power and width of the confidence limits. It is recommended that with modest sample sizes, confidence limits be obtained using computer simulations based on the score statistic. Although nuclear worker studies are emphasized in this report, its results are relevant for any study investigating linear dose-response functions with highly skewed exposure distributions. 22 refs., 14 tabs

  12. Advancements in the Development of an Operational Lightning Jump Algorithm for GOES-R GLM

    Shultz, Chris; Petersen, Walter; Carey, Lawrence

    2011-01-01

    Rapid increases in total lightning have been shown to precede the manifestation of severe weather at the surface. These rapid increases have been termed lightning jumps, and are the current focus of algorithm development for the GOES-R Geostationary Lightning Mapper (GLM). Recent lightning jump algorithm work has focused on evaluation of algorithms in three additional regions of the country, as well as, markedly increasing the number of thunderstorms in order to evaluate the each algorithm s performance on a larger population of storms. Lightning characteristics of just over 600 thunderstorms have been studied over the past four years. The 2 lightning jump algorithm continues to show the most promise for an operational lightning jump algorithm, with a probability of detection of 82%, a false alarm rate of 35%, a critical success index of 57%, and a Heidke Skill Score of 0.73 on the entire population of thunderstorms. Average lead time for the 2 algorithm on all severe weather is 21.15 minutes, with a standard deviation of +/- 14.68 minutes. Looking at tornadoes alone, the average lead time is 18.71 minutes, with a standard deviation of +/-14.88 minutes. Moreover, removing the 2 lightning jumps that occur after a jump has been detected, and before severe weather is detected at the ground, the 2 lightning jump algorithm s false alarm rate drops from 35% to 21%. Cold season, low topped, and tropical environments cause problems for the 2 lightning jump algorithm, due to their relative dearth in lightning as compared to a supercellular or summertime airmass thunderstorm environment.

  13. Partially linear varying coefficient models stratified by a functional covariate

    Maity, Arnab; Huang, Jianhua Z.

    2012-01-01

    We consider the problem of estimation in semiparametric varying coefficient models where the covariate modifying the varying coefficients is functional and is modeled nonparametrically. We develop a kernel-based estimator of the nonparametric

  14. A linear time layout algorithm for business process models

    Gschwind, T.; Pinggera, J.; Zugal, S.; Reijers, H.A.; Weber, B.

    2014-01-01

    The layout of a business process model influences how easily it can beunderstood. Existing layout features in process modeling tools often rely on graph representations, but do not take the specific properties of business process models into account. In this paper, we propose an algorithm that is

  15. Cross-beam energy transfer: On the accuracy of linear stationary models in the linear kinetic regime

    Debayle, A.; Masson-Laborde, P.-E.; Ruyer, C.; Casanova, M.; Loiseau, P.

    2018-05-01

    We present an extensive numerical study by means of particle-in-cell simulations of the energy transfer that occurs during the crossing of two laser beams. In the linear regime, when ions are not trapped in the potential well induced by the laser interference pattern, a very good agreement is obtained with a simple linear stationary model, provided the laser intensity is sufficiently smooth. These comparisons include different plasma compositions to cover the strong and weak Landau damping regimes as well as the multispecies case. The correct evaluation of the linear Landau damping at the phase velocity imposed by the laser interference pattern is essential to estimate the energy transfer rate between the laser beams, once the stationary regime is reached. The transient evolution obtained in kinetic simulations is also analysed by means of a full analytical formula that includes 3D beam energy exchange coupled with the ion acoustic wave response. Specific attention is paid to the energy transfer when the laser presents small-scale inhomogeneities. In particular, the energy transfer is reduced when the laser inhomogeneities are comparable with the Landau damping characteristic length of the ion acoustic wave.

  16. The Data-Constrained Generalized Maximum Entropy Estimator of the GLM: Asymptotic Theory and Inference

    Nicholas Scott Cardell

    2013-05-01

    Full Text Available Maximum entropy methods of parameter estimation are appealing because they impose no additional structure on the data, other than that explicitly assumed by the analyst. In this paper we prove that the data constrained GME estimator of the general linear model is consistent and asymptotically normal. The approach we take in establishing the asymptotic properties concomitantly identifies a new computationally efficient method for calculating GME estimates. Formulae are developed to compute asymptotic variances and to perform Wald, likelihood ratio, and Lagrangian multiplier statistical tests on model parameters. Monte Carlo simulations are provided to assess the performance of the GME estimator in both large and small sample situations. Furthermore, we extend our results to maximum cross-entropy estimators and indicate a variant of the GME estimator that is unbiased. Finally, we discuss the relationship of GME estimators to Bayesian estimators, pointing out the conditions under which an unbiased GME estimator would be efficient.

  17. Free-piston engine linear generator for hybrid vehicles modeling study

    Callahan, T. J.; Ingram, S. K.

    1995-05-01

    Development of a free piston engine linear generator was investigated for use as an auxiliary power unit for a hybrid electric vehicle. The main focus of the program was to develop an efficient linear generator concept to convert the piston motion directly into electrical power. Computer modeling techniques were used to evaluate five different designs for linear generators. These designs included permanent magnet generators, reluctance generators, linear DC generators, and two and three-coil induction generators. The efficiency of the linear generator was highly dependent on the design concept. The two-coil induction generator was determined to be the best design, with an efficiency of approximately 90 percent.

  18. Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It

    Grünwald, P.; van Ommen, T.

    2017-01-01

    We empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting. We use the standard linear model, which assumes homoskedasticity, whereas the data are

  19. Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

    Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

    2006-01-01

    Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

  20. Genomic prediction based on data from three layer lines using non-linear regression models

    Huang, H.; Windig, J.J.; Vereijken, A.; Calus, M.P.L.

    2014-01-01

    Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate

  1. Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties

    Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon

    2012-01-01

    Purpose: The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency (F[subscript 0]) during anterior-posterior stretching. Method: Three materially linear and 3 materially nonlinear models were…

  2. Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it

    P.D. Grünwald (Peter); T. van Ommen (Thijs)

    2017-01-01

    textabstractWe empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting. We use the standard linear model, which assumes homoskedasticity, whereas the data

  3. Non-linear characterisation of the physical model of an ancient masonry bridge

    Fragonara, L Zanotti; Ceravolo, R; Matta, E; Quattrone, A; De Stefano, A; Pecorelli, M

    2012-01-01

    This paper presents the non-linear investigations carried out on a scaled model of a two-span masonry arch bridge. The model has been built in order to study the effect of the central pile settlement due to riverbank erosion. Progressive damage was induced in several steps by applying increasing settlements at the central pier. For each settlement step, harmonic shaker tests were conducted under different excitation levels, this allowing for the non-linear identification of the progressively damaged system. The shaker tests have been performed at resonance with the modal frequency of the structure, which were determined from a previous linear identification. Estimated non-linearity parameters, which result from the systematic application of restoring force based identification algorithms, can corroborate models to be used in the reassessment of existing structures. The method used for non-linear identification allows monitoring the evolution of non-linear parameters or indicators which can be used in damage and safety assessment.

  4. Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming

    Cardoso, Goncalo; Stadler, Michael; Siddiqui, Afzal; Marnay, Chris; DeForest, Nicholas; Barbosa-Povoa, Ana; Ferrao, Paulo

    2013-05-23

    This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.

  5. Utilizing ISS Camera Systems for Scientific Analysis of Lightning Characteristics and comparison with ISS-LIS and GLM

    Schultz, C. J.; Lang, T. J.; Leake, S.; Runco, M.; Blakeslee, R. J.

    2017-12-01

    Video and still frame images from cameras aboard the International Space Station (ISS) are used to inspire, educate, and provide a unique vantage point from low-Earth orbit that is second to none; however, these cameras have overlooked capabilities for contributing to scientific analysis of the Earth and near-space environment. The goal of this project is to study how georeferenced video/images from available ISS camera systems can be useful for scientific analysis, using lightning properties as a demonstration. Camera images from the crew cameras and high definition video from the Chiba University Meteor Camera were combined with lightning data from the National Lightning Detection Network (NLDN), ISS-Lightning Imaging Sensor (ISS-LIS), the Geostationary Lightning Mapper (GLM) and lightning mapping arrays. These cameras provide significant spatial resolution advantages ( 10 times or better) over ISS-LIS and GLM, but with lower temporal resolution. Therefore, they can serve as a complementarity analysis tool for studying lightning and thunderstorm processes from space. Lightning sensor data, Visible Infrared Imaging Radiometer Suite (VIIRS) derived city light maps, and other geographic databases were combined with the ISS attitude and position data to reverse geolocate each image or frame. An open-source Python toolkit has been developed to assist with this effort. Next, the locations and sizes of all flashes in each frame or image were computed and compared with flash characteristics from all available lightning datasets. This allowed for characterization of cloud features that are below the 4-km and 8-km resolution of ISS-LIS and GLM which may reduce the light that reaches the ISS-LIS or GLM sensor. In the case of video, consecutive frames were overlaid to determine the rate of change of the light escaping cloud top. Characterization of the rate of change in geometry, more generally the radius, of light escaping cloud top was integrated with the NLDN, ISS-LIS and

  6. Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data

    Cheng, Guang; Zhou, Lan; Huang, Jianhua Z.

    2014-01-01

    We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled nonparametrically, and propose an estimation procedure based

  7. Approximate reduction of linear population models governed by stochastic differential equations: application to multiregional models.

    Sanz, Luis; Alonso, Juan Antonio

    2017-12-01

    In this work we develop approximate aggregation techniques in the context of slow-fast linear population models governed by stochastic differential equations and apply the results to the treatment of populations with spatial heterogeneity. Approximate aggregation techniques allow one to transform a complex system involving many coupled variables and in which there are processes with different time scales, by a simpler reduced model with a fewer number of 'global' variables, in such a way that the dynamics of the former can be approximated by that of the latter. In our model we contemplate a linear fast deterministic process together with a linear slow process in which the parameters are affected by additive noise, and give conditions for the solutions corresponding to positive initial conditions to remain positive for all times. By letting the fast process reach equilibrium we build a reduced system with a lesser number of variables, and provide results relating the asymptotic behaviour of the first- and second-order moments of the population vector for the original and the reduced system. The general technique is illustrated by analysing a multiregional stochastic system in which dispersal is deterministic and the rate growth of the populations in each patch is affected by additive noise.

  8. Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

    Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

    2012-12-01

    In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

  9. Modeling exposure–lag–response associations with distributed lag non-linear models

    Gasparrini, Antonio

    2014-01-01

    In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24027094

  10. Linear regression models for quantitative assessment of left ...

    Changes in left ventricular structures and function have been reported in cardiomyopathies. No prediction models have been established in this environment. This study established regression models for prediction of left ventricular structures in normal subjects. A sample of normal subjects was drawn from a large urban ...

  11. Non-linear modeling of active biohybrid materials

    Paetsch, C.; Dorfmann, A.

    2013-01-01

    , such as those of Manduca sexta. In this study, we propose a model to assist in the analysis of biohybrid constructs by generalizing a recently proposed constitutive law for Manduca muscle tissue. The continuum model accounts (i) for the stimulation of muscle

  12. Nonstandard Finite Difference Method Applied to a Linear Pharmacokinetics Model

    Oluwaseun Egbelowo

    2017-05-01

    Full Text Available We extend the nonstandard finite difference method of solution to the study of pharmacokinetic–pharmacodynamic models. Pharmacokinetic (PK models are commonly used to predict drug concentrations that drive controlled intravenous (I.V. transfers (or infusion and oral transfers while pharmacokinetic and pharmacodynamic (PD interaction models are used to provide predictions of drug concentrations affecting the response of these clinical drugs. We structure a nonstandard finite difference (NSFD scheme for the relevant system of equations which models this pharamcokinetic process. We compare the results obtained to standard methods. The scheme is dynamically consistent and reliable in replicating complex dynamic properties of the relevant continuous models for varying step sizes. This study provides assistance in understanding the long-term behavior of the drug in the system, and validation of the efficiency of the nonstandard finite difference scheme as the method of choice.

  13. Nonlinearity measure and internal model control based linearization in anti-windup design

    Perev, Kamen [Systems and Control Department, Technical University of Sofia, 8 Cl. Ohridski Blvd., 1756 Sofia (Bulgaria)

    2013-12-18

    This paper considers the problem of internal model control based linearization in anti-windup design. The nonlinearity measure concept is used for quantifying the control system degree of nonlinearity. The linearizing effect of a modified internal model control structure is presented by comparing the nonlinearity measures of the open-loop and closed-loop systems. It is shown that the linearization properties are improved by increasing the control system local feedback gain. However, it is emphasized that at the same time the stability of the system deteriorates. The conflicting goals of stability and linearization are resolved by solving the design problem in different frequency ranges.

  14. Non-linear mixed-effects pharmacokinetic/pharmacodynamic modelling in NLME using differential equations

    Tornøe, Christoffer Wenzel; Agersø, Henrik; Madsen, Henrik

    2004-01-01

    The standard software for non-linear mixed-effect analysis of pharmacokinetic/phar-macodynamic (PK/PD) data is NONMEM while the non-linear mixed-effects package NLME is an alternative as tong as the models are fairly simple. We present the nlmeODE package which combines the ordinary differential...... equation (ODE) solver package odesolve and the non-Linear mixed effects package NLME thereby enabling the analysis of complicated systems of ODEs by non-linear mixed-effects modelling. The pharmacokinetics of the anti-asthmatic drug theophylline is used to illustrate the applicability of the nlme...

  15. Partially linear varying coefficient models stratified by a functional covariate

    Maity, Arnab

    2012-10-01

    We consider the problem of estimation in semiparametric varying coefficient models where the covariate modifying the varying coefficients is functional and is modeled nonparametrically. We develop a kernel-based estimator of the nonparametric component and a profiling estimator of the parametric component of the model and derive their asymptotic properties. Specifically, we show the consistency of the nonparametric functional estimates and derive the asymptotic expansion of the estimates of the parametric component. We illustrate the performance of our methodology using a simulation study and a real data application.

  16. Modeling results for a linear simulator of a divertor

    Hooper, E.B.; Brown, M.D.; Byers, J.A.; Casper, T.A.; Cohen, B.I.; Cohen, R.H.; Jackson, M.C.; Kaiser, T.B.; Molvik, A.W.; Nevins, W.M.; Nilson, D.G.; Pearlstein, L.D.; Rognlien, T.D.

    1993-01-01

    A divertor simulator, IDEAL, has been proposed by S. Cohen to study the difficult power-handling requirements of the tokamak program in general and the ITER program in particular. Projections of the power density in the ITER divertor reach ∼ 1 Gw/m 2 along the magnetic fieldlines and > 10 MW/m 2 on a surface inclined at a shallow angle to the fieldlines. These power densities are substantially greater than can be handled reliably on the surface, so new techniques are required to reduce the power density to a reasonable level. Although the divertor physics must be demonstrated in tokamaks, a linear device could contribute to the development because of its flexibility, the easy access to the plasma and to tested components, and long pulse operation (essentially cw). However, a decision to build a simulator requires not just the recognition of its programmatic value, but also confidence that it can meet the required parameters at an affordable cost. Accordingly, as reported here, it was decided to examine the physics of the proposed device, including kinetic effects resulting from the intense heating required to reach the plasma parameters, and to conduct an independent cost estimate. The detailed role of the simulator in a divertor program is not explored in this report

  17. A Multiphase Non-Linear Mixed Effects Model: An Application to Spirometry after Lung Transplantation

    Rajeswaran, Jeevanantham; Blackstone, Eugene H.

    2014-01-01

    In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time varying coefficients. PMID:24919830

  18. Modeling winter precipitation over the Juneau Icefield, Alaska, using a linear model of orographic precipitation

    Roth, Aurora; Hock, Regine; Schuler, Thomas V.; Bieniek, Peter A.; Pelto, Mauri; Aschwanden, Andy

    2018-03-01

    Assessing and modeling precipitation in mountainous areas remains a major challenge in glacier mass balance modeling. Observations are typically scarce and reanalysis data and similar climate products are too coarse to accurately capture orographic effects. Here we use the linear theory of orographic precipitation model (LT model) to downscale winter precipitation from a regional climate model over the Juneau Icefield, one of the largest ice masses in North America (>4000 km2), for the period 1979-2013. The LT model is physically-based yet computationally efficient, combining airflow dynamics and simple cloud microphysics. The resulting 1 km resolution precipitation fields show substantially reduced precipitation on the northeastern portion of the icefield compared to the southwestern side, a pattern that is not well captured in the coarse resolution (20 km) WRF data. Net snow accumulation derived from the LT model precipitation agrees well with point observations across the icefield. To investigate the robustness of the LT model results, we perform a series of sensitivity experiments varying hydrometeor fall speeds, the horizontal resolution of the underlying grid, and the source of the meteorological forcing data. The resulting normalized spatial precipitation pattern is similar for all sensitivity experiments, but local precipitation amounts vary strongly, with greatest sensitivity to variations in snow fall speed. Results indicate that the LT model has great potential to provide improved spatial patterns of winter precipitation for glacier mass balance modeling purposes in complex terrain, but ground observations are necessary to constrain model parameters to match total amounts.

  19. Non-linear modeling of active biohybrid materials

    Paetsch, C.

    2013-11-01

    Recent advances in engineered muscle tissue attached to a synthetic substrate motivate the development of appropriate constitutive and numerical models. Applications of active materials can be expanded by using robust, non-mammalian muscle cells, such as those of Manduca sexta. In this study, we propose a model to assist in the analysis of biohybrid constructs by generalizing a recently proposed constitutive law for Manduca muscle tissue. The continuum model accounts (i) for the stimulation of muscle fibers by introducing multiple stress-free reference configurations for the active and passive states and (ii) for the hysteretic response by specifying a pseudo-elastic energy function. A simple example representing uniaxial loading-unloading is used to validate and verify the characteristics of the model. Then, based on experimental data of muscular thin films, a more complex case shows the qualitative potential of Manduca muscle tissue in active biohybrid constructs. © 2013 Elsevier Ltd. All rights reserved.

  20. Eddy current modeling in linear and nonlinear multifilamentary composite materials

    Menana, Hocine; Farhat, Mohamad; Hinaje, Melika; Berger, Kevin; Douine, Bruno; Lévêque, Jean

    2018-04-01

    In this work, a numerical model is developed for a rapid computation of eddy currents in composite materials, adaptable for both carbon fiber reinforced polymers (CFRPs) for NDT applications and multifilamentary high temperature superconductive (HTS) tapes for AC loss evaluation. The proposed model is based on an integro-differential formulation in terms of the electric vector potential in the frequency domain. The high anisotropy and the nonlinearity of the considered materials are easily handled in the frequency domain.

  1. Operator-based linearization for efficient modeling of geothermal processes

    Khait, M.; Voskov, D.V.

    2018-01-01

    Numerical simulation is one of the most important tools required for financial and operational management of geothermal reservoirs. The modern geothermal industry is challenged to run large ensembles of numerical models for uncertainty analysis, causing simulation performance to become a critical issue. Geothermal reservoir modeling requires the solution of governing equations describing the conservation of mass and energy. The robust, accurate and computationally efficient implementation of ...

  2. Evaluating significance in linear mixed-effects models in R.

    Luke, Steven G

    2017-08-01

    Mixed-effects models are being used ever more frequently in the analysis of experimental data. However, in the lme4 package in R the standards for evaluating significance of fixed effects in these models (i.e., obtaining p-values) are somewhat vague. There are good reasons for this, but as researchers who are using these models are required in many cases to report p-values, some method for evaluating the significance of the model output is needed. This paper reports the results of simulations showing that the two most common methods for evaluating significance, using likelihood ratio tests and applying the z distribution to the Wald t values from the model output (t-as-z), are somewhat anti-conservative, especially for smaller sample sizes. Other methods for evaluating significance, including parametric bootstrapping and the Kenward-Roger and Satterthwaite approximations for degrees of freedom, were also evaluated. The results of these simulations suggest that Type 1 error rates are closest to .05 when models are fitted using REML and p-values are derived using the Kenward-Roger or Satterthwaite approximations, as these approximations both produced acceptable Type 1 error rates even for smaller samples.

  3. A predictive modeling approach to increasing the economic effectiveness of disease management programs.

    Bayerstadler, Andreas; Benstetter, Franz; Heumann, Christian; Winter, Fabian

    2014-09-01

    Predictive Modeling (PM) techniques are gaining importance in the worldwide health insurance business. Modern PM methods are used for customer relationship management, risk evaluation or medical management. This article illustrates a PM approach that enables the economic potential of (cost-) effective disease management programs (DMPs) to be fully exploited by optimized candidate selection as an example of successful data-driven business management. The approach is based on a Generalized Linear Model (GLM) that is easy to apply for health insurance companies. By means of a small portfolio from an emerging country, we show that our GLM approach is stable compared to more sophisticated regression techniques in spite of the difficult data environment. Additionally, we demonstrate for this example of a setting that our model can compete with the expensive solutions offered by professional PM vendors and outperforms non-predictive standard approaches for DMP selection commonly used in the market.

  4. Linear and quadrature models for data from treshold measurements of the transient visual system

    Brinker, den A.C.

    1986-01-01

    III this paper two models are considered for the transient visual system at threshold. One is a linear model and the other a model contain ing a quadrature element. Both models are commonly used on evidence from different experimental sources. It is shown that both models act in a similar fashion

  5. A versatile curve-fit model for linear to deeply concave rank abundance curves

    Neuteboom, J.H.; Struik, P.C.

    2005-01-01

    A new, flexible curve-fit model for linear to concave rank abundance curves was conceptualized and validated using observational data. The model links the geometric-series model and log-series model and can also fit deeply concave rank abundance curves. The model is based ¿ in an unconventional way

  6. Genetic demixing and evolution in linear stepping stone models

    Korolev, K. S.; Avlund, Mikkel; Hallatschek, Oskar; Nelson, David R.

    2010-04-01

    Results for mutation, selection, genetic drift, and migration in a one-dimensional continuous population are reviewed and extended. The population is described by a continuous limit of the stepping stone model, which leads to the stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms describing mutations. Although the stepping stone model was first proposed for population genetics, it is closely related to “voter models” of interest in nonequilibrium statistical mechanics. The stepping stone model can also be regarded as an approximation to the dynamics of a thin layer of actively growing pioneers at the frontier of a colony of micro-organisms undergoing a range expansion on a Petri dish. The population tends to segregate into monoallelic domains. This segregation slows down genetic drift and selection because these two evolutionary forces can only act at the boundaries between the domains; the effects of mutation, however, are not significantly affected by the segregation. Although fixation in the neutral well-mixed (or “zero-dimensional”) model occurs exponentially in time, it occurs only algebraically fast in the one-dimensional model. An unusual sublinear increase is also found in the variance of the spatially averaged allele frequency with time. If selection is weak, selective sweeps occur exponentially fast in both well-mixed and one-dimensional populations, but the time constants are different. The relatively unexplored problem of evolutionary dynamics at the edge of an expanding circular colony is studied as well. Also reviewed are how the observed patterns of genetic diversity can be used for statistical inference and the differences are highlighted between the well-mixed and one-dimensional models. Although the focus is on two alleles or variants, q -allele Potts-like models of gene segregation are considered as well. Most of the analytical results are checked with simulations and could be tested against recent spatial

  7. Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models.

    Shah, A A; Xing, W W; Triantafyllidis, V

    2017-04-01

    In this paper, we develop reduced-order models for dynamic, parameter-dependent, linear and nonlinear partial differential equations using proper orthogonal decomposition (POD). The main challenges are to accurately and efficiently approximate the POD bases for new parameter values and, in the case of nonlinear problems, to efficiently handle the nonlinear terms. We use a Bayesian nonlinear regression approach to learn the snapshots of the solutions and the nonlinearities for new parameter values. Computational efficiency is ensured by using manifold learning to perform the emulation in a low-dimensional space. The accuracy of the method is demonstrated on a linear and a nonlinear example, with comparisons with a global basis approach.

  8. A new approach to modeling linear accelerator systems

    Gillespie, G.H.; Hill, B.W.; Jameson, R.A.

    1994-01-01

    A novel computer code is being developed to generate system level designs of radiofrequency ion accelerators with specific applications to machines of interest to Accelerator Driven Transmutation Technologies (ADTT). The goal of the Accelerator System Model (ASM) code is to create a modeling and analysis tool that is easy to use, automates many of the initial design calculations, supports trade studies used in accessing alternate designs and yet is flexible enough to incorporate new technology concepts as they emerge. Hardware engineering parameters and beam dynamics are to be modeled at comparable levels of fidelity. Existing scaling models of accelerator subsystems were used to produce a prototype of ASM (version 1.0) working within the Shell for Particle Accelerator Related Code (SPARC) graphical user interface. A small user group has been testing and evaluating the prototype for about a year. Several enhancements and improvements are now being developed. The current version of ASM is described and examples of the modeling and analysis capabilities are illustrated. The results of an example study, for an accelerator concept typical of ADTT applications, is presented and sample displays from the computer interface are shown

  9. Electricity consumption forecasting in Italy using linear regression models

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)

    2009-09-15

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  10. Diet models with linear goal programming: impact of achievement functions.

    Gerdessen, J C; de Vries, J H M

    2015-11-01

    Diet models based on goal programming (GP) are valuable tools in designing diets that comply with nutritional, palatability and cost constraints. Results derived from GP models are usually very sensitive to the type of achievement function that is chosen.This paper aims to provide a methodological insight into several achievement functions. It describes the extended GP (EGP) achievement function, which enables the decision maker to use either a MinSum achievement function (which minimizes the sum of the unwanted deviations) or a MinMax achievement function (which minimizes the largest unwanted deviation), or a compromise between both. An additional advantage of EGP models is that from one set of data and weights multiple solutions can be obtained. We use small numerical examples to illustrate the 'mechanics' of achievement functions. Then, the EGP achievement function is demonstrated on a diet problem with 144 foods, 19 nutrients and several types of palatability constraints, in which the nutritional constraints are modeled with fuzzy sets. Choice of achievement function affects the results of diet models. MinSum achievement functions can give rise to solutions that are sensitive to weight changes, and that pile all unwanted deviations on a limited number of nutritional constraints. MinMax achievement functions spread the unwanted deviations as evenly as possible, but may create many (small) deviations. EGP comprises both types of achievement functions, as well as compromises between them. It can thus, from one data set, find a range of solutions with various properties.

  11. Electricity consumption forecasting in Italy using linear regression models

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio

    2009-01-01

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  12. Linear summation of outputs in a balanced network model of motor cortex.

    Capaday, Charles; van Vreeswijk, Carl

    2015-01-01

    Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis.

  13. Downscaling of rainfall in Peru using Generalised Linear Models

    Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.

    2012-04-01

    The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the downscaling model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim downscaling model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim downscaling model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.

  14. Estimation of group means when adjusting for covariates in generalized linear models.

    Qu, Yongming; Luo, Junxiang

    2015-01-01

    Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

  15. A Linear Mixed-Effects Model of Wireless Spectrum Occupancy

    Pagadarai Srikanth

    2010-01-01

    Full Text Available We provide regression analysis-based statistical models to explain the usage of wireless spectrum across four mid-size US cities in four frequency bands. Specifically, the variations in spectrum occupancy across space, time, and frequency are investigated and compared between different sites within the city as well as with other cities. By applying the mixed-effects models, several conclusions are drawn that give the occupancy percentage and the ON time duration of the licensed signal transmission as a function of several predictor variables.

  16. Analysis of baseline, average, and longitudinally measured blood pressure data using linear mixed models.

    Hossain, Ahmed; Beyene, Joseph

    2014-01-01

    This article compares baseline, average, and longitudinal data analysis methods for identifying genetic variants in genome-wide association study using the Genetic Analysis Workshop 18 data. We apply methods that include (a) linear mixed models with baseline measures, (b) random intercept linear mixed models with mean measures outcome, and (c) random intercept linear mixed models with longitudinal measurements. In the linear mixed models, covariates are included as fixed effects, whereas relatedness among individuals is incorporated as the variance-covariance structure of the random effect for the individuals. The overall strategy of applying linear mixed models decorrelate the data is based on Aulchenko et al.'s GRAMMAR. By analyzing systolic and diastolic blood pressure, which are used separately as outcomes, we compare the 3 methods in identifying a known genetic variant that is associated with blood pressure from chromosome 3 and simulated phenotype data. We also analyze the real phenotype data to illustrate the methods. We conclude that the linear mixed model with longitudinal measurements of diastolic blood pressure is the most accurate at identifying the known single-nucleotide polymorphism among the methods, but linear mixed models with baseline measures perform best with systolic blood pressure as the outcome.

  17. Identification of linear error-models with projected dynamical systems

    Krejčí, Pavel; Kuhnen, K.

    2004-01-01

    Roč. 10, č. 1 (2004), s. 59-91 ISSN 1387-3954 Keywords : identification * error models * projected dynamical systems Subject RIV: BA - General Mathematics Impact factor: 0.292, year: 2004 http://www.informaworld.com/smpp/content~db=all~content=a713682517

  18. Operator-based linearization for efficient modeling of geothermal processes

    Khait, M.; Voskov, D.V.

    2018-01-01

    Numerical simulation is one of the most important tools required for financial and operational management of geothermal reservoirs. The modern geothermal industry is challenged to run large ensembles of numerical models for uncertainty analysis, causing simulation performance to become a critical

  19. Linearity and Misspecification Tests for Vector Smooth Transition Regression Models

    Teräsvirta, Timo; Yang, Yukai

    The purpose of the paper is to derive Lagrange multiplier and Lagrange multiplier type specification and misspecification tests for vector smooth transition regression models. We report results from simulation studies in which the size and power properties of the proposed asymptotic tests in small...

  20. Solving large linear systems in an implicit thermohaline ocean model

    de Niet, Arie Christiaan

    2007-01-01

    The climate on earth is largely determined by the global ocean circulation. Hence it is important to predict how the flow will react to perturbation by for example melting icecaps. To answer questions about the stability of the global ocean flow, a computer model has been developed that is able to

  1. Adjoint based model adaptation for a linear problem

    Cnossen, J.M.; Bijl, H.; Koren, B.; Brummelen, van E.H.

    2004-01-01

    In aerospace engineering CFD is often applied to obtain values for quantities of interest which are global functionals of the solution. To optimise the balance between accuracy of the computed functional and CPU time we focus on dual-weighted adaptive hierarchical modelling of fluid flow. In this

  2. Multiple Linear Regression Model for Estimating the Price of a ...

    Ghana Mining Journal ... In the modeling, the Ordinary Least Squares (OLS) normality assumption which could introduce errors in the statistical analyses was dealt with by log transformation of the data, ensuring the data is normally ... The resultant MLRM is: Ŷi MLRM = (X'X)-1X'Y(xi') where X is the sample data matrix.

  3. Revisited global drift fluid model for linear devices

    Reiser, Dirk

    2012-01-01

    The problem of energy conserving global drift fluid simulations is revisited. It is found that for the case of cylindrical plasmas in a homogenous magnetic field, a straightforward reformulation is possible avoiding simplifications leading to energetic inconsistencies. The particular new feature is the rigorous treatment of the polarisation drift by a generalization of the vorticity equation. The resulting set of model equations contains previous formulations as limiting cases and is suitable for efficient numerical techniques. Examples of applications on studies of plasma blobs and its impact on plasma target interaction are presented. The numerical studies focus on the appearance of plasma blobs and intermittent transport and its consequences on the release of sputtered target materials in the plasma. Intermittent expulsion of particles in radial direction can be observed and it is found that although the neutrals released from the target show strong fluctuations in their propagation into the plasma column, the overall effect on time averaged profiles is negligible for the conditions considered. In addition, the numerical simulations are utilised to perform an a-posteriori assessment of the magnitude of energetic inconsistencies in previously used simplified models. It is found that certain popular approximations, in particular by the use of simplified vorticity equations, do not significantly affect energetics. However, popular model simplifications with respect to parallel advection are found to provide significant deterioration of the model consistency.

  4. Performances of some estimators of linear model with ...

    The estimators are compared by examing the finite properties of estimators namely; sum of biases, sum of absolute biases, sum of variances and sum of the mean squared error of the estimated parameter of the model. Results show that when the autocorrelation level is small (ρ=0.4), the MLGD estimator is best except when ...

  5. A fuzzy Bi-linear management model in reverse logistic chains

    Tadić Danijela

    2016-01-01

    Full Text Available The management of the electrical and electronic waste (WEEE problem in the uncertain environment has a critical effect on the economy and environmental protection of each region. The considered problem can be stated as a fuzzy non-convex optimization problem with linear objective function and a set of linear and non-linear constraints. The original problem is reformulated by using linear relaxation into a fuzzy linear programming problem. The fuzzy rating of collecting point capacities and fix costs of recycling centers are modeled by triangular fuzzy numbers. The optimal solution of the reformulation model is found by using optimality concept. The proposed model is verified through an illustrative example with real-life data. The obtained results represent an input for future research which should include a good benchmark base for tested reverse logistic chains and their continuous improvement. [Projekat Ministarstva nauke Republike Srbije, br. 035033: Sustainable development technology and equipment for the recycling of motor vehicles

  6. Portfolio optimization by using linear programing models based on genetic algorithm

    Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

    2018-01-01

    In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

  7. Stochastic Modelling of Linear Programming Application to Brewing Operational Systems

    Akanbi O.P.

    2014-07-01

    Full Text Available System where a large number of interrelated operations exist, technically-based operational mechanism is always required to achieve potential. An intuitive solution, which is common practice in most of the breweries, perhaps may not uncover the optimal solution, as there is hardly any guarantee to satisfy the best policy application. There is always high foreign exchange involved in procurement of imported raw materials and thus increases the cost of production, abandonment and poor utilization of available locally-sourced raw materials. This study focuses on the approaches which highlight the steps and mechanisms involved in optimizing the wort extract by the use of different types of adjuncts and formulating wort production models which are useful in proffering expected solutions. Optimization techniques, the generalized models and an overview of typical brewing processes were considered.

  8. Linear facility location in three dimensions - Models and solution methods

    Brimberg, Jack; Juel, Henrik; Schöbel, Anita

    2002-01-01

    We consider the problem of locating a line or a line segment in three-dimensional space, such that the sum of distances from the facility represented by the line (segment) to a given set of points is minimized. An example is planning the drilling of a mine shaft, with access to ore deposits through...... horizontal tunnels connecting the deposits and the shaft. Various models of the problem are developed and analyzed, and efficient solution methods are given....

  9. Perfect observables for the hierarchical non-linear O(N)-invariant σ-model

    Wieczerkowski, C.; Xylander, Y.

    1995-05-01

    We compute moving eigenvalues and the eigenvectors of the linear renormalization group transformation for observables along the renormalized trajectory of the hierarchical non-linear O(N)-invariant σ-model by means of perturbation theory in the running coupling constant. Moving eigenvectors are defined as solutions to a Callan-Symanzik type equation. (orig.)

  10. Mathematical models of non-linear phenomena, processes and systems: from molecular scale to planetary atmosphere

    2013-01-01

    This book consists of twenty seven chapters, which can be divided into three large categories: articles with the focus on the mathematical treatment of non-linear problems, including the methodologies, algorithms and properties of analytical and numerical solutions to particular non-linear problems; theoretical and computational studies dedicated to the physics and chemistry of non-linear micro-and nano-scale systems, including molecular clusters, nano-particles and nano-composites; and, papers focused on non-linear processes in medico-biological systems, including mathematical models of ferments, amino acids, blood fluids and polynucleic chains.

  11. Delta-tilde interpretation of standard linear mixed model results

    Brockhoff, Per Bruun; Amorim, Isabel de Sousa; Kuznetsova, Alexandra

    2016-01-01

    effects relative to the residual error and to choose the proper effect size measure. For multi-attribute bar plots of F-statistics this amounts, in balanced settings, to a simple transformation of the bar heights to get them transformed into depicting what can be seen as approximately the average pairwise...... data set and compared to actual d-prime calculations based on Thurstonian regression modeling through the ordinal package. For more challenging cases we offer a generic "plug-in" implementation of a version of the method as part of the R-package SensMixed. We discuss and clarify the bias mechanisms...

  12. lmerTest Package: Tests in Linear Mixed Effects Models

    Kuznetsova, Alexandra; Brockhoff, Per B.; Christensen, Rune Haubo Bojesen

    2017-01-01

    One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions...... by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using...

  13. Modelling time course gene expression data with finite mixtures of linear additive models.

    Grün, Bettina; Scharl, Theresa; Leisch, Friedrich

    2012-01-15

    A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time. The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).

  14. Using Set Covering with Item Sampling to Analyze the Infeasibility of Linear Programming Test Assembly Models

    Huitzing, Hiddo A.

    2004-01-01

    This article shows how set covering with item sampling (SCIS) methods can be used in the analysis and preanalysis of linear programming models for test assembly (LPTA). LPTA models can construct tests, fulfilling a set of constraints set by the test assembler. Sometimes, no solution to the LPTA model exists. The model is then said to be…

  15. The Simulation and Correction to the Brain Deformation Based on the Linear Elastic Model in IGS

    MU Xiao-lan; SONG Zhi-jian

    2004-01-01

    @@ The brain deformation is a vital factor affecting the precision of the IGS and it becomes a hotspot to simulate and correct the brain deformation recently.The research organizations, which firstly resolved the brain deformation with the physical models, have the Image Processing and Analysis department of Yale University, Biomedical Modeling Lab of Vanderbilt University and so on. The former uses the linear elastic model; the latter uses the consolidation model.The linear elastic model only needs to drive the model using the surface displacement of exposed brain cortex,which is more convenient to be measured in the clinic.

  16. Non-linear σ-models and string theories

    Sen, A.

    1986-10-01

    The connection between σ-models and string theories is discussed, as well as how the σ-models can be used as tools to prove various results in string theories. Closed bosonic string theory in the light cone gauge is very briefly introduced. Then, closed bosonic string theory in the presence of massless background fields is discussed. The light cone gauge is used, and it is shown that in order to obtain a Lorentz invariant theory, the string theory in the presence of background fields must be described by a two-dimensional conformally invariant theory. The resulting constraints on the background fields are found to be the equations of motion of the string theory. The analysis is extended to the case of the heterotic string theory and the superstring theory in the presence of the massless background fields. It is then shown how to use these results to obtain nontrivial solutions to the string field equations. Another application of these results is shown, namely to prove that the effective cosmological constant after compactification vanishes as a consequence of the classical equations of motion of the string theory. 34 refs

  17. Huffman and linear scanning methods with statistical language models.

    Roark, Brian; Fried-Oken, Melanie; Gibbons, Chris

    2015-03-01

    Current scanning access methods for text generation in AAC devices are limited to relatively few options, most notably row/column variations within a matrix. We present Huffman scanning, a new method for applying statistical language models to binary-switch, static-grid typing AAC interfaces, and compare it to other scanning options under a variety of conditions. We present results for 16 adults without disabilities and one 36-year-old man with locked-in syndrome who presents with complex communication needs and uses AAC scanning devices for writing. Huffman scanning with a statistical language model yielded significant typing speedups for the 16 participants without disabilities versus any of the other methods tested, including two row/column scanning methods. A similar pattern of results was found with the individual with locked-in syndrome. Interestingly, faster typing speeds were obtained with Huffman scanning using a more leisurely scan rate than relatively fast individually calibrated scan rates. Overall, the results reported here demonstrate great promise for the usability of Huffman scanning as a faster alternative to row/column scanning.

  18. Modeling and non-linear responses of MEMS capacitive accelerometer

    Sri Harsha C.

    2014-01-01

    Full Text Available A theoretical investigation of an electrically actuated beam has been illustrated when the electrostatic-ally actuated micro-cantilever beam is separated from the electrode by a moderately large gap for two distinct types of geometric configurations of MEMS accelerometer. Higher order nonlinear terms have been taken into account for studying the pull in voltage analysis. A nonlinear model of gas film squeezing damping, another source of nonlinearity in MEMS devices is included in obtaining the dynamic responses. Moreover, in the present work, the possible source of nonlinearities while formulating the mathematical model of a MEMS accelerometer and their influences on the dynamic responses have been investigated. The theoretical results obtained by using MATLAB has been verified with the results obtained in FE software and has been found in good agreement. Criterion towards stable micro size accelerometer for each configuration has been investigated. This investigation clearly provides an understanding of nonlinear static and dynamics characteristics of electrostatically micro cantilever based device in MEMS.

  19. Isobio software: biological dose distribution and biological dose volume histogram from physical dose conversion using linear-quadratic-linear model.

    Jaikuna, Tanwiwat; Khadsiri, Phatchareewan; Chawapun, Nisa; Saekho, Suwit; Tharavichitkul, Ekkasit

    2017-02-01

    To develop an in-house software program that is able to calculate and generate the biological dose distribution and biological dose volume histogram by physical dose conversion using the linear-quadratic-linear (LQL) model. The Isobio software was developed using MATLAB version 2014b to calculate and generate the biological dose distribution and biological dose volume histograms. The physical dose from each voxel in treatment planning was extracted through Computational Environment for Radiotherapy Research (CERR), and the accuracy was verified by the differentiation between the dose volume histogram from CERR and the treatment planning system. An equivalent dose in 2 Gy fraction (EQD 2 ) was calculated using biological effective dose (BED) based on the LQL model. The software calculation and the manual calculation were compared for EQD 2 verification with pair t -test statistical analysis using IBM SPSS Statistics version 22 (64-bit). Two and three-dimensional biological dose distribution and biological dose volume histogram were displayed correctly by the Isobio software. Different physical doses were found between CERR and treatment planning system (TPS) in Oncentra, with 3.33% in high-risk clinical target volume (HR-CTV) determined by D 90% , 0.56% in the bladder, 1.74% in the rectum when determined by D 2cc , and less than 1% in Pinnacle. The difference in the EQD 2 between the software calculation and the manual calculation was not significantly different with 0.00% at p -values 0.820, 0.095, and 0.593 for external beam radiation therapy (EBRT) and 0.240, 0.320, and 0.849 for brachytherapy (BT) in HR-CTV, bladder, and rectum, respectively. The Isobio software is a feasible tool to generate the biological dose distribution and biological dose volume histogram for treatment plan evaluation in both EBRT and BT.

  20. How linear response shaped models of neural circuits and the quest for alternatives.

    Herfurth, Tim; Tchumatchenko, Tatjana

    2017-10-01

    In the past decades, many mathematical approaches to solve complex nonlinear systems in physics have been successfully applied to neuroscience. One of these tools is the concept of linear response functions. However, phenomena observed in the brain emerge from fundamentally nonlinear interactions and feedback loops rather than from a composition of linear filters. Here, we review the successes achieved by applying the linear response formalism to topics, such as rhythm generation and synchrony and by incorporating it into models that combine linear and nonlinear transformations. We also discuss the challenges encountered in the linear response applications and argue that new theoretical concepts are needed to tackle feedback loops and non-equilibrium dynamics which are experimentally observed in neural networks but are outside of the validity regime of the linear response formalism. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Reflexion on linear regression trip production modelling method for ensuring good model quality

    Suprayitno, Hitapriya; Ratnasari, Vita

    2017-11-01

    Transport Modelling is important. For certain cases, the conventional model still has to be used, in which having a good trip production model is capital. A good model can only be obtained from a good sample. Two of the basic principles of a good sampling is having a sample capable to represent the population characteristics and capable to produce an acceptable error at a certain confidence level. It seems that this principle is not yet quite understood and used in trip production modeling. Therefore, investigating the Trip Production Modelling practice in Indonesia and try to formulate a better modeling method for ensuring the Model Quality is necessary. This research result is presented as follows. Statistics knows a method to calculate span of prediction value at a certain confidence level for linear regression, which is called Confidence Interval of Predicted Value. The common modeling practice uses R2 as the principal quality measure, the sampling practice varies and not always conform to the sampling principles. An experiment indicates that small sample is already capable to give excellent R2 value and sample composition can significantly change the model. Hence, good R2 value, in fact, does not always mean good model quality. These lead to three basic ideas for ensuring good model quality, i.e. reformulating quality measure, calculation procedure, and sampling method. A quality measure is defined as having a good R2 value and a good Confidence Interval of Predicted Value. Calculation procedure must incorporate statistical calculation method and appropriate statistical tests needed. A good sampling method must incorporate random well distributed stratified sampling with a certain minimum number of samples. These three ideas need to be more developed and tested.

  2. Evaluation of a Linear Mixing Model to Retrieve Soil and Vegetation Temperatures of Land Targets

    Yang, Jinxin; Jia, Li; Cui, Yaokui; Zhou, Jie; Menenti, Massimo

    2014-01-01

    A simple linear mixing model of heterogeneous soil-vegetation system and retrieval of component temperatures from directional remote sensing measurements by inverting this model is evaluated in this paper using observations by a thermal camera. The thermal camera was used to obtain multi-angular TIR (Thermal Infra-Red) images over vegetable and orchard canopies. A whole thermal camera image was treated as a pixel of a satellite image to evaluate the model with the two-component system, i.e. soil and vegetation. The evaluation included two parts: evaluation of the linear mixing model and evaluation of the inversion of the model to retrieve component temperatures. For evaluation of the linear mixing model, the RMSE is 0.2 K between the observed and modelled brightness temperatures, which indicates that the linear mixing model works well under most conditions. For evaluation of the model inversion, the RMSE between the model retrieved and the observed vegetation temperatures is 1.6K, correspondingly, the RMSE between the observed and retrieved soil temperatures is 2.0K. According to the evaluation of the sensitivity of retrieved component temperatures on fractional cover, the linear mixing model gives more accurate retrieval accuracies for both soil and vegetation temperatures under intermediate fractional cover conditions

  3. An R2 statistic for fixed effects in the linear mixed model.

    Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

    2008-12-20

    Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

  4. A model structure for identification of linear models of the UH-60 helicopter in hover and forward flight

    1995-08-01

    A linear model structure applicable to identification of the UH-60 flight : dynamics in hover and forward flight without rotor-state data is developed. The : structure of the model is determined through consideration of the important : dynamic modes ...

  5. ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA

    KOMANG CANDRA IVAN

    2016-05-01

    Full Text Available Circular data are data which the value in form of vector is circular data. Statistic analysis that is used in analyzing circular data is circular statistics analysis. In regression analysis, if any of predictor or response variables or both are circular then the regression analysis used is called circular regression analysis. Observation data in circular statistic which use direction and time units usually don’t satisfy all of the parametric assumptions, thus making nonparametric regression as a good solution. Nonparametric regression function estimation is using epanechnikov kernel estimator for the linier variables and von Mises kernel estimator for the circular variable. This study showed that the result of circular analysis by using circular descriptive statistic is better than common statistic. Multiple circular-linier nonparametric regressions with Epanechnikov and von Mises kernel estimator didn’t create estimation model explicitly as parametric regression does, but create estimation from its observation knots instead.

  6. A Linear Algorithm for Black Scholes Economic Model

    Dumitru FANACHE

    2008-01-01

    Full Text Available The pricing of options is a very important problem encountered in financial domain. The famous Black-Scholes model provides explicit closed form solution for the values of certain (European style call and put options. But for many other options, either there are no closed form solution, or if such closed form solutions exist, the formulas exhibiting them are complicated and difficult to evaluate accurately by conventional methods. The aim of this paper is to study the possibility of obtaining the numerical solution for the Black-Scholes equation in parallel, by means of several processors, using the finite difference method. A comparison between the complexity of the parallel algorithm and the serial one is given.

  7. A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.

    Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S

    2017-06-01

    The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were

  8. A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

    Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

    2018-04-01

    In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

  9. Study of Piezoelectric Vibration Energy Harvester with non-linear conditioning circuit using an integrated model

    Manzoor, Ali; Rafique, Sajid; Usman Iftikhar, Muhammad; Mahmood Ul Hassan, Khalid; Nasir, Ali

    2017-08-01

    Piezoelectric vibration energy harvester (PVEH) consists of a cantilever bimorph with piezoelectric layers pasted on its top and bottom, which can harvest power from vibrations and feed to low power wireless sensor nodes through some power conditioning circuit. In this paper, a non-linear conditioning circuit, consisting of a full-bridge rectifier followed by a buck-boost converter, is employed to investigate the issues of electrical side of the energy harvesting system. An integrated mathematical model of complete electromechanical system has been developed. Previously, researchers have studied PVEH with sophisticated piezo-beam models but employed simplistic linear circuits, such as resistor, as electrical load. In contrast, other researchers have worked on more complex non-linear circuits but with over-simplified piezo-beam models. Such models neglect different aspects of the system which result from complex interactions of its electrical and mechanical subsystems. In this work, authors have integrated the distributed parameter-based model of piezo-beam presented in literature with a real world non-linear electrical load. Then, the developed integrated model is employed to analyse the stability of complete energy harvesting system. This work provides a more realistic and useful electromechanical model having realistic non-linear electrical load unlike the simplistic linear circuit elements employed by many researchers.

  10. A study of the linear free energy model for DNA structures using the generalized Hamiltonian formalism

    Yavari, M., E-mail: yavari@iaukashan.ac.ir [Islamic Azad University, Kashan Branch (Iran, Islamic Republic of)

    2016-06-15

    We generalize the results of Nesterenko [13, 14] and Gogilidze and Surovtsev [15] for DNA structures. Using the generalized Hamiltonian formalism, we investigate solutions of the equilibrium shape equations for the linear free energy model.

  11. Development of demand functions and their inclusion in linear programming forecasting models

    Chamberlin, J.H.

    1976-05-01

    The purpose of the paper is to present a method for including demand directly within a linear programming model, and to use this method to analyze the effect of the Liquid Metal Fast Breeder Reactor upon the nuclear energy system

  12. SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

    As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

  13. Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

    Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

    2017-12-01

    We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

  14. A Linear Programming Approach to Complex Games: An Application to Nuclear Exchange Models

    Oelrich, I

    2002-01-01

    .... Like the MESA model, the exchange is cast in terms of game theory, using linear approximations and an optimal allocation defined by a user-specified objective function Solutions are better using...

  15. Risk evaluations of aging phenomena: The linear aging reliability model and its extensions

    Vesely, W.E.; Wolford, A.J.

    1988-01-01

    A model for component failure rates due to aging mechanisms is developed from basic phenomenological considerations. In the treatment, the occurrences of deterioration are modeled as following a Poisson process. The severity of damage is allowed to have any distribution, however the damage is assumed to accumulate independently. Finally, the failure rate is modeled as being proportional to the accumulated damage. Using this treatment, the linear aging failure rate model is obtained. The applicability of the linear aging model to various mechanisms is discussed. Extensions of the model to cover nonlinear and dependent aging phenomena are also described. The implementability of the linear aging model is demonstrated by applying it to the aging data collected in the U.S. NRC Nuclear Plant Aging Research (NPAR) Program. (orig./HP)

  16. Risk evaluations of aging phenomena: the linear aging reliability model and its extensions

    Vesely, W.E.

    1987-01-01

    A model for component failure rates due to aging mechanisms has been developed from basic phenomenological considerations. In the treatment, the occurrences of deterioration are modeled as following a Poisson process. The severity of damage is allowed to have any distribution, however the damage is assumed to accumulate independently. Finally, the failure rate is modeled as being proportional to the accumulated damage. Using this treatment, the linear aging failure rate model is obtained. The applicability of the linear aging model to various mechanisms is discussed. The model can be extended to cover nonlinear and dependent aging phenomena. The implementability of the linear aging model is demonstrated by applying it to the aging data collected in NRC's Nuclear Plant Aging Research (NPAR) Program. The applications show that aging as observed in collected data have significant effects on the component failure probability and component reliability when aging is not effectively detected and controlled by testing and maintenance

  17. Risk evaluations of aging phenomena: The linear aging reliability model and its extensions

    Vesely, W.E.

    1986-01-01

    A model for component failure rates due to aging mechanisms has been developed from basic phenomenological considerations. In the treatment, the occurrences of deterioration are modeled as following a Poisson process. The severity of damage is allowed to have any distribution, however the damage is assumed to accumulate independently. Finally, the failure rate is modeled as being proportional to the accumulated damage. Using this treatment, the linear aging failure rate model is obtained. The applicability of the linear aging model to various mechanisms is discussed. The model can be extended to cover nonlinear and dependent aging phenomena. The implementability of the linear aging model is demonstrated by applying it of the aging data collected in NRC's Nuclear Plant Aging Research (NPAR) Program. The applications show that aging as observed in collected data have significant effects on the component failure probability and component reliability when aging is not effectively detected and controlled by testing and maintenance

  18. A national-scale model of linear features improves predictions of farmland biodiversity.

    Sullivan, Martin J P; Pearce-Higgins, James W; Newson, Stuart E; Scholefield, Paul; Brereton, Tom; Oliver, Tom H

    2017-12-01

    Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies.We assessed whether a novel spatial dataset mapping linear and woody-linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites, respectively.Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. Synthesis and applications . This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features such as hedgerows will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems and also can potentially be used to assess habitat connectivity.

  19. A componential model of human interaction with graphs: 1. Linear regression modeling

    Gillan, Douglas J.; Lewis, Robert

    1994-01-01

    Task analyses served as the basis for developing the Mixed Arithmetic-Perceptual (MA-P) model, which proposes (1) that people interacting with common graphs to answer common questions apply a set of component processes-searching for indicators, encoding the value of indicators, performing arithmetic operations on the values, making spatial comparisons among indicators, and repsonding; and (2) that the type of graph and user's task determine the combination and order of the components applied (i.e., the processing steps). Two experiments investigated the prediction that response time will be linearly related to the number of processing steps according to the MA-P model. Subjects used line graphs, scatter plots, and stacked bar graphs to answer comparison questions and questions requiring arithmetic calculations. A one-parameter version of the model (with equal weights for all components) and a two-parameter version (with different weights for arithmetic and nonarithmetic processes) accounted for 76%-85% of individual subjects' variance in response time and 61%-68% of the variance taken across all subjects. The discussion addresses possible modifications in the MA-P model, alternative models, and design implications from the MA-P model.

  20. Linear and nonlinear modeling of light propagation in hollow-core photonic crystal fiber

    Roberts, John; Lægsgaard, Jesper

    2009-01-01

    Hollow core photonic crystal fibers (HC-PCFs) find applications which include quantum and non-linear optics, gas detection and short high-intensity laser pulse delivery. Central to most applications is an understanding of the linear and nonlinear optical properties. These require careful modeling....... The intricacies of modeling various forms of HC-PCF are reviewed. An example of linear dispersion engineering, aimed at reducing and flattening the group velocity dispersion, is then presented. Finally, a study of short high intensity pulse delivery using HC-PCF in both dispersive and nonlinear (solitonic...

  1. A gauge model describing N relativistic particles bound by linear forces

    Filippov, A.T.

    1988-01-01

    A relativistic model of N particles bound by linear forces is obtained by applying the gauging procedure to the linear canonical symmteries of a simple (rudimentary) nonrelativistic N-particle Lagrangian extended to relativistic phase space. The new (gauged) Lagrangian is formally Poincare invariant, the Hamiltonian is a linear combination of first-class constraints which are closed with respect to Pisson brackets and generate the localized canonical symmteries. The gauge potentials appear as the Lagrange multipliers of the constraints. Gauge fixing and quantization of the model are also briefly discussed. 11 refs

  2. Predicting musically induced emotions from physiological inputs: linear and neural network models.

    Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

    2013-01-01

    Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

  3. Anti-symmetrically fused model and non-linear integral equations in the three-state Uimin-Sutherland model

    Fujii, Akira; Kluemper, Andreas

    1999-01-01

    We derive the non-linear integral equations determining the free energy of the three-state pure bosonic Uimin-Sutherland model. In order to find a complete set of auxiliary functions, the anti-symmetric fusion procedure is utilized. We solve the non-linear integral equations numerically and see that the low-temperature behavior coincides with that predicted by conformal field theory. The magnetization and magnetic susceptibility are also calculated by means of the non-linear integral equation

  4. Mathematical Modelling and the Learning Trajectory: Tools to Support the Teaching of Linear Algebra

    Cárcamo Bahamonde, Andrea Dorila; Fortuny Aymemí, Josep Maria; Gómez i Urgellés, Joan Vicenç

    2017-01-01

    In this article we present a didactic proposal for teaching linear algebra based on two compatible theoretical models: emergent models and mathematical modelling. This proposal begins with a problematic situation related to the creation and use of secure passwords, which leads students toward the construction of the concepts of spanning set and…

  5. Predicting Longitudinal Change in Language Production and Comprehension in Individuals with Down Syndrome: Hierarchical Linear Modeling.

    Chapman, Robin S.; Hesketh, Linda J.; Kistler, Doris J.

    2002-01-01

    Longitudinal change in syntax comprehension and production skill, measured over six years, was modeled in 31 individuals (ages 5-20) with Down syndrome. The best fitting Hierarchical Linear Modeling model of comprehension uses age and visual and auditory short-term memory as predictors of initial status, and age for growth trajectory. (Contains…

  6. Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

    Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

    2013-01-01

    Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

  7. Measuring Teacher Effectiveness through Hierarchical Linear Models: Exploring Predictors of Student Achievement and Truancy

    Subedi, Bidya Raj; Reese, Nancy; Powell, Randy

    2015-01-01

    This study explored significant predictors of student's Grade Point Average (GPA) and truancy (days absent), and also determined teacher effectiveness based on proportion of variance explained at teacher level model. We employed a two-level hierarchical linear model (HLM) with student and teacher data at level-1 and level-2 models, respectively.…

  8. A Linear Programming Model to Optimize Various Objective Functions of a Foundation Type State Support Program.

    Matzke, Orville R.

    The purpose of this study was to formulate a linear programming model to simulate a foundation type support program and to apply this model to a state support program for the public elementary and secondary school districts in the State of Iowa. The model was successful in producing optimal solutions to five objective functions proposed for…

  9. The effect of workload constraints in linear programming models for production planning

    Jansen, M.M.; Kok, de A.G.; Adan, I.J.B.F.

    2011-01-01

    Linear programming (LP) models for production planning incorporate a model of the manufacturing system that is necessarily deterministic. Although these deterministic models are the current state-of-the-art, it should be recognized that they are used in an environment that is inherently stochastic.

  10. Modeling of non-linear CHP efficiency curves in distributed energy systems

    Milan, Christian; Stadler, Michael; Cardoso, Gonçalo

    2015-01-01

    Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation...... for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation......, which can be even of non-linear nature. Since considering these characteristics would turn the models into non-linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two...

  11. Linearization of the interaction principle: Analytic Jacobians in the 'Radiant' model

    Spurr, R.J.D.; Christi, M.J.

    2007-01-01

    In this paper we present a new linearization of the Radiant radiative transfer model. Radiant uses discrete ordinates for solving the radiative transfer equation in a multiply-scattering anisotropic medium with solar and thermal sources, but employs the adding method (interaction principle) for the stacking of reflection and transmission matrices in a multilayer atmosphere. For the linearization, we show that the entire radiation field is analytically differentiable with respect to any surface or atmospheric parameter for which we require Jacobians (derivatives of the radiance field). Derivatives of the discrete ordinate solutions are based on existing methods developed for the LIDORT radiative transfer models. Linearization of the interaction principle is completely new and constitutes the major theme of the paper. We discuss the application of the Radiant model and its linearization in the Level 2 algorithm for the retrieval of columns of carbon dioxide as the main target of the Orbiting Carbon Observatory (OCO) mission

  12. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

    Kawashima, Issaku; Kumano, Hiroaki

    2017-01-01

    Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  13. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

    Issaku Kawashima

    2017-07-01

    Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  14. GLM Proxy Data Generation: Methods for Stroke/Pulse Level Inter-Comparison of Ground-Based Lightning Reference Networks

    Cummins, Kenneth L.; Carey, Lawrence D.; Schultz, Christopher J.; Bateman, Monte G.; Cecil, Daniel J.; Rudlosky, Scott D.; Petersen, Walter Arthur; Blakeslee, Richard J.; Goodman, Steven J.

    2011-01-01

    In order to produce useful proxy data for the GOES-R Geostationary Lightning Mapper (GLM) in regions not covered by VLF lightning mapping systems, we intend to employ data produced by ground-based (regional or global) VLF/LF lightning detection networks. Before using these data in GLM Risk Reduction tasks, it is necessary to have a quantitative understanding of the performance of these networks, in terms of CG flash/stroke DE, cloud flash/pulse DE, location accuracy, and CLD/CG classification error. This information is being obtained through inter-comparison with LMAs and well-quantified VLF/LF lightning networks. One of our approaches is to compare "bulk" counting statistics on the spatial scale of convective cells, in order to both quantify relative performance and observe variations in cell-based temporal trends provided by each network. In addition, we are using microsecond-level stroke/pulse time correlation to facilitate detailed inter-comparisons at a more-fundamental level. The current development status of our ground-based inter-comparison and evaluation tools will be presented, and performance metrics will be discussed through a comparison of Vaisala s Global Lightning Dataset (GLD360) with the NLDN at locations within and outside the U.S.

  15. Effect of Process Parameters on Friction Model in Computer Simulation of Linear Friction Welding

    A. Yamileva

    2014-07-01

    Full Text Available The friction model is important part of a numerical model of linear friction welding. Its selection determines the accuracy of the results. Existing models employ the classical law of Amonton-Coulomb where the friction coefficient is either constant or linearly dependent on a single parameter. Determination of the coefficient of friction is a time consuming process that requires a lot of experiments. So the feasibility of determinating the complex dependence should be assessing by analysis of effect of approximating law for friction model on simulation results.

  16. New classical r-matrices from integrable non-linear sigma-models

    Laartz, J.; Bordemann, M.; Forger, M.; Schaper, U.

    1993-01-01

    Non-linear sigma models on Riemannian symmetric spaces constitute the most general class of classical non-linear sigma models which are known to be integrable. Using the current algebra structure of these models their canonical structure is analyzed and it is shown that their non-ultralocal fundamental Poisson bracket relation is governed by a field dependent non antisymmetric r-matrix obeying a dynamical Yang Baxter equation. The fundamental Poisson bracket relations and the r-matrix are derived explicitly and a new kind of algebra is found that is supposed to replace the classical Yang Baxter algebra governing the canonical structure of ultralocal models. (Author) 9 refs

  17. The Relationship between Economic Growth and Money Laundering – a Linear Regression Model

    Daniel Rece

    2009-09-01

    Full Text Available This study provides an overview of the relationship between economic growth and money laundering modeled by a least squares function. The report analyzes statistically data collected from USA, Russia, Romania and other eleven European countries, rendering a linear regression model. The study illustrates that 23.7% of the total variance in the regressand (level of money laundering is “explained” by the linear regression model. In our opinion, this model will provide critical auxiliary judgment and decision support for anti-money laundering service systems.

  18. Iterated non-linear model predictive control based on tubes and contractive constraints.

    Murillo, M; Sánchez, G; Giovanini, L

    2016-05-01

    This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Available pressure amplitude of linear compressor based on phasor triangle model

    Duan, C. X.; Jiang, X.; Zhi, X. Q.; You, X. K.; Qiu, L. M.

    2017-12-01

    The linear compressor for cryocoolers possess the advantages of long-life operation, high efficiency, low vibration and compact structure. It is significant to study the match mechanisms between the compressor and the cold finger, which determines the working efficiency of the cryocooler. However, the output characteristics of linear compressor are complicated since it is affected by many interacting parameters. The existing matching methods are simplified and mainly focus on the compressor efficiency and output acoustic power, while neglecting the important output parameter of pressure amplitude. In this study, a phasor triangle model basing on analyzing the forces of the piston is proposed. It can be used to predict not only the output acoustic power, the efficiency, but also the pressure amplitude of the linear compressor. Calculated results agree well with the measurement results of the experiment. By this phasor triangle model, the theoretical maximum output pressure amplitude of the linear compressor can be calculated simply based on a known charging pressure and operating frequency. Compared with the mechanical and electrical model of the linear compressor, the new model can provide an intuitionistic understanding on the match mechanism with faster computational process. The model can also explain the experimental phenomenon of the proportional relationship between the output pressure amplitude and the piston displacement in experiments. By further model analysis, such phenomenon is confirmed as an expression of the unmatched design of the compressor. The phasor triangle model may provide an alternative method for the compressor design and matching with the cold finger.

  20. Wireless Positioning Based on a Segment-Wise Linear Approach for Modeling the Target Trajectory

    Figueiras, Joao; Pedersen, Troels; Schwefel, Hans-Peter

    2008-01-01

    Positioning solutions in infrastructure-based wireless networks generally operate by exploiting the channel information of the links between the Wireless Devices and fixed networking Access Points. The major challenge of such solutions is the modeling of both the noise properties of the channel...... measurements and the user mobility patterns. One class of typical human being movement patterns is the segment-wise linear approach, which is studied in this paper. Current tracking solutions, such as the Constant Velocity model, hardly handle such segment-wise linear patterns. In this paper we propose...... a segment-wise linear model, called the Drifting Points model. The model results in an increased performance when compared with traditional solutions....

  1. Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

    Yang, Runhua; Cohn, Stephen E.; daSilva, Arlindo; Joiner, Joanna; Houser, Paul R.

    1997-01-01

    The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface

  2. Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models.

    Godin, Bruno; Mayer, Frédéric; Agneessens, Richard; Gerin, Patrick; Dardenne, Pierre; Delfosse, Philippe; Delcarte, Jérôme

    2015-01-01

    The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models.

    Wilson, Machelle D; Sethi, Sunjay; Lein, Pamela J; Keil, Kimberly P

    2017-03-01

    The Sholl technique is widely used to quantify dendritic morphology. Data from such studies, which typically sample multiple neurons per animal, are often analyzed using simple linear models. However, simple linear models fail to account for intra-class correlation that occurs with clustered data, which can lead to faulty inferences. Mixed effects models account for intra-class correlation that occurs with clustered data; thus, these models more accurately estimate the standard deviation of the parameter estimate, which produces more accurate p-values. While mixed models are not new, their use in neuroscience has lagged behind their use in other disciplines. A review of the published literature illustrates common mistakes in analyses of Sholl data. Analysis of Sholl data collected from Golgi-stained pyramidal neurons in the hippocampus of male and female mice using both simple linear and mixed effects models demonstrates that the p-values and standard deviations obtained using the simple linear models are biased downwards and lead to erroneous rejection of the null hypothesis in some analyses. The mixed effects approach more accurately models the true variability in the data set, which leads to correct inference. Mixed effects models avoid faulty inference in Sholl analysis of data sampled from multiple neurons per animal by accounting for intra-class correlation. Given the widespread practice in neuroscience of obtaining multiple measurements per subject, there is a critical need to apply mixed effects models more widely. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Linear models of income patterns in consumer demand for foods and evaluation of its elasticity

    Pavel Syrovátka

    2005-01-01

    Full Text Available The paper is focused on the use of the linear constructions for developing of Engel’s demand models in the field of the food-consumer demand. In the theoretical part of the paper, the linear approximations of this demand models are analysed on the bases of the linear interpolation. In the same part of this text, the hyperbolic elasticity function was defined for the linear Engel model. The behaviour of the hyperbolic elasticity function and its properties were consequently investigated too. The behaviour of the determined elasticity function was investigated according to the values of the intercept point and the direction parameter in the original linear Engel model. The obtained theoretical findings were tested using the real data of Czech Statistical Office. The developed linear Engel model was explicitly dynamised, because the achieved database was formed into the time series. With respect to the two variables definitions of the hyperbolic function in the theoretical part of the text, the determined dynamic model of the Engel demand for food was transformed into the form with parametric intercept point:ret* = At + 0.0946 · rmt*,where the values of absolute member are defined as:At = 1773.0973 + 9.3064 · t – 0.3023 · t2; (t = 1, 2, ... 32.The value of At in the parametric linear model of Engel consumer demand for food was during the observed period (1995–2002 always positive. Thus, the hyperbolic elasticity function achieved the elasticity coefficients from the interval:ηt ∈〈+0; +1.Within quantitative analysis of Engel demand for food in the Czech Republic during the given time period, it was founded, that income elasticity of food expenditures of the average Czech household was moved between +0.4080 and +0.4511. The Czech-household demand for food is thus income inelastic with the normal income reactions.

  5. Thermal radiation analysis for small satellites with single-node model using techniques of equivalent linearization

    Anh, N.D.; Hieu, N.N.; Chung, P.N.; Anh, N.T.

    2016-01-01

    Highlights: • Linearization criteria are presented for a single-node model of satellite thermal. • A nonlinear algebraic system for linearization coefficients is obtained. • The temperature evolutions obtained from different methods are explored. • The temperature mean and amplitudes versus the heat capacity are discussed. • The dual criterion approach yields smaller errors than other approximate methods. - Abstract: In this paper, the method of equivalent linearization is extended to the thermal analysis of satellite using both conventional and dual criteria of linearization. These criteria are applied to a differential nonlinear equation of single-node model of the heat transfer of a small satellite in the Low Earth Orbit. A system of nonlinear algebraic equations for linearization coefficients is obtained in the closed form and then solved by the iteration method. The temperature evolution, average values and amplitudes versus the heat capacity obtained by various approaches including Runge–Kutta algorithm, conventional and dual criteria of equivalent linearization, and Grande's approach are compared together. Numerical results reveal that temperature responses obtained from the method of linearization and Grande's approach are quite close to those obtained from the Runge–Kutta method. The dual criterion yields smaller errors than those of the remaining methods when the nonlinearity of the system increases, namely, when the heat capacity varies in the range [1.0, 3.0] × 10 4  J K −1 .

  6. OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

    Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

    2012-01-01

    The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

  7. Taming waveform inversion non-linearity through phase unwrapping of the model and objective functions

    Alkhalifah, Tariq Ali

    2012-09-25

    Traveltime inversion focuses on the geometrical features of the waveform (traveltimes), which is generally smooth, and thus, tends to provide averaged (smoothed) information of the model. On other hand, general waveform inversion uses additional elements of the wavefield including amplitudes to extract higher resolution information, but this comes at the cost of introducing non-linearity to the inversion operator, complicating the convergence process. We use unwrapped phase-based objective functions in waveform inversion as a link between the two general types of inversions in a domain in which such contributions to the inversion process can be easily identified and controlled. The instantaneous traveltime is a measure of the average traveltime of the energy in a trace as a function of frequency. It unwraps the phase of wavefields yielding far less non-linearity in the objective function than that experienced with conventional wavefields, yet it still holds most of the critical wavefield information in its frequency dependency. However, it suffers from non-linearity introduced by the model (or reflectivity), as reflections from independent events in our model interact with each other. Unwrapping the phase of such a model can mitigate this non-linearity as well. Specifically, a simple modification to the inverted domain (or model), can reduce the effect of the model-induced non-linearity and, thus, make the inversion more convergent. Simple numerical examples demonstrate these assertions.

  8. Taming waveform inversion non-linearity through phase unwrapping of the model and objective functions

    Alkhalifah, Tariq Ali; Choi, Yun Seok

    2012-01-01

    Traveltime inversion focuses on the geometrical features of the waveform (traveltimes), which is generally smooth, and thus, tends to provide averaged (smoothed) information of the model. On other hand, general waveform inversion uses additional elements of the wavefield including amplitudes to extract higher resolution information, but this comes at the cost of introducing non-linearity to the inversion operator, complicating the convergence process. We use unwrapped phase-based objective functions in waveform inversion as a link between the two general types of inversions in a domain in which such contributions to the inversion process can be easily identified and controlled. The instantaneous traveltime is a measure of the average traveltime of the energy in a trace as a function of frequency. It unwraps the phase of wavefields yielding far less non-linearity in the objective function than that experienced with conventional wavefields, yet it still holds most of the critical wavefield information in its frequency dependency. However, it suffers from non-linearity introduced by the model (or reflectivity), as reflections from independent events in our model interact with each other. Unwrapping the phase of such a model can mitigate this non-linearity as well. Specifically, a simple modification to the inverted domain (or model), can reduce the effect of the model-induced non-linearity and, thus, make the inversion more convergent. Simple numerical examples demonstrate these assertions.

  9. Nonlinear aeroacoustic characterization of Helmholtz resonators with a local-linear neuro-fuzzy network model

    Förner, K.; Polifke, W.

    2017-10-01

    The nonlinear acoustic behavior of Helmholtz resonators is characterized by a data-based reduced-order model, which is obtained by a combination of high-resolution CFD simulation and system identification. It is shown that even in the nonlinear regime, a linear model is capable of describing the reflection behavior at a particular amplitude with quantitative accuracy. This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels. A so-called fuzzy-neuron layer distributes the input signal over the linear submodels, depending on the root mean square of the particle velocity at the resonator surface. The resulting model structure is referred to as an local-linear neuro-fuzzy network. System identification techniques are used to estimate the free parameters of this model from training data. The training data are generated by CFD simulations of the resonator, with persistent acoustic excitation over a wide range of frequencies and sound pressure levels. The estimated nonlinear, reduced-order models show good agreement with CFD and experimental data over a wide range of amplitudes for several test cases.

  10. On Active Surge Control of Compression Systems via Characteristic Linearization and Model Nonlinearity Cancellation

    Yohannes S.M. Simamora

    2014-09-01

    Full Text Available A simple approach of active surge control of compression systems is presented. Specifically, nonlinear components of the pressure ratio and rotating speed states of the Moore-Greitzer model are transferred into the input vectors. Subsequently, the compressor characteristic is linearized into two modes, which describe the stable region and the unstable region respectively. As a result, the system’s state and input matrices both appear linear, to which linear realization and analysis are applicable. A linear quadratic regulator plus integrator is then chosen as closed-loop controller. By simulation it was shown that the modified model and characteristics can describe surge behavior, while the closed-loop controller can stabilize the system in the unstable operating region. The last-mentioned was achieved when massflow was 5.38 per cent less than the surge point.

  11. An axisymmetrical non-linear finite element model for induction heating in injection molding tools

    Guerrier, Patrick; Nielsen, Kaspar Kirstein; Menotti, Stefano

    2016-01-01

    To analyze the heating and cooling phase of an induction heated injection molding tool accurately, the temperature dependent magnetic properties, namely the non-linear B-H curves, need to be accounted for in an induction heating simulation. Hence, a finite element model has been developed......, including the non-linear temperature dependent magnetic data described by a three-parameter modified Frohlich equation fitted to the magnetic saturation curve, and solved with an iterative procedure. The numerical calculations are compared with experiments conducted with two types of induction coils, built...... in to the injection molding tool. The model shows very good agreement with the experimental temperature measurements. It is also shown that the non-linearity can be used without the temperature dependency in some cases, and a proposed method is presented of how to estimate an effective linear permeability to use...

  12. Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data

    Ivan Castillo-Riffart

    2017-07-01

    Full Text Available Peatlands are ecosystems of great relevance, because they have an important number of ecological functions that provide many services to mankind. However, studies focusing on plant diversity, addressed from the remote sensing perspective, are still scarce in these environments. In the present study, predictions of vascular plant richness and diversity were performed in three anthropogenic peatlands on Chiloé Island, Chile, using free satellite data from the sensors OLI, ASTER, and MSI. Also, we compared the suitability of these sensors using two modeling methods: random forest (RF and the generalized linear model (GLM. As predictors for the empirical models, we used the spectral bands, vegetation indices and textural metrics. Variable importance was estimated using recursive feature elimination (RFE. Fourteen out of the 17 predictors chosen by RFE were textural metrics, demonstrating the importance of the spatial context to predict species richness and diversity. Non-significant differences were found between the algorithms; however, the GLM models often showed slightly better results than the RF. Predictions obtained by the different satellite sensors did not show significant differences; nevertheless, the best models were obtained with ASTER (richness: R2 = 0.62 and %RMSE = 17.2, diversity: R2 = 0.71 and %RMSE = 20.2, obtained with RF and GLM respectively, followed by OLI and MSI. Diversity obtained higher accuracies than richness; nonetheless, accurate predictions were achieved for both, demonstrating the potential of free satellite data for the prediction of relevant community characteristics in anthropogenic peatland ecosystems.

  13. su(1,2) Algebraic Structure of XYZ Antiferromagnetic Model in Linear Spin-Wave Frame

    Jin Shuo; Xie Binghao; Yu Zhaoxian; Hou Jingmin

    2008-01-01

    The XYZ antiferromagnetic model in linear spin-wave frame is shown explicitly to have an su(1,2) algebraic structure: the Hamiltonian can be written as a linear function of the su(1,2) algebra generators. Based on it, the energy eigenvalues are obtained by making use of the similar transformations, and the algebraic diagonalization method is investigated. Some numerical solutions are given, and the results indicate that only one group solution could be accepted in physics

  14. Model-Checking of Linear-Time Properties in Multi-Valued Systems

    Li, Yongming; Droste, Manfred; Lei, Lihui

    2012-01-01

    In this paper, we study model-checking of linear-time properties in multi-valued systems. Safety property, invariant property, liveness property, persistence and dual-persistence properties in multi-valued logic systems are introduced. Some algorithms related to the above multi-valued linear-time properties are discussed. The verification of multi-valued regular safety properties and multi-valued $\\omega$-regular properties using lattice-valued automata are thoroughly studied. Since the law o...

  15. Vibration Stabilization of a Mechanical Model of a X-Band Linear Collider Final Focus Magnet

    Frisch, J; Decker, V; Hendrickson, L; Markiewicz, T W; Partridge, R; Seryi, Andrei

    2004-01-01

    The small beam sizes at the interaction point of a X-band linear collider require mechanical stabilization of the final focus magnets at the nanometer level. While passive systems provide adequate performance at many potential sites, active mechanical stabilization is useful if the natural or cultural ground vibration is higher than expected. A mechanical model of a room temperature linear collider final focus magnet has been constructed and actively stabilized with an accelerometer based system.

  16. Vibration Stabilization of a Mechanical Model of a X-Band Linear Collider Final Focus Magnet

    Frisch, Josef; Chang, Allison; Decker, Valentin; Doyle, Eric; Eriksson, Leif; Hendrickson, Linda; Himel, Thomas; Markiewicz, Thomas; Partridge, Richard; Seryi, Andrei; SLAC

    2006-01-01

    The small beam sizes at the interaction point of a X-band linear collider require mechanical stabilization of the final focus magnets at the nanometer level. While passive systems provide adequate performance at many potential sites, active mechanical stabilization is useful if the natural or cultural ground vibration is higher than expected. A mechanical model of a room temperature linear collider final focus magnet has been constructed and actively stabilized with an accelerometer based system

  17. Renormalization a la BRS of the non-linear σ-model

    Blasi, A.; Collina, R.

    1987-01-01

    We characterize the non-linear O(N+1) σ-model in an arbitrary parametrization with a nihilpotent BRS operator obtained from the symmetry transformation by the use of anticommuting parameters. The identity can be made compatible with the presence of a mass term in the model, so we can analyze its stability and prove that the model is anomaly free. This procedure avoids many problems encountered in the conventional analysis; in particular the introduction of an infinite number of sources coupled to the successive variations of the field is not necessary and the linear O(N) symmetry is respected as a consequence of the identity. The approach may provide useful in discussing the renormalizability of a wider class of models with non-linear symmetries. (orig.)

  18. Finiteness of Ricci flat supersymmetric non-linear sigma-models

    Alvarez-Gaume, L.; Ginsparg, P.

    1985-01-01

    Combining the constraints of Kaehler differential geometry with the universality of the normal coordinate expansion in the background field method, we study the ultraviolet behavior of 2-dimensional supersymmetric non-linear sigma-models with target space an arbitrary riemannian manifold M. We show that the constraint of N=2 supersymmetry requires that all counterterms to the metric beyond one-loop order are cohomologically trivial. It follows that such supersymmetric non-linear sigma-models defined on locally symmetric spaces are super-renormalizable and that N=4 models are on-shell ultraviolet finite to all orders of perturbation theory. (orig.)

  19. A quantitative analysis of instabilities in the linear chiral sigma model

    Nemes, M.C.; Nielsen, M.; Oliveira, M.M. de; Providencia, J. da

    1990-08-01

    We present a method to construct a complete set of stationary states corresponding to small amplitude motion which naturally includes the continuum solution. The energy wheighted sum rule (EWSR) is shown to provide for a quantitative criterium on the importance of instabilities which is known to occur in nonasymptotically free theories. Out results for the linear σ model showed be valid for a large class of models. A unified description of baryon and meson properties in terms of the linear σ model is also given. (author)

  20. Warped Linear Prediction of Physical Model Excitations with Applications in Audio Compression and Instrument Synthesis

    Glass, Alexis; Fukudome, Kimitoshi

    2004-12-01

    A sound recording of a plucked string instrument is encoded and resynthesized using two stages of prediction. In the first stage of prediction, a simple physical model of a plucked string is estimated and the instrument excitation is obtained. The second stage of prediction compensates for the simplicity of the model in the first stage by encoding either the instrument excitation or the model error using warped linear prediction. These two methods of compensation are compared with each other, and to the case of single-stage warped linear prediction, adjustments are introduced, and their applications to instrument synthesis and MPEG4's audio compression within the structured audio format are discussed.