Unstable volatility functions: the break preserving local linear estimator
DEFF Research Database (Denmark)
Casas, Isabel; Gijbels, Irene
The objective of this paper is to introduce the break preserving local linear (BPLL) estimator for the estimation of unstable volatility functions. Breaks in the structure of the conditional mean and/or the volatility functions are common in Finance. Markov switching models (Hamilton, 1989) and t...
Estimating monotonic rates from biological data using local linear regression.
Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R
2017-03-01
Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.
Error Estimation for the Linearized Auto-Localization Algorithm
Directory of Open Access Journals (Sweden)
Fernando Seco
2012-02-01
Full Text Available The Linearized Auto-Localization (LAL algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs, using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL, the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method.
Bistatic Sonar Localization Based on Best Linear Unbiased Estimation
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A best linear unbiased estimation (BLUE) algorithm for bistatic sonar localization is proposed. The Cramer-Rao bound for bistatic sonar and the geometrical dilution of precision (GDOP) in different conditions are given. The simulation results show that the location accuracy of BLUE algorithm is higher than the weighted least square method.
Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan
2012-01-01
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.
Linear Minimum variance estimation fusion
Institute of Scientific and Technical Information of China (English)
ZHU Yunmin; LI Xianrong; ZHAO Juan
2004-01-01
This paper shows that a general mulitisensor unbiased linearly weighted estimation fusion essentially is the linear minimum variance (LMV) estimation with linear equality constraint, and the general estimation fusion formula is developed by extending the Gauss-Markov estimation to the random paramem of distributed estimation fusion in the LMV setting.In this setting ,the fused estimator is a weighted sum of local estimatess with a matrix quadratic optimization problem subject to a convex linear equality constraint. Second, we present a unique solution to the above optimization problem, which depends only on the covariance matrixCK. Third, if a priori information, the expectation and covariance, of the estimated quantity is unknown, a necessary and sufficient condition for the above LMV fusion becoming the best unbiased LMV estimation with dnown prior information as the above is presented. We also discuss the generality and usefulness of the LMV fusion formulas developed. Finally, we provied and off-line recursion of Ck for a class of multisensor linear systems with coupled measurement noises.
A consistent local linear estimator of the covariate adjusted correlation coefficient.
Nguyen, Danh V; Sentürk, Damla
2009-08-01
Consider the correlation between two random variables (X, Y), both not directly observed. One only observes X̃ = φ(1)(U)X + φ(2)(U) and Ỹ = ψ(1)(U)Y + ψ(2)(U), where all four functions {φ(l)(·),ψ(l)(·), l = 1, 2} are unknown/unspecified smooth functions of an observable covariate U. We consider consistent estimation of the correlation between the unobserved variables X and Y, adjusted for the above general dual additive and multiplicative effects of U, based on the observed data (X̃, Ỹ, U).
Liang, Liang; Martin, Caitlin; Wang, Qian; Sun, Wei; Duncan, James
2016-03-01
Aortic valve (AV) disease is a significant cause of morbidity and mortality. The preferred treatment modality for severe AV disease is surgical resection and replacement of the native valve with either a mechanical or tissue prosthetic. In order to develop effective and long-lasting treatment methods, computational analyses, e.g., structural finite element (FE) and computational fluid dynamic simulations, are very effective for studying valve biomechanics. These computational analyses are based on mesh models of the aortic valve, which are usually constructed from 3D CT images though many hours of manual annotation, and therefore an automatic valve shape reconstruction method is desired. In this paper, we present a method for estimating the aortic valve shape from 3D cardiac CT images, which is represented by triangle meshes. We propose a pipeline for aortic valve shape estimation which includes novel algorithms for building local shape dictionaries and for building landmark detectors and curve detectors using local shape dictionaries. The method is evaluated on real patient image dataset using a leave-one-out approach and achieves an average accuracy of 0.69 mm. The work will facilitate automatic patient-specific computational modeling of the aortic valve.
Linear parameter estimation of rational biokinetic functions
Doeswijk, T.G.; Keesman, K.J.
2009-01-01
For rational biokinetic functions such as the Michaelis-Menten equation, in general, a nonlinear least-squares method is a good estimator. However, a major drawback of a nonlinear least-squares estimator is that it can end up in a local minimum. Rearranging and linearizing rational biokinetic
Hippocampus Segmentation Based on Local Linear Mapping
Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin
2017-04-01
We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.
On Bayes linear unbiased estimation of estimable functions for the singular linear model
Institute of Scientific and Technical Information of China (English)
ZHANG Weiping; WEI Laisheng
2005-01-01
The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under mean square error matrix (MSEM)criterion.
Spectral clustering based on local linear approximations
Arias-Castro, Ery; Lerman, Gilad
2010-01-01
In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface, possibly contaminated with outliers. We consider a prototype for a higher-order spectral clustering method based on the residual from a local linear approximation. In an asymptotic setting where the number of points becomes large, we obtain theoretical guaranties for this algorithm and show that, both in terms of separation and robustness to outliers, it outperforms the standard spectral clustering algorithm based on pairwise distances of Ng, Jordan and Weiss (NIPS, 2001). Under some conditions on the dimension of, and the incidence angle at, an intersection, the algorithm is able to recover the intersecting clusters. The optimal choice for some of the tuning parameters depends on the dimension and thickness of the clusters. We provide estimators that come close enough for our purposes. We discuss the cases of clusters of mixed dimensions and of clus...
On Linear Coherent Estimation with Spatial Collaboration
Kar, Swarnendu
2012-01-01
We consider a power-constrained sensor network, consisting of multiple sensor nodes and a fusion center (FC), that is deployed for the purpose of estimating a common random parameter of interest. In contrast to the distributed framework, the sensor nodes are allowed to update their individual observations by (linearly) combining observations from neighboring nodes. The updated observations are communicated to the FC using an analog amplify-and-forward modulation scheme and through a coherent multiple access channel. The optimal collaborative strategy is obtained by minimizing the cumulative transmission power subject to a maximum distortion constraint. For the distributed scenario (i.e., with no observation sharing), the solution reduces to the power-allocation problem considered by [Xiao, TSP08]. Collaboration among neighbors significantly improves power efficiency of the network in the low local-SNR regime, as demonstrated through an insightful example and numerical simulations.
Locally Linear Discriminate Embedding for Face Recognition
Directory of Open Access Journals (Sweden)
Eimad E. Abusham
2009-01-01
Full Text Available A novel method based on the local nonlinear mapping is presented in this research. The method is called Locally Linear Discriminate Embedding (LLDE. LLDE preserves a local linear structure of a high-dimensional space and obtains a compact data representation as accurately as possible in embedding space (low dimensional before recognition. For computational simplicity and fast processing, Radial Basis Function (RBF classifier is integrated with the LLDE. RBF classifier is carried out onto low-dimensional embedding with reference to the variance of the data. To validate the proposed method, CMU-PIE database has been used and experiments conducted in this research revealed the efficiency of the proposed methods in face recognition, as compared to the linear and non-linear approaches.
Deformation in locally convex topological linear spaces
Institute of Scientific and Technical Information of China (English)
DING; Yanheng
2004-01-01
We are concerned with a deformation theory in locally convex topological linear spaces. A special "nice" partition of unity is given. This enables us to construct certain vector fields which are locally Lipschitz continuous with respect to the locally convex topology. The existence, uniqueness and continuous dependence of flows associated to the vector fields are established. Deformations related to strongly indefinite functionals are then obtained. Finally, as applications, we prove some abstract critical point theorems.
Fliess, Michel; Sira-Ramirez, Hebertt
2007-01-01
Non-linear state estimation and some related topics, like parametric estimation, fault diagnosis, and perturbation attenuation, are tackled here via a new methodology in numerical differentiation. The corresponding basic system theoretic definitions and properties are presented within the framework of differential algebra, which permits to handle system variables and their derivatives of any order. Several academic examples and their computer simulations, with on-line estimations, are illustrating our viewpoint.
Localizing the Angular Momentum of Linear Gravity
Butcher, Luke M; Hobson, Michael; 10.1103/PhysRevD.86.084012
2012-01-01
In a previous article [Phys. Rev. D 82 104040 (2010)], we derived an energy-momentum tensor for linear gravity that exhibited positive energy density and causal energy flux. Here we extend this framework by localizing the angular momentum of the linearized gravitational field, deriving a gravitational spin tensor which possesses similarly desirable properties. By examining the local exchange of angular momentum (between matter and gravity) we find that gravitational intrinsic spin is localized, separately from orbital angular momentum, in terms of a gravitational spin tensor. This spin tensor is then uniquely determined by requiring that it obey two simple physically motivated algebraic conditions. Firstly, the spin of an arbitrary (harmonic-gauge) gravitational plane wave is required to flow in the direction of propagation of the wave. Secondly, the spin tensor of any transverse-traceless gravitational field is required to be traceless. (The second condition ensures that local field redefinitions suffice to ...
Surface tensor estimation from linear sections
DEFF Research Database (Denmark)
Kousholt, Astrid; Kiderlen, Markus; Hug, Daniel
From Crofton's formula for Minkowski tensors we derive stereological estimators of translation invariant surface tensors of convex bodies in the n-dimensional Euclidean space. The estimators are based on one-dimensional linear sections. In a design based setting we suggest three types of estimators....... These are based on isotropic uniform random lines, vertical sections, and non-isotropic random lines, respectively. Further, we derive estimators of the specific surface tensors associated with a stationary process of convex particles in the model based setting....
Surface tensor estimation from linear sections
DEFF Research Database (Denmark)
Kousholt, Astrid; Kiderlen, Markus; Hug, Daniel
2015-01-01
From Crofton’s formula for Minkowski tensors we derive stereological estimators of translation invariant surface tensors of convex bodies in the n-dimensional Euclidean space. The estimators are based on one-dimensional linear sections. In a design based setting we suggest three types of estimators....... These are based on isotropic uniform random lines, vertical sections, and non-isotropic random lines, respectively. Further, we derive estimators of the specific surface tensors associated with a stationary process of convex particles in the model based setting....
Fliess, Michel; Join, Cédric; Sira-Ramirez, Hebertt
2008-01-01
International audience; Non-linear state estimation and some related topics, like parametric estimation, fault diagnosis, and perturbation attenuation, are tackled here via a new methodology in numerical differentiation. The corresponding basic system theoretic definitions and properties are presented within the framework of differential algebra, which permits to handle system variables and their derivatives of any order. Several academic examples and their computer simulations, with on-line ...
Spectral Experts for Estimating Mixtures of Linear Regressions
Chaganty, Arun Tejasvi; Liang, Percy
2013-01-01
Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suffer from local optima. In this paper, we develop a new computationally efficient and provably consistent estimator for a mixture of linear regressions, a simple instance of a discriminative latent-variable model. Our approach relies on a low-rank linear regression to recover a symmetric tensor, which can be factorized into the parameters using a tensor power method. We prove rates of ...
Relative linear power contribution with estimation statistics
Lohnberg, P.
1983-01-01
The relative contribution by a noiselessly observed input signal to the power of a possibly disturbed observed stationary output signal from a linear system is expressed into signal spectral densities. Approximations of estimator statistics and derived confidence limits agree fairly well with
Local Linear Regression for Data with AR Errors
Institute of Scientific and Technical Information of China (English)
Runze Li; Yan Li
2009-01-01
In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques.We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one.From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.
Estimation of linear functionals in emission tomography
Energy Technology Data Exchange (ETDEWEB)
Kuruc, A.
1995-08-01
In emission tomography, the spatial distribution of a radioactive tracer is estimated from a finite sample of externally-detected photons. We present an algorithm-independent theory of statistical accuracy attainable in emission tomography that makes minimal assumptions about the underlying image. Let f denote the tracer density as a function of position (i.e., f is the image being estimated). We consider the problem of estimating the linear functional {Phi}(f) {triple_bond} {integral}{phi}(x)f(x) dx, where {phi} is a smooth function, from n independent observations identically distributed according to the Radon transform of f. Assuming only that f is bounded above and below away from 0, we construct statistically efficient estimators for {Phi}(f). By definition, the variance of the efficient estimator is a best-possible lower bound (depending on and f) on the variance of unbiased estimators of {Phi}(f). Our results show that, in general, the efficient estimator will have a smaller variance than the standard estimator based on the filtered-backprojection reconstruction algorithm. The improvement in performance is obtained by exploiting the range properties of the Radon transform.
Variable bandwidth and one-step local M-estimator
Institute of Scientific and Technical Information of China (English)
范剑青; 蒋建成
2000-01-01
A robust version of local linear regression smoothers augmented with variable bandwidth is studied. The proposed method inherits the advantages of local polynomial regression and overcomes the shortcoming of lack of robustness of least-squares techniques. The use of variable bandwidth enhances the flexibility of the resulting local M- estimators and makes them possible to cope well with spatially inho-mogeneous curves, heteroscedastic errors and nonuniform design densities. Under appropriate regularity conditions, it is shown that the proposed estimators exist and are asymptotically normal. Based on the robust estimation equation, one-step local M-estimators are introduced to reduce computational burden. It is demonstrated that the one-step local M-estimators share the same asymptotic distributions as the fully iterative M-estimators, as long as the initial estimators are good enough. In other words, the one-step local M-estimators reduce significantly the computation cost of the fully iterative M-estim
Local Linear Embedding Algorithm with Adaptively Determining Neighborhood
Directory of Open Access Journals (Sweden)
Zhenduo Wang
2014-06-01
Full Text Available Local linear embedding is a kind of very competitive nonlinear dimensionality reduction technique with good representational capacity for a broader range of manifolds and high computational efficiency. However, it is based on the assumption that the whole data manifolds are evenly distributed so that it determines the neighborhood for all points with the same neighborhood size. Accordingly, it fails to nicely deal with most real problems that are unevenly distributed. This paper presents a new approach that takes the general conceptual framework of Hessian locally linear embedding so as to guarantee its correctness in the setting of local isometry for an open connected subset, but dynamically determines the local neighborhood size for each point. This approach estimates the approximate geodesic distance between any two points by the shortest path in the local neighborhood graph, and then determines the neighborhood size for each point by using the relationship between its local estimated geodesic distance matrix and local Euclidean distance matrix. This approach has clear geometry intuition as well as the better performance and stability. It deals with the sparsely sampled or noise contaminated data sets that are often unevenly distributed. The conducted experiments on benchmark data sets validate the proposed approach
An Entropic Estimator for Linear Inverse Problems
Directory of Open Access Journals (Sweden)
Amos Golan
2012-05-01
Full Text Available In this paper we examine an Information-Theoretic method for solving noisy linear inverse estimation problems which encompasses under a single framework a whole class of estimation methods. Under this framework, the prior information about the unknown parameters (when such information exists, and constraints on the parameters can be incorporated in the statement of the problem. The method builds on the basics of the maximum entropy principle and consists of transforming the original problem into an estimation of a probability density on an appropriate space naturally associated with the statement of the problem. This estimation method is generic in the sense that it provides a framework for analyzing non-normal models, it is easy to implement and is suitable for all types of inverse problems such as small and or ill-conditioned, noisy data. First order approximation, large sample properties and convergence in distribution are developed as well. Analytical examples, statistics for model comparisons and evaluations, that are inherent to this method, are discussed and complemented with explicit examples.
Local Linear Regression on Manifolds and its Geometric Interpretation
Cheng, Ming-Yen
2012-01-01
We study nonparametric regression with high-dimensional data, when the predictors lie on an unknown, lower-dimensional manifold. In this context, recently \\cite{aswani_bickel:2011} suggested performing the conventional local linear regression (LLR) in the ambient space and regularizing the estimation problem using information obtained from learning the manifold locally. By contrast, our approach is to reduce the dimensionality first and then construct the LLR directly on a tangent plane approximation to the manifold. Under mild conditions, asymptotic expressions for the conditional mean squared error of the proposed estimator are derived for both the interior and the boundary cases. One implication of these results is that the optimal convergence rate depends only on the intrinsic dimension $d$ of the manifold, but not on the ambient space dimension $p$. Another implication is that the estimator is design adaptive and automatically adapts to the boundary of the unknown manifold. The bias and variance expressi...
Linear perturbations of spatially locally homogeneous spacetimes
Tanimoto, M
2003-01-01
Methods and properties regarding the linear perturbations are discussed for some spatially closed (vacuum) solutions of Einstein's equation. The main focus is on two kinds of spatially locally homogeneous solution; one is the Bianchi III (Thurston's H^2 x R) type, while the other is the Bianchi II (Thurston's Nil) type. With a brief summary of previous results on the Bianchi III perturbations, asymptotic solutions for the gauge-invariant variables for the Bianchi III are shown, with which (in)stability of the background solution is also examined. The issue of linear stability for a Bianchi II solution is still an open problem. To approach it, appropriate eigenfunctions are presented for an explicitly compactified Bianchi II manifold and based on that, some field equations on the Bianchi II background spacetime are studied. Differences between perturbation analyses for Bianchi class B (to which Bianchi III belongs) and class A (to which Bianchi II belongs) are stressed for an intention to be helpful for applic...
Misaligned Image Integration With Local Linear Model.
Baba, Tatsuya; Matsuoka, Ryo; Shirai, Keiichiro; Okuda, Masahiro
2016-05-01
We present a new image integration technique for a flash and long-exposure image pair to capture a dark scene without incurring blurring or noisy artifacts. Most existing methods require well-aligned images for the integration, which is often a burdensome restriction in practical use. We address this issue by locally transferring the colors of the flash images using a small fraction of the corresponding pixels in the long-exposure images. We formulate the image integration as a convex optimization problem with the local linear model. The proposed method makes it possible to integrate the color of the long-exposure image with the detail of the flash image without causing any harmful effects to its contrast, where we do not need perfect alignment between the images by virtue of our new integration principle. We show that our method successfully outperforms the state of the art in the image integration and reference-based color transfer for challenging misaligned data sets.
Modeling local item dependence with the hierarchical generalized linear model.
Jiao, Hong; Wang, Shudong; Kamata, Akihito
2005-01-01
Local item dependence (LID) can emerge when the test items are nested within common stimuli or item groups. This study proposes a three-level hierarchical generalized linear model (HGLM) to model LID when LID is due to such contextual effects. The proposed three-level HGLM was examined by analyzing simulated data sets and was compared with the Rasch-equivalent two-level HGLM that ignores such a nested structure of test items. The results demonstrated that the proposed model could capture LID and estimate its magnitude. Also, the two-level HGLM resulted in larger mean absolute differences between the true and the estimated item difficulties than those from the proposed three-level HGLM. Furthermore, it was demonstrated that the proposed three-level HGLM estimated the ability distribution variance unaffected by the LID magnitude, while the two-level HGLM with no LID consideration increasingly underestimated the ability variance as the LID magnitude increased.
Efficient Quantile Estimation for Functional-Coefficient Partially Linear Regression Models
Institute of Scientific and Technical Information of China (English)
Zhangong ZHOU; Rong JIANG; Weimin QIAN
2011-01-01
The quantile estimation methods are proposed for functional-coefficient partially linear regression (FCPLR) model by combining nonparametric and functional-coefficient regression (FCR) model.The local linear scheme and the integrated method are used to obtain local quantile estimators of all unknown functions in the FCPLR model.These resulting estimators are asymptotically normal,but each of them has big variance.To reduce variances of these quantile estimators,the one-step backfitting technique is used to obtain the efficient quantile estimators of all unknown functions,and their asymptotic normalities are derived.Two simulated examples are carried out to illustrate the proposed estimation methodology.
Local Polynomial Estimation of Distribution Functions
Institute of Scientific and Technical Information of China (English)
LI Yong-hong; ZENG Xia
2007-01-01
Under the condition that the total distribution function is continuous and bounded on (-∞,∞), we constructed estimations for distribution and hazard functions with local polynomial method, and obtained the rate of strong convergence of the estimations.
The improved local linear prediction of chaotic time series
Institute of Scientific and Technical Information of China (English)
Meng Qing-Fang; Peng Yu-Hua; Sun Jia
2007-01-01
Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time aeries. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time aeries and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.
Improved linear least squares estimation using bounded data uncertainty
Ballal, Tarig
2015-04-01
This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.
Time signal filtering by relative neighborhood graph localized linear approximation
DEFF Research Database (Denmark)
Sørensen, John Aasted
1994-01-01
A time signal filtering algorithm based on the relative neighborhood graph (RNG) used for localization of linear filters is proposed. The filter is constructed from a training signal during two stages. During the first stage an RNG is constructed. During the second stage, localized linear filters...
Adjoint Error Estimation for Linear Advection
Energy Technology Data Exchange (ETDEWEB)
Connors, J M; Banks, J W; Hittinger, J A; Woodward, C S
2011-03-30
An a posteriori error formula is described when a statistical measurement of the solution to a hyperbolic conservation law in 1D is estimated by finite volume approximations. This is accomplished using adjoint error estimation. In contrast to previously studied methods, the adjoint problem is divorced from the finite volume method used to approximate the forward solution variables. An exact error formula and computable error estimate are derived based on an abstractly defined approximation of the adjoint solution. This framework allows the error to be computed to an arbitrary accuracy given a sufficiently well resolved approximation of the adjoint solution. The accuracy of the computable error estimate provably satisfies an a priori error bound for sufficiently smooth solutions of the forward and adjoint problems. The theory does not currently account for discontinuities. Computational examples are provided that show support of the theory for smooth solutions. The application to problems with discontinuities is also investigated computationally.
Estimation for the simple linear Boolean model
2006-01-01
We consider the simple linear Boolean model, a fundamental coverage process also known as the Markov/General/infinity queue. In the model, line segments of independent and identically distributed length are located at the points of a Poisson process. The segments may overlap, resulting in a pattern of "clumps"-regions of the line that are covered by one or more segments-alternating with uncovered regions or "spacings". Study and application of the model have been impeded by the difficult...
Local correlation detection with linearity enhancement in streaming data
Xie, Qing
2013-01-01
This paper addresses the challenges in detecting the potential correlation between numerical data streams, which facilitates the research of data stream mining and pattern discovery. We focus on local correlation with delay, which may occur in burst at different time in different streams, and last for a limited period. The uncertainty on the correlation occurrence and the time delay make it diff cult to monitor the correlation online. Furthermore, the conventional correlation measure lacks the ability of ref ecting visual linearity, which is more desirable in reality. This paper proposes effective methods to continuously detect the correlation between data streams. Our approach is based on the Discrete Fourier Transform to make rapid cross-correlation calculation with time delay allowed. In addition, we introduce a shape-based similarity measure into the framework, which ref nes the results by representative trend patterns to enhance the signif cance of linearity. The similarity of proposed linear representations can quickly estimate the correlation, and the window sliding strategy in segment level improves the eff ciency for online detection. The empirical study demonstrates the accuracy of our detection approach, as well as more than 30% improvement of eff ciency. Copyright 2013 ACM.
Variable bandwidth and one-step local M-estimator
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A robust version of local linear regression smoothers augmented with variable bandwidth is studied. The proposed method inherits the advantages of local polynomial regression and overcomes the shortcoming of lack of robustness of least-squares techniques. The use of variable bandwidth enhances the flexibility of the resulting local M-estimators and makes them possible to cope well with spatially inhomogeneous curves, heteroscedastic errors and nonuniform design densities. Under appropriate regularity conditions, it is shown that the proposed estimators exist and are asymptotically normal. Based on the robust estimation equation, one-step local M-estimators are introduced to reduce computational burden. It is demonstrated that the one-step local M-estimators share the same asymptotic distributions as the fully iterative M-estimators, as long as the initial estimators are good enough. In other words, the one-step local M-estimators reduce significantly the computation cost of the fully iterative M-estimators without deteriorating their performance. This fact is also illustrated via simulations.
Linear Factor Models and the Estimation of Expected Returns
Sarisoy, Cisil; de Goeij, Peter; Werker, Bas
2016-01-01
Linear factor models of asset pricing imply a linear relationship between expected returns of assets and exposures to one or more sources of risk. We show that exploiting this linear relationship leads to statistical gains of up to 31% in variances when estimating expected returns on individual asse
Linear Factor Models and the Estimation of Expected Returns
Sarisoy, Cisil; de Goeij, Peter; Werker, Bas
2015-01-01
Estimating expected returns on individual assets or portfolios is one of the most fundamental problems of finance research. The standard approach, using historical averages,produces noisy estimates. Linear factor models of asset pricing imply a linear relationship between expected returns and exposu
Algorithms for non-linear M-estimation
DEFF Research Database (Denmark)
Madsen, Kaj; Edlund, O; Ekblom, H
1997-01-01
a sequence of estimation problems for linearized models is solved. In the testing we apply four estimators to ten non-linear data fitting problems. The test problems are also solved by the Generalized Levenberg-Marquardt method and standard optimization BFGS method. It turns out that the new method...
Robust control of robots via linear estimated state feedback
Berghuis, Harry; Nijmeijer, Henk
1994-01-01
In this note we propose a robust tracking controller for robots that requires only position measurements. The controller consists of two parts: a linear observer part that generates an estimated error state from the error on the joint position and a linear feedback part that utilizes this estimated
Local linear logic for locality consciousness in multiset transformation
McEvoy, H.; Hartel, P.H.
1995-01-01
We use Girard's linear logic (LL) to produce a semantics for Gamma, a multiset transformation language. The semantics improves on the existing structured operational semantics (SOS) of the language by highlighting Gamma's inefficiencies, which were hidden by the SOS. We propose a new logic called lo
Local Linear Logic for Locality Consciousness in Multiset Transformation
McEvoy, H.; Hartel, P.H.
1995-01-01
We use Girard's linear logic (LL) to produce a semantics for Gamma, a multiset transformation language. The semantics improves on the existing structured operational semantics (SOS) of the language by highlighting Gamma's inefficiencies, which were hidden by the SOS. We propose a new logic called lo
Efficient estimation of moments in linear mixed models
Wu, Ping; Zhu, Li-Xing; 10.3150/10-BEJ330
2012-01-01
In the linear random effects model, when distributional assumptions such as normality of the error variables cannot be justified, moments may serve as alternatives to describe relevant distributions in neighborhoods of their means. Generally, estimators may be obtained as solutions of estimating equations. It turns out that there may be several equations, each of them leading to consistent estimators, in which case finding the efficient estimator becomes a crucial problem. In this paper, we systematically study estimation of moments of the errors and random effects in linear mixed models.
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.
Virtual estimator for piecewise linear systems based on observability analysis.
Morales-Morales, Cornelio; Adam-Medina, Manuel; Cervantes, Ilse; Vela-Valdés, Luis G; Beltrán, Carlos Daniel García
2013-02-27
This article proposes a virtual sensor for piecewise linear systems based on observability analysis that is in function of a commutation law related with the system's outpu. This virtual sensor is also known as a state estimator. Besides, it presents a detector of active mode when the commutation sequences of each linear subsystem are arbitrary and unknown. For the previous, this article proposes a set of virtual estimators that discern the commutation paths of the system and allow estimating their output. In this work a methodology in order to test the observability for piecewise linear systems with discrete time is proposed. An academic example is presented to show the obtained results.
Virtual Estimator for Piecewise Linear Systems Based on Observability Analysis
Morales-Morales, Cornelio; Adam-Medina, Manuel; Cervantes, Ilse; Vela-Valdés and, Luis G.; García Beltrán, Carlos Daniel
2013-01-01
This article proposes a virtual sensor for piecewise linear systems based on observability analysis that is in function of a commutation law related with the system's outpu. This virtual sensor is also known as a state estimator. Besides, it presents a detector of active mode when the commutation sequences of each linear subsystem are arbitrary and unknown. For the previous, this article proposes a set of virtual estimators that discern the commutation paths of the system and allow estimating their output. In this work a methodology in order to test the observability for piecewise linear systems with discrete time is proposed. An academic example is presented to show the obtained results. PMID:23447007
Estimating Mutual Information by Local Gaussian Approximation
Gao, Shuyang; Galstyan, Aram
2015-01-01
Estimating mutual information (MI) from samples is a fundamental problem in statistics, machine learning, and data analysis. Recently it was shown that a popular class of non-parametric MI estimators perform very poorly for strongly dependent variables and have sample complexity that scales exponentially with the true MI. This undesired behavior was attributed to the reliance of those estimators on local uniformity of the underlying (and unknown) probability density function. Here we present a novel semi-parametric estimator of mutual information, where at each sample point, densities are {\\em locally} approximated by a Gaussians distribution. We demonstrate that the estimator is asymptotically unbiased. We also show that the proposed estimator has a superior performance compared to several baselines, and is able to accurately measure relationship strengths over many orders of magnitude.
BAND GAP EFFECTS IN PERIODIC CHAIN WITH LOCAL LINEAR OR NON-LINEAR OSCILLATORS
DEFF Research Database (Denmark)
Lazarov, Boyan Stefanov; Jensen, Jakob Søndergaard
2007-01-01
attached linear oscillators. The stop band is located around the resonant frequency of the local oscillators, and thus a stop band can be created in the lower frequency range. In this paper, wave propagation in one-dimensional infinite periodic chains with attached linear and non-linear local oscillators...... within bands of frequencies called stop bands. Stop bands in structures with periodic or random inclusions are located mainly in the high frequency range, as the wave length has to be comparable with the distance between the alternating parts. Wave attenuation is also possible in structures with locally...
A new estimate of the parameters in linear mixed models
Institute of Scientific and Technical Information of China (English)
王松桂; 尹素菊
2002-01-01
In linear mixed models, there are two kinds of unknown parameters: one is the fixed effect, theother is the variance component. In this paper, new estimates of these parameters, called the spectral decom-position estimates, are proposed, Some important statistical properties of the new estimates are established,in particular the linearity of the estimates of the fixed effects with many statistical optimalities. A new methodis applied to two important models which are used in economics, finance, and mechanical fields. All estimatesobtained have good statistical and practical meaning.
Manifold-Based Reinforcement Learning via Locally Linear Reconstruction.
Xu, Xin; Huang, Zhenhua; Zuo, Lei; He, Haibo
2017-04-01
Feature representation is critical not only for pattern recognition tasks but also for reinforcement learning (RL) methods to solve learning control problems under uncertainties. In this paper, a manifold-based RL approach using the principle of locally linear reconstruction (LLR) is proposed for Markov decision processes with large or continuous state spaces. In the proposed approach, an LLR-based feature learning scheme is developed for value function approximation in RL, where a set of smooth feature vectors is generated by preserving the local approximation properties of neighboring points in the original state space. By using the proposed feature learning scheme, an LLR-based approximate policy iteration (API) algorithm is designed for learning control problems with large or continuous state spaces. The relationship between the value approximation error of a new data point and the estimated values of its nearest neighbors is analyzed. In order to compare different feature representation and learning approaches for RL, a comprehensive simulation and experimental study was conducted on three benchmark learning control problems. It is illustrated that under a wide range of parameter settings, the LLR-based API algorithm can obtain better learning control performance than the previous API methods with different feature representation schemes.
Institute of Scientific and Technical Information of China (English)
Juan ZHAO; Yunmin ZHU
2009-01-01
The optimally weighted least squares estimate and the linear minimum variance estimate are two of the most popular estimation methods for a linear model. In this paper, the authors make a comprehensive discussion about the relationship between the two estimates. Firstly, the authors consider the classical linear model in which the coefficient matrix of the linear model is deterministic,and the necessary and sufficient condition for equivalence of the two estimates is derived. Moreover,under certain conditions on variance matrix invertibility, the two estimates can be identical provided that they use the same a priori information of the parameter being estimated. Secondly, the authors consider the linear model with random coefficient matrix which is called the extended linear model;under certain conditions on variance matrix invertibility, it is proved that the former outperforms the latter when using the same a priori information of the parameter.
Is the local linearity of space-time inherited from the linearity of probabilities?
Mueller, Markus P; Hoehn, Philipp A
2016-01-01
The appearance of linear spaces, describing physical quantities by vectors and tensors, is ubiquitous in all of physics, from classical mechanics to the modern notion of local Lorentz invariance. However, as natural as this seems to the physicist, most computer scientists would argue that something like a "local linear tangent space" is not very typical and in fact a quite surprising property of any conceivable world or algorithm. In this paper, we take the perspective of the computer scientist seriously, and ask whether there could be any inherently information-theoretic reason to expect this notion of linearity to appear in physics. We give a series of simple arguments, spanning quantum information theory, group representation theory, and renormalization in quantum gravity, that supports a surprising thesis: namely, that the local linearity of space-time might ultimately be a consequence of the linearity of probabilities. While our arguments involve a fair amount of speculation, they have the virtue of bein...
Nonlinear Alignment and Its Local Linear Iterative Solution
Directory of Open Access Journals (Sweden)
Sumin Zhang
2016-01-01
Full Text Available In manifold learning, the aim of alignment is to derive the global coordinate of manifold from the local coordinates of manifold’s patches. At present, most of manifold learning algorithms assume that the relation between the global and local coordinates is locally linear and based on this linear relation align the local coordinates of manifold’s patches into the global coordinate of manifold. There are two contributions in this paper. First, the nonlinear relation between the manifold’s global and local coordinates is deduced by making use of the differentiation of local pullback functions defined on the differential manifold. Second, the method of local linear iterative alignment is used to align the manifold’s local coordinates into the manifold’s global coordinate. The experimental results presented in this paper show that the errors of noniterative alignment are considerably large and can be reduced to almost zero within the first two iterations. The large errors of noniterative/linear alignment verify the nonlinear nature of alignment and justify the necessity of iterative alignment.
Estimating linear temporal trends from aggregated environmental monitoring data
Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.
2017-01-01
Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.
Discontinuous Galerkin error estimation for linear symmetric hyperbolic systems
Adjerid, Slimane; Weinhart, Thomas
2009-01-01
In this manuscript we present an error analysis for the discontinuous Galerkin discretization error of multi-dimensional first-order linear symmetric hyperbolic systems of partial differential equations. We perform a local error analysis by writing the local error as a series and showing that its le
Vuori, Kaarina; Strandén, Ismo; Sevón-Aimonen, Marja-Liisa; Mäntysaari, Esa A
2006-01-01
A method based on Taylor series expansion for estimation of location parameters and variance components of non-linear mixed effects models was considered. An attractive property of the method is the opportunity for an easily implemented algorithm. Estimation of non-linear mixed effects models can be done by common methods for linear mixed effects models, and thus existing programs can be used after small modifications. The applicability of this algorithm in animal breeding was studied with simulation using a Gompertz function growth model in pigs. Two growth data sets were analyzed: a full set containing observations from the entire growing period, and a truncated time trajectory set containing animals slaughtered prematurely, which is common in pig breeding. The results from the 50 simulation replicates with full data set indicate that the linearization approach was capable of estimating the original parameters satisfactorily. However, estimation of the parameters related to adult weight becomes unstable in the case of a truncated data set.
CONSISTENCY OF LS ESTIMATOR IN SIMPLE LINEAR EV REGRESSION MODELS
Institute of Scientific and Technical Information of China (English)
Liu Jixue; Chen Xiru
2005-01-01
Consistency of LS estimate of simple linear EV model is studied. It is shown that under some common assumptions of the model, both weak and strong consistency of the estimate are equivalent but it is not so for quadratic-mean consistency.
A least squares estimation method for the linear learning model
B. Wierenga (Berend)
1978-01-01
textabstractThe author presents a new method for estimating the parameters of the linear learning model. The procedure, essentially a least squares method, is easy to carry out and avoids certain difficulties of earlier estimation procedures. Applications to three different data sets are reported, a
A Direct Estimation Approach to Sparse Linear Discriminant Analysis
Cai, Tony
2011-01-01
This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix $\\O$ and the difference $\\de$ of the mean vectors, we introduce a simple and effective classifier by estimating the product $\\O\\de$ directly through constrained $\\ell_1$ minimization. The estimator can be implemented efficiently using linear programming and the resulting classifier is called the linear programming discriminant (LPD) rule. The LPD rule is shown to have desirable theoretical and numerical properties. It exploits the approximate sparsity of $\\O\\de$ and as a consequence allows cases where it can still perform well even when $\\O$ and/or $\\de$ cannot be estimated consistently. Asymptotic properties of the LPD rule are investigated and consistency and rate of convergence results are given. The LPD classifier has superior finite sample performance and significant computational advantages over the existing methods that req...
Adaptive Unified Biased Estimators of Parameters in Linear Model
Institute of Scientific and Technical Information of China (English)
Hu Yang; Li-xing Zhu
2004-01-01
To tackle multi collinearity or ill-conditioned design matrices in linear models,adaptive biased estimators such as the time-honored Stein estimator,the ridge and the principal component estimators have been studied intensively.To study when a biased estimator uniformly outperforms the least squares estimator,some suficient conditions are proposed in the literature.In this paper,we propose a unified framework to formulate a class of adaptive biased estimators.This class includes all existing biased estimators and some new ones.A suficient condition for outperforming the least squares estimator is proposed.In terms of selecting parameters in the condition,we can obtain all double-type conditions in the literature.
Indirect linear locally distributed damping of coupled systems
Directory of Open Access Journals (Sweden)
Annick BEYRATH
2004-11-01
Full Text Available The aim of this paper is to prove indirect internal stabilization results for diﬀerent coupled systems with linear locally distributed damping (coupled wave equations, wave equations with diﬀerent speeds of propagation. In our case, a linear local damping term appears only in the ﬁrst equation whereas no damping term is applied to the second one (this is indirect stabilization, see [11]. Using thepiecewise multiplier method we prove that the full system is stabilized and that the total energy of the solution of this system decays polynomially.
Virtual Estimator for Piecewise Linear Systems Based on Observability Analysis
Directory of Open Access Journals (Sweden)
Ilse Cervantes
2013-02-01
Full Text Available This article proposes a virtual sensor for piecewise linear systems based on observability analysis that is in function of a commutation law related with the system’s outpu. This virtual sensor is also known as a state estimator. Besides, it presents a detector of active mode when the commutation sequences of each linear subsystem are arbitrary and unknown. For the previous, this article proposes a set of virtual estimators that discern the commutation paths of the system and allow estimating their output. In this work a methodology in order to test the observability for piecewise linear systems with discrete time is proposed. An academic example is presented to show the obtained results.
ROBUST ESTIMATION IN PARTIAL LINEAR MIXED MODEL FOR LONGITUDINAL DATA
Institute of Scientific and Technical Information of China (English)
Qin Guoyou; Zhu Zhongyi
2008-01-01
In this article, robust generalized estimating equation for the analysis of par- tial linear mixed model for longitudinal data is used. The authors approximate the non- parametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.
Estimation in partial linear EV models with replicated observations
Institute of Scientific and Technical Information of China (English)
CUI; Hengjian
2004-01-01
The aim of this work is to construct the parameter estimators in the partial linear errors-in-variables (EV) models and explore their asymptotic properties. Unlike other related References, the assumption of known error covariance matrix is removed when the sample can be repeatedly drawn at each designed point from the model. The estimators of interested regression parameters, and the model error variance, as well as the nonparametric function, are constructed. Under some regular conditions, all of the estimators prove strongly consistent. Meanwhile, the asymptotic normality for the estimator of regression parameter is also presented. A simulation study is reported to illustrate our asymptotic results.
Adaptive quasi-likelihood estimate in generalized linear models
Institute of Scientific and Technical Information of China (English)
CHEN Xia; CHEN Xiru
2005-01-01
This paper gives a thorough theoretical treatment on the adaptive quasilikelihood estimate of the parameters in the generalized linear models. The unknown covariance matrix of the response variable is estimated by the sample. It is shown that the adaptive estimator defined in this paper is asymptotically most efficient in the sense that it is asymptotic normal, and the covariance matrix of the limit distribution coincides with the one for the quasi-likelihood estimator for the case that the covariance matrix of the response variable is completely known.
Penalized maximum likelihood estimation for generalized linear point processes
DEFF Research Database (Denmark)
Hansen, Niels Richard
2010-01-01
A generalized linear point process is specified in terms of an intensity that depends upon a linear predictor process through a fixed non-linear function. We present a framework where the linear predictor is parametrized by a Banach space and give results on Gateaux differentiability of the log-likelihood....... Of particular interest is when the intensity is expressed in terms of a linear filter parametrized by a Sobolev space. Using that the Sobolev spaces are reproducing kernel Hilbert spaces we derive results on the representation of the penalized maximum likelihood estimator in a special case and the gradient...... of the negative log-likelihood in general. The latter is used to develop a descent algorithm in the Sobolev space. We conclude the paper by extensions to multivariate and additive model specifications. The methods are implemented in the R-package ppstat....
Locally supersymmetric D=3 non-linear sigma models
Wit, B. de; Tollsten, A. K.; Nicolai, H.
1992-01-01
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 Kahler, respectively. All N>2 theories are associated with Einstein spaces. For N=3 the target space is quaternionic, while for N=4 it general
Estimating WISC-IV indexes: proration versus linear scaling.
Glass, Laura A; Ryan, Joseph J; Bartels, Jared M; Morris, Jeri
2008-10-01
This investigation compared proration and linear scaling for estimating Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) verbal comprehension (VCI) and perceptual reasoning (PRI) composites from all relevant two subtest combinations. Using 57 primary school students and 41 clinical referrals, actual VCI and PRI scores were highly correlated with estimated index scores based on proration and linear scaling (all rs> or =.90). In the school sample, significant mean score differences between the actual and estimated composites were found in two comparisons; however, differences between mean scores were less than three points. No significant differences emerged in the clinical sample. Results indicate that any of the two subtest combinations produced reasonably accurate estimates of actual indexes. There was no advantage of one computational method over the other. Copyright 2008 Wiley Periodicals, Inc.
Spectral covolatility estimation from noisy observations using local weights
Bibinger, Markus
2011-01-01
We propose localized spectral estimators for the quadratic covariation and the spot covolatility of diffusion processes which are observed discretely with additive observation noise. The eligibility of this approach to lead to an appropriate estimation for time-varying volatilities stems from an asymptotic equivalence of the underlying statistical model to a white noise model with correlation and volatility processes being constant over small intervals. The asymptotic equivalence of the continuous-time and the discrete-time experiments are proved by a construction with linear interpolation in one direction and local means for the other. The new estimator outperforms earlier nonparametric approaches in the considered model. We investigate its finite sample size characteristics in simulations and draw a comparison between the various proposed methods.
Linearized versus non-linear inverse methods for seismic localization of underground sources
DEFF Research Database (Denmark)
Oh, Geok Lian; Jacobsen, Finn
2013-01-01
The problem of localization of underground sources from seismic measurements detected by several geophones located on the ground surface is addressed. Two main approaches to the solution of the problem are considered: a beamforming approach that is derived from the linearized inversion problem...... Difference elastic wave-field numerical method. In this paper, the accuracy and performance of the linear beamformer and nonlinear inverse methods to localize a underground seismic source are checked and compared using computer generated synthetic experimental data. © 2013 Acoustical Society of America....
Estimation linear model using block generalized inverse of a matrix
Jasińska, Elżbieta; Preweda, Edward
2013-01-01
The work shows the principle of generalized linear model, point estimation, which can be used as a basis for determining the status of movements and deformations of engineering objects. The structural model can be put on any boundary conditions, for example, to ensure the continuity of the deformations. Estimation by the method of least squares was carried out taking into account the terms and conditions of the Gauss- Markov for quadratic forms stored using Lagrange function. The original sol...
Linear irreversible heat engines based on local equilibrium assumptions
Izumida, Yuki; Okuda, Koji
2015-08-01
We formulate an endoreversible finite-time Carnot cycle model based on the assumptions of local equilibrium and constant energy flux, where the efficiency and the power are expressed in terms of the thermodynamic variables of the working substance. By analyzing the entropy production rate caused by the heat transfer in each isothermal process during the cycle, and using the endoreversible condition applied to the linear response regime, we identify the thermodynamic flux and force of the present system and obtain a linear relation that connects them. We calculate the efficiency at maximum power in the linear response regime by using the linear relation, which agrees with the Curzon-Ahlborn (CA) efficiency known as the upper bound in this regime. This reason is also elucidated by rewriting our model into the form of the Onsager relations, where our model turns out to satisfy the tight-coupling condition leading to the CA efficiency.
Admissibilities of linear estimator in a class of linear models with a multivariate t error variable
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This paper discusses admissibilities of estimators in a class of linear models,which include the following common models:the univariate and multivariate linear models,the growth curve model,the extended growth curve model,the seemingly unrelated regression equations,the variance components model,and so on.It is proved that admissible estimators of functions of the regression coefficient β in the class of linear models with multivariate t error terms,called as Model II,are also ones in the case that error terms have multivariate normal distribution under a strictly convex loss function or a matrix loss function.It is also proved under Model II that the usual estimators of β are admissible for p 2 with a quadratic loss function,and are admissible for any p with a matrix loss function,where p is the dimension of β.
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.
SNR Estimation in Linear Systems with Gaussian Matrices
Suliman, Mohamed A.
2017-09-27
This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results show that the proposed method is very accurate.
Is the local linearity of space-time inherited from the linearity of probabilities?
Müller, Markus P.; Carrozza, Sylvain; Höhn, Philipp A.
2017-02-01
The appearance of linear spaces, describing physical quantities by vectors and tensors, is ubiquitous in all of physics, from classical mechanics to the modern notion of local Lorentz invariance. However, as natural as this seems to the physicist, most computer scientists would argue that something like a ‘local linear tangent space’ is not very typical and in fact a quite surprising property of any conceivable world or algorithm. In this paper, we take the perspective of the computer scientist seriously, and ask whether there could be any inherently information-theoretic reason to expect this notion of linearity to appear in physics. We give a series of simple arguments, spanning quantum information theory, group representation theory, and renormalization in quantum gravity, that supports a surprising thesis: namely, that the local linearity of space-time might ultimately be a consequence of the linearity of probabilities. While our arguments involve a fair amount of speculation, they have the virtue of being independent of any detailed assumptions on quantum gravity, and they are in harmony with several independent recent ideas on emergent space-time in high-energy physics.
Institute of Scientific and Technical Information of China (English)
Yee LEUNG; WU Kefa; DONG Tianxin
2001-01-01
In this paper, a multivariate linear functional relationship model, where the covariance matrix of the observational errors is not restricted, is considered. The parameter estimation of this model is discussed. The estimators are shown to be a strongly consistent estimation under some mild conditions on the incidental parameters.
Mode localized MEMS transducers with voltage-controlled linear coupling
Manav, M.; Srikantha Phani, A.; Cretu, E.
2017-05-01
Recent studies have demonstrated mode localized resonant micro-electro-mechanical systems (MEMS) sensing devices with orders of magnitude improvement in sensitivity. Avoided crossings or eigenvalue veering is the physical mechanism exploited to achieve the enhancement in sensitivity of devices operating either in vacuum or in air. The mode localized MEMS devices are typically designed to be symmetric and use gap-varying electrostatic springs to couple motions of two or more resonators. The role of asymmetry in the design of devices and its influence on sensitivity is not fully understood. Furthermore, gap-varying electrostatic springs suffer from nonlinearities when gap variation between coupling plates becomes large due to mode localization, imposing limitations on the device performance. To address these shortcomings, this contribution has two principal objectives. The first objective is to critically assess the role of asymmetry in the device design and operation. We show, based on energy analysis, that carefully designed asymmetry in devices can lead to even higher sensitivities than reported in the literature. Our second objective is to design and implement linear, tunable, electrostatic springs, using shaped combs, which allow large vibration amplitudes of resonators thereby increasing the signal to noise ratio. We experimentally demonstrate linear electrostatic coupling in a two oscillator device. Our study suggests that a future avenue for progress in the mode localized resonant sensing technology is to combine asymmetric devices with tunable linear coupling designs.
Linear minimax estimation for random vectors with parametric uncertainty
Bitar, E
2010-06-01
In this paper, we take a minimax approach to the problem of computing a worst-case linear mean squared error (MSE) estimate of X given Y , where X and Y are jointly distributed random vectors with parametric uncertainty in their distribution. We consider two uncertainty models, PA and PB. Model PA represents X and Y as jointly Gaussian whose covariance matrix Λ belongs to the convex hull of a set of m known covariance matrices. Model PB characterizes X and Y as jointly distributed according to a Gaussian mixture model with m known zero-mean components, but unknown component weights. We show: (a) the linear minimax estimator computed under model PA is identical to that computed under model PB when the vertices of the uncertain covariance set in PA are the same as the component covariances in model PB, and (b) the problem of computing the linear minimax estimator under either model reduces to a semidefinite program (SDP). We also consider the dynamic situation where x(t) and y(t) evolve according to a discrete-time LTI state space model driven by white noise, the statistics of which is modeled by PA and PB as before. We derive a recursive linear minimax filter for x(t) given y(t).
Directory of Open Access Journals (Sweden)
Mäntysaari Esa A
2006-06-01
Full Text Available Abstract A method based on Taylor series expansion for estimation of location parameters and variance components of non-linear mixed effects models was considered. An attractive property of the method is the opportunity for an easily implemented algorithm. Estimation of non-linear mixed effects models can be done by common methods for linear mixed effects models, and thus existing programs can be used after small modifications. The applicability of this algorithm in animal breeding was studied with simulation using a Gompertz function growth model in pigs. Two growth data sets were analyzed: a full set containing observations from the entire growing period, and a truncated time trajectory set containing animals slaughtered prematurely, which is common in pig breeding. The results from the 50 simulation replicates with full data set indicate that the linearization approach was capable of estimating the original parameters satisfactorily. However, estimation of the parameters related to adult weight becomes unstable in the case of a truncated data set.
Precise Asymptotics of Error Variance Estimator in Partially Linear Models
Institute of Scientific and Technical Information of China (English)
Shao-jun Guo; Min Chen; Feng Liu
2008-01-01
In this paper, we focus our attention on the precise asymptoties of error variance estimator in partially linear regression models, yi = xTi β + g(ti) +εi, 1 ≤i≤n, {εi,i = 1,... ,n } are i.i.d random errors with mean 0 and positive finite variance q2. Following the ideas of Allan Gut and Aurel Spataru[7,8] and Zhang[21],on precise asymptotics in the Baum-Katz and Davis laws of large numbers and precise rate in laws of the iterated logarithm, respectively, and subject to some regular conditions, we obtain the corresponding results in partially linear regression models.
Linear Hand Burn Contracture Release under Local Anesthesia without Tourniquet.
Prasetyono, Theddeus O H; Koswara, Astrid F
2015-10-01
The objective of this report is to present a case of hand burn linear contracture release performed under local anesthesia. It also introduces the one-per-mil tumescent solution consisted of 0.2% lidocaine and 1:1.000.000 epinephrine as a local anesthesia formula, which has the potential of providing adequate anesthesia as well as hemostatic effect during surgery of the hand without tourniquet. The surgery was performed on a 19 year-old male patient with multiple thumb and fingers flexion linear contracture for 105 minutes without any obstacle. The patient did not complain any pain and discomfort during the procedure; while bloodless operative field was successfully achieved. At four-month follow up, the patient could fully extend his thumb, middle and ring finger, while the index was limited by 10° at the DIP joint. Overall, the patient was satisfied with the outcome.
Local energy decay for linear wave equations with variable coefficients
Ikehata, Ryo
2005-06-01
A uniform local energy decay result is derived to the linear wave equation with spatial variable coefficients. We deal with this equation in an exterior domain with a star-shaped complement. Our advantage is that we do not assume any compactness of the support on the initial data, and its proof is quite simple. This generalizes a previous famous result due to Morawetz [The decay of solutions of the exterior initial-boundary value problem for the wave equation, Comm. Pure Appl. Math. 14 (1961) 561-568]. In order to prove local energy decay, we mainly apply two types of ideas due to Ikehata-Matsuyama [L2-behaviour of solutions to the linear heat and wave equations in exterior domains, Sci. Math. Japon. 55 (2002) 33-42] and Todorova-Yordanov [Critical exponent for a nonlinear wave equation with damping, J. Differential Equations 174 (2001) 464-489].
Locally linear approximation for Kernel methods : the Railway Kernel
Muñoz, Alberto; González, Javier
2008-01-01
In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capability of the pr...
Local regularization of linear inverse problems via variational filtering
Lamm, Patricia K.
2017-08-01
We develop local regularization methods for ill-posed linear inverse problems governed by general Fredholm integral operators. The methods are executed as filtering algorithms which are simple to implement and computationally efficient for a large class of problems. We establish a convergence theory and give convergence rates for such methods, and illustrate their computational speed in numerical tests for inverse problems in geomagnetic exploration and imaging.
Local polynomial Whittle estimation covering non-stationary fractional processes
DEFF Research Database (Denmark)
Nielsen, Frank
This paper extends the local polynomial Whittle estimator of Andrews & Sun (2004) to fractionally integrated processes covering stationary and non-stationary regions. We utilize the notion of the extended discrete Fourier transform and periodogram to extend the local polynomial Whittle estimator ...... study illustrates the performance of the proposed estimator compared to the classical local Whittle estimator and the local polynomial Whittle estimator. The empirical justi.cation of the proposed estimator is shown through an analysis of credit spreads....
Remote sensing image fusion based on Bayesian linear estimation
Institute of Scientific and Technical Information of China (English)
GE ZhiRong; WANG Bin; ZHANG LiMing
2007-01-01
A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spatial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM+ demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation,PCA-based method and wavelet-based method.
Unbiased bootstrap error estimation for linear discriminant analysis.
Vu, Thang; Sima, Chao; Braga-Neto, Ulisses M; Dougherty, Edward R
2014-12-01
Convex bootstrap error estimation is a popular tool for classifier error estimation in gene expression studies. A basic question is how to determine the weight for the convex combination between the basic bootstrap estimator and the resubstitution estimator such that the resulting estimator is unbiased at finite sample sizes. The well-known 0.632 bootstrap error estimator uses asymptotic arguments to propose a fixed 0.632 weight, whereas the more recent 0.632+ bootstrap error estimator attempts to set the weight adaptively. In this paper, we study the finite sample problem in the case of linear discriminant analysis under Gaussian populations. We derive exact expressions for the weight that guarantee unbiasedness of the convex bootstrap error estimator in the univariate and multivariate cases, without making asymptotic simplifications. Using exact computation in the univariate case and an accurate approximation in the multivariate case, we obtain the required weight and show that it can deviate significantly from the constant 0.632 weight, depending on the sample size and Bayes error for the problem. The methodology is illustrated by application on data from a well-known cancer classification study.
Multiview locally linear embedding for effective medical image retrieval.
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Hualei Shen
Full Text Available Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE, principal component analysis (PCA, or laplacian eigenmaps (LE can be employed to reduce the "curse of dimensionality". Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods.
Localized linear IgA/IgG bullous dermatosis.
Shimizu, Satoko; Natsuga, Ken; Shinkuma, Satoru; Yasui, Chikako; Tsuchiya, Kikuo; Shimizu, Hiroshi
2010-11-01
Linear IgA/IgG bullous dermatosis (LAGBD) is an auto-immune blistering disease characterized by the local accumulation of IgA- and IgG-class anti-basement membrane autoantibodies. It typically presents as a generalized pruritic vesiculobullous eruption. No cases of localized LAGBD have yet been reported. We report a case of a 78-year-old man with LAGBD localized to the perianal area. The patient complained of suffering from persistent ulcers around the anus for more than 3 years. Physical examination revealed several blisters and ulcers up to 2-cm in diameter around the anus. No lesions were found elsewhere on the body. Histological analysis of a skin biopsy revealed subepidermal blistering, while direct immunofluorescence showed the linear deposition of IgA and IgG antibodies at the dermoepidermal junction. Indirect immunofluorescence of normal human skin whose layers had been separated using 1M NaCl showed the binding of both IgA and IgG to the epidermal side. Immunoblotting demonstrated the presence of circulating IgA and IgG autoantibodies that bound to a 120-kDa protein. This is the first case of localized LAGBD whose skin lesions were restricted to the perianal region.
Estimating dynamic equilibrium economies: linear versus nonlinear likelihood
2004-01-01
This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a sequential Monte Carlo filter proposed by Fernández-Villaverde and Rubio-Ramírez (2004) and the Kalman filter. The sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. The authors report two main results...
Gradient estimates for parabolic and elliptic systems from linear laminates
Dong, Hongjie
2012-01-01
We establish several gradient estimates for second-order divergence type parabolic and elliptic systems. The coefficients and data are assumed to be H\\"older or Dini continuous in the time variable and all but one spatial variables. This type of systems arises from the problems of linearly elastic laminates and composite materials. For the proof, we use Campanato's approach in a novel way. Non-divergence type equations under a similar condition are also discussed.
Application of linear mean-square estimation in ocean engineering
Wang, Li-ping; Chen, Bai-yu; Chen, Chao; Chen, Zheng-shou; Liu, Gui-lin
2016-03-01
The attempt to obtain long-term observed data around some sea areas we concern is usually very hard or even impossible in practical offshore and ocean engineering situations. In this paper, by means of linear mean-square estimation method, a new way to extend short-term data to long-term ones is developed. The long-term data about concerning sea areas can be constructed via a series of long-term data obtained from neighbor oceanographic stations, through relevance analysis of different data series. It is effective to cover the insufficiency of time series prediction method's overdependence upon the length of data series, as well as the limitation of variable numbers adopted in multiple linear regression model. The storm surge data collected from three oceanographic stations located in Shandong Peninsula are taken as examples to analyze the number-selection effect of reference oceanographic stations (adjacent to the concerning sea area) and the correlation coefficients between sea sites which are selected for reference and for engineering projects construction respectively. By comparing the N-year return-period values which are calculated from observed raw data and processed data which are extended from finite data series by means of the linear mean-square estimation method, one can draw a conclusion that this method can give considerably good estimation in practical ocean engineering, in spite of different extreme value distributions about raw and processed data.
Localized density matrix minimization and linear scaling algorithms
Lai, Rongjie
2015-01-01
We propose a convex variational approach to compute localized density matrices for both zero temperature and finite temperature cases, by adding an entry-wise $\\ell_1$ regularization to the free energy of the quantum system. Based on the fact that the density matrix decays exponential away from the diagonal for insulating system or system at finite temperature, the proposed $\\ell_1$ regularized variational method provides a nice way to approximate the original quantum system. We provide theoretical analysis of the approximation behavior and also design convergence guaranteed numerical algorithms based on Bregman iteration. More importantly, the $\\ell_1$ regularized system naturally leads to localized density matrices with banded structure, which enables us to develop approximating algorithms to find the localized density matrices with computation cost linearly dependent on the problem size.
DPRESS: Localizing estimates of predictive uncertainty
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Clark Robert D
2009-07-01
conservative even when the training set was biased, but not excessively so. Conclusion DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, a posteriori approach to defining applicability domains in terms of localized uncertainty.
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Linear vs. nonlinear porosity estimation of NMR oil reservoir data
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Mohsen Abdou Abou Mandour
2010-09-01
Full Text Available Nuclear magnetic resonance is widely used to assess oil reservoir properties especially those that can not be evaluated using conventional techniques. In this regard, porosity determination and the related estimation of the oil present play a very important role in assessing the eco1nomic value of the oil wells. Nuclear Magnetic Resonance data is usually fit to the sum of decaying exponentials. The resulting distribution; i.e. T2 distribution; is directly related to porosity determination. In this work, three reservoir core samples (Tight Sandstone and two Carbonate samples were analyzed. Linear Least Square method (LLS and non-linear least square fitting using Levenberg-Marquardt method were used to calculate the T2 distribution and the resulting incremental porosity. Parametric analysis for the two methods was performed to evaluate the impact of number of exponentials, and effect of the regularization parameter (? on the smoothing of the solution. Effect of the type of solution on porosity determination was carried out. It was found that 12 exponentials is the optimum number of exponentials for both the linear and nonlinear solutions. In the mean time, it was shown that the linear solution begins to be smooth at α = 0.5 which corresponds to the standard industrial value for the regularization parameter. The order of magnitude of time needed for the linear solution is in the range of few minutes while it is in the range of few hours for the nonlinear solution. Regardless of the fact that small differences exist between the linear and nonlinear solutions, these small values make an appreciable difference in porosity. The nonlinear solution predicts 12% less porosity for the tight sandstone sample and 4.5 % and 13 % more porosity in the two carbonate samples respectively.
A Comparison of Alternative Estimators of Linearly Aggregated Macro Models
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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.
Adaptive Error Estimation in Linearized Ocean General Circulation Models
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
Linear and nonlinear buckling analysis of a locally stretched plate
Energy Technology Data Exchange (ETDEWEB)
Kilardj, Madina; Ikhenzzen, Ghania [University of Sciences and Technology Houari Boumediene (U.S.T.H.B), Bab Ezzouar, Algiers (Algeria); Merssager, Tanguy; Kanit, Toufik [Laboratoire de Mecanique de Lille Universite Lille 1, Cite ScientifiqueVilleneuve d' Ascq cedex (France)
2016-08-15
Uniformly stretched thin plates do not buckle unless they are in special boundary conditions. However, buckling commonly occurs around discontinuities, such as cracks, cuts, narrow slits, holes, and different openings, of such plates. This study aims to show that buckling can also occur in thin plates that contain no defect or singularity when the stretching is local. This specific stability problem is analyzed with the finite element method. A brief literature review on stretched plates is presented. Linear and nonlinear buckling stress analyses are conducted for a partially stretched rectangular plate, and various load cases are considered to investigate the influence of the partial loading expanse on the critical tensile buckling load. Results are summarized in iso-stress areas, tables and graphs. Local stretching on one end of the plate induces buckling in the thin plate even without geometrical imperfection.
OPTIMAL ERROR ESTIMATES OF THE PARTITION OF UNITY METHOD WITH LOCAL POLYNOMIAL APPROXIMATION SPACES
Institute of Scientific and Technical Information of China (English)
Yun-qing Huang; Wei Li; Fang Su
2006-01-01
In this paper, we provide a theoretical analysis of the partition of unity finite element method(PUFEM), which belongs to the family of meshfree methods. The usual error analysis only shows the order of error estimate to the same as the local approximations[12].Using standard linear finite element base functions as partition of unity and polynomials as local approximation space, in 1-d case, we derive optimal order error estimates for PUFEM interpolants. Our analysis show that the error estimate is of one order higher than the local approximations. The interpolation error estimates yield optimal error estimates for PUFEM solutions of elliptic boundary value problems.
Local magnitude estimate at Mt. Etna
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V. Maiolino
2005-06-01
Full Text Available In order to verify the duration magnitude MD we calculated local magnitude ML values of 288 earthquakes occurring from October 2002 to April 2003 at Mt. Etna. The analysis was computed at three digital stations of the permanent seismic network of Istituto Nazionale di Geofisica e Vulcanologia of Catania, using the relationship ML = logA+alog?-b, where A is maximum half-amplitude of the horizontal component of the seismic recording measured in mm and the term «+alog?-b» takes the place of the term «-logA0» of Richter relationship. In particular, a = 0.15 for ?<200 km, b=0.16 for ?<200 km. Duration magnitude MD values, moment magnitude MW values and other local magnitude values were compared. Differences between ML and MD were obtained for the strong seismic swarms occurring on October 27, during the onset of 2002-2003 Mt. Etna eruption, characterized by a high earthquake rate, with very strong events (seismogram results clipped in amplitude on drum recorder trace and high level of volcanic tremor, which not permit us to estimate the duration of the earthquakes correctly. ML and MD relationships were related and therefore a new relationship for MD is proposed. Cumulative strain release calculated after the eruption using ML values is about 1.75E+06 J1/2 higher than the one calculated using MD values.
Estimation of Log-Linear-Binomial Distribution with Applications
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Elsayed Ali Habib
2010-01-01
Full Text Available Log-linear-binomial distribution was introduced for describing the behavior of the sum of dependent Bernoulli random variables. The distribution is a generalization of binomial distribution that allows construction of a broad class of distributions. In this paper, we consider the problem of estimating the two parameters of log-linearbinomial distribution by moment and maximum likelihood methods. The distribution is used to fit genetic data and to obtain the sampling distribution of the sign test under dependence among trials.
Taming Chaos by Linear Regulation with Bound Estimation
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Jiqiang Wang
2015-01-01
Full Text Available Chaos control has become an important area of research and consequently many approaches have been proposed to control chaos. This paper proposes a linear regulation method. Different from the existing approaches is that it can provide region of attraction while estimating the bounding behaviour of the norm of the states. The proposed method also possesses design flexibility and can be easily used to cater for special requirement such that control signal should be generated via single input, single state, static feedback and so forth. The applications to the Tigan system, the Genesio chaotic system, the novel chaotic system, and the Lorenz chaotic system justify the above claims.
The Optimal Selection for Restricted Linear Models with Average Estimator
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Qichang Xie
2014-01-01
Full Text Available The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC, which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.
Portfolio optimization using local linear regression ensembles in RapidMiner
Gabor Nagy; Gergo Barta; Tamas Henk
2015-01-01
In this paper we implement a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The estimates and the historical returns of the committees are used to compute the weights of the portfolio from the 453 stock. The proposed method outperforms benchmark portfolio selection strategies that optimize the growth rate of the capital. We investigate the effect of algorithm parameter m: the number of selected stocks on achieved average annua...
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.
Two-stage local M-estimation of additive models
Institute of Scientific and Technical Information of China (English)
JIANG JianCheng; LI JianTao
2008-01-01
This paper studies local M-estimation of the nonparametric components of additive models. A two-stage local M-estimation procedure is proposed for estimating the additive components and their derivatives. Under very mild conditions, the proposed estimators of each additive component and its derivative are jointly asymptotically normal and share the same asymptotic distributions as they would be if the other components were known. The established asymptotic results also hold for two particular local M-estimations: the local least squares and least absolute deviation estimations. However,for general two-stage local M-estimation with continuous and nonlinear ψ-functions, its implementation is time-consuming. To reduce the computational burden, one-step approximations to the two-stage local M-estimators are developed. The one-step estimators are shown to achieve the same efficiency as the fully iterative two-stage local M-estimators, which makes the two-stage local M-estimation more feasible in practice. The proposed estimators inherit the advantages and at the same time overcome the disadvantages of the local least-squares based smoothers. In addition, the practical implementation of the proposed estimation is considered in details. Simulations demonstrate the merits of the two-stage local M-estimation, and a real example illustrates the performance of the methodology.
Two-stage local M-estimation of additive models
Institute of Scientific and Technical Information of China (English)
2008-01-01
This paper studies local M-estimation of the nonparametric components of additive models.A two-stage local M-estimation procedure is proposed for estimating the additive components and their derivatives.Under very mild conditions,the proposed estimators of each additive component and its derivative are jointly asymptotically normal and share the same asymptotic distributions as they would be if the other components were known.The established asymptotic results also hold for two particular local M-estimations:the local least squares and least absolute deviation estimations.However,for general two-stage local M-estimation with continuous and nonlinear ψ-functions,its implementation is time-consuming.To reduce the computational burden,one-step approximations to the two-stage local M-estimators are developed.The one-step estimators are shown to achieve the same effciency as the fully iterative two-stage local M-estimators,which makes the two-stage local M-estimation more feasible in practice.The proposed estimators inherit the advantages and at the same time overcome the disadvantages of the local least-squares based smoothers.In addition,the practical implementation of the proposed estimation is considered in details.Simulations demonstrate the merits of the two-stage local M-estimation,and a real example illustrates the performance of the methodology.
Generalized linear model for estimation of missing daily rainfall data
Rahman, Nurul Aishah; Deni, Sayang Mohd; Ramli, Norazan Mohamed
2017-04-01
The analysis of rainfall data with no missingness is vital in various applications including climatological, hydrological and meteorological study. The issue of missing data is a serious concern since it could introduce bias and lead to misleading conclusions. In this study, five imputation methods including simple arithmetic average, normal ratio method, inverse distance weighting method, correlation coefficient weighting method and geographical coordinate were used to estimate the missing data. However, these imputation methods ignored the seasonality in rainfall dataset which could give more reliable estimation. Thus this study is aimed to estimate the missingness in daily rainfall data by using generalized linear model with gamma and Fourier series as the link function and smoothing technique, respectively. Forty years daily rainfall data for the period from 1975 until 2014 which consists of seven stations at Kelantan region were selected for the analysis. The findings indicated that the imputation methods could provide more accurate estimation values based on the least mean absolute error, root mean squared error and coefficient of variation root mean squared error when seasonality in the dataset are considered.
Leaf area estimation of cassava from linear dimensions
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SAMARA ZANETTI
2017-08-01
Full Text Available ABSTRACT The objective of this study was to determine predictor models of leaf area of cassava from linear leaf measurements. The experiment was carried out in greenhouse in the municipality of Botucatu, São Paulo state, Brazil. The stem cuttings with 5-7 nodes of the cultivar IAC 576-70 were planted in boxes filled with about 320 liters of soil, keeping soil moisture at field capacity, monitored by puncturing tensiometers. At 80 days after planting, 140 leaves were randomly collected from the top, middle third and base of cassava plants. We evaluated the length and width of the central lobe of leaves, number of lobes and leaf area. The measurements of leaf areas were correlated with the length and width of the central lobe and the number of lobes of the leaves, and adjusted to polynomial and multiple regression models. The linear function that used the length of the central lobe LA = -69.91114 + 15.06462L and linear multiple functions LA = -69.9188 + 15.5102L + 0.0197726K - 0.0768998J or LA = -69.9346 + 15.0106L + 0.188931K - 0.0264323H are suitable models to estimate leaf area of cassava cultivar IAC 576-70.
The non-linear evolution of edge localized modes
Energy Technology Data Exchange (ETDEWEB)
Wenninger, Ronald
2013-01-09
Edge localized modes (ELMs) are instabilities in the edge of tokamak plasmas in the high confinement regime (H-mode). Without them the edge transport in ordinary H-mode plasmas is too low to establish a stationary situation. However in a future device large unmitigated ELMs are believed to cause divertor power flux densities far in excess of tolerable material limits. Hence the size of energy loss per ELM and the resulting ELM frequency must be controlled. To proceed in understanding how the ELM size is determined and how ELM mitigation methods work it is necessary to characterize the non-linear evolution of pedestal erosion. In order to achieve this experimental data is compared to the results of ELM simulations with the code JOREK (reduced MHD, non-linear) applying a specially developed synthetic magnetic diagnostic. The experimental data are acquired by several fast sampling diagnostics at the experiments ASDEX Upgrade and TCV at a large number of toroidal/poloidal positions. A central element of the presented work is the detailed characterization of dominant magnetic perturbations during ELMs. These footprints of the instability can be observed most intensely in close temporal vicinity to the onset of pedestal erosion. Dominant magnetic perturbations are caused by current perturbations located at or inside the last closed flux surface. In ASDEX Upgrade under certain conditions dominant magnetic perturbations like other H-mode edge instabilities display a similarity to solitons. Furthermore - as expected - they are often observed to be correlated to a perturbation of electron temperature. In TCV it is possible to characterize the evolution of the toroidal structure of dominant magnetic perturbations. Between growing above the level of background fluctuations and the maximum perturbation level for all time instance a similar toroidal structure is observed. This rigid mode-structure is an indication for non-linear coupling. Most frequently the dominant toroidal
A Posteriori Error Estimation for Finite Element Methods and Iterative Linear Solvers
Energy Technology Data Exchange (ETDEWEB)
Melboe, Hallgeir
2001-10-01
This thesis addresses a posteriori error estimation for finite element methods and iterative linear solvers. Adaptive finite element methods have gained a lot of popularity over the last decades due to their ability to produce accurate results with limited computer power. In these methods a posteriori error estimates play an essential role. Not only do they give information about how large the total error is, they also indicate which parts of the computational domain should be given a more sophisticated treatment in order to reduce the error. A posteriori error estimates are traditionally aimed at estimating the global error, but more recently so called goal oriented error estimators have been shown a lot of interest. The name reflects the fact that they estimate the error in user-defined local quantities. In this thesis the main focus is on global error estimators for highly stretched grids and goal oriented error estimators for flow problems on regular grids. Numerical methods for partial differential equations, such as finite element methods and other similar techniques, typically result in a linear system of equations that needs to be solved. Usually such systems are solved using some iterative procedure which due to a finite number of iterations introduces an additional error. Most such algorithms apply the residual in the stopping criterion, whereas the control of the actual error may be rather poor. A secondary focus in this thesis is on estimating the errors that are introduced during this last part of the solution procedure. The thesis contains new theoretical results regarding the behaviour of some well known, and a few new, a posteriori error estimators for finite element methods on anisotropic grids. Further, a goal oriented strategy for the computation of forces in flow problems is devised and investigated. Finally, an approach for estimating the actual errors associated with the iterative solution of linear systems of equations is suggested. (author)
Brachytherapy seed localization using geometric and linear programming techniques.
Singh, Vikas; Mukherjee, Lopamudra; Xu, Jinhui; Hoffmann, Kenneth R; Dinu, Petru M; Podgorsak, Matthew
2007-09-01
We propose an optimization algorithm to solve the brachytherapy seed localization problem in prostate brachytherapy. Our algorithm is based on novel geometric approaches to exploit the special structure of the problem and relies on a number of key observations which help us formulate the optimization problem as a minimization integer program (IP). Our IP model precisely defines the feasibility polyhedron for this problem using a polynomial number of half-spaces; the solution to its corresponding linear program is rounded to yield an integral solution to our task of determining correspondences between seeds in multiple projection images. The algorithm is efficient in theory as well as in practice and performs well on simulation data (approximately 98% accuracy) and real X-ray images (approximately 95% accuracy). We present in detail the underlying ideas and an extensive set of performance evaluations based on our implementation.
Spatial Signature Estimation with an Uncalibrated Uniform Linear Array
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Xiang Cao
2015-06-01
Full Text Available In this paper, the problem of spatial signature estimation using a uniform linear array (ULA with unknown sensor gain and phase errors is considered. As is well known, the directions-of-arrival (DOAs can only be determined within an unknown rotational angle in this array model. However, the phase ambiguity has no impact on the identification of the spatial signature. Two auto-calibration methods are presented for spatial signature estimation. In our methods, the rotational DOAs and model error parameters are firstly obtained, and the spatial signature is subsequently calculated. The first method extracts two subarrays from the ULA to construct an estimator, and the elements of the array can be used several times in one subarray. The other fully exploits multiple invariances in the interior of the sensor array, and a multidimensional nonlinear problem is formulated. A Gauss–Newton iterative algorithm is applied for solving it. The first method can provide excellent initial inputs for the second one. The effectiveness of the proposed algorithms is demonstrated by several simulation results.
Nearly best linear estimates of logistic parameters based on complete ordered statistics
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Deals with the determination of the nearly best linear estimates of location and scale parameters of a logistic population, when both parameters are unknown, by introducing Bloms semi-empirical α, β-correction′into the asymptotic mean and covariance formulae with complete and ordered samples taken into consideration and various nearly best linear estimates established and points out the high efficiency of these estimators relative to the best linear unbiased estimators (BLUEs) and other linear estimators makes them useful in practice.
Linear inverse source estimate of combined EEG and MEG data related to voluntary movements.
Babiloni, F; Carducci, F; Cincotti, F; Del Gratta, C; Pizzella, V; Romani, G L; Rossini, P M; Tecchio, F; Babiloni, C
2001-12-01
A method for the modeling of human movement-related cortical activity from combined electroencephalography (EEG) and magnetoencephalography (MEG) data is proposed. This method includes a subject's multi-compartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multi-dipole source model, and a regularized linear inverse source estimate based on boundary element mathematics. Linear inverse source estimates of cortical activity were regularized by taking into account the covariance of background EG and MEG sensor noise. EEG (121 sensors) and MEG (43 sensors) data were recorded in separate sessions whereas normal subjects executed voluntary right one-digit movements. Linear inverse source solution of EEG, MEG, and EEG-MEG data were quantitatively evaluated by using three performance indexes. The first two indexes (Dipole Localization Error [DLE] and Spatial Dispersion [SDis]) were used to compute the localization power for the source solutions obtained. Such indexes were based on the information provided by the column of the resolution matrix (i.e., impulse response). Ideal DLE values tend to zero (the source current was correctly retrieved by the procedure). In contrast, high DLE values suggest severe mislocalization in the source reconstruction. A high value of SDis at a source space point mean that such a source will be retrieved by a large area with the linear inverse source estimation. The remaining performance index assessed the quality of the source solution based on the information provided by the rows of the resolution matrix R, i.e., resolution kernels. The i-th resolution kernels of the matrix R describe how the estimation of the i-th source is distorted by the concomitant activity of all other sources. A statistically significant lower dipole localization error was observed and lower spatial dispersion in source solutions produced by combined EEG-MEG data than from EEG and MEG data considered separately (P < 0
K factor estimation in distribution transformers using linear regression models
Directory of Open Access Journals (Sweden)
Juan Miguel Astorga Gómez
2016-06-01
Full Text Available Background: Due to massive incorporation of electronic equipment to distribution systems, distribution transformers are subject to operation conditions other than the design ones, because of the circulation of harmonic currents. It is necessary to quantify the effect produced by these harmonic currents to determine the capacity of the transformer to withstand these new operating conditions. The K-factor is an indicator that estimates the ability of a transformer to withstand the thermal effects caused by harmonic currents. This article presents a linear regression model to estimate the value of the K-factor, from total current harmonic content obtained with low-cost equipment.Method: Two distribution transformers that feed different loads are studied variables, current total harmonic distortion factor K are recorded, and the regression model that best fits the data field is determined. To select the regression model the coefficient of determination R2 and the Akaike Information Criterion (AIC are used. With the selected model, the K-factor is estimated to actual operating conditions.Results: Once determined the model it was found that for both agricultural cargo and industrial mining, present harmonic content (THDi exceeds the values that these transformers can drive (average of 12.54% and minimum 8,90% in the case of agriculture and average value of 18.53% and a minimum of 6.80%, for industrial mining case.Conclusions: When estimating the K factor using polynomial models it was determined that studied transformers can not withstand the current total harmonic distortion of their current loads. The appropriate K factor for studied transformer should be 4; this allows transformers support the current total harmonic distortion of their respective loads.
Best linear unbiased estimation of the nuclear masses
Bouriquet, Bertrand
2009-01-01
This paper presents methods to provide an optimal evaluation of the nuclear masses. The techniques used for this purpose come from data assimilation (DA) that allows combining, in an optimal and consistent way, information coming from experiment and from numerical modelling. Using all the available information, it leads to improve not only masses evaluations, but also their uncertainties. Each newly evaluated mass value is associated with some accuracy that is sensibly reduced with respect to the values given in tables, especially in the case of the less well-known masses. In this paper, we first introduce a useful tool of DA, the Best Linear Unbiased Estimation (BLUE). This BLUE method is applied to nuclear mass tables and some results of improvement are shown. Then finally, some post validation diagnostics, demonstrating that the method has been used in optimal conditions, are described and used to validate the results.
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...
Linear Estimation of Location and Scale Parameters Using Partial Maxima
Papadatos, Nickos
2010-01-01
Consider an i.i.d. sample X^*_1,X^*_2,...,X^*_n from a location-scale family, and assume that the only available observations consist of the partial maxima (or minima)sequence, X^*_{1:1},X^*_{2:2},...,X^*_{n:n}, where X^*_{j:j}=max{X^*_1,...,X^*_j}. This kind of truncation appears in several circumstances, including best performances in athletics events. In the case of partial maxima, the form of the BLUEs (best linear unbiased estimators) is quite similar to the form of the well-known Lloyd's (1952, Least-squares estimation of location and scale parameters using order statistics, Biometrika, vol. 39, pp. 88-95) BLUEs, based on (the sufficient sample of) order statistics, but, in contrast to the classical case, their consistency is no longer obvious. The present paper is mainly concerned with the scale parameter, showing that the variance of the partial maxima BLUE is at most of order O(1/log n), for a wide class of distributions.
Binary Classifier Calibration Using an Ensemble of Linear Trend Estimation
Naeini, Mahdi Pakdaman; Cooper, Gregory F.
2017-01-01
Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called ensemble of linear trend estimation (ELiTE). ELiTE utilizes the recently proposed ℓ1 trend ltering signal approximation method [22] to find the mapping from uncalibrated classification scores to the calibrated probability estimates. ELiTE is designed to address the key limitations of the histogram binning-based calibration methods which are (1) the use of a piecewise constant form of the calibration mapping using bins, and (2) the assumption of independence of predicted probabilities for the instances that are located in different bins. The method post-processes the output of a binary classifier to obtain calibrated probabilities. Thus, it can be applied with many existing classification models. We demonstrate the performance of ELiTE on real datasets for commonly used binary classification models. Experimental results show that the method outperforms several common binary-classifier calibration methods. In particular, ELiTE commonly performs statistically significantly better than the other methods, and never worse. Moreover, it is able to improve the calibration power of classifiers, while retaining their discrimination power. The method is also computationally tractable for large scale datasets, as it is practically O(N log N) time, where N is the number of samples.
Are local wind power resources well estimated?
Lundtang Petersen, Erik; Troen, Ib; Jørgensen, Hans E.; Mann, Jakob
2013-03-01
Planning and financing of wind power installations require very importantly accurate resource estimation in addition to a number of other considerations relating to environment and economy. Furthermore, individual wind energy installations cannot in general be seen in isolation. It is well known that the spacing of turbines in wind farms is critical for maximum power production. It is also well established that the collective effect of wind turbines in large wind farms or of several wind farms can limit the wind power extraction downwind. This has been documented by many years of production statistics. For the very large, regional sized wind farms, a number of numerical studies have pointed to additional adverse changes to the regional wind climate, most recently by the detailed studies of Adams and Keith [1]. They show that the geophysical limit to wind power production is likely to be lower than previously estimated. Although this problem is of far future concern, it has to be considered seriously. In their paper they estimate that a wind farm larger than 100 km2 is limited to about 1 W m-2. However, a 20 km2 off shore farm, Horns Rev 1, has in the last five years produced 3.98 W m-2 [5]. In that light it is highly unlikely that the effects pointed out by [1] will pose any immediate threat to wind energy in coming decades. Today a number of well-established mesoscale and microscale models exist for estimating wind resources and design parameters and in many cases they work well. This is especially true if good local data are available for calibrating the models or for their validation. The wind energy industry is still troubled by many projects showing considerable negative discrepancies between calculated and actually experienced production numbers and operating conditions. Therefore it has been decided on a European Union level to launch a project, 'The New European Wind Atlas', aiming at reducing overall uncertainties in determining wind conditions. The
Multiple Shooting-Local Linearization method for the identification of dynamical systems
Carbonell, F.; Iturria-Medina, Y.; Jimenez, J. C.
2016-08-01
The combination of the multiple shooting strategy with the generalized Gauss-Newton algorithm turns out in a recognized method for estimating parameters in ordinary differential equations (ODEs) from noisy discrete observations. A key issue for an efficient implementation of this method is the accurate integration of the ODE and the evaluation of the derivatives involved in the optimization algorithm. In this paper, we study the feasibility of the Local Linearization (LL) approach for the simultaneous numerical integration of the ODE and the evaluation of such derivatives. This integration approach results in a stable method for the accurate approximation of the derivatives with no more computational cost than that involved in the integration of the ODE. The numerical simulations show that the proposed Multiple Shooting-Local Linearization method recovers the true parameters value under different scenarios of noisy data.
LLSURE: local linear SURE-based edge-preserving image filtering.
Qiu, Tianshuang; Wang, Aiqi; Yu, Nannan; Song, Aimin
2013-01-01
In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.
Penalized maximum likelihood estimation for generalized linear point processes
2010-01-01
A generalized linear point process is specified in terms of an intensity that depends upon a linear predictor process through a fixed non-linear function. We present a framework where the linear predictor is parametrized by a Banach space and give results on Gateaux differentiability of the log-likelihood. Of particular interest is when the intensity is expressed in terms of a linear filter parametrized by a Sobolev space. Using that the Sobolev spaces are reproducing kernel Hilbert spaces we...
Admissible estimation of linear functions of characteristic values of a finite population
Institute of Scientific and Technical Information of China (English)
邹国华; 成平; 冯士雍
1997-01-01
The problem on admissibility of estimators is considered based on the point of view of the superpopu-tation model. The necessary and sufficient conditions for linear estimators of an arbitrary linear function of characteristic values of a finite population to be admissible in the class of linear or all estimators are obtained respectively.
Directory of Open Access Journals (Sweden)
Liu Gang
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.
Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding
Directory of Open Access Journals (Sweden)
Xiang Wang
2015-07-01
Full Text Available Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD, and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.
Non-local in time perturbations of linear hyperbolic PDEs
Lechner, Gandalf
2013-01-01
Linear Integro-differential equations of the form $(D+\\lambda W)f=0$ are studied, where $D$ is a normal or prenormal hyperbolic differential operator on $\\mathbb{R}^n$, $\\lambda\\in\\mathbb{C}$ is a coupling constant, and $W$ is a regular integral operator with compactly supported kernel. In particular, $W$ can be non-local in time, so that a Hamiltonian formulation is not possible. It is shown that for sufficiently small $|\\lambda|$, the hyperbolic character of $D$ is essentially preserved. Unique advanced/retarded fundamental solutions are constructed by means of a convergent expansion in $\\lambda$, and the solution spaces are analyzed. It is shown that the acausal behavior of the solutions is well-controlled, but the Cauchy problem is ill-posed in general. Nonetheless, a scattering operator can be calculated which describes the effect of $W$ on the space of solutions of $D$. It is also described how these structures occur in the context of wave or Dirac equations on noncommutative deformations of Minkowski s...
Probing the Locality of Excited States with Linear Algebra.
Etienne, Thibaud
2015-04-14
This article reports a novel theoretical approach related to the analysis of molecular excited states. The strategy introduced here involves gathering two pieces of physical information, coming from Hilbert and direct space operations, into a general, unique quantum mechanical descriptor of electronic transitions' locality. Moreover, the projection of Hilbert and direct space-derived indices in an Argand plane delivers a straightforward way to visually probe the ability of a dye to undergo a long- or short-range charge-transfer. This information can be applied, for instance, to the analysis of the electronic response of families of dyes to light absorption by unveiling the trend of a given push-pull chromophore to increase the electronic cloud polarization magnitude of its main transition with respect to the size extension of its conjugated spacer. We finally demonstrate that all the quantities reported in this article can be reliably approximated by a linear algebraic derivation, based on the contraction of detachment/attachment density matrices from canonical to atomic space. This alternative derivation has the remarkable advantage of a very low computational cost with respect to the previously used numerical integrations, making fast and accurate characterization of large molecular systems' excited states easily affordable.
Institute of Scientific and Technical Information of China (English)
Hasan ABBASI NOZARI; Hamed DEHGHAN BANADAKI; Mohammad MOKHTARE; Somaveh HEKMATI VAHED
2012-01-01
This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system.A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT),which is an incremental tree-based learning algorithm.The proposed NF models are compared with other known intelligent identifiers,namely multilayer perceptron (MLP) and radial basis function (RBF).Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system.Experimental results show the effectiveness of our proposed NF modelling approach.
Estimation of scale parameters of logistic distribution by linear functions of sample quantiles
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The large sample estimation of standard deviation of logistic distribution employs the asymptotically best linear unbiased estimators based on sample quantiles. The sample quantiles are established from a pair of single spacing. Finally, a table of the variances and efficiencies of the estimator for 5 ≤ n ≤ 65 is provided and comparison is made with other linear estimators.
Localization estimation and global vs. local information measures
Energy Technology Data Exchange (ETDEWEB)
Pennini, F. [Departamento de Fisica, Universidad Catolica del Norte, Casilla 1280, Antofagasta (Chile) and Instituto de Fisica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina' s National Research Council (CONICET) C.C. 727, 1900 La Plata (Argentina)]. E-mail: fpennini@ucn.cl; Plastino, A. [Instituto de Fisica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina' s National Research Council (CONICET) C.C. 727, 1900 La Plata (Argentina)]. E-mail: plastino@fisica.unlp.edu.ar
2007-06-04
The maximum entropy principle is one of the great ideas of the last 50 years, with a multitude of applications in many areas of science. Its main ingredient is an information measure. We show that global and local information measures provide different types of physical information, which requires handling them with some care. The concomitant differences are illustrated with reference to the problem of localization in phase space, placing emphasis on some details of the smoothing of Wigner functions, as described in [G. Manfredi, M.R. Feix, Phys. Rev. E 62 (2000) 4665]. Our discussion is made in terms of a special version of Fisher's information measure, called the shift-invariant one.
Indoor Localization Accuracy Estimation from Fingerprint Data
DEFF Research Database (Denmark)
Nikitin, Artyom; Laoudias, Christos; Chatzimilioudis, Georgios
2017-01-01
The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks...... on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our...
Idiart, Martín I.; Lahellec, Noel
2016-12-01
New estimates are derived for the overall properties of linear solids with pointwise heterogeneous local properties. The derivation relies on the use of 'comparison solids' which, unlike comparison solids considered previously, are themselves pointwise heterogeneous. The estimates are then exploited within an incremental homogenization scheme to determine the overall response of multiphase elasto-viscoplastic solids under arbitrary loading histories. By way of example, the scheme is applied to incompressible Maxwellian solids with power-law plastic dissipation; particularly simple estimates of the Hashin-Shtrikman type are obtained. Predictions are confronted with full-field simulations for particulate composites under cyclic and rotating loading conditions. Good agreement is found for all cases considered. In particular, elasto-plastic transitions, tension-compression asymmetries (Bauschinger effect) and stress-path distortions induced by material heterogeneity are all well-captured, thus improving significantly on commonly used elastic-plastic decoupled schemes.
Estimating Mutual Information by Local Gaussian Approximation
2015-07-13
proposed a variety of methods to overcome the bias, such as the reflection method (Schuster, 1985), ( Silverman , 1986); the boundary kernel method...communication. The Bell System Technical Journal, 27:379423, 1948. Bernard W Silverman . Density estimation for statistics and data analysis, volume 26. CRC press
Moderate Deviations for M-estimators in Linear Models with φ-mixing Errors
Institute of Scientific and Technical Information of China (English)
Jun FAN
2012-01-01
In this paper,the moderate deviations for the M-estimators of regression parameter in a linear model are obtained when the errors form a strictly stationary φ-mixing sequence.The results are applied to study many different types of M-estimators such as Huber's estimator,Lp-regression estimator,least squares estimator and least absolute deviation estimator.
Human Age Estimation Based on Locality and Ordinal Information.
Li, Changsheng; Liu, Qingshan; Dong, Weishan; Zhu, Xiaobin; Liu, Jing; Lu, Hanqing
2015-11-01
In this paper, we propose a novel feature selection-based method for facial age estimation. The face aging is a typical temporal process, and facial images should have certain ordinal patterns in the aging feature space. From the geometrical perspective, a facial image can be usually seen as sampled from a low-dimensional manifold embedded in the original high-dimensional feature space. Thus, we first measure the energy of each feature in preserving the underlying local structure information and the ordinal information of the facial images, respectively, and then we intend to learn a low-dimensional aging representation that can maximally preserve both kinds of information. To further improve the performance, we try to eliminate the redundant local information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation among features. Finally, we formulate all these issues into a unified optimization problem, which is similar to linear discriminant analysis in format. Since it is expensive to collect the labeled facial aging images in practice, we extend the proposed supervised method to a semi-supervised learning mode including the semi-supervised feature selection method and the semi-supervised age prediction algorithm. Extensive experiments are conducted on the FACES dataset, the Images of Groups dataset, and the FG-NET aging dataset to show the power of the proposed algorithms, compared to the state-of-the-arts.
PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA
Institute of Scientific and Technical Information of China (English)
QianWeimin; LiYumei
2005-01-01
The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.
Local Ray-Based Traveltime Computation Using the Linearized Eikonal Equation
Almubarak, Mohammed S.
2013-05-01
The computation of traveltimes plays a critical role in the conventional implementations of Kirchhoff migration. Finite-difference-based methods are considered one of the most effective approaches for traveltime calculations and are therefore widely used. However, these eikonal solvers are mainly used to obtain early-arrival traveltime. Ray tracing can be used to pick later traveltime branches, besides the early arrivals, which may lead to an improvement in velocity estimation or in seismic imaging. In this thesis, I improved the accuracy of the solution of the linearized eikonal equation by constructing a linear system of equations (LSE) based on finite-difference approximation, which is of second-order accuracy. The ill-conditioned LSE is initially regularized and subsequently solved to calculate the traveltime update. Numerical tests proved that this method is as accurate as the second-order eikonal solver. Later arrivals are picked using ray tracing. These traveltimes are binned to the nearest node on a regular grid and empty nodes are estimated by interpolating the known values. The resulting traveltime field is used as an input to the linearized eikonal algorithm, which improves the accuracy of the interpolated nodes and yields a local ray-based traveltime. This is a preliminary study and further investigation is required to test the efficiency and the convergence of the solutions.
Localization of Non-Linearly Modeled Autonomous Mobile Robots Using Out-of-Sequence Measurements
Directory of Open Access Journals (Sweden)
Jesus M. de la Cruz
2012-02-01
Full Text Available This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.
Localization of non-linearly modeled autonomous mobile robots using out-of-sequence measurements.
Besada-Portas, Eva; Lopez-Orozco, Jose A; Lanillos, Pablo; de la Cruz, Jesus M
2012-01-01
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.
DEFF Research Database (Denmark)
Tabatabaeipour, Seyed Mojtaba; Bak, Thomas
2012-01-01
In this paper we consider the problem of fault estimation and accommodation for discrete time piecewise linear systems. A robust fault estimator is designed to estimate the fault such that the estimation error converges to zero and H∞ performance of the fault estimation is minimized. Then...
A Stochastic Restricted Principal Components Regression Estimator in the Linear Model
Directory of Open Access Journals (Sweden)
Daojiang He
2014-01-01
Full Text Available We propose a new estimator to combat the multicollinearity in the linear model when there are stochastic linear restrictions on the regression coefficients. The new estimator is constructed by combining the ordinary mixed estimator (OME and the principal components regression (PCR estimator, which is called the stochastic restricted principal components (SRPC regression estimator. Necessary and sufficient conditions for the superiority of the SRPC estimator over the OME and the PCR estimator are derived in the sense of the mean squared error matrix criterion. Finally, we give a numerical example and a Monte Carlo study to illustrate the performance of the proposed estimator.
Two biased estimation techniques in linear regression: Application to aircraft
Klein, Vladislav
1988-01-01
Several ways for detection and assessment of collinearity in measured data are discussed. Because data collinearity usually results in poor least squares estimates, two estimation techniques which can limit a damaging effect of collinearity are presented. These two techniques, the principal components regression and mixed estimation, belong to a class of biased estimation techniques. Detection and assessment of data collinearity and the two biased estimation techniques are demonstrated in two examples using flight test data from longitudinal maneuvers of an experimental aircraft. The eigensystem analysis and parameter variance decomposition appeared to be a promising tool for collinearity evaluation. The biased estimators had far better accuracy than the results from the ordinary least squares technique.
Estimation of Physical Parameters in Linear and Nonlinear Dynamic Systems
DEFF Research Database (Denmark)
Knudsen, Morten
and estimation of physical parameters in particular. 2. To apply the new methods for modelling of specific objects, such as loudspeakers, ac- and dc-motors wind turbines and beat exchangers. A reliable quality measure of an obtained parameter estimate is a prerequisite for any reasonable use of the result...
DEFF Research Database (Denmark)
Tabatabaeipour, Seyed Mojtaba; Bak, Thomas
2012-01-01
In this paper we consider the problem of fault estimation and accommodation for discrete time piecewise linear systems. A robust fault estimator is designed to estimate the fault such that the estimation error converges to zero and H∞ performance of the fault estimation is minimized. Then......, the estimate of fault is used to compensate for the effect of the fault. Hence, using the estimate of fault, a fault tolerant controller using a piecewise linear static output feedback is designed such that it stabilizes the system and provides an upper bound on the H∞ performance of the faulty system....... Sufficient conditions for the existence of robust fault estimator and fault tolerant controller are derived in terms of linear matrix inequalities. Upper bounds on the H∞ performance can be minimized by solving convex optimization problems with linear matrix inequality constraints. The efficiency...
DEFF Research Database (Denmark)
Cook, Gerald; Lin, Ching-Fang
1980-01-01
The local linearization algorithm is presented as a possible numerical integration scheme to be used in real-time simulation. A second-order nonlinear example problem is solved using different methods. The local linearization approach is shown to require less computing time and give significant...... improvement in accuracy over the classical second-order integration methods....
Design of reduced-order state estimators for linear time-varying multivariable systems
Nguyen, Charles C.
1987-01-01
The design of reduced-order state estimators for linear time-varying multivariable systems is considered. Employing the concepts of matrix operators and the method of canonical transformations, this paper shows that there exists a reduced-order state estimator for linear time-varying systems that are 'lexicography-fixedly observable'. In addition, the eigenvalues of the estimator can be arbitrarily assigned. A simple algorithm is proposed for the design of the state estimator.
Asymptotic Parameter Estimation for a Class of Linear Stochastic Systems Using Kalman-Bucy Filtering
Directory of Open Access Journals (Sweden)
Xiu Kan
2012-01-01
Full Text Available The asymptotic parameter estimation is investigated for a class of linear stochastic systems with unknown parameter θ:dXt=(θα(t+β(tXtdt+σ(tdWt. Continuous-time Kalman-Bucy linear filtering theory is first used to estimate the unknown parameter θ based on Bayesian analysis. Then, some sufficient conditions on coefficients are given to analyze the asymptotic convergence of the estimator. Finally, the strong consistent property of the estimator is discussed by comparison theorem.
Kumar, K Vasanth; Sivanesan, S
2005-08-31
Comparison analysis of linear least square method and non-linear method for estimating the isotherm parameters was made using the experimental equilibrium data of safranin onto activated carbon at two different solution temperatures 305 and 313 K. Equilibrium data were fitted to Freundlich, Langmuir and Redlich-Peterson isotherm equations. All the three isotherm equations showed a better fit to the experimental equilibrium data. The results showed that non-linear method could be a better way to obtain the isotherm parameters. Redlich-Peterson isotherm is a special case of Langmuir isotherm when the Redlich-Peterson isotherm constant g was unity.
Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation
Ebtehaj, Ardeshir Mohammad; Foufoula-Georgiou, Efi
2014-01-01
This paper introduces a new approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology relies on modern supervised manifold learning and regularization paradigms, which makes use of two joint dictionaries of coincidental rainfall profiles and their upwelling spectral radiative fluxes. A sequential detection-estimation strategy is adopted which relies on a geometrical perception that similar rainfall intensity values and their spectral radiances lie on or live close to some sufficiently smooth manifolds with analogous geometrical structure. The detection step employs of a nearest neighborhood classification rule, while the estimation scheme is equipped with a constrained shrinkage estimator to ensure sufficiently stable retrieval and some physical consistency. The algorithm is examined using coincidental observations of the active precipitation radar (PR) and passive microwave imager (TMI) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. We present impro...
THE SUPERIORITY OF EMPIRICAL BAYES ESTIMATION OF PARAMETERS IN PARTITIONED NORMAL LINEAR MODEL
Institute of Scientific and Technical Information of China (English)
Zhang Weiping; Wei Laisheng
2008-01-01
In this article, the empirical Bayes (EB) estimators are constructed for the estimable functions of the parameters in partitioned normal linear model. The superiorities of the EB estimators over ordinary least-squares (LS) estimator are investigated under mean square error matrix (MSEM) criterion.
Localized Recursive Estimation in Energy Constrained Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Bang Wang
2006-06-01
Full Text Available This paper proposes a localized recursive estimation scheme for parameter estimation in wireless sensor networks. Given any parameter of a target occurring at some location and time, a number of sensors recursively estimate the parameter by using their local measurements of the parameter that is attenuated with the distance between a sensor and the target location and corrupted by noise. Compared with centralized estimation schemes that transmit all encoded measurements to a sink (or a fusion center, the recursive scheme needs only to transmit the final estimate to a sink. When the sink is faraway from the sensors and multihop communications have to be used, using localized recursive estimation can help to reduce energy consumption and reduce network traffic load. A sensor sequence with the fastest convergence rate is identified, by which the variance of estimation error reduces faster than all other sequences. In the case of adjustable transmission power, a heuristic has been proposed to find a sensor sequence with the minimum total transmission power when performing the recursive estimation. Numerical examples have been used to compare the performance of the proposed scheme with that of a centralized estimation scheme and have also shown the effectiveness of the proposed heuristic.
Remodeling and Estimation for Sparse Partially Linear Regression Models
Directory of Open Access Journals (Sweden)
Yunhui Zeng
2013-01-01
Full Text Available When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains significant variables. But it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the first stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. The simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction.
Image Quality Assessment Based on Local Linear Information and Distortion-Specific Compensation.
Wang, Hanli; Fu, Jie; Lin, Weisi; Hu, Sudeng; Kuo, C-C Jay; Zuo, Lingxuan
2016-12-14
Image Quality Assessment (IQA) is a fundamental yet constantly developing task for computer vision and image processing. Most IQA evaluation mechanisms are based on the pertinence of subjective and objective estimation. Each image distortion type has its own property correlated with human perception. However, this intrinsic property may not be fully exploited by existing IQA methods. In this paper, we make two main contributions to the IQA field. First, a novel IQA method is developed based on a local linear model that examines the distortion between the reference and the distorted images for better alignment with human visual experience. Second, a distortion-specific compensation strategy is proposed to offset the negative effect on IQA modeling caused by different image distortion types. These score offsets are learned from several known distortion types. Furthermore, for an image with an unknown distortion type, a Convolutional Neural Network (CNN) based method is proposed to compute the score offset automatically. Finally, an integrated IQA metric is proposed by combining the aforementioned two ideas. Extensive experiments are performed to verify the proposed IQA metric, which demonstrate that the local linear model is useful in human perception modeling, especially for individual image distortion, and the overall IQA method outperforms several state-of-the-art IQA approaches.
Pladdy, Christopher; Nerayanuru, Sreenivasa M.; Fimoff, Mark; Özen, Serdar; Zoltowski, Michael
2004-01-01
We present a low complexity approximate method for semi-blind best linear unbiased estimation (BLUE) of a channel impulse response vector (CIR) for a communication system, which utilizes a periodically transmitted training sequence, within a continuous stream of information symbols. The algorithm achieves slightly degraded results at a much lower complexity than directly computing the BLUE CIR estimate. In addition, the inverse matrix required to invert the weighted normal equations to solve ...
Computational Issues in Linear Least-Squares Estimation and Control
1979-06-06
Algorithms for Parallel Processing in Optimal Estimation," to appear in Automatica, May, 1979. Newton, Issac, [1926], Philosophe Naturalis Principia ... Mathematica , Ii. Pemberton, Ed. (G. & J. Innys, London, ed. 3). , [1934], Mathematical Principles of Natural Philosophy, A. Motte, Translation, 7. Cajori, Ed
Local polynomial Whittle estimation of perturbed fractional processes
DEFF Research Database (Denmark)
Frederiksen, Per; Nielsen, Frank; Nielsen, Morten Ørregaard
We propose a semiparametric local polynomial Whittle with noise (LPWN) estimator of the memory parameter in long memory time series perturbed by a noise term which may be serially correlated. The estimator approximates the spectrum of the perturbation as well as that of the short-memory component...... for d ε (0, 3/4), and if the spectral density is infinitely smooth near frequency zero, the rate of convergence can become arbitrarily close to the parametric rate, pn. A Monte Carlo study reveals that the LPWN estimator performs well in the presence of a serially correlated perturbation term....... Furthermore, an empirical investigation of the 30 DJIA stocks shows that this estimator indicates stronger persistence in volatility than the standard local Whittle estimator....
Method and system for non-linear motion estimation
Lu, Ligang (Inventor)
2011-01-01
A method and system for extrapolating and interpolating a visual signal including determining a first motion vector between a first pixel position in a first image to a second pixel position in a second image, determining a second motion vector between the second pixel position in the second image and a third pixel position in a third image, determining a third motion vector between one of the first pixel position in the first image and the second pixel position in the second image, and the second pixel position in the second image and the third pixel position in the third image using a non-linear model, determining a position of the fourth pixel in a fourth image based upon the third motion vector.
dglars: An R Package to Estimate Sparse Generalized Linear Models
Directory of Open Access Journals (Sweden)
Luigi Augugliaro
2014-09-01
Full Text Available dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013, developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004. The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013, and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012. The latter algorithm, as shown here, is significantly faster than the predictor-corrector algorithm. For comparison purposes, we have implemented both algorithms.
Monopole and dipole estimation for multi-frequency sky maps by linear regression
Wehus, I K; Eriksen, H K; Banday, A J; Dickinson, C; Ghosh, T; Gorski, K M; Lawrence, C R; Leahy, J P; Maino, D; Reich, P; Reich, W
2014-01-01
We describe a simple but efficient method for deriving a consistent set of monopole and dipole corrections for multi-frequency sky map data sets, allowing robust parametric component separation with the same data set. The computational core of this method is linear regression between pairs of frequency maps, often called "T-T plots". Individual contributions from monopole and dipole terms are determined by performing the regression locally in patches on the sky, while the degeneracy between different frequencies is lifted when ever the dominant foreground component exhibits a significant spatial spectral index variation. Based on this method, we present two different, but each internally consistent, sets of monopole and dipole coefficients for the 9-year WMAP, Planck 2013, SFD 100 um, Haslam 408 MHz and Reich & Reich 1420 MHz maps. The two sets have been derived with different analysis assumptions and data selection, and provides an estimate of residual systematic uncertainties. In general, our values are...
ESTIMATE OF DISCRETE NONLINEARITIES IN A MAINLY LINEAR DYNAMIC SYSTEM
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The class of system considered is a single degree of freedom undamped vibrating system with a clearance in which the dynamical behavior is described by a state-space representation in real time. The direct identification technique for the estimate of a clearance and other parameters in the system is presented in terms of least squares method and stepby-step iteration approach. For numerical simulation purpose, the simulated data are achieved by corrupting the modeled responses. The mathematical algorithm, which is put forward, has proven to be effective through a practical numerical example.
Optimal linear shrinkage corrections of sample LMMSE and MVDR estimators
2012-01-01
La proposició d'estimadors shrinkage òptims que corregeixen la degradació dels mètodes sample LMMSE i sample MUDR en el règim on el número de mostres és petit en comparació a la dimensió de les observacions. [ANGLÈS] This master thesis proposes optimal shrinkage estimators that counteract the performance degradation of the sample LMMSE and sample MVDR methods in the regime where the sample size is small compared to the observation dimension. [CASTELLÀ] Esta máster tesis propone estimado...
Note: Localization based on estimated source energy homogeneity
Turkaya, Semih; Toussaint, Renaud; Eriksen, Fredrik Kvalheim; Lengliné, Olivier; Daniel, Guillaume; Flekkøy, Eirik G.; Mâløy, Knut Jørgen
2016-09-01
Acoustic signal localization is a complex problem with a wide range of industrial and academic applications. Herein, we propose a localization method based on energy attenuation and inverted source amplitude comparison (termed estimated source energy homogeneity, or ESEH). This inversion is tested on both synthetic (numerical) data using a Lamb wave propagation model and experimental 2D plate data (recorded with 4 accelerometers sensitive up to 26 kHz). We compare the performance of this technique with classic source localization algorithms: arrival time localization, time reversal localization, and localization based on energy amplitude. Our technique is highly versatile and out-performs the conventional techniques in terms of error minimization and cost (both computational and financial).
Linear scaling calculation of maximally localized Wannier functions with atomic basis set.
Xiang, H J; Li, Zhenyu; Liang, W Z; Yang, Jinlong; Hou, J G; Zhu, Qingshi
2006-06-21
We have developed a linear scaling algorithm for calculating maximally localized Wannier functions (MLWFs) using atomic orbital basis. An O(N) ground state calculation is carried out to get the density matrix (DM). Through a projection of the DM onto atomic orbitals and a subsequent O(N) orthogonalization, we obtain initial orthogonal localized orbitals. These orbitals can be maximally localized in linear scaling by simple Jacobi sweeps. Our O(N) method is validated by applying it to water molecule and wurtzite ZnO. The linear scaling behavior of the new method is demonstrated by computing the MLWFs of boron nitride nanotubes.
On asymptotics of t-type regression estimation in multiple linear model
Institute of Scientific and Technical Information of China (English)
无
2004-01-01
We consider a robust estimator (t-type regression estimator) of multiple linear regression model by maximizing marginal likelihood of a scaled t-type error t-distribution.The marginal likelihood can also be applied to the de-correlated response when the withinsubject correlation can be consistently estimated from an initial estimate of the model based on the independent working assumption. This paper shows that such a t-type estimator is consistent.
Linnet, K
1990-12-01
The linear relationship between the measurements of two methods is estimated on the basis of a weighted errors-in-variables regression model that takes into account a proportional relationship between standard deviations of error distributions and true variable levels. Weights are estimated by an interative procedure. As shown by simulations, the regression procedure yields practically unbiased slope estimates in realistic situations. Standard errors of slope and location difference estimations are derived by the jackknife principle. For illustration, the linear relationship is estimated between the measurements of two albumin methods with proportional errors.
Extracting harmonic signal from a chaotic background with local linear model
Li, Chenlong; Su, Liyun
2017-02-01
In this paper, the problems of blind detection and estimation of harmonic signal in strong chaotic background are analyzed, and new methods by using local linear (LL) model are put forward. The LL model has been exhaustively researched and successfully applied for fitting and forecasting chaotic signal in many chaotic fields. We enlarge the modeling capacity substantially. Firstly, we can predict the short-term chaotic signal and obtain the fitting error based on the LL model. Then we detect the frequencies from the fitting error by periodogram, a property on the fitting error is proposed which has not been addressed before, and this property ensures that the detected frequencies are similar to that of harmonic signal. Secondly, we establish a two-layer LL model to estimate the determinate harmonic signal in strong chaotic background. To estimate this simply and effectively, we develop an efficient backfitting algorithm to select and optimize the parameters that are hard to be exhaustively searched for. In the method, based on sensitivity to initial value of chaos motion, the minimum fitting error criterion is used as the objective function to get the estimation of the parameters of the two-layer LL model. Simulation shows that the two-layer LL model and its estimation technique have appreciable flexibility to model the determinate harmonic signal in different chaotic backgrounds (Lorenz, Henon and Mackey-Glass (M-G) equations). Specifically, the harmonic signal can be extracted well with low SNR and the developed background algorithm satisfies the condition of convergence in repeated 3-5 times.
Robust observer-based fault estimation and accommodation of discrete-time piecewise linear systems
DEFF Research Database (Denmark)
Tabatabaeipour, Mojtaba; Bak, Thomas
2013-01-01
In this paper a new integrated observer-based fault estimation and accommodation strategy for discrete-time piecewise linear (PWL) systems subject to actuator faults is proposed. A robust estimator is designed to simultaneously estimate the state of the system and the actuator fault. Then, the es...
Boedeker, Peter
2017-01-01
Hierarchical linear modeling (HLM) is a useful tool when analyzing data collected from groups. There are many decisions to be made when constructing and estimating a model in HLM including which estimation technique to use. Three of the estimation techniques available when analyzing data with HLM are maximum likelihood, restricted maximum…
Set-membership state estimation framework for uncertain linear differential-algebraic equations
Zhuk, Serhiy
2008-01-01
We investigate a problem of state estimation for the dynamical system described by the linear operator equation with unknown parameters in Hilbert space. We present explicit expressions for linear minimax estimation and error provided that any pair of uncertain parameters belongs to the quadratic bounding set. As an application of the introduced approach we introduce a notion of minimax directional observability and index of non-causality for linear noncausal DAEs. Application of these notions to the problem of state estimation for the linear uncertain noncausal DAEs allows to construct the state estimation in the form of the recursive minimax filter. A numerical example of the state estimation for 3D non-causal descriptor system is presented.
Input and state estimation for linear systems with a rank-deficient direct feedthrough matrix.
Wang, Haokun; Zhao, Jun; Xu, Zuhua; Shao, Zhijiang
2015-07-01
The problem of joint input and state estimation for linear stochastic systems with a rank-deficient direct feedthrough matrix is discussed in this paper. Results from previous studies only solve the state estimation problem; globally optimal estimation of the unknown input is not provided. Based on linear minimum-variance unbiased estimation, a five-step recursive filter with global optimality is proposed to estimate both the unknown input and the state. The relationship between the proposed filter and the existing results is addressed. We show that the unbiased input estimation does not require any new information or additional constraints. Both the state and the unknown input can be estimated under the same unbiasedness condition. Global optimalities of both the state estimator and the unknown input estimator are proven in the minimum-variance unbiased sense.
Institute of Scientific and Technical Information of China (English)
Tao Hu; Heng-jian Cui; Xing-wei Tong
2009-01-01
This article considers a semiparametric varying-coefficient partially linear regression model with current status data. The semiparametric varying-coefficient partially linear regression model which is a gen-eralization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estima-tor for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are conducted to examine the small-sample properties of the proposed estimates and a real dataset is used to illustrate our approach.
Directory of Open Access Journals (Sweden)
Kesavan.E
2013-04-01
Full Text Available This paper suggests an idea to design an adaptive PID controller for Non-linear liquid tank System and is implemented in PLC. Online estimation of linear parameters (Time constant and Gain brings an exact model of the process to take perfect control action. Based on these estimated values, the controller parameters will be well tuned by internal model control. Internal model control is an unremarkably used technique and provides well tuned controller in order to have a good controlling process. PLC with its ability to have both continues control for PID Control and digital control for fault diagnosis which ascertains faults in the system and provides alerts about the status of the entire process.
truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models
Directory of Open Access Journals (Sweden)
Maria Karlsson
2014-05-01
Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.
Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks
Directory of Open Access Journals (Sweden)
Xiaoxue Feng
2014-11-01
Full Text Available Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS, which gets better filtering performance than NILS without constraint.
Matijevič, Gal; Prša, Andrej; Orosz, Jerome A.; Welsh, William F.; Bloemen, Steven; Barclay, Thomas
2012-05-01
We present an automated classification of 2165 Kepler eclipsing binary (EB) light curves that accompanied the second Kepler data release. The light curves are classified using locally linear embedding, a general nonlinear dimensionality reduction tool, into morphology types (detached, semi-detached, overcontact, ellipsoidal). The method, related to a more widely used principal component analysis, produces a lower-dimensional representation of the input data while preserving local geometry and, consequently, the similarity between neighboring data points. We use this property to reduce the dimensionality in a series of steps to a one-dimensional manifold and classify light curves with a single parameter that is a measure of "detachedness" of the system. This fully automated classification correlates well with the manual determination of morphology from the data release, and also efficiently highlights any misclassified objects. Once a lower-dimensional projection space is defined, the classification of additional light curves runs in a negligible time and the method can therefore be used as a fully automated classifier in pipeline structures. The classifier forms a tier of the Kepler EB pipeline that pre-processes light curves for the artificial intelligence based parameter estimator.
Estimation of the FRF Through the Improved Local Bandwidth Selection in the Local Polynomial Method
DEFF Research Database (Denmark)
Thummala, Prasanth; Schoukens, Johan
2012-01-01
This paper presents a nonparametric method to measure an improved frequency response function (FRF) of a linear dynamic system excited by a random input. Recently, the local polynomial method (LPM) has been proposed as a technique to reduce the leakage errors on FRF measurements. The noise...
Efficent Estimation of the Non-linear Volatility and Growth Model
2009-01-01
Ramey and Ramey (1995) introduced a non-linear model relating volatility to growth. The solution of this model by generalised computer algorithms for non-linear maximum likelihood estimation encounters the usual difficulties and is, at best, tedious. We propose an algebraic solution for the model that provides fully efficient estimators and is elementary to implement as a standard ordinary least squares procedure. This eliminates issues such as the ‘guesstimation’ of initial values and mul...
Linear regressive model structures for estimation and prediction of compartmental diffusive systems
Vries, D.; Keesman, K.J.; Zwart, H.
2006-01-01
Abstract In input-output relations of (compartmental) diffusive systems, physical parameters appear non-linearly, resulting in the use of (constrained) non-linear parameter estimation techniques with its short-comings regarding global optimality and computational effort. Given a LTI system in state
Linear regressive model structures for estimation and prediction of compartmental diffusive systems
Vries, D.; Keesman, K.J.; Zwart, H.J.
2006-01-01
In input-output relations of (compartmental) diffusive systems, physical parameters appear non-linearly, resulting in the use of (constrained) non-linear parameter estimation techniques with its short-comings regarding global optimality and computational effort. Given a LTI system in state space for
A practical localization solution for wireless sensor networks deployed in linear topography
Zhang, Kui; Guo, Peng; Meratnia, Nirvana; Havinga, Paul J.M.
2010-01-01
In this paper, we propose a practical range-free localization solution for wireless sensor networks (WSNs). Different from existing localization approaches, the proposed solution is specially designed for an ultra sparse mobile WSNs deployed in coal mine tunnels with linear topography. To obtain mor
Estimations of non-linearities in structural vibrations of string musical instruments
Ege, Kerem; Boutillon, Xavier
2012-01-01
Under the excitation of strings, the wooden structure of string instruments is generally assumed to undergo linear vibrations. As an alternative to the direct measurement of the distortion rate at several vibration levels and frequencies, we characterise weak non-linearities by a signal-model approach based on cascade of Hammerstein models. In this approach, in a chain of two non-linear systems, two measurements are sufficient to estimate the non-linear contribution of the second (sub-)system which cannot be directly linearly driven, as a function of the exciting frequency. The experiment consists in exciting the instrument acoustically. The linear and non-linear contributions to the response of (a) the loudspeaker coupled to the room, (b) the instrument can be separated. Some methodological issues will be discussed. Findings pertaining to several instruments - one piano, two guitars, one violin - will be presented.
Improving Empirical Approaches to Estimating Local Greenhouse Gas Emissions
Blackhurst, M.; Azevedo, I. L.; Lattanzi, A.
2016-12-01
Evidence increasingly indicates our changing climate will have significant global impacts on public health, economies, and ecosystems. As a result, local governments have become increasingly interested in climate change mitigation. In the U.S., cities and counties representing nearly 15% of the domestic population plan to reduce 300 million metric tons of greenhouse gases over the next 40 years (or approximately 1 ton per capita). Local governments estimate greenhouse gas emissions to establish greenhouse gas mitigation goals and select supporting mitigation measures. However, current practices produce greenhouse gas estimates - also known as a "greenhouse gas inventory " - of empirical quality often insufficient for robust mitigation decision making. Namely, current mitigation planning uses sporadic, annual, and deterministic estimates disaggregated by broad end use sector, obscuring sources of emissions uncertainty, variability, and exogeneity that influence mitigation opportunities. As part of AGU's Thriving Earth Exchange, Ari Lattanzi of City of Pittsburgh, PA recently partnered with Dr. Inez Lima Azevedo (Carnegie Mellon University) and Dr. Michael Blackhurst (University of Pittsburgh) to improve the empirical approach to characterizing Pittsburgh's greenhouse gas emissions. The project will produce first-order estimates of the underlying sources of uncertainty, variability, and exogeneity influencing Pittsburgh's greenhouse gases and discuss implications of mitigation decision making. The results of the project will enable local governments to collect more robust greenhouse gas inventories to better support their mitigation goals and improve measurement and verification efforts.
Localized linear IgA dermatosis induced by UV light-treatment for herpes zoster.
He, Chundi; Xu, Honghui; Xiao, Ting; Geng, Long; Chen, Hong-Duo
2007-05-01
We report a case of localized linear IgA dermatosis (LID). The patient suffered from herpes zoster on the right waist and received three localized ultraviolet (UV) light treatments. One month later he presented with bullae on the same site. Direct immunofluorescence showed deposition of linear IgA and weak C3 along the basement membrane zone. Indirect immunofluorescence on the salt-split human skin demonstrated that IgA antibodies were bound to the epidermal side. To our knowledge, this is the first case of localized LID induced by UV light treatment for herpes zoster. It is also the third case of LID induced by UV light.
Asymptotically exact Discontinuous Galerkin error estimates for linear symmetric hyperbolic systems
Adjerid, S.; Weinhart, T.
2014-01-01
We present an a posteriori error analysis for the discontinuous Galerkin discretization error of first-order linear symmetric hyperbolic systems of partial differential equations with smooth solutions. We perform a local error analysis by writing the local error as a series and showing that its lead
Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images
Institute of Scientific and Technical Information of China (English)
Yusei Kobori; Satoshi Mizuta
2016-01-01
Graphical representation of DNA sequences is one of the most popular techniques for alignment-free sequence comparison. Here, we propose a new method for the feature extraction of DNA sequences represented by binary images, by estimating the similarity between DNA sequences using the frequency histograms of local bitmap patterns of images. Our method shows linear time complexity for the length of DNA sequences, which is practical even when long sequences, such as whole genome sequences, are compared. We tested five distance measures for the estimation of sequence similarities, and found that the histogram intersection and Manhattan distance are the most appropriate ones for phylogenetic analyses.
Local Whittle estimation of multivariate fractionally integrated processes
DEFF Research Database (Denmark)
Nielsen, Frank
This paper derives a semiparametric estimator of multivariate fractionally integrated processes covering both stationary and non-stationary values of d. We utilize the notion of the extended discrete Fourier transform and periodogram to extend the multivariate local Whittle estimator of Shimotsu ...... analysis of log spot exchange rates. We find that the log spot exchange rates of Germany, United Kingdom, Japan, Canada, France, Italy, and Switzerland against the US Dollar for the period January 1974 until December 2001 are well decribed as I (1) processes....
Collective vs local measurements in qubit mixed state estimation
Bagán, E; Muñoz-Tàpia, R; Rodríguez, A
2004-01-01
We discuss the problem of estimating a general (mixed) qubit state. We give the optimal guess that can be inferred from any given set of measurements. For collective measurements and for a large number $N$ of copies, we show that the error in the estimation goes as 1/N. For local measurements we focus on the simpler case of states lying on the equatorial plane of the Bloch sphere. We show that standard tomographic techniques lead to an error proportional to $1/N^{1/4}$, while with our optimal data processing it is proportional to $1/N^{3/4}$.
Tightness of M-estimators for multiple linear regression in time series
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Bent
We show tightness of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semi-continuous and sufficiently large for large argument: Particular cases are the Huber-skip and quantile regression. Tightness requires...
robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models
Directory of Open Access Journals (Sweden)
Manuel Koller
2016-12-01
Full Text Available As any real-life data, data modeled by linear mixed-effects models often contain outliers or other contamination. Even little contamination can drive the classic estimates far away from what they would be without the contamination. At the same time, datasets that require mixed-effects modeling are often complex and large. This makes it difficult to spot contamination. Robust estimation methods aim to solve both problems: to provide estimates where contamination has only little influence and to detect and flag contamination. We introduce an R package, robustlmm, to robustly fit linear mixed-effects models. The package's functions and methods are designed to closely equal those offered by lme4, the R package that implements classic linear mixed-effects model estimation in R. The robust estimation method in robustlmm is based on the random effects contamination model and the central contamination model. Contamination can be detected at all levels of the data. The estimation method does not make any assumption on the data's grouping structure except that the model parameters are estimable. robustlmm supports hierarchical and non-hierarchical (e.g., crossed grouping structures. The robustness of the estimates and their asymptotic efficiency is fully controlled through the function interface. Individual parts (e.g., fixed effects and variance components can be tuned independently. In this tutorial, we show how to fit robust linear mixed-effects models using robustlmm, how to assess the model fit, how to detect outliers, and how to compare different fits.
DOA Estimation with Local-Peak-Weighted CSP
Directory of Open Access Journals (Sweden)
Ichikawa Osamu
2010-01-01
Full Text Available This paper proposes a novel weighting algorithm for Cross-power Spectrum Phase (CSP analysis to improve the accuracy of direction of arrival (DOA estimation for beamforming in a noisy environment. Our sound source is a human speaker and the noise is broadband noise in an automobile. The harmonic structures in the human speech spectrum can be used for weighting the CSP analysis, because harmonic bins must contain more speech power than the others and thus give us more reliable information. However, most conventional methods leveraging harmonic structures require pitch estimation with voiced-unvoiced classification, which is not sufficiently accurate in noisy environments. In our new approach, the observed power spectrum is directly converted into weights for the CSP analysis by retaining only the local peaks considered to be harmonic structures. Our experiment showed the proposed approach significantly reduced the errors in localization, and it showed further improvements when used with other weighting algorithms.
A speed estimation unit for induction motors based on adaptive linear combiner
Energy Technology Data Exchange (ETDEWEB)
Marei, Mostafa I.; Shaaban, Mostafa F.; El-Sattar, Ahmed A. [Department of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo 11517 (Egypt)
2009-07-15
This paper presents a new induction motor speed estimation technique, which can estimate the rotor resistance as well, from the measured voltage and current signals. Moreover, the paper utilizes a novel adaptive linear combiner (ADALINE) structure for speed and rotor resistance estimations. This structure can deal with the multi-output systems and it is called MO-ADALINE. The model of the induction motor is arranged in a linear form, in the stationary reference frame, to cope with the proposed speed estimator. There are many advantages of the proposed unit such as wide speed range capability, immunity against harmonics of measured waveforms, and precise estimation of the speed and the rotor resistance at different dynamic changes. Different types of induction motor drive systems are used to evaluate the dynamic performance and to examine the accuracy of the proposed unit for speed and rotor resistance estimation. (author)
Design of Non-fragile Satisfactory Estimator for Linear Continuous Perturbed Stochastic Systems
Institute of Scientific and Technical Information of China (English)
ZANG Wen-li; WANG Yuan-gang; GUO Zhi
2006-01-01
The design problem of non-fragile estimator is addressed for a class of perturbed linear continuous systems. The perturbations occur on the plant and estimator parameters. The estimator designed should force the error system to achieve the desired decay rate and force the steady error variance less than the specified upper bound irrelevancy of the admissible plant perturbations and estimator perturbations. Consistency problem of the decay rate with the variance upper bound is first considered via linear matrix inequality (LMI) approach. The solution of the estimator parameter under specifications to be consistent is then discussed. The consistency condition of specifications and estimator parameter solution are transformed to feasible or minimum problems subject to a set of LMI respectively. The method is illustrated by a numerical example.
Solutions to estimation problems for scalar hamilton-jacobi equations using linear programming
Claudel, Christian G.
2014-01-01
This brief presents new convex formulations for solving estimation problems in systems modeled by scalar Hamilton-Jacobi (HJ) equations. Using a semi-analytic formula, we show that the constraints resulting from a HJ equation are convex, and can be written as a set of linear inequalities. We use this fact to pose various (and seemingly unrelated) estimation problems related to traffic flow-engineering as a set of linear programs. In particular, we solve data assimilation and data reconciliation problems for estimating the state of a system when the model and measurement constraints are incompatible. We also solve traffic estimation problems, such as travel time estimation or density estimation. For all these problems, a numerical implementation is performed using experimental data from the Mobile Century experiment. In the context of reproducible research, the code and data used to compute the results presented in this brief have been posted online and are accessible to regenerate the results. © 2013 IEEE.
LOCAL ESTIMATES OF SINGULAR SOLUTION TO GAUSSIAN CURVATURE EQUATION
Institute of Scientific and Technical Information of China (English)
杨云雁
2003-01-01
In this paper, we derive the local estimates of a singular solution near its singular set Z of the Gaussian curvature equation △u(x) + K(x)eu(x) = 0 in Ω \\ Z,in the case that K(x) may be zero on Z, where Ω R2 is a bounded open domain, and Z is a set of finite points.
An Adaptive Finite Element Method Based on Optimal Error Estimates for Linear Elliptic Problems
Institute of Scientific and Technical Information of China (English)
汤雁
2004-01-01
The subject of the work is to propose a series of papers about adaptive finite element methods based on optimal error control estimate. This paper is the third part in a series of papers on adaptive finite element methods based on optimal error estimates for linear elliptic problems on the concave corner domains. In the preceding two papers (part 1:Adaptive finite element method based on optimal error estimate for linear elliptic problems on concave corner domain; part 2:Adaptive finite element method based on optimal error estimate for linear elliptic problems on nonconvex polygonal domains), we presented adaptive finite element methods based on the energy norm and the maximum norm. In this paper, an important result is presented and analyzed. The algorithm for error control in the energy norm and maximum norm in part 1 and part 2 in this series of papers is based on this result.
Directory of Open Access Journals (Sweden)
KAYODE AYINDE
2012-11-01
Full Text Available Performances of estimators of linear regression model with autocorrelated error term have been attributed to the nature and specification of the explanatory variables. The violation of assumption of the independence of the explanatory variables is not uncommon especially in business, economic and social sciences, leading to the development of many estimators. Moreover, prediction is one of the main essences of regression analysis. This work, therefore, attempts to examine the parameter estimates of the Ordinary Least Square estimator (OLS, Cochrane-Orcutt estimator (COR, Maximum Likelihood estimator (ML and the estimators based on Principal Component analysis (PC in prediction of linear regression model with autocorrelated error terms under the violations of assumption of independent regressors (multicollinearity using Monte-Carlo experiment approach. With uniform variables as regressors, it further identifies the best estimator that can be used for prediction purpose by averaging the adjusted co-efficient of determination of each estimator over the number of trials. Results reveal that the performances of COR and ML estimators at each level of multicollinearity over the levels of autocorrelation are convex – like while that of the OLS and PC estimators are concave; and that asthe level of multicollinearity increases, the estimators perform much better at all the levels of autocorrelation. Except when the sample size is small (n=10, the performances of the COR and ML estimators are generally best and asymptotically the same. When the sample size is small, the COR estimator is still best except when the autocorrelation level is low. At these instances, the PC estimator is either best or competes with the best estimator. Moreover, at low level of autocorrelation in all the sample sizes, the OLS estimator competes with the best estimator in all the levels of multicollinearity.
On the use of Lineal Energy Measurements to Estimate Linear Energy Transfer Spectra
Adams, David A.; Howell, Leonard W., Jr.; Adam, James H., Jr.
2007-01-01
This paper examines the error resulting from using a lineal energy spectrum to represent a linear energy transfer spectrum for applications in the space radiation environment. Lineal energy and linear energy transfer spectra are compared in three diverse but typical space radiation environments. Different detector geometries are also studied to determine how they affect the error. LET spectra are typically used to compute dose equivalent for radiation hazard estimation and single event effect rates to estimate radiation effects on electronics. The errors in the estimations of dose equivalent and single event rates that result from substituting lineal energy spectra for linear energy spectra are examined. It is found that this substitution has little effect on dose equivalent estimates in interplanetary quiet-time environment regardless of detector shape. The substitution has more of an effect when the environment is dominated by solar energetic particles or trapped radiation, but even then the errors are minor especially if a spherical detector is used. For single event estimation, the effect of the substitution can be large if the threshold for the single event effect is near where the linear energy spectrum drops suddenly. It is judged that single event rate estimates made from lineal energy spectra are unreliable and the use of lineal energy spectra for single event rate estimation should be avoided.
Bourgeois, Brian S.; Elmore, Paul A.; Avera, William E.; Zambo, Samantha J.
2016-07-01
This paper examines and contrasts two estimation methods, Kalman filtering and linear smoothing, for creating interpolated data products from bathymetry measurements. Using targeted examples, we demonstrate previously obscured behavior showing the dependence of linear smoothers on the spatial arrangement of the measurements, yielding markedly different estimation results than the Kalman filter. For bathymetry data, we have modified the variance estimates from both the Kalman filter and linear smoothers to obtain comparable estimators for dense data. These comparable estimators produce uncertainty estimates that have statistically insignificant differences via hypothesis testing. Achieving comparable estimation is accomplished by applying the "propagated uncertainty" concept and a numerical realization of Tobler's principle to the measurement data prior to the computation of the estimate. We show new mathematical derivations for these modifications. In addition, we show test results with (a) synthetic data and (b) gridded bathymetry in the area of the Scripps and La Jolla Canyons. Our tenfold cross-validation for case (b) shows that the modified equations create comparable uncertainty for both gridding algorithms with null hypothesis acceptance rates of greater than 99.95% of the data points. In contrast, bilinear interpolation has 10 times the amount of rejection. We then discuss how the uncertainty estimators are, in principle, applicable to interpolate geophysical data other than bathymetry.
FEH Local: Improving flood estimates using historical data
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Prosdocimi Ilaria
2016-01-01
Full Text Available The traditional approach to design flood estimation (for example, to derive the 100-year flood is to apply a statistical model to time series of peak river flow measured by gauging stations. Such records are typically not very long, for example in the UK only about 10% of the stations have records that are more than 50 years in length. Along-explored way to augment the data available from a gauging station is to derive information about historical flood events and paleo-floods, which can be obtained from careful exploration of archives, old newspapers, flood marks or other signs of past flooding that are still discernible in the catchment, and the history of settlements. The inclusion of historical data in flood frequency estimation has been shown to substantially reduce the uncertainty around the estimated design events and is likely to provide insight into the rarest events which might have pre-dated the relatively short systematic records. Among other things, the FEH Local project funded by the Environment Agency aims to develop methods to easily incorporate historical information into the standard method of statistical flood frequency estimation in the UK. Different statistical estimation procedures are explored, namely maximum likelihood and partial probability weighted moments, and the strengths and weaknesses of each method are investigated. The project assesses the usefulness of historical data and aims to provide practitioners with useful guidelines to indicate in what circumstances the inclusion of historical data is likely to be beneficial in terms of reducing both the bias and the variability of the estimated flood frequency curves. The guidelines are based on the results of a large Monte Carlo simulation study, in which different estimation procedures and different data availability scenarios are studied. The study provides some indication of the situations under which different estimation procedures might give a better performance.
Direction of Arrival Estimation Based on MUSIC Algorithm Using Uniform and Non-Uniform Linear Arrays
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Eva Kwizera
2017-03-01
Full Text Available In signal processing, the direction of arrival (DOA estimation denotes the direction from which a propagating wave arrives at a point, where a set of antennas is located. Using the array antenna has an advantage over the single antenna in achieving an improved performance by applying Multiple Signal Classification (MUSIC algorithm. This paper focuses on estimating the DOA using uniform linear array (ULA and non-uniform linear array (NLAof antennas to analyze the performance factors that affect the accuracy and resolution of the system based on MUSIC algorithm. The direction of arrival estimation is simulated on a MATLAB platform with a set of input parameters such as array elements, signal to noise ratio, number of snapshots and number of signal sources. An extensive simulation has been conducted and the results show that the NLA with DOA estimation for co-prime array can achieve an accurate and efficient DOA estimation
Sieve M-estimation for semiparametric varying-coefficient partially linear regression model
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This article considers a semiparametric varying-coefficient partially linear regression model.The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable.A sieve M-estimation method is proposed and the asymptotic properties of the proposed estimators are discussed.Our main object is to estimate the nonparametric component and the unknown parameters simultaneously.It is easier to compute and the required computation burden is much less than the existing two-stage estimation method.Furthermore,the sieve M-estimation is robust in the presence of outliers if we choose appropriate ρ(·).Under some mild conditions,the estimators are shown to be strongly consistent;the convergence rate of the estimator for the unknown nonparametric component is obtained and the estimator for the unknown parameter is shown to be asymptotically normally distributed.Numerical experiments are carried out to investigate the performance of the proposed method.
Local Lyapunov exponents sublimiting growth rates of linear random differential equations
Siegert, Wolfgang
2009-01-01
Establishing a new concept of local Lyapunov exponents the author brings together two separate theories, namely Lyapunov exponents and the theory of large deviations. Specifically, a linear differential system is considered which is controlled by a stochastic process that during a suitable noise-intensity-dependent time is trapped near one of its so-called metastable states. The local Lyapunov exponent is then introduced as the exponential growth rate of the linear system on this time scale. Unlike classical Lyapunov exponents, which involve a limit as time increases to infinity in a fixed system, here the system itself changes as the noise intensity converges, too.
Dynamical Stability of an Ion in a Linear Trap as a Solid-State Problem of Electron Localization
Berman, G P; James, D F V; Hughes, R J; Kamenev, D I
2000-01-01
When an ion confined in a linear ion trap interacts with a coherent laser field, the internal degrees of freedom, related to the electron transitions, couple to the vibrational degree of freedom of the ion. As a result of this interaction, quantum dynamics of the vibrational degree of freedom becomes complicated, and in some ranges of parameters even chaotic. We analyze the vibrational ion dynamics using a formal analogy with the solid-state problem of electron localization. In particular, we show how the resonant approximation used in analysis of the ion dynamics, leads to a transition from a two-dimensional (2D) to a one-dimensional problem (1D) of electron localization. The localization length in the solid-state problem is estimated in cases of weak and strong interaction between the cites of the 2D cell by using the methods of resonance perturbation theory, common in analysis of 1D time-dependent dynamical systems.
Brazhnyi, Valeriy A
2011-01-01
We study the dynamics of two-dimensional (2D) localized modes in the nonlinear lattice described by the discrete nonlinear Schr\\"{o}dinger (DNLS) equation, including a local linear or nonlinear defect. Discrete solitons pinned to the defects are investigated by means of the numerical continuation from the anti-continuum limit and also using the variational approximation (VA), which features a good agreement for strongly localized modes. The models with the time-modulated strengths of the linear or nonlinear defect are considered too. In that case, one can temporarily shift the critical norm, below which localized 2D modes cannot exists, to a level above the norm of the given soliton, which triggers the irreversible delocalization transition.
The Solution Structure and Error Estimation for The Generalized Linear Complementarity Problem
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Tingfa Yan
2014-07-01
Full Text Available In this paper, we consider the generalized linear complementarity problem (GLCP. Firstly, we develop some equivalent reformulations of the problem under milder conditions, and then characterize the solution of the GLCP. Secondly, we also establish the global error estimation for the GLCP by weakening the assumption. These results obtained in this paper can be taken as an extension for the classical linear complementarity problems.
Directory of Open Access Journals (Sweden)
Yueyang Li
2014-01-01
Full Text Available This paper investigates the H∞ fixed-lag fault estimator design for linear discrete time-varying (LDTV systems with intermittent measurements, which is described by a Bernoulli distributed random variable. Through constructing a novel partially equivalent dynamic system, the fault estimator design is converted into a deterministic quadratic minimization problem. By applying the innovation reorganization technique and the projection formula in Krein space, a necessary and sufficient condition is obtained for the existence of the estimator. The parameter matrices of the estimator are derived by recursively solving two standard Riccati equations. An illustrative example is provided to show the effectiveness and applicability of the proposed algorithm.
A process fault estimation strategy for non-linear dynamic systems
Pazera, Marcin; Korbicz, Józef
2017-01-01
The paper deals with the problem of simultaneous state and process fault estimation for non-linear dynamic systems. Instead of estimating the fault directly, its product with state and the state itself are estimated. To derive the fault from the product, a simple algebraic approach is proposed. The estimation strategy is based on the quadratic boundedness approach. The final part of the paper presents an illustrative example concerning a laboratory multi-tank system. The real data experiments clearly exhibit the performance of the proposed approach.
Variance estimation for complex indicators of poverty and inequality using linearization techniques
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Guillaume Osier
2009-12-01
Full Text Available The paper presents the Eurostat experience in calculating measures of precision, including standard errors, confidence intervals and design effect coefficients - the ratio of the variance of a statistic with the actual sample design to the variance of that statistic with a simple random sample of same size - for the "Laeken" indicators, that is, a set of complex indicators of poverty and inequality which had been set out in the framework of the EU-SILC project (European Statistics on Income and Living Conditions. The Taylor linearization method (Tepping, 1968; Woodruff, 1971; Wolter, 1985; Tille, 2000 is actually a well-established method to obtain variance estimators for nonlinear statistics such as ratios, correlation or regression coefficients. It consists of approximating a nonlinear statistic with a linear function of the observations by using first-order Taylor Series expansions. Then, an easily found variance estimator of the linear approximation is used as an estimator of the variance of the nonlinear statistic. Although the Taylor linearization method handles all the nonlinear statistics which can be expressed as a smooth function of estimated totals, the approach fails to encompass the "Laeken" indicators since the latter are having more complex mathematical expressions. Consequently, a generalized linearization method (Deville, 1999, which relies on the concept of influence function (Hampel, Ronchetti, Rousseeuw and Stahel, 1986, has been implemented. After presenting the EU-SILC instrument and the main target indicators for which variance estimates are needed, the paper elaborates on the main features of the linearization approach based on influence functions. Ultimately, estimated standard errors, confidence intervals and design effect coefficients obtained from this approach are presented and discussed.
Localization of periodic orbits of polynomial vector fields of even degree by linear functions
Energy Technology Data Exchange (ETDEWEB)
Starkov, Konstantin E. [CITEDI-IPN, Av. del Parque 1310, Mesa de Otay, Tijuana, BC (Mexico)] e-mail: konst@citedi.mx
2005-08-01
This paper is concerned with the localization problem of periodic orbits of polynomial vector fields of even degree by using linear functions. Conditions of the localization of all periodic orbits in sets of a simple structure are obtained. Our results are based on the solution of the conditional extremum problem and the application of homogeneous polynomial forms of even degrees. As examples, the Lanford system, the jerky system with one quadratic monomial and a quartically perturbed harmonic oscillator are considered.
Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.
Choi, Jae-Seok; Kim, Munchurl
2017-03-01
Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower
A note on constrained M-estimation and its recursive analog in multivariate linear regression models
Institute of Scientific and Technical Information of China (English)
RAO; Calyampudi; R
2009-01-01
In this paper,the constrained M-estimation of the regression coeffcients and scatter parameters in a general multivariate linear regression model is considered.Since the constrained M-estimation is not easy to compute,an up-dating recursion procedure is proposed to simplify the com-putation of the estimators when a new observation is obtained.We show that,under mild conditions,the recursion estimates are strongly consistent.In addition,the asymptotic normality of the recursive constrained M-estimators of regression coeffcients is established.A Monte Carlo simulation study of the recursion estimates is also provided.Besides,robustness and asymptotic behavior of constrained M-estimators are briefly discussed.
Robust state estimation for uncertain linear systems with deterministic input signals
Institute of Scientific and Technical Information of China (English)
Huabo LIU; Tong ZHOU
2014-01-01
In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.
Monopole and dipole estimation for multi-frequency sky maps by linear regression
Wehus, I. K.; Fuskeland, U.; Eriksen, H. K.; Banday, A. J.; Dickinson, C.; Ghosh, T.; Górski, K. M.; Lawrence, C. R.; Leahy, J. P.; Maino, D.; Reich, P.; Reich, W.
2017-01-01
We describe a simple but efficient method for deriving a consistent set of monopole and dipole corrections for multi-frequency sky map data sets, allowing robust parametric component separation with the same data set. The computational core of this method is linear regression between pairs of frequency maps, often called T-T plots. Individual contributions from monopole and dipole terms are determined by performing the regression locally in patches on the sky, while the degeneracy between different frequencies is lifted whenever the dominant foreground component exhibits a significant spatial spectral index variation. Based on this method, we present two different, but each internally consistent, sets of monopole and dipole coefficients for the nine-year WMAP, Planck 2013, SFD 100 μm, Haslam 408 MHz and Reich & Reich 1420 MHz maps. The two sets have been derived with different analysis assumptions and data selection, and provide an estimate of residual systematic uncertainties. In general, our values are in good agreement with previously published results. Among the most notable results are a relative dipole between the WMAP and Planck experiments of 10-15μK (depending on frequency), an estimate of the 408 MHz map monopole of 8.9 ± 1.3 K, and a non-zero dipole in the 1420 MHz map of 0.15 ± 0.03 K pointing towards Galactic coordinates (l,b) = (308°,-36°) ± 14°. These values represent the sum of any instrumental and data processing offsets, as well as any Galactic or extra-Galactic component that is spectrally uniform over the full sky.
GPS/DR Error Estimation for Autonomous Vehicle Localization.
Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In
2015-08-21
Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.
GPS/DR Error Estimation for Autonomous Vehicle Localization
Directory of Open Access Journals (Sweden)
Byung-Hyun Lee
2015-08-01
Full Text Available Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.
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Chandra Nagasuma R
2009-02-01
Full Text Available Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN from transcript profiling data. Results The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting problem and solved finally by formulating a Linear Program (LP. A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known
Estimation of local rainfall erosivity using artificial neural network
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Paulo Tarso Sanches Oliveira
2011-08-01
Full Text Available The information retrieval of local values of rainfall erosivity is essential for soil loss estimation with the Universal Soil Loss Equation (USLE, and thus is very useful in soil and water conservation planning. In this manner, the objective of this study was to develop an Artificial Neural Network (ANN with the capacity of estimating, with satisfactory accuracy, the rainfall erosivity in any location of the Mato Grosso do Sul state. We used data from rain erosivity, latitude, longitude, altitude of pluviometric and pluviographic stations located in the state to train and test an ANN. After training with various network configurations, we selected the best performance and higher coefficient of determination calculated on the basis of data erosivity of the sample test and the values estimated by ANN. In evaluating the results, the confidence and the agreement indices were used in addition to the coefficient of determination. It was found that it is possible to estimate the rainfall erosivity for any location in the state of Mato Grosso do Sul, in a reliable way, using only data of geographical coordinates and altitude.
Simultaneous Robust Fault and State Estimation for Linear Discrete-Time Uncertain Systems
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Feten Gannouni
2017-01-01
Full Text Available We consider the problem of robust simultaneous fault and state estimation for linear uncertain discrete-time systems with unknown faults which affect both the state and the observation matrices. Using transformation of the original system, a new robust proportional integral filter (RPIF having an error variance with an optimized guaranteed upper bound for any allowed uncertainty is proposed to improve robust estimation of unknown time-varying faults and to improve robustness against uncertainties. In this study, the minimization problem of the upper bound of the estimation error variance is formulated as a convex optimization problem subject to linear matrix inequalities (LMI for all admissible uncertainties. The proportional and the integral gains are optimally chosen by solving the convex optimization problem. Simulation results are given in order to illustrate the performance of the proposed filter, in particular to solve the problem of joint fault and state estimation.
A Low-Complexity ESPRIT-Based DOA Estimation Method for Co-Prime Linear Arrays
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Fenggang Sun
2016-08-01
Full Text Available The problem of direction-of-arrival (DOA estimation is investigated for co-prime array, where the co-prime array consists of two uniform sparse linear subarrays with extended inter-element spacing. For each sparse subarray, true DOAs are mapped into several equivalent angles impinging on the traditional uniform linear array with half-wavelength spacing. Then, by applying the estimation of signal parameters via rotational invariance technique (ESPRIT, the equivalent DOAs are estimated, and the candidate DOAs are recovered according to the relationship among equivalent and true DOAs. Finally, the true DOAs are estimated by combining the results of the two subarrays. The proposed method achieves a better complexity–performance tradeoff as compared to other existing methods.
A Low-Complexity ESPRIT-Based DOA Estimation Method for Co-Prime Linear Arrays.
Sun, Fenggang; Gao, Bin; Chen, Lizhen; Lan, Peng
2016-08-25
The problem of direction-of-arrival (DOA) estimation is investigated for co-prime array, where the co-prime array consists of two uniform sparse linear subarrays with extended inter-element spacing. For each sparse subarray, true DOAs are mapped into several equivalent angles impinging on the traditional uniform linear array with half-wavelength spacing. Then, by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), the equivalent DOAs are estimated, and the candidate DOAs are recovered according to the relationship among equivalent and true DOAs. Finally, the true DOAs are estimated by combining the results of the two subarrays. The proposed method achieves a better complexity-performance tradeoff as compared to other existing methods.
Bellili, Faouzi; Meftehi, Rabii; Affes, Sofiene; Stephenne, Alex
2015-01-01
In this paper, we tackle for the first time the problem of maximum likelihood (ML) estimation of the signal-to-noise ratio (SNR) parameter over time-varying single-input multiple-output (SIMO) channels. Both the data-aided (DA) and the non-data-aided (NDA) schemes are investigated. Unlike classical techniques where the channel is assumed to be slowly time-varying and, therefore, considered as constant over the entire observation period, we address the more challenging problem of instantaneous (i.e., short-term or local) SNR estimation over fast time-varying channels. The channel variations are tracked locally using a polynomial-in-time expansion. First, we derive in closed-form expressions the DA ML estimator and its bias. The latter is subsequently subtracted in order to obtain a new unbiased DA estimator whose variance and the corresponding Cram\\'er-Rao lower bound (CRLB) are also derived in closed form. Due to the extreme nonlinearity of the log-likelihood function (LLF) in the NDA case, we resort to the expectation-maximization (EM) technique to iteratively obtain the exact NDA ML SNR estimates within very few iterations. Most remarkably, the new EM-based NDA estimator is applicable to any linearly-modulated signal and provides sufficiently accurate soft estimates (i.e., soft detection) for each of the unknown transmitted symbols. Therefore, hard detection can be easily embedded in the iteration loop in order to improve its performance at low to moderate SNR levels. We show by extensive computer simulations that the new estimators are able to accurately estimate the instantaneous per-antenna SNRs as they coincide with the DA CRLB over a wide range of practical SNRs.
Para-product operators and para-linearization on locally compact Vilenkin groups
Institute of Scientific and Technical Information of China (English)
苏维宜
1995-01-01
The concept of para-product operators over locally compact Vilenkin groups is established and the applications to the para-linearization in nonlinear problems are studied. This kind of operators plays a special role in dealing with those functions which do not have the classical derivatives.
Application of local area networks to accelerator control systems at the Stanford Linear Accelerator
Energy Technology Data Exchange (ETDEWEB)
Fox, J.D.; Linstadt, E.; Melen, R.
1983-03-01
The history and current status of SLAC's SDLC networks for distributed accelerator control systems are discussed. These local area networks have been used for instrumentation and control of the linear accelerator. Network topologies, protocols, physical links, and logical interconnections are discussed for specific applications in distributed data acquisition and control system, computer networks and accelerator operations.
Object matching using a locally affine invariant and linear programming techniques.
Li, Hongsheng; Huang, Xiaolei; He, Lei
2013-02-01
In this paper, we introduce a new matching method based on a novel locally affine-invariant geometric constraint and linear programming techniques. To model and solve the matching problem in a linear programming formulation, all geometric constraints should be able to be exactly or approximately reformulated into a linear form. This is a major difficulty for this kind of matching algorithm. We propose a novel locally affine-invariant constraint which can be exactly linearized and requires a lot fewer auxiliary variables than other linear programming-based methods do. The key idea behind it is that each point in the template point set can be exactly represented by an affine combination of its neighboring points, whose weights can be solved easily by least squares. Errors of reconstructing each matched point using such weights are used to penalize the disagreement of geometric relationships between the template points and the matched points. The resulting overall objective function can be solved efficiently by linear programming techniques. Our experimental results on both rigid and nonrigid object matching show the effectiveness of the proposed algorithm.
Quantum Local Symmetry of the D-Dimensional Non-Linear Sigma Model: A Functional Approach
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Andrea Quadri
2014-04-01
Full Text Available We summarize recent progress on the symmetric subtraction of the Non-Linear Sigma Model in D dimensions, based on the validity of a certain Local Functional Equation (LFE encoding the invariance of the SU(2 Haar measure under local left transformations. The deformation of the classical non-linearly realized symmetry at the quantum level is analyzed by cohomological tools. It is shown that all the divergences of the one-particle irreducible (1-PI amplitudes (both on-shell and off-shell can be classified according to the solutions of the LFE. Applications to the non-linearly realized Yang-Mills theory and to the electroweak theory, which is directly relevant to the model-independent analysis of LHC data, are briefly addressed.
A novel suboptimal algorithm for state estimation of Markov jump linear systems
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
This paper is concerned with state estimation problem for Markov jump linear systems where the disturbances involved in the systems equations and measurement equations are assumed to be Gaussian noise sequences.Based on two properties of conditional expectation,orthogonal projective theorem is applied to the state estimation problem of the considered systems so that a novel suboptimal algorithm is obtained.The novelty of the algorithm lies in using orthogonal projective theorem instead of Kalman filters to ...
Estimation of LISS(local input-to-state stability) properties for nonlinear systems
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
Compared with input-to-state stability(ISS) in global version,the concept of local input-to-state stability(LISS) is more relevant and meaningful in practice.The key of assessing LISS properties lies in investigating three main ingredients,the local region of initial states,the local region of external inputs and the asymptotic gain.It is the objective of this paper to propose a numerical algorithm for estimating LISS properties on the theoretical foundation of quadratic form LISS-Lyapunov function.Given developments of linear matrix inequality(LMI) methods,this algorithm is effective and powerful.A typical power electronics based system with common DC bus is served as a demonstration for quantitative results.
The variance of the locally measured Hubble parameter explained with different estimators
DEFF Research Database (Denmark)
Odderskov, Io; Hannestad, Steen; Brandbyge, Jacob
2017-01-01
We study the expected variance of measurements of the Hubble constant, H0, as calculated in either linear perturbation theory or using non-linear velocity power spectra derived from N-body simulations. We compare the variance with that obtained by carrying out mock observations in the N-body simu......We study the expected variance of measurements of the Hubble constant, H0, as calculated in either linear perturbation theory or using non-linear velocity power spectra derived from N-body simulations. We compare the variance with that obtained by carrying out mock observations in the N......-body simulations, and show that the estimator typically used for the local Hubble constant in studies based on perturbation theory is different from the one used in studies based on N-body simulations. The latter gives larger weight to distant sources, which explains why studies based on N-body simulations tend...... of the percent determination of the Hubble constant in the local universe....
Asymptotics of Huber-Dutter Estimators for Partial Linear Model with Nonstochastic Designs
Institute of Scientific and Technical Information of China (English)
Xing-wei Tong; Heng-jian Cui; Hui Zhao
2005-01-01
For partial linear model Y = Xτ0 + g0(T) + e with unknown β0 ∈ Rd and an unknown smooth function go, this paper considers the Huber-Dutter estimators ofβ0, scale σ for the errors and the function go respectively, in which the smoothing B-spline function is used. Under some regular conditions, it is shown that the Huber-Dutter estimators of β0 and σ are asymptotically normal with convergence rate n-1/2 and the B-spline Huber-Dutter estimator of g0 achieves the optimal convergence rate in nonparametric regression.A simulation study demonstrates that the Huber-Dutter estimator ofβ0 is competitive with its M-estimator without scale parameter and the ordinary least square estimator. An example is presented after the simulation study.
Consistency and normality of Huber-Dutter estimators for partial linear model
Institute of Scientific and Technical Information of China (English)
2008-01-01
For partial linear model Y = Xτβ0 + g0(T) + with unknown β0 ∈ Rd and an unknown smooth function g0, this paper considers the Huber-Dutter estimators of β0, scale σ for the errors and the function g0 approximated by the smoothing B-spline functions, respectively. Under some regularity conditions, the Huber-Dutter estimators of β0 and σ are shown to be asymptotically normal with the rate of convergence n-1/2 and the B-spline Huber-Dutter estimator of g0 achieves the optimal rate of convergence in nonparametric regression. A simulation study and two examples demonstrate that the Huber-Dutter estimator of β0 is competitive with its M-estimator without scale parameter and the ordinary least square estimator.
A Class of Biased Estimators Besed on SVD in Linear Model
Institute of Scientific and Technical Information of China (English)
GUIQing-ming; DUANQing-tang; GUOJian-feng; ZHOUQiao-yun
2003-01-01
In this paper,a class of new biased estimators for linear model is proposed by modifying the singular values of the design matrix so as to directly overcome the difficulties caused by ill-conditioning in the design matrix.Some important properties of these new estimators are obtained.By appropriate choices of the biased parameters,we construct many useful and important estimators.An application of these new estimators in three-dimensional position adjustment by distance in a spatial coordiate surveys is given.The results show that the proposed biased estimators can effectively overcome ill-conditioning and their numerical stabilities are preferable to ordinary least square estimation.
DEFF Research Database (Denmark)
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 ...... Carlo experiment. We find that estimation of the parameters in the transition function can be problematic but that there may be significant benefits in terms of forecast performance....... 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...
Consistency and normality of Huber-Dutter estimators for partial linear model
Institute of Scientific and Technical Information of China (English)
TONG XingWei; CUI HengJian; YU Peng
2008-01-01
For partial linear model Y = Xτβ0 + g0(T) + ∈ with unknown/β0 ∈ Rd and an unknown smooth function g0,this paper considers the Huber-Dutter estimators of/β0,scale σ for the errors and the function g0 approximated by the smoothing B-spline functions,respectively.Under some regularity conditions,the Huber-Dutter estimators of/β0 and σ are shown to be asymptotically normal with the rate of convergence n-1/2 and the B-spline Huber-Dutter estimator of go achieves the optimal rate of convergence in nonparametric regression.A simulation study and two examples demonstrate that the Huber-Dutter estimator of/β0 is competitive with its M-estimator without scale parameter and the ordinary least square estimator.
Zhou, Mu; Tian, Zengshan; Xu, Kunjie; Yu, Xiang; Wu, Haibo
2014-01-01
This paper studies the statistical errors for the fingerprint-based RADAR neighbor matching localization with the linearly calibrated reference points (RPs) in logarithmic received signal strength (RSS) varying Wi-Fi environment. To the best of our knowledge, little comprehensive analysis work has appeared on the error performance of neighbor matching localization with respect to the deployment of RPs. However, in order to achieve the efficient and reliable location-based services (LBSs) as well as the ubiquitous context-awareness in Wi-Fi environment, much attention has to be paid to the highly accurate and cost-efficient localization systems. To this end, the statistical errors by the widely used neighbor matching localization are significantly discussed in this paper to examine the inherent mathematical relations between the localization errors and the locations of RPs by using a basic linear logarithmic strength varying model. Furthermore, based on the mathematical demonstrations and some testing results, the closed-form solutions to the statistical errors by RADAR neighbor matching localization can be an effective tool to explore alternative deployment of fingerprint-based neighbor matching localization systems in the future.
Directory of Open Access Journals (Sweden)
Mu Zhou
2014-01-01
Full Text Available This paper studies the statistical errors for the fingerprint-based RADAR neighbor matching localization with the linearly calibrated reference points (RPs in logarithmic received signal strength (RSS varying Wi-Fi environment. To the best of our knowledge, little comprehensive analysis work has appeared on the error performance of neighbor matching localization with respect to the deployment of RPs. However, in order to achieve the efficient and reliable location-based services (LBSs as well as the ubiquitous context-awareness in Wi-Fi environment, much attention has to be paid to the highly accurate and cost-efficient localization systems. To this end, the statistical errors by the widely used neighbor matching localization are significantly discussed in this paper to examine the inherent mathematical relations between the localization errors and the locations of RPs by using a basic linear logarithmic strength varying model. Furthermore, based on the mathematical demonstrations and some testing results, the closed-form solutions to the statistical errors by RADAR neighbor matching localization can be an effective tool to explore alternative deployment of fingerprint-based neighbor matching localization systems in the future.
Apply a hydrological model to estimate local temperature trends
Igarashi, Masao; Shinozawa, Tatsuya
2014-03-01
Continuous times series {f(x)} such as a depth of water is written f(x) = T(x)+P(x)+S(x)+C(x) in hydrological science where T(x),P(x),S(x) and C(x) are called the trend, periodic, stochastic and catastrophic components respectively. We simplify this model and apply it to the local temperature data such as given E. Halley (1693), the UK (1853-2010), Germany (1880-2010), Japan (1876-2010). We also apply the model to CO2 data. The model coefficients are evaluated by a symbolic computation by using a standard personal computer. The accuracy of obtained nonlinear curve is evaluated by the arithmetic mean of relative errors between the data and estimations. E. Halley estimated the temperature of Gresham College from 11/1692 to 11/1693. The simplified model shows that the temperature at the time rather cold compared with the recent of London. The UK and Germany data sets show that the maximum and minimum temperatures increased slowly from the 1890s to 1940s, increased rapidly from the 1940s to 1980s and have been decreasing since the 1980s with the exception of a few local stations. The trend of Japan is similar to these results.
Institute of Scientific and Technical Information of China (English)
Jie Li DING; Xi Ru CHEN
2006-01-01
For generalized linear models (GLM), in case the regressors are stochastic and have different distributions, the asymptotic properties of the maximum likelihood estimate (MLE)(β^)n of the parameters are studied. Under reasonable conditions, we prove the weak, strong consistency and asymptotic normality of(β^)n.
Point Estimates and Confidence Intervals for Variable Importance in Multiple Linear Regression
Thomas, D. Roland; Zhu, PengCheng; Decady, Yves J.
2007-01-01
The topic of variable importance in linear regression is reviewed, and a measure first justified theoretically by Pratt (1987) is examined in detail. Asymptotic variance estimates are used to construct individual and simultaneous confidence intervals for these importance measures. A simulation study of their coverage properties is reported, and an…
Measurement Error in Income and Schooling and the Bias of Linear Estimators
DEFF Research Database (Denmark)
Bingley, Paul; Martinello, Alessandro
2017-01-01
We propose a general framework for determining the extent of measurement error bias in ordinary least squares and instrumental variable (IV) estimators of linear models while allowing for measurement error in the validation source. We apply this method by validating Survey of Health, Ageing and R...
Institute of Scientific and Technical Information of China (English)
MA Qinghua; YANG Enhao
2000-01-01
An estimation method for solutions to the general linear system of Volterratype integral inequalities containing several iterated integral functionals is obtained. This method is based on a result proved by the present second author in Journ. Math. Anal. Appl.(1984). A certain two-dimensional system of nonlinear ordinary differential equations is also discussed to demonstrate the usefulness of our method.
STRONG CONSISTENCY OF M ESTIMATOR IN LINEAR MODEL FOR NEGATIVELY ASSOCIATED SAMPLES
Institute of Scientific and Technical Information of China (English)
Qunying WU
2006-01-01
This paper discusses the strong consistency of M estimator of regression parameter in linear model for negatively associated samples. As a result, the author extends Theorem 1 and Theorem 2 of Shanchao YANG (2002) to the NA errors without necessarily imposing any extra condition.
The fastclime Package for Linear Programming and Large-Scale Precision Matrix Estimation in R.
Pang, Haotian; Liu, Han; Vanderbei, Robert
2014-02-01
We develop an R package fastclime for solving a family of regularized linear programming (LP) problems. Our package efficiently implements the parametric simplex algorithm, which provides a scalable and sophisticated tool for solving large-scale linear programs. As an illustrative example, one use of our LP solver is to implement an important sparse precision matrix estimation method called CLIME (Constrained L1 Minimization Estimator). Compared with existing packages for this problem such as clime and flare, our package has three advantages: (1) it efficiently calculates the full piecewise-linear regularization path; (2) it provides an accurate dual certificate as stopping criterion; (3) it is completely coded in C and is highly portable. This package is designed to be useful to statisticians and machine learning researchers for solving a wide range of problems.
Li, Shanzhi; Wang, Haoping; Aitouche, Abdel; Tian, Yang; Christov, Nicolai
2017-01-01
This paper proposes a robust unknown input observer for state estimation and fault detection using linear parameter varying model. Since the disturbance and actuator fault is mixed together in the physical system, it is difficult to isolate the fault from the disturbance. Using the state transforation, the estimation of the original state becomes to associate with the transform state. By solving the linear matrix inequalities (LMIs)and linear matrix equalities (LMEs), the parameters of the UIO can be obtained. The convergence of the UIO is also analysed by the Layapunov theory. Finally, a wind turbine system with disturbance and actuator fault is tested for the proposed method. From the simulations, it demonstrates the effectiveness and performances of the proposed method.
Basin, M.; Maldonado, J. J.; Zendejo, O.
2016-07-01
This paper proposes new mean-square filter and parameter estimator design for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered as combinations of Gaussian and Poisson white noises. The problem is treated by reducing the original problem to a filtering problem for an extended state vector that includes parameters as additional states, modelled as combinations of independent Gaussian and Poisson processes. The solution to this filtering problem is based on the mean-square filtering equations for incompletely polynomial states confused with Gaussian and Poisson noises over linear observations. The resulting mean-square filter serves as an identifier for the unknown parameters. Finally, a simulation example shows effectiveness of the proposed mean-square filter and parameter estimator.
DEFF Research Database (Denmark)
Chon, K H; Cohen, R J; Holstein-Rathlou, N H
1997-01-01
A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving...... average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre...... function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes...
Effects of linear trends on estimation of noise in GNSS position time-series
Dmitrieva, K.; Segall, P.; Bradley, A. M.
2017-01-01
A thorough understanding of time-dependent noise in Global Navigation Satellite System (GNSS) position time-series is necessary for computing uncertainties in any signals found in the data. However, estimation of time-correlated noise is a challenging task and is complicated by the difficulty in separating noise from signal, the features of greatest interest in the time-series. In this paper, we investigate how linear trends affect the estimation of noise in daily GNSS position time-series. We use synthetic time-series to study the relationship between linear trends and estimates of time-correlated noise for the six most commonly cited noise models. We find that the effects of added linear trends, or conversely de-trending, vary depending on the noise model. The commonly adopted model of random walk (RW), flicker noise (FN) and white noise (WN) is the most severely affected by de-trending, with estimates of low-amplitude RW most severely biased. FN plus WN is least affected by adding or removing trends. Non-integer power-law noise estimates are also less affected by de-trending, but are very sensitive to the addition of trend when the spectral index is less than one. We derive an analytical relationship between linear trends and the estimated RW variance for the special case of pure RW noise. Overall, we find that to ascertain the correct noise model for GNSS position time-series and to estimate the correct noise parameters, it is important to have independent constraints on the actual trends in the data.
Consistency of EEG source localization and connectivity estimates.
Mahjoory, Keyvan; Nikulin, Vadim V; Botrel, Loïc; Linkenkaer-Hansen, Klaus; Fato, Marco M; Haufe, Stefan
2017-05-15
As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure. Copyright © 2017 Elsevier Inc. All rights reserved.
Local solutions of Maximum Likelihood Estimation in Quantum State Tomography
Gonçalves, Douglas S; Lavor, Carlile; Farías, Osvaldo Jiménez; Ribeiro, P H Souto
2011-01-01
Maximum likelihood estimation is one of the most used methods in quantum state tomography, where the aim is to find the best density matrix for the description of a physical system. Results of measurements on the system should match the expected values produced by the density matrix. In some cases however, if the matrix is parameterized to ensure positivity and unit trace, the negative log-likelihood function may have several local minima. In several papers in the field, authors associate a source of errors to the possibility that most of these local minima are not global, so that optimization methods can be trapped in the wrong minimum, leading to a wrong density matrix. Here we show that, for convex negative log-likelihood functions, all local minima are global. We also show that a practical source of errors is in fact the use of optimization methods that do not have global convergence property or present numerical instabilities. The clarification of this point has important repercussion on quantum informat...
Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes
Bardet, Jean-Marc
2010-01-01
This paper is first devoted to study an adaptive wavelet based estimator of the long memory parameter for linear processes in a general semi-parametric frame. This is an extension of Bardet {\\it et al.} (2008) which only concerned Gaussian processes. Moreover, the definition of the long memory parameter estimator is modified and asymptotic results are improved even in the Gaussian case. Finally an adaptive goodness-of-fit test is also built and easy to be employed: it is a chi-square type test. Simulations confirm the interesting properties of consistency and robustness of the adaptive estimator and test.
Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation
Vaiter, Samuel; Peyré, Gabriel; Dossal, Charles; Fadili, Jalal
2012-01-01
This paper studies the recovery of an unknown signal $x_0$ from low dimensional noisy observations $y = \\Phi x_0 + w$, where $\\Phi$ is an ill-posed linear operator and $w$ accounts for some noise. We focus our attention to sparse analysis regularization. The recovery is performed by minimizing the sum of a quadratic data fidelity term and the $\\lun$-norm of the correlations between the sought after signal and atoms in a given (generally overcomplete) dictionary. The $\\lun$ prior is weighted by a regularization parameter $\\lambda > 0$ that accounts for the noise level. In this paper, we prove that minimizers of this problem are piecewise-affine functions of the observations $y$ and the regularization parameter $\\lambda$. As a byproduct, we exploit these properties to get an objectively guided choice of $\\lambda$. More precisely, we propose an extension of the Generalized Stein Unbiased Risk Estimator (GSURE) and show that it is an unbiased estimator of an appropriately defined risk. This encompasses special ca...
Method for quantitative estimation of position perception using a joystick during linear movement.
Wada, Y; Tanaka, M; Mori, S; Chen, Y; Sumigama, S; Naito, H; Maeda, M; Yamamoto, M; Watanabe, S; Kajitani, N
1996-12-01
We designed a method for quantitatively estimating self-motion perceptions during passive body movement on a sled. The subjects were instructed to tilt a joystick in proportion to perceived displacement from a giving starting position during linear movement with varying displacements of 4 m, 10 m and 16 m induced by constant acceleration of 0.02 g, 0.05 g and 0.08 g along the antero-posterior axis. With this method, we could monitor not only subjective position perceptions but also response latencies for the beginning (RLbgn) and end (RLend) of the linear movement. Perceived body position fitted Stevens' power law, where R=kSn (R is output of the joystick, k is a constant, S is the displacement from the linear movement and n is an exponent). RLbgn decreased as linear acceleration increased. We conclude that this method is useful in analyzing the features and sensitivities of self-motion perceptions during movement.
Varadarajan, Divya; Haldar, Justin P
2017-08-19
The data measured in diffusion MRI can be modeled as the Fourier transform of the Ensemble Average Propagator (EAP), a probability distribution that summarizes the molecular diffusion behavior of the spins within each voxel. This Fourier relationship is potentially advantageous because of the extensive theory that has been developed to characterize the sampling requirements, accuracy, and stability of linear Fourier reconstruction methods. However, existing diffusion MRI data sampling and signal estimation methods have largely been developed and tuned without the benefit of such theory, instead relying on approximations, intuition, and extensive empirical evaluation. This paper aims to address this discrepancy by introducing a novel theoretical signal processing framework for diffusion MRI. The new framework can be used to characterize arbitrary linear diffusion estimation methods with arbitrary q-space sampling, and can be used to theoretically evaluate and compare the accuracy, resolution, and noise-resilience of different data acquisition and parameter estimation techniques. The framework is based on the EAP, and makes very limited modeling assumptions. As a result, the approach can even provide new insight into the behavior of model-based linear diffusion estimation methods in contexts where the modeling assumptions are inaccurate. The practical usefulness of the proposed framework is illustrated using both simulated and real diffusion MRI data in applications such as choosing between different parameter estimation methods and choosing between different q-space sampling schemes. Copyright © 2017 Elsevier Inc. All rights reserved.
Efficient Algorithms for Estimating the Absorption Spectrum within Linear Response TDDFT
Energy Technology Data Exchange (ETDEWEB)
Brabec, Jiri; Lin, Lin; Shao, Meiyue; Govind, Niranjan; Yang, Chao; Saad, Yousef; Ng, Esmond
2015-10-06
We present two iterative algorithms for approximating the absorption spectrum of molecules within linear response of time-dependent density functional theory (TDDFT) framework. These methods do not attempt to compute eigenvalues or eigenvectors of the linear response matrix. They are designed to approximate the absorption spectrum as a function directly. They take advantage of the special structure of the linear response matrix. Neither method requires the linear response matrix to be constructed explicitly. They only require a procedure that performs the multiplication of the linear response matrix with a vector. These methods can also be easily modified to efficiently estimate the density of states (DOS) of the linear response matrix without computing the eigenvalues of this matrix. We show by computational experiments that the methods proposed in this paper can be much more efficient than methods that are based on the exact diagonalization of the linear response matrix. We show that they can also be more efficient than real-time TDDFT simulations. We compare the pros and cons of these methods in terms of their accuracy as well as their computational and storage cost.
Estimation of Multiple Point Sources for Linear Fractional Order Systems Using Modulating Functions
Belkhatir, Zehor
2017-06-28
This paper proposes an estimation algorithm for the characterization of multiple point inputs for linear fractional order systems. First, using polynomial modulating functions method and a suitable change of variables the problem of estimating the locations and the amplitudes of a multi-pointwise input is decoupled into two algebraic systems of equations. The first system is nonlinear and solves for the time locations iteratively, whereas the second system is linear and solves for the input’s amplitudes. Second, closed form formulas for both the time location and the amplitude are provided in the particular case of single point input. Finally, numerical examples are given to illustrate the performance of the proposed technique in both noise-free and noisy cases. The joint estimation of pointwise input and fractional differentiation orders is also presented. Furthermore, a discussion on the performance of the proposed algorithm is provided.
Stochastic error whitening algorithm for linear filter estimation with noisy data.
Rao, Yadunandana N; Erdogmus, Deniz; Rao, Geetha Y; Principe, Jose C
2003-01-01
Mean squared error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel stochastic gradient algorithm based on the recently proposed error whitening criterion (EWC) to tackle the problem of linear filter estimation in the presence of additive white disturbances. We will briefly motivate the theory behind the new criterion and derive an online stochastic gradient algorithm. Convergence proof of the stochastic gradient algorithm is derived making mild assumptions. Further, we will propose some extensions to the stochastic gradient algorithm to ensure faster, step-size independent convergence. We will perform extensive simulations and compare the results with MSE as well as total-least squares in a parameter estimation problem. The stochastic EWC algorithm has many potential applications. We will use this in designing robust inverse controllers with noisy data.
A volume law for specification of linear channel storage for estimation of large floods
Zhang, Shangyou; Cordery, Ian; Sharma, Ashish
2000-02-01
A method of estimating large floods using a linear storage-routing approach is presented. The differences between the proposed approach and those traditionally used are (1) that the flood producing properties of basins are represented by a linear system, (2) the storage parameters of the distributed model are determined using a volume law which, unlike other storage-routing models, accounts for the distribution of storage in natural basins, and (3) the basin outflow hydrograph is determined analytically and expressed in a succinct mathematical form. The single model parameter is estimated from observed data without direct fitting, unlike most traditionally used methods. The model was tested by showing it could reproduce observed large floods on a number of basins. This paper compares the proposed approach with a traditionally used storage routing approach using observed flood data from the Hacking River basin in New South Wales, Australia. Results confirm the usefulness of the proposed approach for estimation of large floods.
Farano, Mirko; Cherubini, Stefania; Robinet, Jean-Christophe; De Palma, Pietro
2016-12-01
Subcritical transition in plane Poiseuille flow is investigated by means of a Lagrange-multiplier direct-adjoint optimization procedure with the aim of finding localized three-dimensional perturbations optimally growing in a given time interval (target time). Space localization of these optimal perturbations (OPs) is achieved by choosing as objective function either a p-norm (with p\\gg 1) of the perturbation energy density in a linear framework; or the classical (1-norm) perturbation energy, including nonlinear effects. This work aims at analyzing the structure of linear and nonlinear localized OPs for Poiseuille flow, and comparing their transition thresholds and scenarios. The nonlinear optimization approach provides three types of solutions: a weakly nonlinear, a hairpin-like and a highly nonlinear optimal perturbation, depending on the value of the initial energy and the target time. The former shows localization only in the wall-normal direction, whereas the latter appears much more localized and breaks the spanwise symmetry found at lower target times. Both solutions show spanwise inclined vortices and large values of the streamwise component of velocity already at the initial time. On the other hand, p-norm optimal perturbations, although being strongly localized in space, keep a shape similar to linear 1-norm optimal perturbations, showing streamwise-aligned vortices characterized by low values of the streamwise velocity component. When used for initializing direct numerical simulations, in most of the cases nonlinear OPs provide the most efficient route to transition in terms of time to transition and initial energy, even when they are less localized in space than the p-norm OP. The p-norm OP follows a transition path similar to the oblique transition scenario, with slightly oscillating streaks which saturate and eventually experience secondary instability. On the other hand, the nonlinear OP rapidly forms large-amplitude bent streaks and skips the phases
Zollanvari, Amin
2013-05-24
We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.
Zollanvari, Amin; Genton, Marc G
2013-08-01
We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.
Fast 2D DOA Estimation Algorithm by an Array Manifold Matching Method with Parallel Linear Arrays.
Yang, Lisheng; Liu, Sheng; Li, Dong; Jiang, Qingping; Cao, Hailin
2016-02-23
In this paper, the problem of two-dimensional (2D) direction-of-arrival (DOA) estimation with parallel linear arrays is addressed. Two array manifold matching (AMM) approaches, in this work, are developed for the incoherent and coherent signals, respectively. The proposed AMM methods estimate the azimuth angle only with the assumption that the elevation angles are known or estimated. The proposed methods are time efficient since they do not require eigenvalue decomposition (EVD) or peak searching. In addition, the complexity analysis shows the proposed AMM approaches have lower computational complexity than many current state-of-the-art algorithms. The estimated azimuth angles produced by the AMM approaches are automatically paired with the elevation angles. More importantly, for estimating the azimuth angles of coherent signals, the aperture loss issue is avoided since a decorrelation procedure is not required for the proposed AMM method. Numerical studies demonstrate the effectiveness of the proposed approaches.
Similarity-based semi-local estimation of EMOS models
Lerch, Sebastian
2015-01-01
Weather forecasts are typically given in the form of forecast ensembles obtained from multiple runs of numerical weather prediction models with varying initial conditions and physics parameterizations. Such ensemble predictions tend to be biased and underdispersive and thus require statistical postprocessing. In the ensemble model output statistics (EMOS) approach, a probabilistic forecast is given by a single parametric distribution with parameters depending on the ensemble members. This article proposes two semi-local methods for estimating the EMOS coefficients where the training data for a specific observation station are augmented with corresponding forecast cases from stations with similar characteristics. Similarities between stations are determined using either distance functions or clustering based on various features of the climatology, forecast errors, ensemble predictions and locations of the observation stations. In a case study on wind speed over Europe with forecasts from the Grand Limited Area...
ESTIMATION OF LIVE BODY WEIGHT FROM LINEAR BODY MEASUREMENTS FOR FARTA SHEEP
Directory of Open Access Journals (Sweden)
MENGISTIE TAYE
2012-01-01
Full Text Available A study, to develop regression models for prediction of body weight from other linear body measurements, was conducted in Esite, Farta and Lai-Gaint districts of South Gondar, Amhara region. Records on body weight (BW and other linear body measurements (Body Length (BL, Wither Height (WH, Chest Girth (CH, Pelvic Width (PW and Ear Length (EL were taken from 941 sheep. Non-linear, simple linear and multiple linear regression models were developed using Statistical Package for Social Sciences (SPSS version 12.0. For the multiple linear regressions, step-wise regression procedures were used. Predicting models were developed for different age, sex and for the pool. Positive and significant (P<0.01 correlations were observed between body weight and linear body measurements for all sex and age groups. Among the four linear body measurements, heart girth had the highest correlation coefficient (except ear length in all age and sex groups which is followed by body length, height at wither and pelvic width. Heart girth was the first variable to explain more variation than other variables in both sex and age groups. The models developed had a coefficient of determination of 0.26 to 0.89; the highest coefficient of determination was depicted for male while the lowest was for dentition groups having two permanent incisors. Regression models in general were poor in explaining weight for the dentition groups above one pair of permanent incisors. Heart girth alone was able to estimate weight with a coefficient of determination of 0.77, for both sexes and the pool. The coefficient of determination of the fitted equations (in general decreased as the age of sheep advances indicating that the fitted equations can predict weight for younger sheep with better accuracy than for older ones. In general, much of the variation in weight was explained when many traits were included in the model. However, for ease of use and to avoid complexity at field condition, it is
Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M; Derocher, Andrew E; Lewis, Mark A; Jonsen, Ian D; Mills Flemming, Joanna
2016-05-25
State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.
Kai, Bo; Li, Runze; Zou, Hui
2011-02-01
The complexity of semiparametric models poses new challenges to statistical inference and model selection that frequently arise from real applications. In this work, we propose new estimation and variable selection procedures for the semiparametric varying-coefficient partially linear model. We first study quantile regression estimates for the nonparametric varying-coefficient functions and the parametric regression coefficients. To achieve nice efficiency properties, we further develop a semiparametric composite quantile regression procedure. We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts and show that the estimators achieve the best convergence rate. Moreover, we show that the proposed method is much more efficient than the least-squares-based method for many non-normal errors and that it only loses a small amount of efficiency for normal errors. In addition, it is shown that the loss in efficiency is at most 11.1% for estimating varying coefficient functions and is no greater than 13.6% for estimating parametric components. To achieve sparsity with high-dimensional covariates, we propose adaptive penalization methods for variable selection in the semiparametric varying-coefficient partially linear model and prove that the methods possess the oracle property. Extensive Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed procedures. Finally, we apply the new methods to analyze the plasma beta-carotene level data.
Directory of Open Access Journals (Sweden)
Il Young Song
2015-01-01
Full Text Available This paper focuses on estimation of a nonlinear function of state vector (NFS in discrete-time linear systems with time-delays and model uncertainties. The NFS represents a multivariate nonlinear function of state variables, which can indicate useful information of a target system for control. The optimal nonlinear estimator of an NFS (in mean square sense represents a function of the receding horizon estimate and its error covariance. The proposed receding horizon filter represents the standard Kalman filter with time-delays and special initial horizon conditions described by the Lyapunov-like equations. In general case to calculate an optimal estimator of an NFS we propose using the unscented transformation. Important class of polynomial NFS is considered in detail. In the case of polynomial NFS an optimal estimator has a closed-form computational procedure. The subsequent application of the proposed receding horizon filter and nonlinear estimator to a linear stochastic system with time-delays and uncertainties demonstrates their effectiveness.
Estimating WAIS-IV indexes: proration versus linear scaling in a clinical sample.
Umfleet, Laura Glass; Ryan, Joseph J; Gontkovsky, Sam T; Morris, Jeri
2012-04-01
We compared the accuracy of proration and linear scaling for estimating Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV), Verbal Comprehension Index (VCI), and Perceptual Reasoning Index (PRI) composites from all possible two subtest combinations. The purpose was to provide practice relevant psychometric results in a clinical sample. The present investigation was an archival study that used mostly within-group comparisons. We analyzed WAIS-IV data of a clinical sample comprising 104 patients with brain damage and 37 with no known neurological impairment. In both clinical samples, actual VCI and PRI scores were highly correlated with estimated index scores based on proration and linear scaling (all rs ≥.95). In the brain-impaired sample, significant mean score differences between the actual and estimated composites were found in two comparisons, but these differences were less than three points; no other significant differences emerged. Overall, findings demonstrate that proration and linear scaling methods are feasible procedures when estimating actual Indexes. There was no advantage of one computational method over the other. © 2012 Wiley Periodicals, Inc.
Optimal local dimming for LED-backlit LCD displays via linear programming
DEFF Research Database (Denmark)
Shu, Xiao; Wu, Xiaolin; Forchhammer, Søren
2012-01-01
LED-backlit LCD displays hold the promise of improving the image quality while reducing the energy consumption with signal-dependent local dimming. To fully realize such potentials we propose a novel local dimming technique that jointly optimizes the intensities of LED backlights...... and the attenuations of LCD pixels. The objective is to minimize the distortion in luminance reproduction due to the leakage of LCD and the coarse granularity of the LED lights. The optimization problem is formulated as one of linear programming, and both exact and approximate algorithms are proposed. Simulation...
Linear-scaling evaluation of the local energy in quantum MonteCarlo
Energy Technology Data Exchange (ETDEWEB)
Austin, Brian; Aspuru-Guzik, Alan; Salomon-Ferrer, Romelia; Lester Jr., William A.
2006-02-11
For atomic and molecular quantum Monte Carlo calculations, most of the computational effort is spent in the evaluation of the local energy. We describe a scheme for reducing the computational cost of the evaluation of the Slater determinants and correlation function for the correlated molecular orbital (CMO) ansatz. A sparse representation of the Slater determinants makes possible efficient evaluation of molecular orbitals. A modification to the scaled distance function facilitates a linear scaling implementation of the Schmidt-Moskowitz-Boys-Handy (SMBH) correlation function that preserves the efficient matrix multiplication structure of the SMBH function. For the evaluation of the local energy, these two methods lead to asymptotic linear scaling with respect to the molecule size.
LOCAL A PRIORI AND A POSTERIORI ERROR ESTIMATE OF TQC9 ELEMENT FOR THE BIHARMONIC EQUATION
Institute of Scientific and Technical Information of China (English)
Ming Wang; Weimeng Zhang
2008-01-01
In this paper,local a priori,local a posteriori and global a posteriori error estimates are obtained for TQC9 element for the biharmonic equation.An adaptive algorithm is given based on the a posteriori error estimates.
Estimating Preferences for Treatments in Patients With Localized Prostate Cancer
Energy Technology Data Exchange (ETDEWEB)
Ávila, Mónica [Health Services Research Unit, IMIM (Hospital del Mar Medical Research Institute), Barcelona (Spain); CIBER en Epidemiología y Salud Pública (CIBERESP) (Spain); Universitat Pompeu Fabra, Barcelona (Spain); Becerra, Virginia [Health Services Research Unit, IMIM (Hospital del Mar Medical Research Institute), Barcelona (Spain); Guedea, Ferran [Servicio de Oncología Radioterápica, Institut Català d' Oncologia, L' Hospitalet de Llobregat (Spain); Suárez, José Francisco [Servicio de Urología, Hospital Universitari de Bellvitge, L' Hospitalet de Llobregat (Spain); Fernandez, Pablo [Servicio de Oncología Radioterápica, Instituto Oncológico de Guipúzcoa, San Sebastián (Spain); Macías, Víctor [Servicio de Oncología Radioterápica, Hospital Clínico Universitario de Salamanca, Salamanca (Spain); Servicio de Oncología Radioterápica, Institut Oncologic del Valles-Hospital General de Catalunya, Sant Cugat del Vallès (Spain); Mariño, Alfonso [Servicio de Oncología Radioterápica, Centro Oncológico de Galicia, A Coruña (Spain); and others
2015-02-01
Purpose: Studies of patients' preferences for localized prostate cancer treatments have assessed radical prostatectomy and external radiation therapy, but none of them has evaluated brachytherapy. The aim of our study was to assess the preferences and willingness to pay of patients with localized prostate cancer who had been treated with radical prostatectomy, external radiation therapy, or brachytherapy, and their related urinary, sexual, and bowel side effects. Methods and Materials: This was an observational, prospective cohort study with follow-up until 5 years after treatment. A total of 704 patients with low or intermediate risk localized prostate cancer were consecutively recruited from 2003 to 2005. The estimation of preferences was conducted using time trade-off, standard gamble, and willingness-to-pay methods. Side effects were measured with the Expanded Prostate Index Composite (EPIC), a prostate cancer-specific questionnaire. Tobit models were constructed to assess the impact of treatment and side effects on patients' preferences. Propensity score was applied to adjust for treatment selection bias. Results: Of the 580 patients reporting preferences, 165 were treated with radical prostatectomy, 152 with external radiation therapy, and 263 with brachytherapy. Both time trade-off and standard gamble results indicated that the preferences of patients treated with brachytherapy were 0.06 utilities higher than those treated with radical prostatectomy (P=.01). Similarly, willingness-to-pay responses showed a difference of €57/month (P=.004) between these 2 treatments. Severe urinary incontinence presented an independent impact on the preferences elicited (P<.05), whereas no significant differences were found by bowel and sexual side effects. Conclusions: Our findings indicate that urinary incontinence is the side effect with the highest impact on preferences and that brachytherapy and external radiation therapy are more valued than radical
Local-linear-prediction analysis for underwater acoustic target radiated noise
Institute of Scientific and Technical Information of China (English)
LIANG Juan; LU Jiren
2002-01-01
Local-linear-prediction in phase space is performed for the underwater acoustic target radiated noise. Relation curve of average prediction error versus neighboring points' number is calculated. The result is used in judging the nonlinearity of radiated noise time series, and obtaining the appropriate form and coefficients of predicting model. The line and continuous spectral component are predicted respectively. Choice of some model parameters minimizing the prediction error is also discussed.
Fukushima, Kimichika
2015-01-01
This paper presents analytical eigenenergies for a pair of confined fundamental fermion and antifermion under a linear potential derived from the Wilson loop for the non-Abelian Yang-Mills field. We use basis functions localized in spacetime, and the Hamiltonian matrix of the Dirac equation is analytically diagonalized. The squared system eigenenergies are proportional to the string tension and the absolute value of the Dirac's relativistic quantum number related to the total angular momentum, consistent with the expectation.
Accuracy of panoramic radiography and linear tomography in mandibular canal localization
Directory of Open Access Journals (Sweden)
Bashizadeh Fakhar H.
2008-10-01
Full Text Available "nBackground and Aim: Accurate bone measurements are essential to determine the optimal size and length of dental implants. The magnification factor of radiographic images may vary with the imaging technique used. The purpose of this study was to compare the accuracy of linear tomography and panoramic radiography in vertical measurements, as well as the accuracy of linear tomography in mandibular width estimation. "nMaterials and Methods: In this test evaluation study, the vertical distances between the crest and the superior border of the inferior alveolar canal, marked with a metal ball, was measured by linear tomography and panoramic radiography in 23 sites of four dry mandible bones. Also the mandibular width was measured at the same sites. Then, the bones were sectioned through the marked spots and the radiographic measurements were compared with actual values. "nResults: The vertical magnification factor in tomograms and panoramic radiographs was 1.79 (SD=0.17 and 1.69 (SD=0.23, respectively. The horizontal magnification of tomograms was 1.47 (SD=0.17. A significant correlation was found between the linear tomographic and actual values, regarding vertical dimensions (p<0.001, r=0.968 and width (p<0.001, r=0.813. The correlation was significant but lower in panoramic radiographs (p<0.001, r=0.795. Applying the magnification values suggested by the manufacturer, the mean difference of vertical measurements between the tomographic sections was 2.5 mm (SD=3.4 but 3.8 mm (SD=1.65 in panoramic radiographs. The mean of absolute difference in mandibular width between the tomographic sections and reality was 0.3mm (SD=1.13. In the linear tomograms, 4.3% of vertical and 56.5% of the width measurements were in the ±1mm error limit. Only 4.3% of the vertical measurements were within this range in the panthomographs. The linear regression equation between the actual values and those obtained by radiography in vertical dimensions showed that 87.5% of
Sensor Fault Estimation Filter Design for Discrete-time Linear Time-varying Systems
Institute of Scientific and Technical Information of China (English)
WANG Zhen-Hua; RODRIGUES Mickael; THEILLIOL Didier; SHEN Yi
2014-01-01
This paper proposes a sensor fault diagnosis method for a class of discrete-time linear time-varying (LTV) systems. In this paper, the considered system is firstly formulated as a de-scriptor system representation by considering the sensor faults as auxiliary state variables. Based on the descriptor system model, a fault estimation filter which can simultaneously estimate the state and the sensor fault magnitudes is designed via a minimum-variance principle. Then, a fault diagnosis scheme is presented by using a bank of the proposed fault estimation filters. The novelty of this paper lies in developing a sensor fault diagnosis method for discrete LTV systems without any assumption on the dynamic of fault. Another advantage of the proposed method is its ability to detect, isolate and estimate sensor faults in the presence of process noise and measurement noise. Simulation results are given to illustrate the effectiveness of the proposed method.
Losses estimation in transonic wet steam flow through linear blade cascade
Dykas, Sławomir; Majkut, Mirosław; Strozik, Michał; Smołka, Krystian
2015-04-01
Experimental investigations of non-equilibrium spontaneous condensation in transonic steam flow were carried out in linear blade cascade. The linear cascade consists of the stator blades of the last stage of low pressure steam turbine. The applied experimental test section is a part of a small scale steam power plant located at Silesian University of Technology in Gliwice. The steam parameters at the test section inlet correspond to the real conditions in low pressure part of 200MWe steam turbine. The losses in the cascade were estimated using measured static pressure and temperature behind the cascade and the total parameters at inlet. The static pressure measurements on the blade surface as well as the Schlieren pictures were used to assess the flow field in linear cascade of steam turbine stator blades.
Numerical estimation of 3D mechanical forces exerted by cells on non-linear materials.
Palacio, J; Jorge-Peñas, A; Muñoz-Barrutia, A; Ortiz-de-Solorzano, C; de Juan-Pardo, E; García-Aznar, J M
2013-01-04
The exchange of physical forces in both cell-cell and cell-matrix interactions play a significant role in a variety of physiological and pathological processes, such as cell migration, cancer metastasis, inflammation and wound healing. Therefore, great interest exists in accurately quantifying the forces that cells exert on their substrate during migration. Traction Force Microscopy (TFM) is the most widely used method for measuring cell traction forces. Several mathematical techniques have been developed to estimate forces from TFM experiments. However, certain simplifications are commonly assumed, such as linear elasticity of the materials and/or free geometries, which in some cases may lead to inaccurate results. Here, cellular forces are numerically estimated by solving a minimization problem that combines multiple non-linear FEM solutions. Our simulations, free from constraints on the geometrical and the mechanical conditions, show that forces are predicted with higher accuracy than when using the standard approaches.
Strain estimation in 3D by fitting linear and planar data to the March model
Mulchrone, Kieran F.; Talbot, Christopher J.
2016-08-01
The probability density function associated with the March model is derived and used in a maximum likelihood method to estimate the best fit distribution and 3D strain parameters for a given set of linear or planar data. Typically it is assumed that in the initial state (pre-strain) linear or planar data are uniformly distributed on the sphere which means the number of strain parameters estimated needs to be reduced so that the numerical technique succeeds. Essentially this requires that the data are rotated into a suitable reference frame prior to analysis. The method has been applied to a suitable example from the Dalradian of SW Scotland and results obtained are consistent with those from an independent method of strain analysis. Despite March theory having been incorporated deep into the fabric of geological strain analysis, its full potential as a simple direct 3D strain analytical tool has not been achieved. The method developed here may help remedy this situation.
SECANT-FUZZY LINEAR REGRESSION METHOD FOR HARMONIC COMPONENTS ESTIMATION IN A POWER SYSTEM
Institute of Scientific and Technical Information of China (English)
Garba Inoussa; LUO An
2003-01-01
In order to avoid unnecessary damage of electrical equipments and installations,high quality power should be delivered to the end user and strict control on frequency should be made, Therefore, it is important to estimate the power system's harmonic components with higher accuracy. This paper presents a new approach for estimating harmonic component in a power system using secant - fuzzy linear regression method. In this approach the non - sinusoidal voltage or current waveform is written as I linear function. The coefficient of this function is assumed to be fuzzy number with a membership function that has center and spread value. The time dependent quantity is written as Taylor series with two different time dependent quantities. The objective is to use the sample obtained from the transmission line to find the power system harmonic components and frequencies. We used an experimental voltage signal from a sub power station as a numerical test.
Measurement error in income and schooling, and the bias for linear estimators
DEFF Research Database (Denmark)
Bingley, Paul; Martinello, Alessandro
with Danish administrative registers. We find that measurement error in surveys is classical for annual gross income but non-classical for years of schooling, causing a 21% amplification bias in IV estimators of returns to schooling. Using a 1958 Danish schooling reform, we contextualize our result......The characteristics of measurement error determine the bias of linear estimators. We propose a method for validating economic survey data allowing for measurement error in the validation source, and we apply this method by validating Survey of Health, Ageing and Retirement in Europe (SHARE) data...
Measurement error in income and schooling, and the bias of linear estimators
DEFF Research Database (Denmark)
Bingley, Paul; Martinello, Alessandro
with Danish administrative registers. We find that measurement error in surveys is classical for annual gross income but non-classical for years of schooling, causing a 21% amplification bias in IV estimators of returns to schooling. Using a 1958 Danish schooling reform, we contextualize our result......The characteristics of measurement error determine the bias of linear estimators. We propose a method for validating economic survey data allowing for measurement error in the validation source, and we apply this method by validating Survey of Health, Ageing and Retirement in Europe (SHARE) data...
Linear and Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects
2017-02-22
AFRL-AFOSR-UK-TR-2017-0023 Linear and Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects Marco Martorella... UNIVERSITY DI PISA, DEPARTMENT DI INGEGNERIA Final Report 02/22/2017 DISTRIBUTION A: Distribution approved for public release. AF Office Of Scientific Research...Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-14-1-0183 5c. PROGRAM
Institute of Scientific and Technical Information of China (English)
Ge-mai Chen; Jin-hong You
2005-01-01
Consider a repeated measurement partially linear regression model with an unknown vector pasemiparametric generalized least squares estimator (SGLSE) ofβ, we propose an iterative weighted semiparametric least squares estimator (IWSLSE) and show that it improves upon the SGLSE in terms of asymptotic covariance matrix. An adaptive procedure is given to determine the number of iterations. We also show that when the number of replicates is less than or equal to two, the IWSLSE can not improve upon the SGLSE.These results are generalizations of those in [2] to the case of semiparametric regressions.
Asymptotic Normality of LS Estimate in Simple Linear EV Regression Model
Institute of Scientific and Technical Information of China (English)
Jixue LIU
2006-01-01
Though EV model is theoretically more appropriate for applications in which measurement errors exist, people are still more inclined to use the ordinary regression models and the traditional LS method owing to the difficulties of statistical inference and computation. So it is meaningful to study the performance of LS estimate in EV model.In this article we obtain general conditions guaranteeing the asymptotic normality of the estimates of regression coefficients in the linear EV model. It is noticeable that the result is in some way different from the corresponding result in the ordinary regression model.
Directory of Open Access Journals (Sweden)
Slavica M. Perovich
2011-06-01
Full Text Available The subject of the theoretical analysis presented in this paper is an analytical approach to the temperature estimation, as an inverse problem, for different thermistors – linear resistances structures: series and parallel ones, by the STFT - Special Trans Functions Theory (S.M. Perovich. The mathematical formulae genesis of both cases is given. Some numerical and graphical simulations in MATHEMATICA program have been realized. The estimated temperature intervals for strongly determined values of the equivalent resistances of the nonlinear structures are given, as well.
Institute of Scientific and Technical Information of China (English)
YIN; Changming; ZHAO; Lincheng; WEI; Chengdong
2006-01-01
In a generalized linear model with q × 1 responses, the bounded and fixed (or adaptive) p × q regressors Zi and the general link function, under the most general assumption on the minimum eigenvalue of ∑ni=1 ZiZ'i, the moment condition on responses as weak as possible and the other mild regular conditions, we prove that the maximum quasi-likelihood estimates for the regression parameter vector are asymptotically normal and strongly consistent.
ASYMPTOTIC NORMALITY OF QUASI MAXIMUM LIKELIHOOD ESTIMATE IN GENERALIZED LINEAR MODELS
Institute of Scientific and Technical Information of China (English)
YUE LI; CHEN XIRU
2005-01-01
For the Generalized Linear Model (GLM), under some conditions including that the specification of the expectation is correct, it is shown that the Quasi Maximum Likelihood Estimate (QMLE) of the parameter-vector is asymptotic normal. It is also shown that the asymptotic covariance matrix of the QMLE reaches its minimum (in the positive-definte sense) in case that the specification of the covariance matrix is correct.
Dufrenois, F; Noyer, J C
2013-02-01
Linear discriminant analysis, such as Fisher's criterion, is a statistical learning tool traditionally devoted to separating a training dataset into two or even several classes by the way of linear decision boundaries. In this paper, we show that this tool can formalize the robust linear regression problem as a robust estimator will do. More precisely, we develop a one-class Fischer's criterion in which the maximization provides both the regression parameters and the separation of the data in two classes: typical data and atypical data or outliers. This new criterion is built on the statistical properties of the subspace decomposition of the hat matrix. From this angle, we improve the discriminative properties of the hat matrix which is traditionally used as outlier diagnostic measure in linear regression. Naturally, we call this new approach discriminative hat matrix. The proposed algorithm is fully nonsupervised and needs only the initialization of one parameter. Synthetic and real datasets are used to study the performance both in terms of regression and classification of the proposed approach. We also illustrate its potential application to image recognition and fundamental matrix estimation in computer vision.
Rate of strong consistency of quasi maximum likelihood estimate in generalized linear models
Institute of Scientific and Technical Information of China (English)
无
2004-01-01
［1］McCullagh, P., Nelder, J. A., Generalized Linear Models, New York: Chapman and Hall, 1989.［2］Wedderbum, R. W. M., Quasi-likelihood functions, generalized linear models and Gauss-Newton method,Biometrika, 1974, 61:439-447.［3］Fahrmeir, L., Maximum likelihood estimation in misspecified generalized linear models, Statistics, 1990, 21:487-502.［4］Fahrmeir, L., Kaufmann, H., Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models, Ann. Statist., 1985, 13: 342-368.［5］Melder, J. A., Pregibon, D., An extended quasi-likelihood function, Biometrika, 1987, 74: 221-232.［6］Bennet, G., Probability inequalities for the sum of independent random variables, JASA, 1962, 57: 33-45.［7］Stout, W. F., Almost Sure Convergence, New York:Academic Press, 1974.［8］Petrov, V, V., Sums of Independent Random Variables, Berlin, New York: Springer-Verlag, 1975.
Estimate of influenza cases using generalized linear, additive and mixed models.
Oviedo, Manuel; Domínguez, Ángela; Pilar Muñoz, M
2015-01-01
We investigated the relationship between reported cases of influenza in Catalonia (Spain). Covariates analyzed were: population, age, data of report of influenza, and health region during 2010-2014 using data obtained from the SISAP program (Institut Catala de la Salut - Generalitat of Catalonia). Reported cases were related with the study of covariates using a descriptive analysis. Generalized Linear Models, Generalized Additive Models and Generalized Additive Mixed Models were used to estimate the evolution of the transmission of influenza. Additive models can estimate non-linear effects of the covariates by smooth functions; and mixed models can estimate data dependence and variability in factor variables using correlations structures and random effects, respectively. The incidence rate of influenza was calculated as the incidence per 100 000 people. The mean rate was 13.75 (range 0-27.5) in the winter months (December, January, February) and 3.38 (range 0-12.57) in the remaining months. Statistical analysis showed that Generalized Additive Mixed Models were better adapted to the temporal evolution of influenza (serial correlation 0.59) than classical linear models.
Linear and nonlinear ARMA model parameter estimation using an artificial neural network
Chon, K. H.; Cohen, R. J.
1997-01-01
This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.
Brassey, Charlotte A; Maidment, Susannah C R; Barrett, Paul M
2015-03-01
Body mass is a key biological variable, but difficult to assess from fossils. Various techniques exist for estimating body mass from skeletal parameters, but few studies have compared outputs from different methods. Here, we apply several mass estimation methods to an exceptionally complete skeleton of the dinosaur Stegosaurus. Applying a volumetric convex-hulling technique to a digital model of Stegosaurus, we estimate a mass of 1560 kg (95% prediction interval 1082-2256 kg) for this individual. By contrast, bivariate equations based on limb dimensions predict values between 2355 and 3751 kg and require implausible amounts of soft tissue and/or high body densities. When corrected for ontogenetic scaling, however, volumetric and linear equations are brought into close agreement. Our results raise concerns regarding the application of predictive equations to extinct taxa with no living analogues in terms of overall morphology and highlight the sensitivity of bivariate predictive equations to the ontogenetic status of the specimen. We emphasize the significance of rare, complete fossil skeletons in validating widely applied mass estimation equations based on incomplete skeletal material and stress the importance of accurately determining specimen age prior to further analyses.
Directory of Open Access Journals (Sweden)
Yu. B. Gimpilevich
2013-12-01
Full Text Available Introduction. Radio-frequency identification (RFID systems can be applied for a 2D spatial localization of objects in indoor spaces. For implementing the known localization method of trilateration one needs to build a model of ratio of distance between antenna and RFID tag versus power level of tag response signal. To maximize the accuracy of the model, one needs to collect measurement data from RFID tags placed at known positions. Due to the labor intensity of this process we aim to develop an alternative modification of elliptical trilateration method resulting in elimination of the preliminary data collecting stage. Theory part. We propose to use a linear model of distance vs power ratio for localization of passive RFID tags with small read ranges. Additionally, our model takes into account the possible ellipticity of position figures which happens because of antennas directivity. Also we propose some heuristics for solving the estimates ambiguity problem which arises when response signals from RFID tags are received by one or two antennas. Experimental part. We carried out the experimental analysis of the proposed trilateration variant using the previously developed RFID system in the 5 m ´ 5 m localization field. During the experiment, we compared our variant with a classical trilateration which was based on the polynomial model of distance vs power ratio formed by analyzing preliminarily gathered measurement data from RFID tags. The comparison indicated that our variant had a bigger by 1.6 cm mean localization error. Furthermore, taking into account ellipticity of position figures resulted in decrease of localization error for 18 of the 24 analyzed cases. Conclusions. It was determined that the proposed trilateration modification produced a slightly bigger mean localization error compared to the classical variant of trilateration. However, our approach allows one to eliminate the preliminary labor-intensive stage of collecting
Estimates of linearization discs in $p$-adic dynamics with application to ergodicity
Lindahl, Karl-Olof
2009-01-01
We give lower bounds for the size of linearization discs for power series over $\\mathbb{C}_p$. For quadratic maps, and certain power series containing a `sufficiently large' quadratic term, we find the exact linearization disc. For finite extensions of $\\mathbb{Q}_p$, we give a sufficient condition on the multiplier under which the corresponding linearization disc is maximal (i.e. its radius coincides with that of the maximal disc in $\\mathbb{C}_p$ on which $f$ is one-to-one). In particular, in unramified extensions of $\\mathbb{Q}_p$, the linearization disc is maximal if the multiplier map has a maximal cycle on the unit sphere. Estimates of linearization discs in the remaining types of non-Archimedean fields of dimension one were obtained in \\cite{Lindahl:2004,Lindahl:2009,Lindahl:2009eq}. Moreover, it is shown that, for any complete non-Archimedean field, transitivity is preserved under analytic conjugation. Using results by Oxtoby \\cite{Oxtoby:1952}, we prove that transitivity, and hence minimality, is equ...
Elenchezhiyan, M; Prakash, J
2015-09-01
In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme.
Estimating differential quantities from point cloud based on a linear fitting of normal vectors
Institute of Scientific and Technical Information of China (English)
CHENG ZhangLin; ZHANG XiaoPeng
2009-01-01
Estimation of differential geometric properties on a discrete surface Is a fundamental work in computer graphics and computer vision.In this paper,we present an accurate and robust method for estimating differential quantities from unorganized point cloud.The principal curvatures and principal directions at each point are computed with the help of partial derivatives of the unit normal vector at that point,where the normal derivatives are estimated by fitting a linear function to each component of the normal vectors in a neighborhood.This method takes into account the normal information of all neighboring points and computes curvatures directly from the varlation of unit normal vectors,which improves the accuracy and robustness of curvature estimation on irregular sampled noisy data.The main advantage of our approach is that the estimation of curvatures at a point does not rely on the accuracy of the normal vector at that point,and the normal vectors can he refined In the process of curvature estimation.Compared with the state of the art methods for estimating curvatures and Darboux frames on both synthetic and real point clouds,the approach is shown to be more accurate and robust for noisy and unorganized point cloud data.
Cheng, Guang
2014-02-01
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 on a spline approximation of the nonparametric part of the model and the generalized estimating equations (GEE). Although the model in consideration is natural and useful in many practical applications, the literature on this model is very limited because of challenges in dealing with dependent data for nonparametric additive models. We show that the proposed estimators are consistent and asymptotically normal even if the covariance structure is misspecified. An explicit consistent estimate of the asymptotic variance is also provided. Moreover, we derive the semiparametric efficiency score and information bound under general moment conditions. By showing that our estimators achieve the semiparametric information bound, we effectively establish their efficiency in a stronger sense than what is typically considered for GEE. The derivation of our asymptotic results relies heavily on the empirical processes tools that we develop for the longitudinal/clustered data. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2014 ISI/BS.
Linear Track Estimation Using Double Pulse Sources for Near-Field Underwater Moving Target
Institute of Scientific and Technical Information of China (English)
Zhifei Chen; Hong Hou; Jianhua Yang; Jincai Sun; Qian Wang
2013-01-01
The double pulse sources (DPS) method is presented for linear track estimation in this work.In the field of noise identification of underwater moving target,the Doppler will distort the frequency and amplitude of the radiated noise.To eliminate this,the track estimation is necessary.In the DPS method,we first estimate bearings of two sinusoidal pulse sources installed in the moving target through baseline positioning method.Meanwhile,the emitted and recorded time of each pulse are also acquired.Then the linear track parameters will be achieved based on the geometry pattern with the help of double sources spacing.The simulated results confirm that the DPS improves the performance of the previous double source spacing method.The simulated experiments were carried out using a moving battery car to further evaluate its performance.When the target is 40-60m away,the experiment results show that biases of track azimuth and abeam distance of DPS are under 0.6° and 3.4m,respectively.And the average deviation of estimated velocity is around 0.25m/s.
Blast load estimation using Finite Volume Method and linear heat transfer
Directory of Open Access Journals (Sweden)
Lidner Michał
2016-01-01
Full Text Available From the point of view of people and building security one of the main destroying factor is the blast load. Rational estimating of its results should be preceded with knowledge of complex wave field distribution in time and space. As a result one can estimate the blast load distribution in time. In considered conditions, the values of blast load are estimating using the empirical functions of overpressure distribution in time (Δp(t. The Δp(t functions are monotonic and are the approximation of reality. The distributions of these functions are often linearized due to simplifying of estimating the blast reaction of elements. The article presents a method of numerical analysis of the phenomenon of the air shock wave propagation. The main scope of this paper is getting the ability to make more realistic the Δp(t functions. An explicit own solution using Finite Volume Method was used. This method considers changes in energy due to heat transfer with conservation of linear heat transfer. For validation, the results of numerical analysis were compared with the literature reports. Values of impulse, pressure, and its duration were studied.
Local numerical modelling of ultrasonic guided waves in linear and nonlinear media
Packo, Pawel; Radecki, Rafal; Kijanka, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz; Leamy, Michael J.
2017-04-01
Nonlinear ultrasonic techniques provide improved damage sensitivity compared to linear approaches. The combination of attractive properties of guided waves, such as Lamb waves, with unique features of higher harmonic generation provides great potential for characterization of incipient damage, particularly in plate-like structures. Nonlinear ultrasonic structural health monitoring techniques use interrogation signals at frequencies other than the excitation frequency to detect changes in structural integrity. Signal processing techniques used in non-destructive evaluation are frequently supported by modeling and numerical simulations in order to facilitate problem solution. This paper discusses known and newly-developed local computational strategies for simulating elastic waves, and attempts characterization of their numerical properties in the context of linear and nonlinear media. A hybrid numerical approach combining advantages of the Local Interaction Simulation Approach (LISA) and Cellular Automata for Elastodynamics (CAFE) is proposed for unique treatment of arbitrary strain-stress relations. The iteration equations of the method are derived directly from physical principles employing stress and displacement continuity, leading to an accurate description of the propagation in arbitrarily complex media. Numerical analysis of guided wave propagation, based on the newly developed hybrid approach, is presented and discussed in the paper for linear and nonlinear media. Comparisons to Finite Elements (FE) are also discussed.
An adaptive locally linear embedding manifold learning approach for hyperspectral target detection
Ziemann, Amanda K.; Messinger, David W.
2015-05-01
Algorithms for spectral analysis commonly use parametric or linear models of the data. Research has shown, however, that hyperspectral data -- particularly in materially cluttered scenes -- are not always well-modeled by statistical or linear methods. Here, we propose an approach to hyperspectral target detection that is based on a graph theory model of the data and a manifold learning transformation. An adaptive nearest neighbor (ANN) graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation. The artificial target manifold helps to guide the separation of the target data from the background data in the new, transformed manifold coordinates. Then, target detection is performed in the manifold space using Spectral Angle Mapper. This methodology is an improvement over previous iterations of this approach due to the incorporation of ANN, the artificial target manifold, and the choice of detector in the transformed space. We implement our approach in a spatially local way: the image is delineated into square tiles, and the detection maps are normalized across the entire image. Target detection results will be shown using laboratory-measured and scene-derived target data from the SHARE 2012 collect.
Van der Zee, K.G.; Van Brummelen, E.H.; De Borst, R.
2010-01-01
We develop duality-based a posteriori error estimates for functional outputs of solutions of free-boundary problems via shape-linearization principles. To derive an appropriate dual (linearized adjoint) problem, we linearize the domain dependence of the very weak form and goal functional of interest
Projection-Based Linear Constrained Estimation and Fusion over Long-Haul Links
Energy Technology Data Exchange (ETDEWEB)
Rao, Nageswara S [ORNL
2016-01-01
In this work, we study estimation and fusion with linear dynamics in long-haul sensor networks, wherein a number of sensors are remotely deployed over a large geographical area for performing tasks such as target tracking, and a remote fusion center serves to combine the information provided by these sensors in order to improve the overall tracking accuracy. In reality, the motion of a dynamic target might be subject to certain constraints, for instance, those defined by a road network. We explore the accuracy performance of projection-based constrained estimation and fusion methods that is affected by information loss over the long-haul links. We use a tracking example to compare the tracking errors under various implementations of centralized and distributed projection-based estimation and fusion methods.
Projection-Based linear constrained estimation and fusion over long-haul links
Energy Technology Data Exchange (ETDEWEB)
Rao, Nageswara S [ORNL
2016-01-01
We study estimation and fusion with linear dynamics in long-haul sensor networks, wherein a number of sensors are remotely deployed over a large geographical area for performing tasks such as target tracking, and a remote fusion center serves to combine the information provided by these sensors in order to improve the overall tracking accuracy. In reality, the motion of a dynamic target might be subject to certain constraints, for instance, those defined by a road network. We explore the accuracy performance of projection-based constrained estimation and fusion methods that is affected by information loss over the long-haul links. We use an example to compare the tracking errors under various implementations of centralized and distributed projection-based estimation and fusion methods and demonstrate the effectiveness of using projection-based methods in these settings.
MUSIC 2D-DOA Estimation using Split Vertical Linear and Circular Arrays
Directory of Open Access Journals (Sweden)
Yasser Albagory
2013-06-01
Full Text Available In this paper, the MUSIC 2D-DOA estimation is estimated by splitting the angle into elevation and azimuth components. This technique is based on an array that is composed by a vertical uniform linear array located perpendicularly at the center of another uniform circular array. This array configuration is proposed to reduce the computational burden faced in MUSIC 2D-DOA estimation where the vertical array is used to determine the elevation DOAs (θs which are used subsequently to determine the azimuth DOAs (∅s by the circular array instead of searching in all space of the two angles in the case of using circular array only. The new Split beamformer is investigated and the performance of the MUSIC 2D-DOA under several signal conditions in the presence of noise is studied.
Non-linear shrinkage estimation of large-scale structure covariance
Joachimi, Benjamin
2017-03-01
In many astrophysical settings, covariance matrices of large data sets have to be determined empirically from a finite number of mock realizations. The resulting noise degrades inference and precludes it completely if there are fewer realizations than data points. This work applies a recently proposed non-linear shrinkage estimator of covariance to a realistic example from large-scale structure cosmology. After optimizing its performance for the usage in likelihood expressions, the shrinkage estimator yields subdominant bias and variance comparable to that of the standard estimator with a factor of ∼50 less realizations. This is achieved without any prior information on the properties of the data or the structure of the covariance matrix, at a negligible computational cost.
THE RATES OF CONVERGENCE OF M-ESTIMATORS FOR PARTLY LINEAR MODELS IN DEPENDENT CASES
Institute of Scientific and Technical Information of China (English)
SHIPEIDE; CHENXIRU
1996-01-01
Consider the partly linear model Yi=X'iβ0+g0(Ti)+ei,where{(Ti,Xi)}∞1 is a strictly stationary sequence of random variables,the e'is are i.i.d. random errors, the Yi's are realvalued responses,β0 is a d-vector of parameters,Xi is a d-vector of explanatory variables,Ti is another explanatory variable ranging over a nondegenerate compact interval. Based on a segment of observations(T1,X'1,Y1)…,(Tn,X'n,Yn)，this article investigates the rates of convergence of the M-eatimators for β0 and g0 obtained from the minimixation problem ∑n i=1ρ（Yi-X'iβ-gn(Ti)）=min β∈R2,sn∈Fn where Fn is a space of B-spline fuctions of order m+1 and ρ(·) is a function chosen suitably.Under some regularity condtions, it is shown that the estimator of go achieves the optimal global rate of convergence of estimators for nonparametric regrssion,and the estimator of β0 is asymptotically uormal.The M-estimators here include regression quantile estimators,L1-estimators,Lp-norm estimators, Huber's type M-estimators and usual least squares estimators.Applications of the asymptotic theory to testing the hypothesis H0:A'β0=β are also discussed, Where β ia a given vector and Ais a known d×d0 matrix with rank d0.
A supervised machine learning estimator for the non-linear matter power spectrum - SEMPS
Mohammed, Irshad
2015-01-01
In this article, we argue that models based on machine learning (ML) can be very effective in estimating the non-linear matter power spectrum ($P(k)$). We employ the prediction ability of the supervised ML algorithms to build an estimator for the $P(k)$. The estimator is trained on a set of cosmological models, and redshifts for which the $P(k)$ is known, and it learns to predict $P(k)$ for any other set. We review three ML algorithms -- Random Forest, Gradient Boosting Machines, and K-Nearest Neighbours -- and investigate their prime parameters to optimize the prediction accuracy of the estimator. We also compute an optimal size of the training set, which is realistic enough, and still yields high accuracy. We find that, employing the optimal values of the internal parameters, a set of $50-100$ cosmological models is enough to train the estimator that can predict the $P(k)$ for a wide range of cosmological models, and redshifts. Using this configuration, we build a blackbox -- Supervised Estimator for Matter...
Preservation of local linearity by neighborhood subspace scaling for solving the pre-image problem
Institute of Scientific and Technical Information of China (English)
Sheng-kai YANG; Jian-yi MENG; Hai-bin SHEN
2014-01-01
An important issue involved in kernel methods is the pre-image problem. However, it is an ill-posed problem, as the solution is usually nonexistent or not unique. In contrast to direct methods aimed at minimizing the distance in feature space, indirect methods aimed at constructing approximate equivalent models have shown outstanding performance. In this paper, an indirect method for solving the pre-image problem is proposed. In the proposed algorithm, an inverse mapping process is constructed based on a novel framework that preserves local linearity. In this framework, a local nonlinear transformation is implicitly conducted by neighborhood subspace scaling transformation to preserve the local linearity between feature space and input space. By extending the inverse mapping process to test samples, we can obtain pre-images in input space. The proposed method is non-iterative, and can be used for any kernel functions. Experimental results based on image denoising using kernel principal component analysis (PCA) show that the proposed method outperforms the state-of-the-art methods for solving the pre-image problem.
A New Spectral Local Linearization Method for Nonlinear Boundary Layer Flow Problems
Directory of Open Access Journals (Sweden)
S. S. Motsa
2013-01-01
Full Text Available We propose a simple and efficient method for solving highly nonlinear systems of boundary layer flow problems with exponentially decaying profiles. The algorithm of the proposed method is based on an innovative idea of linearizing and decoupling the governing systems of equations and reducing them into a sequence of subsystems of differential equations which are solved using spectral collocation methods. The applicability of the proposed method, hereinafter referred to as the spectral local linearization method (SLLM, is tested on some well-known boundary layer flow equations. The numerical results presented in this investigation indicate that the proposed method, despite being easy to develop and numerically implement, is very robust in that it converges rapidly to yield accurate results and is more efficient in solving very large systems of nonlinear boundary value problems of the similarity variable boundary layer type. The accuracy and numerical stability of the SLLM can further be improved by using successive overrelaxation techniques.
Growth of linear Ni-filled carbon nanotubes by local arc discharge in liquid ethanol
Energy Technology Data Exchange (ETDEWEB)
Sagara, Takuya [Department of Electric Engineering, Graduated School of Science and Technology, Nihon University, 1-8-14 Surugadai Kanda, Chiyoda, Tokyo 101-8308 (Japan); Kurumi, Satoshi [Department of Electric Engineering, College of Science and Technology, Nihon University, 1-8-14 Surugadai Kanda, Chiyoda, Tokyo 101-8308 (Japan); Suzuki, Kaoru, E-mail: kaoru@ele.cst.nihon-u.ac.jp [Department of Electric Engineering, College of Science and Technology, Nihon University, 1-8-14 Surugadai Kanda, Chiyoda, Tokyo 101-8308 (Japan)
2014-02-15
The cylindrical geometry of carbon nanotubes (CNTs) allows them to be filled with metal catalysts; the resulting metal-filled CNTs possess different properties depending on the filler metal. Here we report the synthesis of Ni-filled CNTs in which Ni is situated linearly and homogeneously by local arc discharge in liquid ethanol. The structural characteristics of synthesized Ni-filled CNTs were determined by transmission electron microscopy (TEM), and the relationship between pyrolysis conditions and the length and diameter of Ni-filled CNTs was examined. The encapsulated Ni was identified by a TEM-equipped energy-dispersive X-ray spectroscope and found to have a single-crystal fcc structure by nano-beam diffraction. The features of linear Ni-filled CNT are expected to be applicable to probes for magnetic force microscopy.
Linear vs. nonlinear acceleration in plasma turbulence. I. Global versus local measures
Energy Technology Data Exchange (ETDEWEB)
Ghosh, Sanjoy [Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland 20723 (United States); Parashar, Tulasi N. [University of Delaware, Newark, Delaware 19716 (United States)
2015-04-15
Magnetized turbulent plasmas are generally characterized as strongly or weakly turbulent based on the average relative strengths of the linear and nonlinear terms. While this description is useful, it does not represent the full picture and can be misleading. We study the variation of linear and nonlinear accelerations in the Fourier space of a magnetohydrodynamic system with a mean magnetic field and broad selection of initial states and plasma parameters. We show that the local picture can show significant departures from what is expected from the general global picture. We find that high cross helicity systems that are traditionally believed to have relatively weaker nonlinearities, compared to low cross helicity systems, can show strong nonlinearities in parts of the Fourier space that are orthogonal to the mean magnetic field direction. In some cases, these nonlinearities can exceed in strength the level of nonlinearities recovered from low cross helicity systems.
Orain, François; Bécoulet, M.; Morales, J.; Huijsmans, G. T. A.; Dif-Pradalier, G.; Hoelzl, M.; Garbet, X.; Pamela, S.; Nardon, E.; Passeron, C.; Latu, G.; Fil, A.; Cahyna, P.
2015-01-01
The dynamics of a multi-edge localized mode (ELM) cycle as well as the ELM mitigation by resonant magnetic perturbations (RMPs) are modeled in realistic tokamak X-point geometry with the non-linear reduced MHD code JOREK. The diamagnetic rotation is found to be a key parameter enabling us to reproduce the cyclical dynamics of the plasma relaxations and to model the near-symmetric ELM power deposition on the inner and outer divertor target plates consistently with experimental measurements. Moreover, the non-linear coupling of the RMPs with unstable modes are found to modify the edge magnetic topology and induce a continuous MHD activity in place of a large ELM crash, resulting in the mitigation of the ELMs. At larger diamagnetic rotation, a bifurcation from unmitigated ELMs—at low RMP current—towards fully suppressed ELMs—at large RMP current—is obtained.
The Dangers of Estimating V˙O2max Using Linear, Nonexercise Prediction Models.
Nevill, Alan M; Cooke, Carlton B
2017-05-01
This study aimed to compare the accuracy and goodness of fit of two competing models (linear vs allometric) when estimating V˙O2max (mL·kg·min) using nonexercise prediction models. The two competing models were fitted to the V˙O2max (mL·kg·min) data taken from two previously published studies. Study 1 (the Allied Dunbar National Fitness Survey) recruited 1732 randomly selected healthy participants, 16 yr and older, from 30 English parliamentary constituencies. Estimates of V˙O2max were obtained using a progressive incremental test on a motorized treadmill. In study 2, maximal oxygen uptake was measured directly during a fatigue limited treadmill test in older men (n = 152) and women (n = 146) 55 to 86 yr old. In both studies, the quality of fit associated with estimating V˙O2max (mL·kg·min) was superior using allometric rather than linear (additive) models based on all criteria (R, maximum log-likelihood, and Akaike information criteria). Results suggest that linear models will systematically overestimate V˙O2max for participants in their 20s and underestimate V˙O2max for participants in their 60s and older. The residuals saved from the linear models were neither normally distributed nor independent of the predicted values nor age. This will probably explain the absence of a key quadratic age term in the linear models, crucially identified using allometric models. Not only does the curvilinear age decline within an exponential function follow a more realistic age decline (the right-hand side of a bell-shaped curve), but the allometric models identified either a stature-to-body mass ratio (study 1) or a fat-free mass-to-body mass ratio (study 2), both associated with leanness when estimating V˙O2max. Adopting allometric models will provide more accurate predictions of V˙O2max (mL·kg·min) using plausible, biologically sound, and interpretable models.
FUNDAMENTAL MATRIX OF LINEAR CONTINUOUS SYSTEM IN THE PROBLEM OF ESTIMATING ITS TRANSPORT DELAY
Directory of Open Access Journals (Sweden)
N. A. Dudarenko
2014-09-01
Full Text Available The paper deals with the problem of quantitative estimation for transport delay of linear continuous systems. The main result is received by means of fundamental matrix of linear differential equations solutions specified in the normal Cauchy form for the cases of SISO and MIMO systems. Fundamental matrix has the dual property. It means that the weight function of the system can be formed as a free motion of systems. Last one is generated by the vector of initial system conditions, which coincides with the matrix input of the system being researched. Thus, using the properties of the system- solving for fundamental matrix has given the possibility to solve the problem of estimating transport linear continuous system delay without the use of derivation procedure in hardware environment and without formation of exogenous Dirac delta function. The paper is illustrated by examples. The obtained results make it possible to solve the problem of modeling the pure delay links using consecutive chain of aperiodic links of the first order with the equal time constants. Modeling results have proved the correctness of obtained computations. Knowledge of transport delay can be used when configuring multi- component technological complexes and in the diagnosis of their possible functional degeneration.
Estimating VDT Mental Fatigue Using Multichannel Linear Descriptors and KPCA-HMM
Directory of Open Access Journals (Sweden)
Yi Ouyang
2008-04-01
Full Text Available The impacts of prolonged visual display terminal (VDT work on central nervous system and autonomic nervous system are observed and analyzed based on electroencephalogram (EEG and heart rate variability (HRV. Power spectral indices of HRV, the P300 components based on visual oddball task, and multichannel linear descriptors of EEG are combined to estimate the change of mental fatigue. The results show that long-term VDT work induces the mental fatigue. The power spectral of HRV, the P300 components, and multichannel linear descriptors of EEG are correlated with mental fatigue level. The cognitive information processing would come down after long-term VDT work. Moreover, the multichannel linear descriptors of EEG can effectively reflect the changes of ÃŽÂ¸, ÃŽÂ±, and ÃŽÂ² waves and may be used as the indices of the mental fatigue level. The kernel principal component analysis (KPCA and hidden Markov model (HMM are combined to differentiate two mental fatigue states. The investigation suggests that the joint KPCA-HMM method can effectively reduce the dimensions of the feature vectors, accelerate the classification speed, and improve the accuracy of mental fatigue to achieve the maximum 88%. Hence KPCA-HMM could be a promising model for the estimation of mental fatigue.
Estimating VDT Mental Fatigue Using Multichannel Linear Descriptors and KPCA-HMM
Zhang, Chong; Zheng, Chongxun; Yu, Xiaolin; Ouyang, Yi
2008-12-01
The impacts of prolonged visual display terminal (VDT) work on central nervous system and autonomic nervous system are observed and analyzed based on electroencephalogram (EEG) and heart rate variability (HRV). Power spectral indices of HRV, the P300 components based on visual oddball task, and multichannel linear descriptors of EEG are combined to estimate the change of mental fatigue. The results show that long-term VDT work induces the mental fatigue. The power spectral of HRV, the P300 components, and multichannel linear descriptors of EEG are correlated with mental fatigue level. The cognitive information processing would come down after long-term VDT work. Moreover, the multichannel linear descriptors of EEG can effectively reflect the changes of θ, α, and β waves and may be used as the indices of the mental fatigue level. The kernel principal component analysis (KPCA) and hidden Markov model (HMM) are combined to differentiate two mental fatigue states. The investigation suggests that the joint KPCA-HMM method can effectively reduce the dimensions of the feature vectors, accelerate the classification speed, and improve the accuracy of mental fatigue to achieve the maximum 88%. Hence KPCA-HMM could be a promising model for the estimation of mental fatigue.
Jaber, Abobaker M; Ismail, Mohd Tahir; Altaher, Alsaidi M
2014-01-01
This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.
Directory of Open Access Journals (Sweden)
Abobaker M. Jaber
2014-01-01
Full Text Available This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD with nonparametric methods of local linear quantile (LLQ. We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.
Full linear perturbations and localization of gravity on $f(R,T)$ brane
Gu, Bao-Min; Yu, Hao; Liu, Yu-Xiao
2016-01-01
We study the thick brane world system constructed in the recently proposed $f(R,T)$ theories of gravity, with $R$ the Ricci scalar and $T$ the trace of the energy-momentum tensor. The analytic solution with a kink scalar field is obtained in a specific model, thus a domain wall configuration is constructed. We also discuss the full linear perturbations, especially the scalar perturbations. It is found that no tachyon state exists in this model and only the massless tensor mode can be localized on the brane, which recovers the effective four-dimensional gravity. These conclusions hold provided that two constraints on the original formalism of the action are satisfied.
Nonparametric Estimates of Gene × Environment Interaction Using Local Structural Equation Modeling
Briley, Daniel A.; Harden, K. Paige; Bates, Timothy C.; Tucker-Drob, Elliot M.
2017-01-01
Gene × Environment (G×E) interaction studies test the hypothesis that the strength of genetic influence varies across environmental contexts. Existing latent variable methods for estimating G×E interactions in twin and family data specify parametric (typically linear) functions for the interaction effect. An improper functional form may obscure the underlying shape of the interaction effect and may lead to failures to detect a significant interaction. In this article, we introduce a novel approach to the behavior genetic toolkit, local structural equation modeling (LOSEM). LOSEM is a highly flexible nonparametric approach for estimating latent interaction effects across the range of a measured moderator. This approach opens up the ability to detect and visualize new forms of G×E interaction. We illustrate the approach by using LOSEM to estimate gene × socioeconomic status (SES) interactions for six cognitive phenotypes. Rather than continuously and monotonically varying effects as has been assumed in conventional parametric approaches, LOSEM indicated substantial nonlinear shifts in genetic variance for several phenotypes. The operating characteristics of LOSEM were interrogated through simulation studies where the functional form of the interaction effect was known. LOSEM provides a conservative estimate of G×E interaction with sufficient power to detect statistically significant G×E signal with moderate sample size. We offer recommendations for the application of LOSEM and provide scripts for implementing these biometric models in Mplus and in OpenMx under R. PMID:26318287
Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data
Directory of Open Access Journals (Sweden)
Lei Wang
2008-06-01
Full Text Available MODIS (Moderate Resolution Imaging Spectroradiometer is a key instrument aboard the Terra (EOS AM and Aqua (EOS PM satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.
Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data
Institute of Scientific and Technical Information of China (English)
2008-01-01
MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites.Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers.Shaoxing county of Zhcjiang Province in China was chosen to be the study site and early rice was selected as the study crop.The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day,which implies that MODIS data could be used as satellite data source for rice cultivation area estimation,possibly rice growth monitoring and yield forecasting on the regional scale.
Development of a web-based simulator for estimating motion errors in linear motion stages
Khim, G.; Oh, J.-S.; Park, C.-H.
2017-08-01
This paper presents a web-based simulator for estimating 5-DOF motion errors in the linear motion stages. The main calculation modules of the simulator are stored on the server computer. The clients uses the client software to send the input parameters to the server and receive the computed results from the server. By using the simulator, we can predict performances such as 5-DOF motion errors, bearing and table stiffness by entering the design parameters in a design step before fabricating the stages. Motion errors are calculated using the transfer function method from the rail form errors which is the most dominant factor on the motion errors. To verify the simulator, the predicted motion errors are compared to the actually measured motion errors in the linear motion stage.
Reducing the Dimensionality of Data: Locally Linear Embedding of Sloan Galaxy Spectra
VanderPlas, J T
2009-01-01
We introduce Locally Linear Embedding (LLE) to the astronomical community as a new classification technique, using SDSS spectra as an example data set. LLE is a nonlinear dimensionality reduction technique which has been studied in the context of computer perception. We compare the performance of LLE to well-known spectral classification techniques, e.g. principal component analysis and line-ratio diagnostics. We find that LLE combines the strengths of both methods in a single, coherent technique, and leads to improved classification of emission-line spectra at a relatively small computational cost. We also present a data subsampling technique that preserves local information content, and proves effective for creating small, efficient training samples from a large, high-dimensional data sets. Software used in this LLE-based classification is made available.
Robust Hessian Locally Linear Embedding Techniques for High-Dimensional Data
Directory of Open Access Journals (Sweden)
Xianglei Xing
2016-05-01
Full Text Available Recently manifold learning has received extensive interest in the community of pattern recognition. Despite their appealing properties, most manifold learning algorithms are not robust in practical applications. In this paper, we address this problem in the context of the Hessian locally linear embedding (HLLE algorithm and propose a more robust method, called RHLLE, which aims to be robust against both outliers and noise in the data. Specifically, we first propose a fast outlier detection method for high-dimensional datasets. Then, we employ a local smoothing method to reduce noise. Furthermore, we reformulate the original HLLE algorithm by using the truncation function from differentiable manifolds. In the reformulated framework, we explicitly introduce a weighted global functional to further reduce the undesirable effect of outliers and noise on the embedding result. Experiments on synthetic as well as real datasets demonstrate the effectiveness of our proposed algorithm.
Global hybrids from the semiclassical atom theory satisfying the local density linear response
Fabiano, E; Cortona, P; Della Sala, F
2015-01-01
We propose global hybrid approximations of the exchange-correlation (XC) energy functional which reproduce well the modified fourth-order gradient expansion of the exchange energy in the semiclassical limit of many-electron neutral atoms and recover the full local density approximation (LDA) linear response. These XC functionals represent the hybrid versions of the APBE functional [Phys. Rev. Lett. 106, 186406, (2011)] yet employing an additional correlation functional which uses the localization concept of the correlation energy density to improve the compatibility with the Hartree-Fock exchange as well as the coupling-constant-resolved XC potential energy. Broad energetical and structural testings, including thermochemistry and geometry, transition metal complexes, non-covalent interactions, gold clusters and small gold-molecule interfaces, as well as an analysis of the hybrid parameters, show that our construction is quite robust. In particular, our testing shows that the resulting hybrid, including 20\\% o...
Markov Jump Linear Systems-Based Position Estimation for Lower Limb Exoskeletons
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Samuel L. Nogueira
2014-01-01
Full Text Available In this paper, we deal with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. The angular positions of an impedance-controlled exoskeleton, designed to help stroke and spinal cord injured patients during walking rehabilitation, are estimated. Standard position estimate approaches adopt Kalman filters (KF to improve the performance of inertial measurement units (IMUs based on individual link configurations. Consequently, for a multi-body system, like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank are not taken into account in other link position estimation (e.g., the foot. In this paper, we propose a collective modeling of all inertial sensors attached to the exoskeleton, combining them in a Markovian estimation model in order to get the best information from each sensor. In order to demonstrate the effectiveness of our approach, simulation results regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton, are presented. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.
Markov jump linear systems-based position estimation for lower limb exoskeletons.
Nogueira, Samuel L; Siqueira, Adriano A G; Inoue, Roberto S; Terra, Marco H
2014-01-22
In this paper, we deal with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. The angular positions of an impedance-controlled exoskeleton, designed to help stroke and spinal cord injured patients during walking rehabilitation, are estimated. Standard position estimate approaches adopt Kalman filters (KF) to improve the performance of inertial measurement units (IMUs) based on individual link configurations. Consequently, for a multi-body system, like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link position estimation (e.g., the foot). In this paper, we propose a collective modeling of all inertial sensors attached to the exoskeleton, combining them in a Markovian estimation model in order to get the best information from each sensor. In order to demonstrate the effectiveness of our approach, simulation results regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton, are presented. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.
Franco-Pérez, Marco; Ayers, Paul W; Gázquez, José L; Vela, Alberto
2015-12-28
We explore the local and nonlocal response functions of the grand canonical potential density functional at nonzero temperature. In analogy to the zero-temperature treatment, local (e.g., the average electron density and the local softness) and nonlocal (e.g., the softness kernel) intrinsic response functions are defined as partial derivatives of the grand canonical potential with respect to its thermodynamic variables (i.e., the chemical potential of the electron reservoir and the external potential generated by the atomic nuclei). To define the local and nonlocal response functions of the electron density (e.g., the Fukui function, the linear density response function, and the dual descriptor), we differentiate with respect to the average electron number and the external potential. The well-known mathematical relationships between the intrinsic response functions and the electron-density responses are generalized to nonzero temperature, and we prove that in the zero-temperature limit, our results recover well-known identities from the density functional theory of chemical reactivity. Specific working equations and numerical results are provided for the 3-state ensemble model.
Energy Technology Data Exchange (ETDEWEB)
Franco-Pérez, Marco, E-mail: francopj@mcmaster.ca, E-mail: ayers@mcmaster.ca, E-mail: jlgm@xanum.uam.mx, E-mail: avela@cinvestav.mx [Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1 (Canada); Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, México, D.F. 09340 (Mexico); Ayers, Paul W., E-mail: francopj@mcmaster.ca, E-mail: ayers@mcmaster.ca, E-mail: jlgm@xanum.uam.mx, E-mail: avela@cinvestav.mx [Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1 (Canada); Gázquez, José L., E-mail: francopj@mcmaster.ca, E-mail: ayers@mcmaster.ca, E-mail: jlgm@xanum.uam.mx, E-mail: avela@cinvestav.mx [Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, México, D.F. 09340 (Mexico); Vela, Alberto, E-mail: francopj@mcmaster.ca, E-mail: ayers@mcmaster.ca, E-mail: jlgm@xanum.uam.mx, E-mail: avela@cinvestav.mx [Departamento de Química, Centro de Investigación y de Estudios Avanzados (Cinvestav), Av. Instituto Politécnico Nacional 2508, México, D.F. 07360 (Mexico)
2015-12-28
We explore the local and nonlocal response functions of the grand canonical potential density functional at nonzero temperature. In analogy to the zero-temperature treatment, local (e.g., the average electron density and the local softness) and nonlocal (e.g., the softness kernel) intrinsic response functions are defined as partial derivatives of the grand canonical potential with respect to its thermodynamic variables (i.e., the chemical potential of the electron reservoir and the external potential generated by the atomic nuclei). To define the local and nonlocal response functions of the electron density (e.g., the Fukui function, the linear density response function, and the dual descriptor), we differentiate with respect to the average electron number and the external potential. The well-known mathematical relationships between the intrinsic response functions and the electron-density responses are generalized to nonzero temperature, and we prove that in the zero-temperature limit, our results recover well-known identities from the density functional theory of chemical reactivity. Specific working equations and numerical results are provided for the 3-state ensemble model.
Yue, Chen; Chen, Shaojie; Sair, Haris I; Airan, Raag; Caffo, Brian S
2015-09-01
Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcm-cEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.
Institute of Scientific and Technical Information of China (English)
Qibing GAO; Yaohua WU; Chunhua ZHU; Zhanfeng WANG
2008-01-01
In generalized linear models with fixed design, under the assumption ~ →∞ and otherregularity conditions, the asymptotic normality of maximum quasi-likelihood estimator (β)n, which is the root of the quasi-likelihood equation with natural link function ∑n/i=1Xi(yi-μ(X1/iβ))=0, is obtained,where λ/-n denotes the minimum eigenvalue of ∑n/i=1XiX/1/i, Xi are bounded p x q regressors, and yi are q × 1 responses.
Bachelard, Nicolas; Sebbah, Patrick; Vanneste, Christian
2014-01-01
We use time-domain numerical simulations of a two-dimensional (2D) scattering system to study the interaction of a collection of emitters resonantly coupled to an Anderson-localized mode. For a small electric field intensity, we observe the strong coupling between the emitters and the mode, which is characterized by linear Rabi oscillations. Remarkably, a larger intensity induces non-linear interaction between the emitters and the mode, referred to as the dynamical Stark effect, resulting in non-linear Rabi oscillations. The transition between both regimes is observed and an analytical model is proposed which accurately describes our numerical observations.
Non-rigid Motion Correction in 3D Using Autofocusing with Localized Linear Translations
Cheng, Joseph Y.; Alley, Marcus T.; Cunningham, Charles H.; Vasanawala, Shreyas S.; Pauly, John M.; Lustig, Michael
2012-01-01
MR scans are sensitive to motion effects due to the scan duration. To properly suppress artifacts from non-rigid body motion, complex models with elements such as translation, rotation, shear, and scaling have been incorporated into the reconstruction pipeline. However, these techniques are computationally intensive and difficult to implement for online reconstruction. On a sufficiently small spatial scale, the different types of motion can be well-approximated as simple linear translations. This formulation allows for a practical autofocusing algorithm that locally minimizes a given motion metric – more specifically, the proposed localized gradient-entropy metric. To reduce the vast search space for an optimal solution, possible motion paths are limited to the motion measured from multi-channel navigator data. The novel navigation strategy is based on the so-called “Butterfly” navigators which are modifications to the spin-warp sequence that provide intrinsic translational motion information with negligible overhead. With a 32-channel abdominal coil, sufficient number of motion measurements were found to approximate possible linear motion paths for every image voxel. The correction scheme was applied to free-breathing abdominal patient studies. In these scans, a reduction in artifacts from complex, non-rigid motion was observed. PMID:22307933
Nonrigid motion correction in 3D using autofocusing with localized linear translations.
Cheng, Joseph Y; Alley, Marcus T; Cunningham, Charles H; Vasanawala, Shreyas S; Pauly, John M; Lustig, Michael
2012-12-01
MR scans are sensitive to motion effects due to the scan duration. To properly suppress artifacts from nonrigid body motion, complex models with elements such as translation, rotation, shear, and scaling have been incorporated into the reconstruction pipeline. However, these techniques are computationally intensive and difficult to implement for online reconstruction. On a sufficiently small spatial scale, the different types of motion can be well approximated as simple linear translations. This formulation allows for a practical autofocusing algorithm that locally minimizes a given motion metric--more specifically, the proposed localized gradient-entropy metric. To reduce the vast search space for an optimal solution, possible motion paths are limited to the motion measured from multichannel navigator data. The novel navigation strategy is based on the so-called "Butterfly" navigators, which are modifications of the spin-warp sequence that provides intrinsic translational motion information with negligible overhead. With a 32-channel abdominal coil, sufficient number of motion measurements were found to approximate possible linear motion paths for every image voxel. The correction scheme was applied to free-breathing abdominal patient studies. In these scans, a reduction in artifacts from complex, nonrigid motion was observed. Copyright © 2012 Wiley Periodicals, Inc.
Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Daohong Song
2012-01-01
Full Text Available We provide a brief overview on our recent experimental work on linear and nonlinear localization of singly charged vortices (SCVs and doubly charged vortices (DCVs in two-dimensional optically induced photonic lattices. In the nonlinear case, vortex propagation at the lattice surface as well as inside the uniform square-shaped photonic lattices is considered. It is shown that, apart from the fundamental (semi-infinite gap discrete vortex solitons demonstrated earlier, the SCVs can self-trap into stable gap vortex solitons under the normal four-site excitation with a self-defocusing nonlinearity, while the DCVs can be stable only under an eight-site excitation inside the photonic lattices. Moreover, the SCVs can also turn into stable surface vortex solitons under the four-site excitation at the surface of a semi-infinite photonics lattice with a self-focusing nonlinearity. In the linear case, bandgap guidance of both SCVs and DCVs in photonic lattices with a tunable negative defect is investigated. It is found that the SCVs can be guided at the negative defect as linear vortex defect modes, while the DCVs tend to turn into quadrupole-like defect modes provided that the defect strength is not too strong.
Quasi-Maximum Likelihood Estimators in Generalized Linear Models with Autoregressive Processes
Institute of Scientific and Technical Information of China (English)
Hong Chang HU; Lei SONG
2014-01-01
The paper studies a generalized linear model (GLM) yt=h(xTtβ)+εt, t=1, 2, . . . , n, whereε1=η1,εt=ρεt-1+ηt, t=2,3,...,n, h is a continuous diff erentiable function,ηt’s are independent and identically distributed random errors with zero mean and finite varianceσ 2. Firstly, the quasi-maximum likelihood (QML) estimators ofβ,ρandσ 2 are given. Secondly, under mild conditions, the asymptotic properties (including the existence, weak consistency and asymptotic distribution) of the QML estimators are investigated. Lastly, the validity of method is illuminated by a simulation example.
Iwaoka, Nobuyuki; Hagita, Katsumi; Takano, Hiroshi
2014-03-01
On the basis of relaxation mode analysis (RMA), we present an efficient method to estimate the linear viscoelasticity of polymer melts in a molecular dynamics (MD) simulation. Slow relaxation phenomena appeared in polymer melts cause a problem that a calculation of the stress relaxation function in MD simulations, especially in the terminal time region, requires large computational efforts. Relaxation mode analysis is a method that systematically extracts slow relaxation modes and rates of the polymer chain from the time correlation of its conformations. We show the computational cost may be drastically reduced by combining a direct calculation of the stress relaxation function based on the Green-Kubo formula with the relaxation rates spectra estimated by RMA. N. I. acknowledges the Graduate School Doctoral Student Aid Program from Keio University.
Yu, Jung-Lang; Chen, Chia-Hao
Orthogonal frequency-division multiplexing (OFDM) systems often use a cyclic prefix (CP) to simplify the equalization design at the cost of bandwidth efficiency. To increase the bandwidth efficiency, we study the blind equalization with linear smoothing [1] for single-input multiple-output (SIMO) OFDM systems without CP insertion in this paper. Due to the block Toeplitz structure of channel matrix, the block matrix scheme is applied to the linear smoothing channel estimation, which equivalently increases the number of sample vectors and thus reduces the perturbation of sample autocorrelation matrix. Compared with the linear smoothing and subspace methods, the proposed block linear smoothing requires the lowest computational complexity. Computer simulations show that the block linear smoothing yields a channel estimation error smaller than that from linear smoothing, and close to that of the subspace method. Evaluating by the minimum mean-square error (MMSE) equalizer, the block linear smoothing and subspace methods have nearly the same bit-error-rates (BERs).
Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel
2016-01-01
Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.
Directory of Open Access Journals (Sweden)
Rubio Gerardo
2011-03-01
Full Text Available We consider the Cauchy problem in ℝd for a class of semilinear parabolic partial differential equations that arises in some stochastic control problems. We assume that the coefficients are unbounded and locally Lipschitz, not necessarily differentiable, with continuous data and local uniform ellipticity. We construct a classical solution by approximation with linear parabolic equations. The linear equations involved can not be solved with the traditional results. Therefore, we construct a classical solution to the linear Cauchy problem under the same hypotheses on the coefficients for the semilinear equation. Our approach is using stochastic differential equations and parabolic differential equations in bounded domains. Finally, we apply the results to a stochastic optimal consumption problem. Nous considérons le problème de Cauchy dans ℝd pour une classe d’équations aux dérivées partielles paraboliques semi linéaires qui se pose dans certains problèmes de contrôle stochastique. Nous supposons que les coefficients ne sont pas bornés et sont localement Lipschitziennes, pas nécessairement différentiables, avec des données continues et ellipticité local uniforme. Nous construisons une solution classique par approximation avec les équations paraboliques linéaires. Les équations linéaires impliquées ne peuvent être résolues avec les résultats traditionnels. Par conséquent, nous construisons une solution classique au problème de Cauchy linéaire sous les mêmes hypothèses sur les coefficients pour l’équation semi-linéaire. Notre approche utilise les équations différentielles stochastiques et les équations différentielles paraboliques dans les domaines bornés. Enfin, nous appliquons les résultats à un problème stochastique de consommation optimale.
Discrete Plane Segmentation and Estimation from a Point Cloud Using Local Geometric Patterns
Institute of Scientific and Technical Information of China (English)
Yukiko Kenmochi; Lilian Buzer; Akihiro Sugimoto; Ikuko Shimizu
2008-01-01
This paper presents a method for segmenting a 3D point cloud into planar surfaces using recently obtained discrete-geometry results. In discrete geometry, a discrete plane is defined as a set of grid points lying between two parallel planes with a small distance, called thickness. In contrast to the continuous case, there exist a finite number of local geometric patterns (LGPs) appearing on discrete planes. Moreover, such an LGP does not possess the unique normal vector but a set of normal vectors. By using those LGP properties, we first reject non-linear points from a point cloud, and then classify non-rejected points whose LGPs have common normal vectors into a planar-surface-point set. From each segmented point set, we also estimate the values of parameters of a discrete plane by minimizing its thickness.
Yi, Feng; Sun, Chao; Bai, Xiao-Hui
2012-11-01
A new signal-subspace high-resolution bearing estimation method based on the orthogonal projections technique is proposed in this paper. Firstly, the received data are calculated step by step to form a set of basis vectors for the signal-subspace, utilizing an orthogonal projections algorithm that does not construct and eigen-decompose the covariance matrix. This procedure retains a linear complexity in computation and guarantees maximum signal energy in the spanned signal-subspace. Then the algorithm exploits the singular value decomposition of the matrix, comprised of the signal-subspace and the modal subspace that is obtained also from the received data, and the source bearings are estimated by detecting the intersection between the estimated signal-subspace and the modal subspace. The computational complexity of the proposed method is compared to that of the subspace intersection method, and its performance is compared to that of the conventional bearing estimation method, including conventional beamforming (CBF), and minimum variance distortionless response beamforming (MVDR). The performance of the proposed method under different condition such as sensor number, sensor inter-space, received signal-noise ratio (SNR), snapshot number is also investigated. Numerical simulation results in typical shallow water demonstrate the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Zhang Han
2009-01-01
Full Text Available We address the problem of superimposed trainings- (STs- based linearly time-varying (LTV channel estimation and symbol detection for orthogonal frequency-division multiplexing access (OFDMA systems at the uplink receiver. The LTV channel coefficients are modeled by truncated discrete Fourier bases (DFBs. By judiciously designing the superimposed pilot symbols, we estimate the LTV channel transfer functions over the whole frequency band by using a weighted average procedure, thereby providing validity for adaptive resource allocation. We also present a performance analysis of the channel estimation approach to derive a closed-form expression for the channel estimation variances. In addition, an iterative symbol detector is presented to mitigate the superimposed training effects on information sequence recovery. By the iterative mitigation procedure, the demodulator achieves a considerable gain in signal-interference ratio and exhibits a nearly indistinguishable symbol error rate (SER performance from that of frequency-division multiplexed trainings. Compared to existing frequency-division multiplexed training schemes, the proposed algorithm does not entail any additional bandwidth while with the advantage for system adaptive resource allocation.
Post-L1-Penalized Estimators in High-Dimensional Linear Regression Models
Belloni, Alexandre
2010-01-01
In this paper we study the post-penalized estimator which applies ordinary, unpenalized linear regression to the model selected by the first step penalized estimators, typically the LASSO. We show that post-LASSO can perform as well or nearly as well as the LASSO in terms of the rate of convergence. We show that this performance occurs even if the LASSO-based model selection "fails", in the sense of missing some components of the "true" regression model. Furthermore, post-LASSO can perform strictly better than LASSO, in the sense of a strictly faster rate of convergence, if the LASSO-based model selection correctly includes all components of the "true" model as a subset and enough sparsity is obtained. Of course, in the extreme case, when LASSO perfectly selects the true model, the past-LASSO estimator becomes the oracle estimator. We show that the results hold in both parametric and non-parametric models; and by the "true" model we mean the best $s$-dimensional approximation to the true regression model, whe...
DEFF Research Database (Denmark)
Damkilde, Lars; Pedersen, Ronnie
2012-01-01
This paper describes a new triangular plane element which can be considered as a linear strain triangular element (LST) extended with incompatible displacement modes. The extended element will have a full cubic interpolation of strains and stresses. The extended LST-element is connected with other...... elements similar to the LST-element i.e. through three corner nodes and three mid-side nodes. The incompatible modes are associated with two displacement gradients at each mid-side node and displacements in the central node. The element passes the patch test and converges to the exact solution. The element...... has been tested on a standard linear test such as Cook’s panel, and is shown as expected to be somewhat more flexible than the LST-element and the compatible quadratic strain element (QST). The extended element has also been applied to material non-linear geotechnical problems. Geotechnical problems...
The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis.
Treder, Matthias S; Porbadnigk, Anne K; Shahbazi Avarvand, Forooz; Müller, Klaus-Robert; Blankertz, Benjamin
2016-04-01
We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.
Directory of Open Access Journals (Sweden)
Abobaker M. Jaber
2014-01-01
Full Text Available Empirical mode decomposition (EMD is particularly useful in analyzing nonstationary and nonlinear time series. However, only partial data within boundaries are available because of the bounded support of the underlying time series. Consequently, the application of EMD to finite time series data results in large biases at the edges by increasing the bias and creating artificial wiggles. This study introduces a new two-stage method to automatically decrease the boundary effects present in EMD. At the first stage, local polynomial quantile regression (LLQ is applied to provide an efficient description of the corrupted and noisy data. The remaining series is assumed to be hidden in the residuals. Hence, EMD is applied to the residuals at the second stage. The final estimate is the summation of the fitting estimates from LLQ and EMD. Simulation was conducted to assess the practical performance of the proposed method. Results show that the proposed method is superior to classical EMD.
Directory of Open Access Journals (Sweden)
Ana Calabrese
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.
Prediction of an outcome using trajectories estimated from a linear mixed model.
Maruyama, Nami; Takahashi, Fumiaki; Takeuchi, Masahiro
2009-09-01
In longitudinal data, interest is usually focused on the repeatedly measured variable itself. In some situations, however, the pattern of variation of the variable over time may contain information about a separate outcome variable. In such situations, longitudinal data provide an opportunity to develop predictive models for future observations of the separate outcome variable given the current data for an individual. In particular, longitudinally changing patterns of repeated measurements of a variable measured up to time t, or trajectories, can be used to predict an outcome measure or event that occurs after time t. In this article, we propose a method for predicting an outcome variable based on a generalized linear model, specifically, a logistic regression model, the covariates of which are variables that characterize the trajectory of an individual. Since the trajectory of an individual contains estimation error, the proposed logistic regression model constitutes a measurement error model. The model is fitted in two steps. First, a linear mixed model is fitted to the longitudinal data to estimate the random effect that characterizes the trajectory for each individual while adjusting for other covariates. In the second step, a conditional likelihood approach is applied to account for the estimation error in the trajectory. Prediction of an outcome variable is based on the logistic regression model in the second step. The receiver operating characteristic curve is used to compare the discrimination ability of a model with trajectories to one without trajectories as covariates. A simulation study is used to assess the performance of the proposed method, and the method is applied to clinical trial data.
Local polynomial Whittle estimation of perturbed fractional processes
DEFF Research Database (Denmark)
Frederiksen, Per; Nielsen, Frank; Nielsen, Morten Ørregaard
for d ε (0, 3/4), and if the spectral density is infinitely smooth near frequency zero, the rate of convergence can become arbitrarily close to the parametric rate, pn. A Monte Carlo study reveals that the LPWN estimator performs well in the presence of a serially correlated perturbation term...... of the signal by two separate polynomials. Including these polynomials we obtain a reduction in the order of magnitude of the bias, but also in‡ate the asymptotic variance of the long memory estimate by a multiplicative constant. We show that the estimator is consistent for d 2 (0; 1), asymptotically normal...
Sparsity-based AOA Estimation for Emitter Localization
Directory of Open Access Journals (Sweden)
Lingwen Zhang
2012-08-01
Full Text Available Angle of arrival (AOA is able to achieve high accuracy when the antenna arrays are deployed much closer to the emitter. However, spatial resolution problem still exists. This paper presents a novel AOA estimation method called sparsity angle sensing (SAS to improve the resolution. It integrates compressive sensing theorem into the parameter estimation formula. Traditional approaches for AOA estimation such as beamforming (BF, minimum variance distortionless response (MVDR, multiple signal classification (MUSIC are compared with SAS, and simulation results are discussed. It is shown that SAS method outperforms the other three methods in spatial resolution and robustness.
Cosmic flows and the expansion of the local Universe from non-linear phase-space reconstructions
Heß, Steffen; Kitaura, Francisco-Shu
2016-03-01
In this work, we investigate the impact of cosmic flows and density perturbations on Hubble constant H0 measurements using non-linear phase-space reconstructions of the Local Universe (LU). In particular, we rely on a set of 25 precise constrained N-body simulations based on Bayesian initial conditions reconstructions of the LU using the Two-Micron Redshift Survey galaxy sample within distances of about 90 h-1 Mpc. These have been randomly extended up to volumes enclosing distances of 360 h-1 Mpc with augmented Lagrangian perturbation theory (750 simulations in total), accounting in this way for gravitational mode coupling from larger scales, correcting for periodic boundary effects, and estimating systematics of missing attractors (σlarge = 134 s-1 km). We report on Local Group (LG) speed reconstructions, which for the first time are compatible with those derived from cosmic microwave background-dipole measurements: |vLG| = 685 ± 137 s-1 km. The direction (l, b) = (260.5° ± 13.3°, 39.1 ± 10.4°) is found to be compatible with the observations after considering the variance of large scales. Considering this effect of large scales, our local bulk flow estimations assuming a Λ cold dark matter model are compatible with the most recent estimates based on velocity data derived from the Tully-Fisher relation. We focus on low-redshift supernova measurements out to 0.01 tension. The first one is caused by the anisotropic distribution of supernovae, which aligns with the velocity dipole and hence induces a systematic boost in H0. The second one is due to the inhomogeneous matter fluctuations in the LU. In particular, a divergent region surrounding the Virgo Supercluster is responsible for an additional positive bias in H0. Taking these effects into account yields a correction of ΔH0 = -1.76 ± 0.21 s- 1 km Mpc- 1, thereby reducing the tension between local probes and more distant probes. Effectively H0 is lower by about 2 per cent.
A Homogeneous Linear Estimation Method for System Error in Data Assimilation
Institute of Scientific and Technical Information of China (English)
WU Wei; WU Zengmao; GAO Shanhong; ZHENG Yi
2013-01-01
In this paper,a new bias estimation method is proposed and applied in a regional ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model.The method is based on a homogeneous linear bias model,and the model bias is estimated using statistics at each assimilation cycle,which is different from the state augmentation methods proposed in previous literatures.The new method provides a good estimation for the model bias of some specific variables,such as sea level pressure (SLP).A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condition.Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems,and the reduction of analysis errors.The background error covariance structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept 'correlation scale' is introduced.However,the new method needs further evaluation with more cases of assimilation.
DOA and polarization estimation via signal reconstruction with linear polarization-sensitive arrays
Directory of Open Access Journals (Sweden)
Liu Zhangmeng
2015-12-01
Full Text Available This paper addresses the problem of direction-of-arrival (DOA and polarization estimation with polarization sensitive arrays (PSA, which has been a hot topic in the area of array signal processing during the past two or three decades. The sparse Bayesian learning (SBL technique is introduced to exploit the sparsity of the incident signals in space to solve this problem and a new method is proposed by reconstructing the signals from the array outputs first and then exploiting the reconstructed signals to realize parameter estimation. Only 1-D searching and numerical calculations are contained in the proposed method, which makes the proposed method computationally much efficient. Based on a linear array consisting of identically structured sensors, the proposed method can be used with slight modifications in PSA with different polarization structures. It also performs well in the presence of coherent signals or signals with different degrees of polarization. Simulation results are given to demonstrate the parameter estimation precision of the proposed method.
Simulating the Effect of Non-Linear Mode-Coupling in Cosmological Parameter Estimation
Kiessling, A; Heavens, A F
2011-01-01
Fisher Information Matrix methods are commonly used in cosmology to estimate the accuracy that cosmological parameters can be measured with a given experiment, and to optimise the design of experiments. However, the standard approach usually assumes both data and parameter estimates are Gaussian-distributed. Further, for survey forecasts and optimisation it is usually assumed the power-spectra covariance matrix is diagonal in Fourier-space. But in the low-redshift Universe, non-linear mode-coupling will tend to correlate small-scale power, moving information from lower to higher-order moments of the field. This movement of information will change the predictions of cosmological parameter accuracy. In this paper we quantify this loss of information by comparing naive Gaussian Fisher matrix forecasts with a Maximum Likelihood parameter estimation analysis of a suite of mock weak lensing catalogues derived from N-body simulations, based on the SUNGLASS pipeline, for a 2-D and tomographic shear analysis of a Eucl...
Estimation of failure probabilities of linear dynamic systems by importance sampling
Indian Academy of Sciences (India)
Anna Ivanova Olsen; Arvid Naess
2006-08-01
An iterative method for estimating the failure probability for certain time-variant reliability problems has been developed. In the paper, the focus is on the displacement response of a linear oscillator driven by white noise. Failure is then assumed to occur when the displacement response exceeds a critical threshold. The iteration procedure is a two-step method. On the ﬁrst iteration, a simple control function promoting failure is constructed using the design point weighting principle. After time discretization, two points are chosen to construct a compound deterministic control function. It is based on the time point when the ﬁrst maximum of the homogenous solution has occurred and on the point at the end of the considered time interval. An importance sampling technique is used in order to estimate the failure probability functional on a set of initial values of state space variables and time. On the second iteration, the concept of optimal control function can be implemented to construct a Markov control which allows much better accuracy in the failure probability estimate than the simple control function. On both iterations, the concept of changing the probability measure by the Girsanov transformation is utilized. As a result the CPU time is substantially reduced compared with the crude Monte Carlo procedure.
Directory of Open Access Journals (Sweden)
Y.A. Abbo
2016-09-01
Full Text Available In this paper we have discussed theoretical concepts and presented numerical results of local field enhancement at the core of different assemblages of metal/dielectric cylindrical nanoinclusions embedded in a linear dielectric host matrix. The obtained results show that for a composite with metal coated inclusions there exist two peak values of the enhancement factor at two different resonant frequencies. The existence of the second maxima becomes more important for a larger volume fraction of the metal part of the inclusion. For dielectric coated metal core inclusions and pure metal inclusions there is only one resonant frequency and one peak value of the enhancement factor. The enhancement of an electromagnetic wave is promising for the existence of nonlinear optical phenomena such as optical bistability which is important in optical communication and in optical computing such as optical switch and memory elements.
Drain current local variability from linear to saturation region in 28 nm bulk NMOSFETs
Karatsori, T. A.; Theodorou, C. G.; Haendler, S.; Dimitriadis, C. A.; Ghibaudo, G.
2017-02-01
In this work, we investigate the impact of the source - drain series resistance mismatch on the drain current variability in 28 nm bulk MOSFETs. For the first time, a mismatch model including the local fluctuations of the threshold voltage (Vt), the drain current gain factor (β) and the source - drain series resistance (RSD) in both linear and saturation regions is presented. Furthermore, it is demonstrated that the influence of the source - drain series resistance mismatch is attenuated in the saturation region, due to the weaker sensitivity of the drain current variability on the series resistance variation. The experimental results were further verified by numerical simulations of the drain current characteristics with sensitivity analysis of the MOSFET parameters Vt, β and RSD.
Full linear perturbations and localization of gravity on f( R, T) brane
Gu, Bao-Min; Zhang, Yu-Peng; Yu, Hao; Liu, Yu-Xiao
2017-02-01
We study the thick brane world system constructed in the recently proposed f( R, T) theories of gravity, with R the Ricci scalar and T the trace of the energy-momentum tensor. We try to get the analytic background solutions and discuss the full linear perturbations, especially the scalar perturbations. We compare how the brane world model is modified with that of general relativity coupled to a canonical scalar field. It is found that some more interesting background solutions are allowed, and only the scalar perturbation mode is modified. There is no tachyon state existing in this model and only the massless tensor mode can be localized on the brane, which recovers the effective four-dimensional gravity. These conclusions hold provided that two constraints on the original formalism of the action are satisfied.
Full linear perturbations and localization of gravity on f(R, T) brane
Energy Technology Data Exchange (ETDEWEB)
Gu, Bao-Min; Zhang, Yu-Peng; Yu, Hao; Liu, Yu-Xiao [Lanzhou University, Institute of Theoretical Physics, Lanzhou (China)
2017-02-15
We study the thick brane world system constructed in the recently proposed f(R, T) theories of gravity, with R the Ricci scalar and T the trace of the energy-momentum tensor. We try to get the analytic background solutions and discuss the full linear perturbations, especially the scalar perturbations. We compare how the brane world model is modified with that of general relativity coupled to a canonical scalar field. It is found that some more interesting background solutions are allowed, and only the scalar perturbation mode is modified. There is no tachyon state existing in this model and only the massless tensor mode can be localized on the brane, which recovers the effective four-dimensional gravity. These conclusions hold provided that two constraints on the original formalism of the action are satisfied. (orig.)
Institute of Scientific and Technical Information of China (English)
Qin Luo; Zheng Tian; Zhixiang Zhao
2008-01-01
Existing manifold learning algorithms use Euclidean distance to measure the proximity of data points. However, in high-dimensional space, Minkowski metrics are no longer stable because the ratio of distance of nearest and farthest neighbors to a given query is almost unit. It will degrade the performance of manifold learning algorithms when applied to dimensionality reduction of high-dimensional data. We introduce a new distance function named shrinkage-divergence-proximity (SDP) to manifold learning, which is meaningful in any high-dimensional space. An improved locally linear embedding (LLE) algorithm named SDP-LLE is proposed in light of the theoretical result. Experiments are conducted on a hyperspectral data set and an image segmentation data set. Experimental results show that the proposed method can efficiently reduce the dimensionality while getting higher classification accuracy.
Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad
2014-10-01
Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets - Australian and German - from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.
Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.
Wu, Panpan; Xia, Kewen; Yu, Hengyong
2016-11-01
Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
L1-norm locally linear representation regularization multi-source adaptation learning.
Tao, Jianwen; Wen, Shiting; Hu, Wenjun
2015-09-01
In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object.
On the local virial theorems for linear and isotropic harmonic oscillator potentials in d dimensions
Energy Technology Data Exchange (ETDEWEB)
Bencheikh, K [Departement de Physique, Laboratoire de physique quantique et systemes dynamiques, Universite de Setif, Setif 19000 (Algeria); Nieto, L M, E-mail: bencheikh.kml@gmail.co [Departamento de Fisica Teorica, Atomica y Optica, Universidad de Valladolid, 47071 Valladolid (Spain)
2010-09-17
For the system of noninteracting fermions in a one-body potential V(r-vector), the local virial theorems (LVT) are relations, at a given point r-vector in space, between this potential, kinetic energy and particle densities. It was recently shown (Brack et al 2010 J. Phys. A: Math. Theor. 43 255204) that for d-dimensional linear and also for isotropic harmonic oscillator potentials these LVTs are exactly satisfied. We present alternative and simple proofs of these theorems, by consideration of the canonical or Bloch density matrix and its relation to the kinetic energy density. The explicit analytical forms of the Bloch density matrix are used for the above-mentioned potentials to achieve the proofs. For the case of linear potential, we obtain a more general result for the so-called semilocal virial theorem, and for the harmonic oscillator potential case we derive a new relationship between the diagonal part of the canonical bloch density and the kinetic energy density.
Sample based 3D face reconstruction from a single frontal image by adaptive locally linear embedding
Institute of Scientific and Technical Information of China (English)
ZHANG Jian; ZHUANG Yue-ting
2007-01-01
In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.
Sharifie, Javad; Lucas, Caro; Araabi, Babak N.
2006-06-01
Disturbance storm time index (Dst) is nonlinearly related to solar wind data. In this paper, Dst past values, Dst derivative, past values of southward interplanetary magnetic field, and the square root of dynamic pressure are used as inputs for modeling and prediction of the Dst index, especially during extreme events. The geoeffective solar wind parameters are selected depending on the physical background of the geomagnetic storm procedure and physical models. A locally linear neurofuzzy model with a progressive tree construction learning algorithm is applied as a powerful tool for nonlinear modeling of Dst index on the basis of its past values and solar wind parameters. The result for modeling and prediction of several intense storms shows that the geomagnetic disturbance Dst index based on geoeffective parameters is a nonlinear model that could be considered as the nonlinear extension of empirical linear physical models. The method is applied for prediction of some geomagnetic storms. Obtained results show that using the proposed method, the predicted values of several extreme storms are highly correlated with observed values. In addition, prediction of the main phase of many storms shows a good match with observed data, which constitutes an appropriate approach for solar storm alerting to vulnerable industries.
DEFF Research Database (Denmark)
Andreasen, Martin Møller; Christensen, Bent Jesper
This paper suggests a new and easy approach to estimate linear and non-linear dynamic term structure models with latent factors. We impose no distributional assumptions on the factors and they may therefore be non-Gaussian. The novelty of our approach is to use many observables (yields or bonds p...
DEFF Research Database (Denmark)
Jimenez, M.J.; Madsen, Henrik; Bloem, J.J.
2008-01-01
(MAP) estimation is presented along with a software implementation. As a case study, the modelling of the thermal characteristics of a building integrated PV component is considered. The EC-JRC Ispra has made experimental data available. Both linear and non-linear models are identified. It is shown...
A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem
Delaigle, Aurore
2009-03-01
Local polynomial estimators are popular techniques for nonparametric regression estimation and have received great attention in the literature. Their simplest version, the local constant estimator, can be easily extended to the errors-in-variables context by exploiting its similarity with the deconvolution kernel density estimator. The generalization of the higher order versions of the estimator, however, is not straightforward and has remained an open problem for the last 15 years. We propose an innovative local polynomial estimator of any order in the errors-in-variables context, derive its design-adaptive asymptotic properties and study its finite sample performance on simulated examples. We provide not only a solution to a long-standing open problem, but also provide methodological contributions to error-invariable regression, including local polynomial estimation of derivative functions.
Indian Academy of Sciences (India)
OMPRAKASH TEMBHURNE; DEEPTI SHRIMANKAR
2017-07-01
A study of abundance estimation has vital importance in spectral unmixing of hyperspectral image. Recently, various methods have been proposed for spectral unmixing to achieve higher performance using an evolutionary approach. However, these methods are based on unconstrained optimisation problems. Theirperformance was also based on proper tuning parameters. We have proposed a new non-parametric algorithm using teaching-learning-based optimisation technique with an inbuilt constraints maintenance mechanism using the linear mixing model. In this approach, the unmixing problem is transformed into a combinatorial optimisation problem by introducing abundance sum to one constraint and abundance non-negative constraint. A comparative analysis of the proposed algorithm is conducted with other two state-of-the-art algorithms.Experimental results in known and unknown environments with varying signal-to-noise ratio on simulated and real hyper spectral data demonstrate that the proposed method outperforms the other methods.
Zhou, Si-Da; Heylen, Ward; Sas, Paul; Liu, Li
2014-05-01
This paper investigates the problem of modal parameter estimation of time-varying structures under unknown excitation. A time-frequency-domain maximum likelihood estimator of modal parameters for linear time-varying structures is presented by adapting the frequency-domain maximum likelihood estimator to the time-frequency domain. The proposed estimator is parametric, that is, the linear time-varying structures are represented by a time-dependent common-denominator model. To adapt the existing frequency-domain estimator for time-invariant structures to the time-frequency methods for time-varying cases, an orthogonal polynomial and z-domain mapping hybrid basis function is presented, which has the advantageous numerical condition and with which it is convenient to calculate the modal parameters. A series of numerical examples have evaluated and illustrated the performance of the proposed maximum likelihood estimator, and a group of laboratory experiments has further validated the proposed estimator.
Zheng, Yu-Lin; Zhen, Yi-Zheng; Chen, Zeng-Bing; Liu, Nai-Le; Chen, Kai; Pan, Jian-Wei
2017-01-01
The striking and distinctive nonlocal features of quantum mechanics were discovered by Einstein, Podolsky, and Rosen (EPR) beyond classical physics. At the core of the EPR argument, it was "steering" that Schrödinger proposed in 1935. Besides its fundamental significance, quantum steering opens up a novel application for quantum communication. Recent work has precisely characterized its properties; however, witnessing the EPR nonlocality remains a big challenge under arbitrary local measurements. Here we present an alternative linear criterion and complement existing results to efficiently testify steering for high-dimensional system in practice. By developing a novel and analytical method to tackle the maximization problem in deriving the bound of a steering criterion, we show how observed correlations can reveal powerfully the EPR nonlocality in an easily accessed manner. Although the criteria is not necessary and sufficient, it can recover some of the known results under a few settings of local measurements and is applicable even if the size of the system or the number of measurement settings are high. Remarkably, a deep connection is explicitly established between the steering and amount of entanglement. The results promise viable paths for secure communication with an untrusted source, providing optional loophole-free tests of the EPR nonlocality for high-dimensional states, as well as motivating solutions for other related problems in quantum information theory.
Institute of Scientific and Technical Information of China (English)
2008-01-01
In this paper,we explore some weakly consistent properties of quasi-maximum likelihood estimates(QMLE) concerning the quasi-likelihood equation in=1 Xi(yi-μ(Xiβ)) = 0 for univariate generalized linear model E(y |X) = μ(X’β).Given uncorrelated residuals {ei = Yi-μ(Xiβ0),1 i n} and other conditions,we prove that βn-β0 = Op(λn-1/2) holds,where βn is a root of the above equation,β0 is the true value of parameter β and λn denotes the smallest eigenvalue of the matrix Sn = ni=1 XiXi.We also show that the convergence rate above is sharp,provided independent non-asymptotically degenerate residual sequence and other conditions.Moreover,paralleling to the elegant result of Drygas(1976) for classical linear regression models,we point out that the necessary condition guaranteeing the weak consistency of QMLE is Sn-1→ 0,as the sample size n →∞.
Institute of Scientific and Technical Information of China (English)
ZHANG SanGuo; LIAO Yuan
2008-01-01
In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates(QMLE)concerning the quasi-likelihood equation ∑ni=1 Xi(yi-μ(X1iβ)) =0 for univariate generalized linear model E(y|X) =μ(X1β). Given uncorrelated residuals{ei=Yi-μ(X1iβ0), 1≤i≤n}and other conditions, we prove that (β)n-β0=Op(λ--1/2n)holds, where (β)n is a root of the above equation,β0 is the true value of parameter β and λ-n denotes the smallest eigenvalue of the matrix Sn=Σni=1 XiX1i. We also show that the convergence rate above is sharp, provided independent nonasymptotically degenerate residual sequence and other conditions. Moreover, paralleling to the elegant result of Drygas(1976)for classical linear regression models,we point out that the necessary condition guaranteeing the weak consistency of QMLE is S-1n→0, as the sample size n→∞.
Nohara, Yoshiro; Andersen, O. K.
2016-08-01
A method for 3D interpolation between hard spheres is described. The function to be interpolated could be the charge density between atoms in condensed matter. Its electrostatic potential is found analytically, and so are various integrals. Periodicity is not required. The interpolation functions are localized structure-adapted linear combinations of spherical waves, the so-called unitary spherical waves (USWs), ψR L(" close=")ɛn)">ɛ ,r , centered at the spheres R , where they have cubic-harmonic character L . Input to the interpolation are the coefficients in the cubic-harmonic expansions of the target function at and slightly outside the spheres; specifically, the values and the three first radial derivatives labeled by d =0 (value) and 1-3 (derivatives). To fit this, we use USWs with four negative energies, ɛ =ɛ1,ɛ2,ɛ3 , and ɛ4. Each interpolation function, ϱd R L(r ), is actually a linear combination of these four sets of USWs with the following properties. (1) It is centered at a specific sphere where it has a specific cubic-harmonic character and radial derivative. (2) Its value and the first three radial derivatives vanish at all other spheres and for all other cubic-harmonic characters, and is therefore highly localized, essentially inside its Voronoi cell. Value-and-derivative (v&d) functions were originally introduced and used by Methfessel [Phys. Rev. B 38, 1537 (1988), 10.1103/PhysRevB.38.1537], but only for the first radial derivative. Explicit expressions are given for the v&d functions and their Coulomb potentials in terms of the USWs at the four energies, plus ɛ0≡0 for the potentials. The coefficients, as well as integrals over the interstitial such as the electrostatic energy, are given entirely in terms of the structure matrix, SR L ,R'L', describing the slopes of the USWs at the five energies and their expansions in Hankel functions. For open structures, additional constraints are installed to pinpoint the interpolated function deep
Estimating Independent Locally Shifted Random Utility Models for Ranking Data
Lam, Kar Yin; Koning, Alex J.; Franses, Philip Hans
2011-01-01
We consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we avoided the computation of high-dimensional integrals. We extended the approximation technique proposed by Henery (1981) in the context of the Thurstone-Mosteller-Daniels model to any…
ACCES: Offline Accuracy Estimation for Fingerprint-Based Localization
DEFF Research Database (Denmark)
Nikitin, Artyom; Laoudias, Christos; Chatzimilioudis, Georgios
2017-01-01
will be able to use our service directly to collect signal measurements over the venue using an Android smartphone; and (ii) Reflection Mode, where attendees will be able to observe the collected measurements and the respective ACCES accuracy estimations in the form of an overlay heatmap....
A new stylolite classification scheme to estimate compaction and local permeability variations
Koehn, D.; Rood, M. P.; Beaudoin, N.; Chung, P.; Bons, P. D.; Gomez-Rivas, E.
2016-12-01
We modeled the geometrical roughening of bedding-parallel, mainly layer-dominated stylolites in order to understand their structural evolution, to present an advanced classification of stylolite shapes and to relate this classification to chemical compaction and permeability variations at stylolites. Stylolites are rough dissolution seams that develop in sedimentary basins during chemical compaction. In the Zechstein 2 carbonate units, an important lean gas reservoir in the southern Permian Zechstein basin in Germany, stylolites influence local fluid flow, mineral replacement reactions and hence the permeability of the reservoir. Our simulations demonstrate that layer-dominated stylolites can grow in three distinct stages: an initial slow nucleation phase, a fast layer-pinning phase and a final freezing phase if the layer is completely dissolved during growth. Dissolution of the pinning layer and thus destruction of the stylolite's compaction tracking capabilities is a function of the background noise in the rock and the dissolution rate of the layer itself. Low background noise needs a slower dissolving layer for pinning to be successful but produces flatter teeth than higher background noise. We present an advanced classification based on our simulations and separate stylolites into four classes: (1) rectangular layer type, (2) seismogram pinning type, (3) suture/sharp peak type and (4) simple wave-like type. Rectangular layer type stylolites are the most appropriate for chemical compaction estimates because they grow linearly and record most of the actual compaction (up to 40 mm in the Zechstein example). Seismogram pinning type stylolites also provide good tracking capabilities, with the largest teeth tracking most of the compaction. Suture/sharp peak type stylolites grow in a non-linear fashion and thus do not record most of the actual compaction. However, when a non-linear growth law is used, the compaction estimates are similar to those making use of the
Second order average estimates on local data of cusp forms
2005-01-01
We specify sufficient conditions for the square modulus of the local parameters of a family of GL(n) cusp forms to be bounded on average. These conditions are global in nature and are at present satisfied for n less than or equal to 4. As an application, we show that Rankin-Selberg L-functions on GL(m) x GL(n), when m and n are less than or equal to 4, satisfy the standard convexity bound.
Localization of periodic orbits of polynomial systems by ellipsoidal estimates
Energy Technology Data Exchange (ETDEWEB)
Starkov, Konstantin E. [CITEDI-IPN, Avenue del Parque 1310, Mesa de Otay, Tijuana, BC (Mexico)]. E-mail: konst@citedi.mx; Krishchenko, Alexander P. [Bauman Moscow State Technical University, 2nd Baumanskaya Street, 5, Moscow 105005 (Russian Federation)]. E-mail: apkri@999.ru
2005-02-01
In this paper we study the localization problem of periodic orbits of multidimensional continuous-time systems in the global setting. Our results are based on the solution of the conditional extremum problem and using sign-definite quadratic and quartic forms. As examples, the Rikitake system and the Lamb's equations for a three-mode operating cavity in a laser are considered.
On the error of estimating the sparsest solution of underdetermined linear systems
Babaie-Zadeh, Massoud; Mohimani, Hosein
2011-01-01
Let A be an n by m matrix with m>n, and suppose that the underdetermined linear system As=x admits a sparse solution s0 for which ||s0||_0 < 1/2 spark(A). Such a sparse solution is unique due to a well-known uniqueness theorem. Suppose now that we have somehow a solution s_hat as an estimation of s0, and suppose that s_hat is only `approximately sparse', that is, many of its components are very small and nearly zero, but not mathematically equal to zero. Is such a solution necessarily close to the true sparsest solution? More generally, is it possible to construct an upper bound on the estimation error ||s_hat-s0||_2 without knowing s0? The answer is positive, and in this paper we construct such a bound based on minimal singular values of submatrices of A. We will also state a tight bound, which is more complicated, but besides being tight, enables us to study the case of random dictionaries and obtain probabilistic upper bounds. We will also study the noisy case, that is, where x=As+n. Moreover, we will s...
Ferranti, Francesco; Rolain, Yves
2017-01-01
This paper proposes a novel state-space matrix interpolation technique to generate linear parameter-varying (LPV) models starting from a set of local linear time-invariant (LTI) models estimated at fixed operating conditions. Since the state-space representation of LTI models is unique up to a similarity transformation, the state-space matrices need to be represented in a common state-space form. This is needed to avoid potentially large variations as a function of the scheduling parameters of the state-space matrices to be interpolated due to underlying similarity transformations, which might degrade the accuracy of the interpolation significantly. Underlying linear state coordinate transformations for a set of local LTI models are extracted by the computation of similarity transformation matrices by means of linear least-squares approximations. These matrices are then used to transform the local LTI state-space matrices into a form suitable to achieve accurate interpolation results. The proposed LPV modeling technique is validated by pertinent numerical results.
DEFF Research Database (Denmark)
Mohd. Azam, Sazuan Nazrah
2017-01-01
In this paper, we used the modified quadruple tank system that represents a multi-input-multi-output (MIMO) system as an example to present the realization of a linear discrete-time state space model and to obtain the state estimation using Kalman filter in a methodical mannered. First, an existing...... dynamics of the system of stochastic differential equations is linearized to produce the deterministic-stochastic linear transfer function. Then the linear transfer function is discretized to produce a linear discrete-time state space model that has a deterministic and a stochastic component. The filtered...... part of the Kalman filter is used to estimates the current state, based on the model and the measurements. The static and dynamic Kalman filter is compared and all results is demonstrated through simulations....
Azam, Sazuan N. M.
2017-01-01
In this paper, we used the modified quadruple tank system that represents a multi-input-multi-output (MIMO) system as an example to present the realization of a linear discrete-time state space model and to obtain the state estimation using Kalman filter in a methodical mannered. First, an existing dynamics of the system of stochastic differential equations is linearized to produce the deterministic-stochastic linear transfer function. Then the linear transfer function is discretized to produce a linear discrete-time state space model that has a deterministic and a stochastic component. The filtered part of the Kalman filter is used to estimates the current state, based on the model and the measurements. The static and dynamic Kalman filter is compared and all results is demonstrated through simulations.
带线性约束的新两参数估计%New Two Parameters Estimation for the Linear Model with Linear Restrictions
Institute of Scientific and Technical Information of China (English)
郭淑妹; 顾勇为; 郭杰
2013-01-01
针对带约束的最小二乘估计在参数估计中处理复共线性的不足，引入随机线性约束，提出了约束新两参数估计。并且得到在均方误差下，约束新两参数估计与约束最小二乘估计，约束岭估计和约束Liu估计相比的优良性。%In order to overcome the shortage of the multicollinearity in ordinary restricted least square estimation with parameter estimate based on the stochastic linear restrictions,a new estimation as restricted linear new two parameters estimation is proposed. In the mean squared error sense,compared with the properties the ordinary restricted least squares estimation,and the restricted ridge estimation,the method we proposed was superior.
Institute of Scientific and Technical Information of China (English)
YANGXiao-Jun; WENGZheng-Xin; TIANZuo-Hua; SHISong-Jiao
2005-01-01
The H∞ hybrid estimation problem for linear continuous time-varying systems is investigated in this paper, where estimated signals are linear combination of state and input. Design objective requires the worst-case energy gain from disturbance to estimation error be less than a prescribed level. Optimal solution of the hybrid estimation problem is the saddle point of a two-player zero sum differential game. Based on the differential game approach, necessary and sufficient solvable conditions for the hybrid estimation problem are provided in terms of solutions to a Riccati differential equation. Moreover, one possible estimator is proposed if the solvable conditions are satisfied.The estimator is characterized by a gain matrix and an output mapping matrix that reflects the internal relations between the unknown input and output estimation error. Both state and unknown inputs estimation are realized by the proposed estimator. Thus, the results in this paper are also capable of dealing with fault diagnosis problems of linear time-varying systems. At last, a numerical example is provided to illustrate the proposed approach.
Hu, L; Zhang, Z G; Mouraux, A; Iannetti, G D
2015-05-01
Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical
Localization of acoustic sensors from passive Green's function estimation.
Nowakowski, Thibault; Daudet, Laurent; de Rosny, Julien
2015-11-01
A number of methods have recently been developed for passive localization of acoustic sensors, based on the assumption that the acoustic field is diffuse. This article presents the more general case of equipartition fields, which takes into account reflections off boundaries and/or scatterers. After a thorough discussion on the fundamental differences between the diffuse and equipartition models, it is shown that the method is more robust when dealing with wideband noise sources. Finally, experimental results show, for two types of boundary conditions, that this approach is especially relevant when acoustic sensors are close to boundaries.
Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration
Douglas, Craig C.
2010-01-01
We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Fréchet derivative when calculating the Jacobian matrix. We analyze the existence of the Fréchet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method. ©2010 IEEE.
2002-01-01
This paper presents recursive least-squares (RLS) estimation algorithms using the covariance information in linear discrete-time distributed parameter systems. The signal is estimated with the observations containing some uncertain observations. In the uncertain observations, there are cases where the observed value does not contain the signal and consists of observation noise only. The probability that the signal exists in the observed value is used in the estimation algorithms. The algorith...
Multi-person localization and orientation estimation in volumetric scene reconstructions
Liem, M.C.
2014-01-01
Accurate localization of persons and estimation of their pose are important topics in current-day computer vision research. As part of the pose estimation, estimating the body orientation of a person (i.e. rotation around torso major axis) conveys important information about the person's current act
Directory of Open Access Journals (Sweden)
J. Szilagyi
2009-05-01
Full Text Available Under simplifying conditions catchment-scale vapor pressure at the drying land surface can be calculated as a function of its watershed-representative temperature (<T_{s}> by the wet-surface equation (WSE, similar to the wet-bulb equation in meteorology for calculating the dry-bulb thermometer vapor pressure of the Complementary Relationship of evaporation. The corresponding watershed ET rate,
Directory of Open Access Journals (Sweden)
Kunju Shi
2014-01-01
Full Text Available Dimensionality reduction is a crucial task in machinery fault diagnosis. Recently, as a popular dimensional reduction technology, manifold learning has been successfully used in many fields. However, most of these technologies are not suitable for the task, because they are unsupervised in nature and fail to discover the discriminate structure in the data. To overcome these weaknesses, kernel local linear discriminate (KLLD algorithm is proposed. KLLD algorithm is a novel algorithm which combines the advantage of neighborhood preserving projections (NPP, Floyd, maximum margin criterion (MMC, and kernel trick. KLLD has four advantages. First of all, KLLD is a supervised dimension reduction method that can overcome the out-of-sample problems. Secondly, short-circuit problem can be avoided. Thirdly, KLLD algorithm can use between-class scatter matrix and inner-class scatter matrix more efficiently. Lastly, kernel trick is included in KLLD algorithm to find more precise solution. The main feature of the proposed method is that it attempts to both preserve the intrinsic neighborhood geometry of the increased data and exact the discriminate information. Experiments have been performed to evaluate the new method. The results show that KLLD has more benefits than traditional methods.
Applying a Locally Linear Embedding Algorithm for Feature Extraction and Visualization of MI-EEG
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Mingai Li
2016-01-01
Full Text Available Robotic-assisted rehabilitation system based on Brain-Computer Interface (BCI is an applicable solution for stroke survivors with a poorly functioning hemiparetic arm. The key technique for rehabilitation system is the feature extraction of Motor Imagery Electroencephalography (MI-EEG, which is a nonlinear time-varying and nonstationary signal with remarkable time-frequency characteristic. Though a few people have made efforts to explore the nonlinear nature from the perspective of manifold learning, they hardly take into full account both time-frequency feature and nonlinear nature. In this paper, a novel feature extraction method is proposed based on the Locally Linear Embedding (LLE algorithm and DWT. The multiscale multiresolution analysis is implemented for MI-EEG by DWT. LLE is applied to the approximation components to extract the nonlinear features, and the statistics of the detail components are calculated to obtain the time-frequency features. Then, the two features are combined serially. A backpropagation neural network is optimized by genetic algorithm and employed as a classifier to evaluate the effectiveness of the proposed method. The experiment results of 10-fold cross validation on a public BCI Competition dataset show that the nonlinear features visually display obvious clustering distribution and the fused features improve the classification accuracy and stability. This paper successfully achieves application of manifold learning in BCI.
Angeli, Celestino; Sparta, Manuel; Cimiraglia, Renzo
2006-03-01
A recently proposed a priori localization technique is used to exploit the possibility to reduce the number of active orbitals in a Complete Active Space Self Consistent Field calculation. The work relies on the fact that the new approach allows a strict control on the nature of the active orbitals and therefore makes it possible to include in the active space only the relevant orbitals. The idea is tested on the calculation of the energy barrier for rigid rotation of linear polyenes. In order to obtain a relevant set of data, a number of possible rotations around double bonds have been considered in the ethylene, butadiene, hexatriene, octatetraene, decapentaene, dodecahexaene molecules. The possibility to reduce the dimension of the active space has been investigated, considering for each possible rotation different active spaces ranging from the minimal dimension of 2 electrons in 2 π orbitals to the π-complete space. The results show that the rigid isomerization in the polyene molecules can be described with a negligible loss in accuracy with active spaces no larger than ten orbitals and ten electrons. In the special case of the rotation around the terminal double bond, the space can be further reduced to six orbitals and six electrons with a large decrease of the computational cost. An interesting summation rule has been found and verified for the stabilization of the energy barriers as a function of the dimension of the conjugated lateral chains and of the dimension of the active space.
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Jessamine P Winer
Full Text Available Most tissue cells grown in sparse cultures on linearly elastic substrates typically display a small, round phenotype on soft substrates and become increasingly spread as the modulus of the substrate increases until their spread area reaches a maximum value. As cell density increases, individual cells retain the same stiffness-dependent differences unless they are very close or in molecular contact. On nonlinear strain-stiffening fibrin gels, the same cell types become maximally spread even when the low strain elastic modulus would predict a round morphology, and cells are influenced by the presence of neighbors hundreds of microns away. Time lapse microscopy reveals that fibroblasts and human mesenchymal stem cells on fibrin deform the substrate by several microns up to five cell lengths away from their plasma membrane through a force limited mechanism. Atomic force microscopy and rheology confirm that these strains locally and globally stiffen the gel, depending on cell density, and this effect leads to long distance cell-cell communication and alignment. Thus cells are acutely responsive to the nonlinear elasticity of their substrates and can manipulate this rheological property to induce patterning.
Localization of twisted N=(0,2) gauged linear sigma models in two dimensions
Energy Technology Data Exchange (ETDEWEB)
Closset, Cyril [Simons Center for Geometry and Physics, State University of New York, Stony Brook, NY 11794 (United States); Gu, Wei [Department of Physics MC 0435, Virginia Tech, 850 West Campus Drive, Blacksburg, VA 24061 (United States); Jia, Bei [Theory Group, Physics Department, University of Texas, Austin, TX 78612 (United States); Sharpe, Eric [Department of Physics MC 0435, Virginia Tech, 850 West Campus Drive, Blacksburg, VA 24061 (United States)
2016-03-14
We study two-dimensional N=(0,2) supersymmetric gauged linear sigma models (GLSMs) using supersymmetric localization. We consider N=(0,2) theories with an R-symmetry, which can always be defined on curved space by a pseudo-topological twist while preserving one of the two supercharges of flat space. For GLSMs which are deformations of N=(2,2) GLSMs and retain a Coulomb branch, we consider the A/2-twist and compute the genus-zero correlation functions of certain pseudo-chiral operators, which generalize the simplest twisted chiral ring operators away from the N=(2,2) locus. These correlation functions can be written in terms of a certain residue operation on the Coulomb branch, generalizing the Jeffrey-Kirwan residue prescription relevant for the N=(2,2) locus. For abelian GLSMs, we reproduce existing results with new formulas that render the quantum sheaf cohomology relations and other properties manifest. For non-abelian GLSMs, our methods lead to new results. As an example, we briefly discuss the quantum sheaf cohomology of the Grassmannian manifold.
Lazo, Edmundo; Garrido, Alejandro; Neira, Félix
2016-11-01
This study investigates the localization properties of dual electric transmission lines with non-linear capacitances. The VC,n voltage across each capacitor is selected as a non-linear function of the electric charge qn, i.e., VC,n = qn(1/Cn -ɛn|qn|2) where Cn is the linear part of the capacitance and ɛn the amplitude of the non-linear term. We follow a binary distribution of values of ɛn, according to the Thue-Morse m-tupling sequence. The localization behavior of this non-linear case indicates that the case m = 2 does not belong to the m ≥ 3, family because when m changes from m = 2 to m = 3, the number of extended states diminishes dramatically. This proves the topological difference of the m = 2 and m = 3 families. However, by increasing m values, localization behavior of the m-tupling family resembles that of the m = 2, case because the system begins to regain its extended states. The exact same result was obtained recently in the study of linear direct transmission lines with m-tupling distribution of inductances. Consequently, we state that the localization behavior of the m-tupling family as a function of the m value is independent of both the linear and the non-linear system under study, but independent of the kind of transmission line (dual or direct). This is curious behavior of the m-tupling family and thus deserves more scholarly attention.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A noninterior continuation method is proposed for semidefinite complementarity problem (SDCP). This method improves the noninterior continuation methods recently developed for SDCP by Chen and Tseng. The main properties of our method are: (i)it is well defined for the monotones SDCP; (ii) it has to solve just one linear system of equations at each step; (iii) it is shown to be both globally linearly convergent and locally quadratically convergent under suitable assumptions.
Estimation of health benefits from a local living wage ordinance.
Bhatia, R; Katz, M
2001-09-01
This study estimated the magnitude of health improvements resulting from a proposed living wage ordinance in San Francisco. Published observational models of the relationship of income to health were applied to predict improvements in health outcomes associated with proposed wage increases in San Francisco. With adoption of a living wage of $11.00 per hour, we predict decreases in premature death from all causes for adults aged 24 to 44 years working full-time in families whose current annual income is $20,000 (for men, relative hazard [RH] = 0.94, 95% confidence interval [CI] = 0.92, 0.97; for women, RH = 0.96, 95% CI = 0.95, 0.98). Improvements in subjectively rated health and reductions in the number of days sick in bed, in limitations of work and activities of daily living, and in depressive symptoms were also predicted, as were increases in daily alcohol consumption. For the offspring of full-time workers currently earning $20,000, a living wage predicts an increase of 0.25 years (95% CI = 0.20, 0.30) of completed education, increased odds of completing high school (odds ratio = 1.34, 95% CI = 1.20, 1.49), and a reduced risk of early childbirth (RH = 0.78, 95% CI = 0.69, 0.86). A living wage in San Francisco is associated with substantial health improvement.
Frommer, A; Lippert, Th; Rittich, H
2012-01-01
The Lanczos process constructs a sequence of orthonormal vectors v_m spanning a nested sequence of Krylov subspaces generated by a hermitian matrix A and some starting vector b. In this paper we show how to cheaply recover a secondary Lanczos process, starting at an arbitrary Lanczos vector v_m and how to use this secondary process to efficiently obtain computable error estimates and error bounds for the Lanczos approximations to a solution of a linear system Ax = b as well as, more generally, for the Lanczos approximations to the action of a rational matrix function on a vector. Our approach uses the relation between the Lanczos process and quadrature as developed by Golub and Meurant. It is different from methods known so far because of its use of the secondary Lanczos process. With our approach, it is now in particular possible to efficiently obtain upper bounds for the error in the 2-norm, provided a lower bound on the smallest eigenvalue of A is known. This holds for the error of the cg iterates as well ...
Directory of Open Access Journals (Sweden)
Do-Sik Yoo
2015-01-01
Full Text Available We propose a low complexity subspace-based direction-of-arrival (DOA estimation algorithm employing a direct signal space construction method (DSPCM by subsampling the autocorrelation matrix of a uniform linear array (ULA. Three major contributions of this paper are as follows. First of all, we introduce the method of autocorrelation matrix subsampling which enables us to employ a low complexity algorithm based on a ULA without computationally complex eigenvalue decomposition or singular-value decomposition. Secondly, we introduce a signal vector separation method to improve the distinguishability among signal vectors, which can greatly improve the performance, particularly, in low signal-to-noise ratio (SNR regime. Thirdly, we provide a root finding (RF method in addition to a spectral search (SS method as the angle finding scheme. Through simulations, we illustrate that the performance of the proposed scheme is reasonably close to computationally much more expensive MUSIC- (MUltiple SIgnal Classification- based algorithms. Finally, we illustrate that the computational complexity of the proposed scheme is reduced, in comparison with those of MUSIC-based schemes, by a factor of O(N2/K, where K is the number of sources and N is the number of antenna elements.
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Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Non-linear parameter estimation for the LTP experiment: analysis of an operational exercise
Congedo, G; Ferraioli, L; Hueller, M; Vitale, S; Hewitson, M; Nofrarias, M; Monsky, A; Armano, M; Grynagier, A; Diaz-Aguilo, M; Plagnol, E; Rais, B
2011-01-01
The precursor ESA mission LISA-Pathfinder, to be flown in 2013, aims at demonstrating the feasibility of the free-fall, necessary for LISA, the upcoming space-born gravitational wave observatory. LISA Technology Package (LTP) is planned to carry out a number of experiments, whose main targets are to identify and measure the disturbances on each test-mass, in order to reach an unprecedented low-level residual force noise. To fulfill this plan, it is then necessary to correctly design, set-up and optimize the experiments to be performed on-flight and do a full system parameter estimation. Here we describe the progress on the non-linear analysis using the methods developed in the framework of the \\textit{LTPDA Toolbox}, an object-oriented MATLAB Data Analysis environment: the effort is to identify the critical parameters and remove the degeneracy by properly combining the results of different experiments coming from a closed-loop system like LTP.
Urrutia, Jackie D.; Tampis, Razzcelle L.; Mercado, Joseph; Baygan, Aaron Vito M.; Baccay, Edcon B.
2016-02-01
The objective of this research is to formulate a mathematical model for the Philippines' Real Gross Domestic Product (Real GDP). The following factors are considered: Consumers' Spending (x1), Government's Spending (x2), Capital Formation (x3) and Imports (x4) as the Independent Variables that can actually influence in the Real GDP in the Philippines (y). The researchers used a Normal Estimation Equation using Matrices to create the model for Real GDP and used α = 0.01.The researchers analyzed quarterly data from 1990 to 2013. The data were acquired from the National Statistical Coordination Board (NSCB) resulting to a total of 96 observations for each variable. The data have undergone a logarithmic transformation particularly the Dependent Variable (y) to satisfy all the assumptions of the Multiple Linear Regression Analysis. The mathematical model for Real GDP was formulated using Matrices through MATLAB. Based on the results, only three of the Independent Variables are significant to the Dependent Variable namely: Consumers' Spending (x1), Capital Formation (x3) and Imports (x4), hence, can actually predict Real GDP (y). The regression analysis displays that 98.7% (coefficient of determination) of the Independent Variables can actually predict the Dependent Variable. With 97.6% of the result in Paired T-Test, the Predicted Values obtained from the model showed no significant difference from the Actual Values of Real GDP. This research will be essential in appraising the forthcoming changes to aid the Government in implementing policies for the development of the economy.
Silva, Juan P.; Lasso, Ana; Lubberding, Henk J.; Peña, Miguel R.; Gijzen, Hubert J.
2015-05-01
The closed static chamber technique is widely used to quantify greenhouse gases (GHG) i.e. CH4, CO2 and N2O from aquatic and wastewater treatment systems. However, chamber-measured fluxes over air-water interfaces appear to be subject to considerable uncertainty, depending on the chamber design, lack of air mixing in the chamber, concentration gradient changes during the deployment, and irregular eruptions of gas accumulated in the sediment. In this study, the closed static chamber technique was tested in an anaerobic pond operating under tropical conditions. The closed static chambers were found to be reliable to measure GHG, but an intrinsic limitation of using closed static chambers is that not all the data for gas concentrations measured within a chamber headspace can be used to estimate the flux due to gradient concentration curves with non-plausible and physical explanations. Based on the total data set, the percentage of curves accepted was 93.6, 87.2, and 73% for CH4, CO2 and N2O, respectively. The statistical analyses demonstrated that only considering linear regression was inappropriate (i.e. approximately 40% of the data for CH4, CO2 and N2O were best fitted to a non-linear regression) for the determination of GHG flux from stabilization ponds by the closed static chamber technique. In this work, it is clear that when R2adj-non-lin > R2adj-lin, the application of linear regression models is not recommended, as it leads to an underestimation of GHG fluxes by 10-50%. This suggests that adopting only or mostly linear regression models will affect the GHG inventories obtained by using closed static chambers. According to our results, the misuse of the usual R2 parameter and only the linear regression model to estimate the fluxes will lead to reporting erroneous information on the real contribution of GHG emissions from wastewater. Therefore, the R2adj and non-linear regression model analysis should be used to reduce the biases in flux estimation by the
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method.The estimators are constructed based on the observations to ensure unbiasedness in the sense that the estimators in a certain class have the same expectation as the mean survival time.The estimators have good properties such as strong consistency (with the rate of O(n-1/2 (log log n)1/2)) and asymptotic normality.The application to linear regression is considered and the simulation reports are given.
Avramopoulos, A; Papadopoulos, M G; Reis, H
2007-03-15
A discrete model based on the multipolar expansion including terms up to hexadecapoles was employed to describe the electrostatic interactions in liquid acetonitrile. Liquid structures obtained form molecular dynamics simulations with different classical, nonpolarizable potentials were used to analyze the electrostatic interactions. The computed average local field was employed for the determination of the environmental effects on the linear and nonlinear electrical molecular properties. Dipole-dipole interactions yield the dominant contribution to the local field, whereas higher multipolar contributions are small but not negligible. Using the effective in-phase properties, macroscopic linear and nonlinear susceptibilities of the liquid were computed. Depending on the partial charges describing the Coulomb interactions of the force field employed, either the linear properties (refractive index and dielectric constant) were reproduced in good agreement with experiment or the nonlinear properties [third-harmonic generation (THG) and electric field induced second-harmonic (EFISH) generation] and the bulk density but never both sets of properties together. It is concluded that the partial charges of the force fields investigated are not suitable for reliable dielectric properties. New methods are probably necessary for the determination of partial charges, which should take into account the collective and long-range nature of electrostatic interactions more precisely.
Sanyal, Amit K.
2005-01-01
There are several attitude estimation algorithms in existence, all of which use local coordinate representations for the group of rigid body orientations. All local coordinate representations of the group of orientations have associated problems. While minimal coordinate representations exhibit kinematic singularities for large rotations, the quaternion representation requires satisfaction of an extra constraint. This paper treats the attitude estimation and filtering problem as an optimizati...
DOA Estimation for Local Scattered CDMA Signals by Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Jhih-Chung Chang
2012-03-01
Full Text Available This paper deals with the direction-of-arrival (DOA estimation of local scattered code-division multiple access (CDMA signals based on a particle swarm optimization (PSO search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison.
DOA estimation for local scattered CDMA signals by particle swarm optimization.
Chang, Jhih-Chung
2012-01-01
This paper deals with the direction-of-arrival (DOA) estimation of local scattered code-division multiple access (CDMA) signals based on a particle swarm optimization (PSO) search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison.
Distributed parameter estimation in wireless sensor networks using fused local observations
Fanaei, Mohammad; Valenti, Matthew C.; Schmid, Natalia A.; Alkhweldi, Marwan M.
2012-05-01
The goal of this paper is to reliably estimate a vector of unknown deterministic parameters associated with an underlying function at a fusion center of a wireless sensor network based on its noisy samples made at distributed local sensors. A set of noisy samples of a deterministic function characterized by a nite set of unknown param- eters to be estimated is observed by distributed sensors. The parameters to be estimated can be some attributes associated with the underlying function, such as its height, its center, its variances in dierent directions, or even the weights of its specic components over a predened basis set. Each local sensor processes its observation and sends its processed sample to a fusion center through parallel impaired communication channels. Two local processing schemes, namely analog and digital, are considered. In the analog local processing scheme, each sensor transmits an amplied version of its local analog noisy observation to the fusion center, acting like a relay in a wireless network. In the digital local processing scheme, each sensor quantizes its noisy observation before trans- mitting it to the fusion center. A at-fading channel model is considered between the local sensors and fusion center. The fusion center combines all of the received locally-processed observations and estimates the vector of unknown parameters of the underlying function. Two dierent well-known estimation techniques, namely maximum-likelihood (ML), for both analog and digital local processing schemes, and expectation maximization (EM), for digital local processing scheme, are considered at the fusion center. The performance of the proposed distributed parameter estimation system is investigated through simulation of practical scenarios for a sample underlying function.
Estimating organic, local, and other price premiums in the Hawaii fluid milk market.
Loke, Matthew K; Xu, Xun; Leung, PingSun
2015-04-01
With retail scanner data, we applied hedonic price modeling to explore price premiums for organic, local, and other product attributes of fluid milk in Hawaii. Within the context of revealed preference, this analysis of organic and local attributes, under a single unified framework, is significant, as research in this area is deficient in the existing literature. This paper finds both organic and local attributes delivered price premiums over imported, conventional, whole fluid milk. However, the estimated price premium for organic milk (24.6%) is significantly lower than findings in the existing literature. Likewise, the price premium for the local attribute is estimated at 17.4%, again substantially lower compared with an earlier, stated preference study in Hawaii. Beyond that, we estimated a robust price premium of 19.7% for nutritional benefits claimed. The magnitude of this estimated coefficient reinforces the notion that nutrition information on food is deemed beneficial and valuable. Finally, package size measures the influence of product weight. With each larger package size, the estimate led to a corresponding larger price discount. This result is consistent with the practice of weight discounting that retailers usually offer with fresh packaged food. Additionally, we estimated a fairly high Armington elasticity of substitution, which suggests a relatively high degree of substitution between local and imported fluid milk when their relative price changes. Overall, this study establishes price premiums for organic, local, and nutrition benefits claimed for fluid milk in Hawaii.
Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery
Stagakis, Stavros; Vanikiotis, Theofilos; Sykioti, Olga
2016-09-01
The advancing technology of hyperspectral remote sensing offers the opportunity of accurate land cover characterization of complex natural environments. In this study, a linear spectral unmixing algorithm that incorporates a novel hierarchical Bayesian approach (BI-ICE) was applied on two spatially and temporally adjacent CHRIS/PROBA images over a forest in North Pindos National Park (Epirus, Greece). The scope is to investigate the potential of this algorithm to discriminate two different forest species (i.e. beech - Fagus sylvatica, pine - Pinus nigra) and produce accurate species-specific abundance maps. The unmixing results were evaluated in uniformly distributed plots across the test site using measured fractions of each species derived by very high resolution aerial orthophotos. Landsat-8 images were also used to produce a conventional discrete-type classification map of the test site. This map was used to define the exact borders of the test site and compare the thematic information of the two mapping approaches (discrete vs abundance mapping). The required ground truth information, regarding training and validation of the applied mapping methodologies, was collected during a field campaign across the study site. Abundance estimates reached very good overall accuracy (R2 = 0.98, RMSE = 0.06). The most significant source of error in our results was due to the shadowing effects that were very intense in some areas of the test site due to the low solar elevation during CHRIS acquisitions. It is also demonstrated that the two mapping approaches are in accordance across pure and dense forest areas, but the conventional classification map fails to describe the natural spatial gradients of each species and the actual species mixture across the test site. Overall, the BI-ICE algorithm presented increased potential to unmix challenging objects with high spectral similarity, such as different vegetation species, under real and not optimum acquisition conditions. Its
El Allaki, Farouk; Christensen, Jette; Vallières, André; Paré, Julie
2014-10-01
The objective of this study was to estimate the population size of Canadian poultry farms in 3 subpopulations (British Columbia, Ontario, and Other) by poultry category. We used data for 2008 to 2011 from the Canadian Notifiable Avian Influenza (NAI) Surveillance System (CanNAISS). Log-linear capture-recapture models were applied to estimate the number of commercial chicken and turkey farms. The estimated size of farm populations was validated by comparing sizes to data provided by the Canadian poultry industry in 2007, which were assumed to be complete and exhaustive. Our results showed that the log-linear modelling approach was an appropriate tool to estimate the population size of Canadian commercial chicken and turkey farms. The 2007 farm population size for each poultry category was included in the 95% confidence intervals of the farm population size estimates. Log-linear capture-recapture modelling might be useful for estimating the number of farms using surveillance data when no comprehensive registry exists.
Directory of Open Access Journals (Sweden)
N.G. HOSSEIN-ZADEH
2008-12-01
Full Text Available Data on stillbirth from the Animal Breeding Center of Iran collected from January 1990 to December 2007 and comprising 668810 Holstein calving events from 2506 herds were analyzed. Linear and threshold animal and sire models were used to estimate genetic parameters and genetic trends for stillbirth in the first, second, and third parities. Mean incidence of stillbirth decreased from first to third parities: 23.7%, 22.1%, and 21.8%, respectively. Phenotypic rates of stillbirth decreased from 1993 to 1998, for first, second and third calvings, and then increased from 1998 to 2007 for the first three parities. Direct heritability estimates of stillbirth for parities 1, 2 and 3 ranged from 2.2 to 8.7%, 0.6 to 5.1% and 0.1 to 3.8%, respectively, and maternal heritability estimates of stillbirth for parities 1, 2 and 3 ranged from 1.4 to 6.3%, 0.5 to 4.2% and 0.08 to 2.0%, respectively, using linear and threshold animal models. The threshold sire model estimates of heritabilities for stillbirth in this study were 0.021 to 0.071, while the linear sire model estimates of heritabilities for stillbirth in the current study were from 0.003 to 0.021 over the parities. There was a slightly increasing genetic trend for stillbirth rate in parities 1 and 2 over time with the analysis of linear animal and linear sire models. There was a significant decreasing genetic trend for stillbirth rate in parity 1 and 3 over time with the analysis of threshold animal and threshold sire models, but the genetic trend for stillbirth rate in parity 2 with these models of analysis was significantly positive. The low estimates of heritability obtained in this study implied that much of the improvement in stillbirth could be attained by improvement of production environment rather than genetic selection.;
A Simple Introduction to Moving Least Squares and Local Regression Estimation
Energy Technology Data Exchange (ETDEWEB)
Garimella, Rao Veerabhadra [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-06-22
In this brief note, a highly simpli ed introduction to esimating functions over a set of particles is presented. The note starts from Global Least Squares tting, going on to Moving Least Squares estimation (MLS) and nally, Local Regression Estimation (LRE).
DEFF Research Database (Denmark)
Jensen, Jørgen Juncher
2007-01-01
In on-board decision support systems efficient procedures are needed for real-time estimation of the maximum ship responses to be expected within the next few hours, given on-line information on the sea state and user defined ranges of possible headings and speeds. For linear responses standard...
Energy Technology Data Exchange (ETDEWEB)
Clark, G
2003-04-28
This report describes a feasibility study. We are interested in calculating the angular and linear velocities of a re-entry vehicle using six acceleration signals from a distributed accelerometer inertial measurement unit (DAIMU). Earlier work showed that angular and linear velocity calculation using classic nonlinear ordinary differential equation (ODE) solvers is not practically feasible, due to mathematical and numerical difficulties. This report demonstrates the theoretical feasibility of using model-based nonlinear state estimation techniques to obtain the angular and linear velocities in this problem. Practical numerical and calibration issues require additional work to resolve. We show that the six accelerometers in the DAIMU are not sufficient to provide observability, so additional measurements of the system states are required (e.g. from a Global Positioning System (GPS) unit). Given the constraint that our system cannot use GPS, we propose using the existing on-board 3-axis magnetometer to measure angular velocity. We further show that the six nonlinear ODE's for the vehicle kinematics can be decoupled into three ODE's in the angular velocity and three ODE's in the linear velocity. This allows us to formulate a three-state Gauss-Markov system model for the angular velocities, using the magnetometer signals in the measurement model. This re-formulated model is observable, allowing us to build an Extended Kalman Filter (EKF) for estimating the angular velocities. Given the angular velocity estimates from the EKF, the three ODE's for the linear velocity become algebraic, and the linear velocity can be calculated by numerical integration. Thus, we do not need direct measurements of the linear velocity to provide observability, and the technique is mathematically feasible. Using a simulation example, we show that the estimator adds value over the numerical ODE solver in the presence of measurement noise. Calculating the velocities in the
Sidik, S. M.
1975-01-01
Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.
污染线性模型的非参数估计%NON-PARAMETRIC ESTIMATION IN CONTAMINATED LINEAR MODEL
Institute of Scientific and Technical Information of China (English)
柴根象; 孙燕; 杨筱菡
2001-01-01
In this paper, the following contaminated linear model is considered: yi=(1-ε)xτiβ+zi, 1≤i≤n, where r.v.'s ｛yi｝ are contaminated with errors ｛zi｝. To assume that the errors have the finite moment of order 2 only. The non-parametric estimation of contaminated coefficient ε and regression parameter β are established, and the strong consistency and convergence rate almost surely of the estimators are obtained. A simulated example is also given to show the visual performance of the estimations.
Time-course window estimator for ordinary differential equations linear in the parameters
Vujacic, Ivan; Dattner, Itai; Gonzalez, Javier; Wit, Ernst
2015-01-01
In many applications obtaining ordinary differential equation descriptions of dynamic processes is scientifically important. In both, Bayesian and likelihood approaches for estimating parameters of ordinary differential equations, the speed and the convergence of the estimation procedure may crucial
Time-course window estimator for ordinary differential equations linear in the parameters
Vujacic, Ivan; Dattner, Itai; Gonzalez, Javier; Wit, Ernst
2015-01-01
In many applications obtaining ordinary differential equation descriptions of dynamic processes is scientifically important. In both, Bayesian and likelihood approaches for estimating parameters of ordinary differential equations, the speed and the convergence of the estimation procedure may
Estimation of central shapes of error distributions in linear regression problems
National Research Council Canada - National Science Library
Lai, P Y; Lee, Stephen M. S
2013-01-01
.... Both methods are motivated by the well-known Hill estimator, which has been extensively studied in the related problem of estimating tail indices, but substitute reciprocals of small L p residuals...
Ozheredov, V. A.; Breus, T. K.
2016-03-01
Several problems can emerge in front of investigators, who take a detailed restoration of dependency. The key of them - is a mathematically rigorous formulation of the desired degree of details. Second in importance is the reliability problem of reproduction of these details. And the third problem is the evaluation of data collection efforts that will ensure the desired depending on the required details and results reliability. In this work the strict concept of spatial resolution of the locally linear algorithm of direct dependence recovery (DDR) is formulated mathematically. Such approach implies approximation of the system reaction (dependent variable) in the case of the assigned value of factors which only utilizes the data (precedents) from a spherical cluster surrounding those assigned value of factors. The concept of reliability of details is formalized through the noise attenuation coefficient. We derive a relationship between the size of the minimum required database, spatial resolution of the recovery algorithm, the number of influencing factors and the noise attenuation coefficient. Analytical findings are verified by numerical experiments. Maximum number of factors, functional dependence on which can be recovered via the database figuring in various helio-biological works published by many authors for several 10 of years, is estimated. It is shown that the minimum required size of the database depends on the number of influencing factors (dimension of space of the independent variable) as a power law. The analysis conducted in this study reveals that the majority of the dimensional potentials of helio-biological databases are significantly higher that dimensions, which are appear in the approaches of authors of these works.
H\\"older Estimates for Singular Non-local Parabolic Equations
Kim, Sunghoon
2011-01-01
In this paper, we establish local H\\"older estimate for non-negative solutions of the singular equation \\eqref{eq-nlocal-PME-1} below, for $m$ in the range of exponents $(\\frac{n-2\\sigma}{n+2\\sigma},1)$. Since we have trouble in finding the local energy inequality of $v$ directly. we use the fact that the operator $(-\\La)^{\\sigma}$ can be thought as the normal derivative of some extension $v^{\\ast}$ of $v$ to the upper half space, \\cite{CS}, i.e., $v$ is regarded as boundary value of $v^{\\ast}$ the solution of some local extension problem. Therefore, the local H\\"older estimate of $v$ can be obtained by the same regularity of $v^{\\ast}$. In addition, it enables us to describe the behaviour of solution of non-local fast diffusion equation near their extinction time.
Onana, Vincent-de-Paul; Trouvé, Emmanuel; Mauris, Gilles; Rudant, Jean-Paul; Tonyé, Emmanuel
2004-01-10
A new linear-features detection method is proposed for extracting straight edges and lines in synthetic-aperture radar images. This method is based on the localized Radon transform, which produces geometrical integrals along straight lines. In the transformed domain, linear features have a specific signature: They appear as strongly contrasted structures, which are easier to extract with the conventional ratio edge detector. The proposed method is dedicated to applications such as geographical map updating for which prior information (approximate length and orientation of features) is available. Experimental results show the method's robustness with respect to poor radiometric contrast and hidden parts and its complementarity to conventional pixel-by-pixel approaches.
Far-field DOA estimation and near-field localization for multipath signals
Elbir, Ahmet M.; Tuncer, T. Engin
2014-09-01
In direction finding and localization applications, multipath signals are important sources of error for parameter estimation. When the antenna array receives multipath reflections which are coherent with the far-field line-of-sight signal, estimating the far- and near-field components becomes an important problem. In this paper, a new method is proposed to estimate the direction-of-arrival (DOA) of the far-field source and to localize its near-field multipaths. Far-field source DOA is estimated using calibration of the antenna array. A near-to-far transformation is proposed for the estimation of the near-field source DOA angles. In order to estimate the near-field range parameters, a compressive sensing approach is presented where a dictionary with near-field sources with different ranges is employed. As a result, the proposed method estimates the far-field and near-field source DOAs as well as the range and the signal amplitudes of the near-field sources. This method is evaluated using close-to-real world data generated by a numerical electromagnetic tool, where the array and transmitter are placed in an irregular terrain and array data are generated using full 3-D propagation model. It is shown that unknown source parameters can be estimated effectively showing the potential of the proposed approach in applications involving high-frequency direction finding and indoor localization.
Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.
2012-04-01
The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical downscaling procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic downscaling of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical downscaling of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic downscaling element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical downscaling element of the methodology consists of an ensemble of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input
Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data
Lei Wang; Satoshi Uchida
2008-01-01
MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classificat...
DEFF Research Database (Denmark)
Tscherning, Carl Christian
2015-01-01
The method of Least-Squares Collocation (LSC) may be used for the modeling of the anomalous gravity potential (T) and for the computation (prediction) of quantities related to T by a linear functional. Errors may also be estimated. However, when using an isotropic covariance function or equivalen...... on gravity anomalies (at 10 km altitude) predicted from GOCE Tzz. This has given an improved agreement between errors based on the differences between values derived from EGM2008 (to degree 512) and predicted gravity anomalies.......The method of Least-Squares Collocation (LSC) may be used for the modeling of the anomalous gravity potential (T) and for the computation (prediction) of quantities related to T by a linear functional. Errors may also be estimated. However, when using an isotropic covariance function or equivalent...... outside the data area. On the other hand, a comparison of predicted quantities with observed values show that the error also varies depending on the local data standard deviation. This quantity may be (and has been) estimated using the GOCE second order vertical derivative, Tzz, in the area covered...
An Iterated Local Search Algorithm for Estimating the Parameters of the Gamma/Gompertz Distribution
Directory of Open Access Journals (Sweden)
Behrouz Afshar-Nadjafi
2014-01-01
Full Text Available Extensive research has been devoted to the estimation of the parameters of frequently used distributions. However, little attention has been paid to estimation of parameters of Gamma/Gompertz distribution, which is often encountered in customer lifetime and mortality risks distribution literature. This distribution has three parameters. In this paper, we proposed an algorithm for estimating the parameters of Gamma/Gompertz distribution based on maximum likelihood estimation method. Iterated local search (ILS is proposed to maximize likelihood function. Finally, the proposed approach is computationally tested using some numerical examples and results are analyzed.
Pascual-Marqui, Roberto D
2007-01-01
This paper deals with the EEG/MEG neuroimaging problem: given measurements of scalp electric potential differences (EEG: electroencephalogram) and extracranial magnetic fields (MEG: magnetoencephalogram), find the 3D distribution of the generating electric neuronal activity. This problem has no unique solution. Only particular solutions with "good" localization properties are of interest, since neuroimaging is concerned with the localization of brain function. In this paper, a general family of linear imaging methods with exact, zero error localization to point-test sources is presented. One particular member of this family is sLORETA (standardized low resolution brain electromagnetic tomography; Pascual-Marqui, Methods Find. Exp. Clin. Pharmacol. 2002, 24D:5-12; http://www.unizh.ch/keyinst/NewLORETA/sLORETA/sLORETA-Math01.pdf). It is shown here that sLORETA has no localization bias in the presence of measurement and biological noise. Another member of this family, denoted as eLORETA (exact low resolution bra...
DEFF Research Database (Denmark)
Niehuesbernd, Jörn; Müller, Clemens; Pantleon, Wolfgang;
2013-01-01
. The local grain orientations determined by EBSD measurements were used to calculate the elastic tensors at several positions along the strain gradient. Based on the geometric mean, the calculated local elastic constants were transferred into global ones by appropriate weighting. Ultrasonic measurements were......Severely deformed materials often show strong plastic strain gradients, which can lead to a variety of gradients in microstructure and texture. Since the elastic behavior of a material is in most cases linked to its crystallographic texture, gradients in the elastic properties are also possible....... Consequently, the macroscopic elastic behavior results from the local elastic properties within the gradient. In the present investigation profiles produced by the linear flow splitting process were examined with respect to local and global elastic anisotropy, which develops during the complex forming process...
Institute of Scientific and Technical Information of China (English)
吴启光; 杨国庆
2002-01-01
In this paper, we study the existence of the uniformly minimum risk equivariant (UMRE) estimators of parameters in a class of normal linear models, which include the normal variance components model,the growth curve model, the extended growth curve model, and the seemingly unrelated regression equations model, and so on. The necessary and sufficient conditions are given for the existence of UMRE estimators of the estimable linear functions of regression coefficients, the covariance matrix V and (trV)a, where a＞ 0is known, in the models under an affine group of transformations for quadratic losses and matrix losses, respectively. Under the (extended) growth curve model and the seemingly unrelated regression equations model,the conclusions given in literature for estimating regression coefficients can be derived by applying the general results in this paper, and the sufficient conditions for non-existence of UMRE estimators of V and tr(V) are expanded to be necessary and sufficient conditions. In addition, the necessary and sufficient conditions that there exist UMRE estimators of parameters in the variance components model are obtained for the first time.
Impacts of altimeter corrections on local linear sea level trends around Taiwan
DEFF Research Database (Denmark)
Cheng, Yongcun; Andersen, Ole Baltazar
2013-01-01
.e. the inverted barometer correction, wet tropospheric correction, and sea state bias correction, have significant impacts on the determination of local LSLT. The trend of default corrections contribute more than 1.4 mm year-1 along the coastline of China mainland and 2.1 mm year-1 to local LSLT in the Taiwan...
A non-local non-autonomous diffusion problem: linear and sublinear cases
Figueiredo-Sousa, Tarcyana S.; Morales-Rodrigo, Cristian; Suárez, Antonio
2017-10-01
In this work we investigate an elliptic problem with a non-local non-autonomous diffusion coefficient. Mainly, we use bifurcation arguments to obtain existence of positive solutions. The structure of the set of positive solutions depends strongly on the balance between the non-local and the reaction terms.
Brassey, CA; Maidment, SC; Barrett, PM
2015-01-01
© 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. The file attached is the published version of the article. Body mass is a key biological variable, but difficult to assess from fossils. Various techniques exist for estimating body mass from skeletal parameters, but few studies have compared outpu...
Quach, Minh; Brunel, Nicolas; d'Alché-Buc, Florence
2007-12-01
Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given. Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach. This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models. Matlab code available on demand.
Linear measurements of the leaf blade in xaraes and massai grasses for estimation of the leaf area
Directory of Open Access Journals (Sweden)
Wilton Ladeira da Silva
2013-09-01
Full Text Available Knowledge on the leaf area of foraging grasses is essential, since it’s one of the most important variables in the evaluation of plant growth. Thus, one aimed at determining equations which allow, through simple measurements of leaf length, as well as average and maximum width, to quickly and accurately estimate the actual leaf area of Brachiaria brizantha cv. Xaraes and Panicum maximum cv. Massai. One measured with millimeter rulers the length along the main vein (L, the maximum width perpendicular to the main vein (Wmax, and the average width (Wave of leaf blades in both species. For determining the actual leaf areas (ALA, one used the Li-Cor®, model LI 3000. Regression and correlation studies were performed between ALA and the leaf area estimated through the linear or exponential equations for choosing the best equations. For xaraes grass the equation with the best accuracy for estimating ALA was the linear 0.53+0.98 LWave and for massai grass the best options were the linear 1.30+0.92 LWave and the exponential 8.86e0.04LWmax and 10.30e0.03LWave. Estimates of the leaf area of xaraes grass and massai grass through simple measurements of leaf length and width have proved to be effective and accurate.
Light propagation in local and linear media: Fresnel-Kummer wave surfaces with 16 singular points
Favaro, Alberto
2016-01-01
It is known that the Fresnel wave surfaces of transparent biaxial media have 4 singular points, located on two special directions. We show that, in more general media, the number of singularities can exceed 4. In fact, a highly symmetric linear material is proposed whose Fresnel surface exhibits 16 singular points. Because, for every linear material, the dispersion equation is quartic, we conclude that 16 is the maximum number of singularities. The identity of Fresnel and Kummer surfaces, which holds true for media with a certain symmetry (zero skewon piece), provides an elegant interpretation of the results. We describe a metamaterial realization for our linear medium with 16 singular points. It is found that an appropriate combination of metal bars, split-ring resonators, and magnetized particles can generate the correct permittivity, permeability, and magnetoelectric moduli. Lastly, we discuss the arrangement of the singularities in terms of Kummer's (16,6)-configuration of points and planes. An investigat...
Non-local investigation of bifurcations of solutions of non-linear elliptic equations
Energy Technology Data Exchange (ETDEWEB)
Il' yasov, Ya Sh
2002-12-31
We justify the projective fibration procedure for functionals defined on Banach spaces. Using this procedure and a dynamical approach to the study with respect to parameters, we prove that there are branches of positive solutions of non-linear elliptic equations with indefinite non-linearities. We investigate the asymptotic behaviour of these branches at bifurcation points. In the general case of equations with p-Laplacian we prove that there are upper bounds of branches of positive solutions with respect to the parameter.
Suliman, Suha Ibrahim
Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Scan Line Corrector (SLC) device, which corrects for the satellite motion, has failed since May 2003 resulting in a loss of about 22% of the data. To improve the reconstruction of Landsat 7 SLC-off images, Locally Linear Manifold (LLM) model is proposed for filling gaps in hyperspectral imagery. In this approach, each spectral band is modeled as a non-linear locally affine manifold that can be learned from the matching bands at different time instances. Moreover, each band is divided into small overlapping spatial patches. In particular, each patch is considered to be a linear combination (approximately on an affine space) of a set of corresponding patches from the same location that are adjacent in time or from the same season of the year. Fill patches are selected from Landsat 5 Thematic Mapper (TM) products of the year 1984 through 2011 which have similar spatial and radiometric resolution as Landsat 7 products. Using this approach, the gap-filling process involves feasible point on the learned manifold to approximate the missing pixels. The proposed LLM framework is compared to some existing single-source (Average and Inverse Distance Weight (IDW)) and multi- source (Local Linear Histogram Matching (LLHM) and Adaptive Window Linear Histogram Matching (AWLHM)) gap-filling methodologies. We analyze the effectiveness of the proposed LLM approach through simulation examples with known ground-truth. It is shown that the LLM-model driven approach outperforms all existing recovery methods considered in this study. The superiority of LLM is illustrated by providing better reconstructed images with higher accuracy even over heterogeneous landscape. Moreover, it is relatively simple to realize algorithmically, and it needs much less computing time when compared to the state- of-the art AWLHM approach.
Estimating local atmosphere-surface fluxes using eddy covariance and numerical Ogive optimization
DEFF Research Database (Denmark)
Sievers, Jakob; Papakyriakou, Tim; Larsen, Søren
2014-01-01
Estimating representative surface-fluxes using eddy covariance leads invariably to questions concerning inclusion or exclusion of low-frequency flux contributions. For studies where fluxes are linked to local physical parameters and up-scaled through numerical modeling efforts, low-frequency cont......Estimating representative surface-fluxes using eddy covariance leads invariably to questions concerning inclusion or exclusion of low-frequency flux contributions. For studies where fluxes are linked to local physical parameters and up-scaled through numerical modeling efforts, low......-frequency contributions interfere with our ability to isolate local biogeochemical processes of interest, as represented by turbulent fluxes. No method currently exists to disentangle low-frequency contributions on flux estimates. Here, we present a novel comprehensive numerical scheme to identify and separate out low...
Pinsker estimators for local helioseismology: inversion of travel times for mass-conserving flows
Fournier, Damien; Gizon, Laurent; Holzke, Martin; Hohage, Thorsten
2016-10-01
A major goal of helioseismology is the three-dimensional reconstruction of the three velocity components of convective flows in the solar interior from sets of wave travel-time measurements. For small amplitude flows, the forward problem is described in good approximation by a large system of convolution equations. The input observations are highly noisy random vectors with a known dense covariance matrix. This leads to a large statistical linear inverse problem. Whereas for deterministic linear inverse problems several computationally efficient minimax optimal regularization methods exist, only one minimax-optimal linear estimator exists for statistical linear inverse problems: the Pinsker estimator. However, it is often computationally inefficient because it requires a singular value decomposition of the forward operator or it is not applicable because of an unknown noise covariance matrix, so it is rarely used for real-world problems. These limitations do not apply in helioseismology. We present a simplified proof of the optimality properties of the Pinsker estimator and show that it yields significantly better reconstructions than traditional inversion methods used in helioseismology, i.e. regularized least squares (Tikhonov regularization) and SOLA (approximate inverse) methods. Moreover, we discuss the incorporation of the mass conservation constraint in the Pinsker scheme using staggered grids. With this improvement we can reconstruct not only horizontal, but also vertical velocity components that are much smaller in amplitude.
Tsai, Cheng-Ying; Li, Rui; Tennant, Chris
2015-01-01
As is known, microbunching instability (MBI) has been one of the most challenging issues in designs of magnetic chicanes for short-wavelength free-electron lasers or linear colliders, as well as those of transport lines for recirculating or energy recovery linac machines. To more accurately quantify MBI in a single-pass system and for more complete analyses, we further extend and continue to increase the capabilities of our previously developed linear Vlasov solver [1] to incorporate more relevant impedance models into the code, including transient and steady-state free-space and/or shielding coherent synchrotron radiation (CSR) impedances, the longitudinal space charge (LSC) impedances, and the linac geometric impedances with extension of the existing formulation to include beam acceleration [2]. Then, we directly solve the linearized Vlasov equation numerically for microbunching gain amplification factor. In this study we apply this code to a beamline lattice of transport arc [3] following an upstream linac...
Knoester, Jasper; Mukamel, Shaul
1989-01-01
Reduced equations of motion for material and radiation field variables in a molecular crystal are presented that allow us to calculate linear- and nonlinear-optical susceptibilities, accounting in a systematic way for intermolecular interactions. These equations are derived starting from the multipo
A Bayesian Estimator for Linear Calibration Error Effects in Thermal Remote Sensing
Morgan, J A
2005-01-01
The Bayesian Land Surface Temperature estimator previously developed has been extended to include the effects of imperfectly known gain and offset calibration errors. It is possible to treat both gain and offset as nuisance parameters and, by integrating over an uninformative range for their magnitudes, eliminate the dependence of surface temperature and emissivity estimates upon the exact calibration error.
Selection of the Linear Regression Model According to the Parameter Estimation
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
In this paper, based on the theory of parameter estimation, we give a selection method and ,in a sense of a good character of the parameter estimation,we think that it is very reasonable. Moreover,we offera calculation method of selection statistic and an applied example.
Directory of Open Access Journals (Sweden)
Muhammed Çetin
2015-01-01
Full Text Available An approximation method based on Lucas polynomials is presented for the solution of the system of high-order linear differential equations with variable coefficients under the mixed conditions. This method transforms the system of ordinary differential equations (ODEs to the linear algebraic equations system by expanding the approximate solutions in terms of the Lucas polynomials with unknown coefficients and by using the matrix operations and collocation points. In addition, the error analysis based on residual function is developed for present method. To demonstrate the efficiency and accuracy of the method, numerical examples are given with the help of computer programmes written in Maple and Matlab.
Belkhatir, Zehor
2017-05-31
This paper proposes a two-stage estimation algorithm to solve the problem of joint estimation of the parameters and the fractional differentiation orders of a linear continuous-time fractional system with non-commensurate orders. The proposed algorithm combines the modulating functions and the first-order Newton methods. Sufficient conditions ensuring the convergence of the method are provided. An error analysis in the discrete case is performed. Moreover, the method is extended to the joint estimation of smooth unknown input and fractional differentiation orders. The performance of the proposed approach is illustrated with different numerical examples. Furthermore, a potential application of the algorithm is proposed which consists in the estimation of the differentiation orders of a fractional neurovascular model along with the neural activity considered as input for this model.
Belkhatir, Zehor
2015-11-05
This paper deals with the joint estimation of the unknown input and the fractional differentiation orders of a linear fractional order system. A two-stage algorithm combining the modulating functions with a first-order Newton method is applied to solve this estimation problem. First, the modulating functions approach is used to estimate the unknown input for a given fractional differentiation orders. Then, the method is combined with a first-order Newton technique to identify the fractional orders jointly with the input. To show the efficiency of the proposed method, numerical examples illustrating the estimation of the neural activity, considered as input of a fractional model of the neurovascular coupling, along with the fractional differentiation orders are presented in both noise-free and noisy cases.
Abdallah, Saeed; Psaromiligkos, Ioannis N.
2012-03-01
We analyze the mean-squared error (MSE) performance of widely linear (WL) and conventional subspace-based channel estimation for single-input multiple-output (SIMO) flat-fading channels employing binary phase-shift-keying (BPSK) modulation when the covariance matrix is estimated using a finite number of samples. The conventional estimator suffers from a phase ambiguity that reduces to a sign ambiguity for the WL estimator. We derive closed-form expressions for the MSE of the two estimators under four different ambiguity resolution scenarios. The first scenario is optimal resolution, which minimizes the Euclidean distance between the channel estimate and the actual channel. The second scenario assumes that a randomly chosen coefficient of the actual channel is known and the third assumes that the one with the largest magnitude is known. The fourth scenario is the more realistic case where pilot symbols are used to resolve the ambiguities. Our work demonstrates that there is a strong relationship between the accuracy of ambiguity resolution and the relative performance of WL and conventional subspace-based estimators, and shows that the less information available about the actual channel for ambiguity resolution, or the lower the accuracy of this information, the higher the performance gap in favor of the WL estimator.
Automated linear regression tools improve RSSI WSN localization in multipath indoor environment
Directory of Open Access Journals (Sweden)
Laermans Eric
2011-01-01
Full Text Available Abstract Received signal strength indication (RSSI-based localization is emerging in wireless sensor networks (WSNs. Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.
National Research Council Canada - National Science Library
Zhou, Mu; Tian, Zengshan; Xu, Kunjie; Yu, Xiang; Wu, Haibo
2014-01-01
...) in logarithmic received signal strength (RSS) varying Wi-Fi environment. To the best of our knowledge, little comprehensive analysis work has appeared on the error performance of neighbor matching localization with respect to the deployment of RPs...
Estimation of azimuth and slowness of teleseismic signals recorded by a local seismic network
Institute of Scientific and Technical Information of China (English)
靳平; 潘常周
2002-01-01
A new method that is applicable to local seismic networks to estimate the azimuth and slowness of teleseismic signals is introduced in the paper. The method is based on the correlation between the arrival times and station positions. The analyzed results indicate that the azimuth and slowness of teleseismic signals can be accurately estimated by the method. Average errors for azimuth and slowness measurements obtained by this method using data of Xi(an Digital Telemetry Seismic Network are 2.0o and 0.34 s/(o), respectively. The conclusions drawn from this study indicate that this method may be very useful to interpret teleseismic records of local seismic networks.
Majeed, Khaqan
2015-12-22
The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15% of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1% of the fingerprinting load, some crowd sourced readings and plan coordinates of the indoor area. The 1% fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50% performance improvement by using this information as compared to using only fingerprints.
Application of Matrix Pencil Algorithm to Mobile Robot Localization Using Hybrid DOA/TOA Estimation
Directory of Open Access Journals (Sweden)
Lan Anh Trinh
2012-12-01
Full Text Available Localization plays an important role in robotics for the tasks of monitoring, tracking and controlling a robot. Much effort has been made to address robot localization problems in recent years. However, despite many proposed solutions and thorough consideration, in terms of developing a low‐cost and fast processing method for multiple‐source signals, the robot localization problem is still a challenge. In this paper, we propose a solution for robot localization with regards to these concerns. In order to locate the position of a robot, both the coordinate and the orientation of a robot are necessary. We develop a localization method using the Matrix Pencil (MP algorithm for hybrid detection of direction of arrival (DOA and time of arrival (TOA. TOA of the signal is estimated for computing the distance between the mobile robot and a base station (BS. Based on the distance and the estimated DOA, we can estimate the mobile robot’s position. The characteristics of the algorithm are examined through analysing simulated experiments and the results demonstrate the advantages of our method over previous works in dealing with the above challenges. The method is constructed based on the low‐cost infrastructure of radio frequency devices; the DOA/TOA estimation is performed with just single value decomposition for fast processing. Finally, the MP algorithm combined with tracking using a Kalman filter allows our proposed method to locate the positions of multiple source signals.
Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method
Directory of Open Access Journals (Sweden)
Hiroaki Ogata
2012-01-01
Full Text Available An application of the empirical likelihood method to non-Gaussian locally stationary processes is presented. Based on the central limit theorem for locally stationary processes, we give the asymptotic distributions of the maximum empirical likelihood estimator and the empirical likelihood ratio statistics, respectively. It is shown that the empirical likelihood method enables us to make inferences on various important indices in a time series analysis. Furthermore, we give a numerical study and investigate a finite sample property.
Weissman-Miller, Deborah
2013-11-02
Point estimation is particularly important in predicting weight loss in individuals or small groups. In this analysis, a new health response function is based on a model of human response over time to estimate long-term health outcomes from a change point in short-term linear regression. This important estimation capability is addressed for small groups and single-subject designs in pilot studies for clinical trials, medical and therapeutic clinical practice. These estimations are based on a change point given by parameters derived from short-term participant data in ordinary least squares (OLS) regression. The development of the change point in initial OLS data and the point estimations are given in a new semiparametric ratio estimator (SPRE) model. The new response function is taken as a ratio of two-parameter Weibull distributions times a prior outcome value that steps estimated outcomes forward in time, where the shape and scale parameters are estimated at the change point. The Weibull distributions used in this ratio are derived from a Kelvin model in mechanics taken here to represent human beings. A distinct feature of the SPRE model in this article is that initial treatment response for a small group or a single subject is reflected in long-term response to treatment. This model is applied to weight loss in obesity in a secondary analysis of data from a classic weight loss study, which has been selected due to the dramatic increase in obesity in the United States over the past 20 years. A very small relative error of estimated to test data is shown for obesity treatment with the weight loss medication phentermine or placebo for the test dataset. An application of SPRE in clinical medicine or occupational therapy is to estimate long-term weight loss for a single subject or a small group near the beginning of treatment.
Lo, Ching F.
1999-01-01
The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.
ASYMPTOTIC ESTIMATION FOR SOLUTION OF A CLASS OF SEMI-LINEAR ROBIN PROBLEMS
Institute of Scientific and Technical Information of China (English)
Cheng Ouyang
2005-01-01
A class of semi-linear Robin problem is considered. Under appropriate assumptions, the existence and asymptotic behavior of its solution are studied more carefully. Using stretched variables, the formal asymptotic expansion of solution for the problem is constructed and the uniform validity of the solution is obtained by using the method of upper and lower solution.
Estimation of saturation and coherence effects in the KGBJS equation - a non-linear CCFM equation
Deak, Michal
2012-01-01
We solve the modified non-linear extension of the CCFM equation - KGBJS equation - numerically for certain initial conditions and compare the resulting gluon Green functions with those obtained from solving the original CCFM equation and the BFKL and BK equations for the same initial conditions. We improve the low transversal momentum behaviour of the KGBJS equation by a small modification.
N.G. HOSSEIN-ZADEH
2008-01-01
Data on stillbirth from the Animal Breeding Center of Iran collected from January 1990 to December 2007 and comprising 668810 Holstein calving events from 2506 herds were analyzed. Linear and threshold animal and sire models were used to estimate genetic parameters and genetic trends for stillbirth in the first, second, and third parities. Mean incidence of stillbirth decreased from first to third parities: 23.7%, 22.1%, and 21.8%, respectively. Phenotypic rates of stillbirth decreased from 1...
Mynard, Jonathan; Penny, Daniel J; Smolich, Joseph J
2008-12-05
Local reflection coefficients (R) provide important insights into the influence of wave reflection on vascular haemodynamics. Using the relatively new time-domain method of wave intensity analysis, R has been calculated as the ratio of the peak intensities (R(PI)) or areas (R(CI)) of incident and reflected waves, or as the ratio of the changes in pressure caused by these waves (R(DeltaP)). While these methods have not yet been compared, it is likely that elastic non-linearities present in large arteries will lead to changes in the size of waves as they propagate and thus errors in the calculation of R(PI) and R(CI). To test this proposition, R(PI), R(CI) and R(DeltaP) were calculated in a non-linear computer model of a single vessel with various degrees of elastic non-linearity, determined by wave speed and pulse amplitude (DeltaP(+)), and a terminal admittance to produce reflections. Results obtained from this model demonstrated that under linear flow conditions (i.e. as DeltaP(+)-->0), R(DeltaP) is equivalent to the square-root of R(PI) and R(CI) (denoted by R(PI)(p) and R(CI)(p)). However for non-linear flow, pressure-increasing (compression) waves undergo amplification while pressure-reducing (expansion) waves undergo attenuation as they propagate. Consequently, significant errors related to the degree of elastic non-linearity arise in R(PI) and R(CI), and also R(PI)(p) and R(CI)(p), with greater errors associated with larger reflections. Conversely, R(Delta)(P) is unaffected by the degree of non-linearity and is thus more accurate than R(PI) and R(CI).
Directory of Open Access Journals (Sweden)
Nengjun Yi
2011-12-01
Full Text Available Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/.
Yi, Nengjun; Liu, Nianjun; Zhi, Degui; Li, Jun
2011-01-01
Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). PMID:22144906
DEFF Research Database (Denmark)
Crimi, Alessandro; Lillholm, Martin; Nielsen, Mads
2011-01-01
the estimates' influence on a missing-data reconstruction task, where high resolution vertebra and cartilage models are reconstructed from incomplete and lower dimensional representations. Our results demonstrate that our methods outperform the traditional ML method and Tikhonov regularization......., and may lead to unreliable results. In this paper, we discuss regularization by prior knowledge using maximum a posteriori (MAP) estimates. We compare ML to MAP using a number of priors and to Tikhonov regularization. We evaluate the covariance estimates on both synthetic and real data, and we analyze...
Directory of Open Access Journals (Sweden)
Marcos Antonio Tavares Lira
2011-09-01
mean wind speed profile obtained from the local Platform for Data Collection (PCD and anemometric tower (TA raw data. The prevailing wind direction data are also used. The logarithmic wind profile equation is used to estimate the values of average wind speed at altitudes of 20, 40 and 60 meters from the observed data at 10m surface, then calculating the correlation coefficients between the estimated altitude data and the observed TA data in the region. Then, the linear regression model is used to estimate new values in altitude. Initially this procedure is done for a period of model calibration and then for a period of validation. In both periods the linear regression model showed a good performance either by the high correlation coefficient values between the estimated and observed data series, or the low values of the errors between the series.
A linear gate and transmitter for improving localization using the charge division method
Busso, L; Marcello, S; Morra, O; Panzarasa, A
2002-01-01
We developed an electronic system which, used with drift chambers, allows to perform a precise charge division measurement of the longitudinal coordinate, even if the sense wire is held at high voltage and a decoupling capacitor is needed. The idea is to create a temporal gate at the arrival of the signal and transmit to the ADC only this part of the signal. The gate remains open for a short period (120 ns), corresponding to the duration of the anode pulse and delivers, at its output, a pulse of amplitude linearly dependent from the input value. In this way the systematic error is better than 1% of the wire length. It introduces a considerable improvement in comparison with previously used software corrections, mainly from the linearity and simplicity point of view.
Institute of Scientific and Technical Information of China (English)
Jinhong YOU; CHEN Min; Gemai CHEN
2004-01-01
Consider a semiparametric regression model with linear time series errors Yκ = x′κβ + g(tκ) + εκ,1 ≤ k ≤ n, where Yκ's are responses, xκ= (xκ1,xκ2,…,xκp)′and tκ ∈ T( ) R are fixed design points, β = (β1,β2,…… ,βp)′ is an unknown parameter vector, g(.) is an unknown bounded real-valued function defined on a compact subset T of the real line R, and εκ is a linear process given by εκ = ∑∞j=0 ψjeκ-j, ψ0 = 1, where ∑∞j=0 |ψj| ＜∞, and ej, j = 0,±1,±2,…, are I.I.d, random variables. In this paper we establish the asymptotic normality of the least squares estimator ofβ, a smooth estimator of g(·), and estimators of the autocovariance and autocorrelation functions of the linear process εκ.
A Linear Algorithm for the CMS Muon Drift Tubes Local Pattern Recognition
De Min, Alberto
2007-01-01
In this note a new, linear reconstruction algorithm is suggested for the CMS barrel muon chambers that detects the presence of a muon track, performs optimal pattern recognition, solves left-right ambiguities and fits the muon track segment within each chamber with a simple single-step procedure. The algorithm uses mixed-integer programming techniques developed in operations research. Compared to the sequential reconstruction method presently used it is potentially unbiased, more reliable and possibly faster.
On the Estimation of Muscle Fiber Conduction Velocity Using a Co-Linear Electrodes Array
2007-11-02
an important parameter of the myoelectric signal which describes muscle fatigue manifestation during voluntary or elicited contractions. It may...Surface Myoelectric Signals Part I: Model Implementation”, IEEE Trans. on BME, Vol. 46, No. 7, pp. 810-820, 1999 [6] W. Muhammad, “Estimation de retards...optimal method to estimate the TD is the Generalized Cross- Correlation method (GCC), but this requires a priori knowledge about signal and noise. In
Exciton Localization in Extended {\\pi}-electron Systems: Comparison of Linear and Cyclic Structures
Thiessen, Alexander; Jester, Stefan-S; Aggarwal, A Vikas; Idelson, Alissa; Bange, Sebastian; Vogelsang, Jan; Höger, Sigurd; Lupton, John M
2015-01-01
We employ five {\\pi}-conjugated model materials of different molecular shape --- oligomers and cyclic structures --- to investigate the extent of exciton self-trapping and torsional motion of the molecular framework following optical excitation. Our studies combine steady-state and transient fluorescence spectroscopy in the ensemble with measurements of polarization anisotropy on single molecules, supported by Monte Carlo simulations. The dimer exhibits a significant spectral red-shift within $\\sim$ 100 ps after photoexcitation which is attributed to torsional relaxation. This relaxation mechanism is inhibited in the structurally rigid macrocyclic analogue. However, both systems show a high degree of exciton localization but with very different consequences: while in the macrocycle the exciton localizes randomly on different parts of the ring, scrambling polarization memory, in the dimer, localization leads to a deterministic exciton position with luminescence characteristics of a dipole. Monte Carlo simulati...
Blackman, Karin; Perret, Laurent
2016-09-01
In the present work, a boundary layer developing over a rough-wall consisting of staggered cubes with a plan area packing density, λp = 25%, is studied within a wind tunnel using combined particle image velocimetry and hot-wire anemometry to investigate the non-linear interactions between large-scale momentum regions and small-scale structures induced by the presence of the roughness. Due to the highly turbulent nature of the roughness sub-layer and measurement equipment limitations, temporally resolved flow measurements are not feasible, making the conventional filtering methods used for triple decomposition unsuitable for the present work. Thus, multi-time delay linear stochastic estimation is used to decompose the flow into large-scales and small-scales. Analysis of the scale-decomposed skewness of the turbulent velocity (u') shows a significant contribution of the non-linear term uL ' uS ' 2 ¯ , which represents the influence of the large-scales ( uL ' ) onto the small-scales ( uS ' ). It is shown that this non-linear influence of the large-scale momentum regions occurs with all three components of velocity in a similar manner. Finally, through two-point spatio-temporal correlation analysis, it is shown quantitatively that large-scale momentum regions influence small-scale structures throughout the boundary layer through a non-linear top-down mechanism.
Indoor Self-Localization and Orientation Estimation of Smartphones Using Acoustic Signals
Directory of Open Access Journals (Sweden)
Héctor A. Sánchez-Hevia
2017-01-01
Full Text Available We propose a new acoustic self-localization and orientation estimation algorithm for smartphones networks composed of commercial off-the-shelf devices equipped with two microphones and a speaker. Each smartphone acts as an acoustic transceiver, which emits and receives acoustic signals. Node locations are found by combining estimates of the range and direction of arrival (DoA between node pairs using a maximum likelihood (ML estimator. A tailored optimization algorithm is proposed to simultaneously solve the DoA uncertainty problem that arises from the use of only 2 microphones per node and obtain the azimuthal orientation of each node without requiring an electronic compass.
Energy Technology Data Exchange (ETDEWEB)
Jang, Hong; Lee, Jay H. [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Braatz, Richard D. [Massachusetts Institute of Technology (MIT), Cambridge (United States)
2016-01-15
This paper proposes a maximum likelihood estimation (MLE) method for estimating time varying local concentration of the target molecule proximate to the sensor from the time profile of monomolecular adsorption and desorption on the surface of the sensor at nanoscale. Recently, several carbon nanotube sensors have been developed that can selectively detect target molecules at a trace concentration level. These sensors use light intensity changes mediated by adsorption or desorption phenomena on their surfaces. The molecular events occurring at trace concentration levels are inherently stochastic, posing a challenge for optimal estimation. The stochastic behavior is modeled by the chemical master equation (CME), composed of a set of ordinary differential equations describing the time evolution of probabilities for the possible adsorption states. Given the significant stochastic nature of the underlying phenomena, rigorous stochastic estimation based on the CME should lead to an improved accuracy over than deterministic estimation formulated based on the continuum model. Motivated by this expectation, we formulate the MLE based on an analytical solution of the relevant CME, both for the constant and the time-varying local concentrations, with the objective of estimating the analyte concentration field in real time from the adsorption readings of the sensor array. The performances of the MLE and the deterministic least squares are compared using data generated by kinetic Monte Carlo (KMC) simulations of the stochastic process. Some future challenges are described for estimating and controlling the concentration field in a distributed domain using the sensor technology.
Estimating local atmosphere-surface fluxes using eddy covariance and numerical Ogive optimization
DEFF Research Database (Denmark)
Sievers, Jakob; Papakyriakou, Tim; Larsen, Søren;
2014-01-01
-frequency contributions interfere with our ability to isolate local biogeochemical processes of interest, as represented by turbulent fluxes. No method currently exists to disentangle low-frequency contributions on flux estimates. Here, we present a novel comprehensive numerical scheme to identify and separate out low...
DEFF Research Database (Denmark)
Hounyo, Ulrich; Varneskov, Rasmus T.
We provide a new resampling procedure - the local stable bootstrap - that is able to mimic the dependence properties of realized power variations for pure-jump semimartingales observed at different frequencies. This allows us to propose a bootstrap estimator and inference procedure for the activi...
Carroll, Raymond
2009-04-23
We consider the efficient estimation of a regression parameter in a partially linear additive nonparametric regression model from repeated measures data when the covariates are multivariate. To date, while there is some literature in the scalar covariate case, the problem has not been addressed in the multivariate additive model case. Ours represents a first contribution in this direction. As part of this work, we first describe the behavior of nonparametric estimators for additive models with repeated measures when the underlying model is not additive. These results are critical when one considers variants of the basic additive model. We apply them to the partially linear additive repeated-measures model, deriving an explicit consistent estimator of the parametric component; if the errors are in addition Gaussian, the estimator is semiparametric efficient. We also apply our basic methods to a unique testing problem that arises in genetic epidemiology; in combination with a projection argument we develop an efficient and easily computed testing scheme. Simulations and an empirical example from nutritional epidemiology illustrate our methods.
Vieira, Vasco M. N. C. S.; Engelen, Aschwin H.; Huanel, Oscar R.; Guillemin, Marie-Laure
2016-01-01
Survival is a fundamental demographic component and the importance of its accurate estimation goes beyond the traditional estimation of life expectancy. The evolutionary stability of isomorphic biphasic life-cycles and the occurrence of its different ploidy phases at uneven abundances are hypothesized to be driven by differences in survival rates between haploids and diploids. We monitored Gracilaria chilensis, a commercially exploited red alga with an isomorphic biphasic life-cycle, having found density-dependent survival with competition and Allee effects. While estimating the linear-in-the-parameters survival function, all model I regression methods (i.e, vertical least squares) provided biased line-fits rendering them inappropriate for studies about ecology, evolution or population management. Hence, we developed an iterative two-step non-linear model II regression (i.e, oblique least squares), which provided improved line-fits and estimates of survival function parameters, while robust to the data aspects that usually turn the regression methods numerically unstable. PMID:27936048
Hansen, Scott K.; Vesselinov, Velimir V.
2016-10-01
We develop empirically-grounded error envelopes for localization of a point contamination release event in the saturated zone of a previously uncharacterized heterogeneous aquifer into which a number of plume-intercepting wells have been drilled. We assume that flow direction in the aquifer is known exactly and velocity is known to within a factor of two of our best guess from well observations prior to source identification. Other aquifer and source parameters must be estimated by interpretation of well breakthrough data via the advection-dispersion equation. We employ high performance computing to generate numerous random realizations of aquifer parameters and well locations, simulate well breakthrough data, and then employ unsupervised machine optimization techniques to estimate the most likely spatial (or space-time) location of the source. Tabulating the accuracy of these estimates from the multiple realizations, we relate the size of 90% and 95% confidence envelopes to the data quantity (number of wells) and model quality (fidelity of ADE interpretation model to actual concentrations in a heterogeneous aquifer with channelized flow). We find that for purely spatial localization of the contaminant source, increased data quantities can make up for reduced model quality. For space-time localization, we find similar qualitative behavior, but significantly degraded spatial localization reliability and less improvement from extra data collection. Since the space-time source localization problem is much more challenging, we also tried a multiple-initial-guess optimization strategy. This greatly enhanced performance, but gains from additional data collection remained limited.
Automotive FMCW Radar-enhanced Range Estimation via a Local Resampling Fourier Transform
Directory of Open Access Journals (Sweden)
Cailing Wang
2016-02-01
Full Text Available In complex traffic scenarios, more accurate measurement and discrimination for an automotive frequency-modulated continuous-wave (FMCW radar is required for intelligent robots, driverless cars and driver-assistant systems. A more accurate range estimation method based on a local resampling Fourier transform (LRFT for a FMCW radar is developed in this paper. Radar signal correlation in the phase space sees a higher signal-noise-ratio (SNR to achieve more accurate ranging, and the LRFT - which acts on a local neighbour as a refinement step - can achieve a more accurate target range. The rough range is estimated through conditional pulse compression (PC and then, around the initial rough estimation, a refined estimation through the LRFT in the local region achieves greater precision. Furthermore, the LRFT algorithm is tested in numerous simulations and physical system experiments, which show that the LRFT algorithm achieves a more precise range estimation than traditional FFT-based algorithms, especially for lower bandwidth signals.
Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
Sun, Xiaodian; Medvedovic, Mario
2016-02-01
Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the JAK-STAT pathway, but this method can be easily extended to large-scale cases.
On the estimate of earthquake magnitude at a local seismic network
Energy Technology Data Exchange (ETDEWEB)
Di Grazia, G.; Langer, H.; Ursino, A.; Scarfi, L. [Istituto Nazionale di Geofisica e Vulcanologia, Sez. di Catania, Priolo-Grgallo, Siracusa (Italy); Gresta, S. [Catania Univ., Catania (Italy). Dipt. di Scienze Geologiche
2001-06-01
It was investigated possible uncertainties and bases of magnitude estimate arising from instrument characteristics site conditions and routine data processing at a local seismic network running in Southeastern Sicily. Differences in instrument characteristics turned out to be of minor importance for small and moderate earthquakes. Magnitudes routinely calculated with the Hypoellipse program are obtained from the peak ground velocities applying a correction for the dominant period. This procedure yields slightly lower values than the standard procedure, where magnitudes are estimated from peak ground displacement. In order to provide the operators in the data center with a tool for an immediate estimate of earthquake size from drum records it was carried out a bivariate regression relating local magnitude (M{sub 1}) to the duration of the signal and the travel time difference of P- and S-waves.
Non-invasive ambient pressure estimation using non-linear ultrasound contrast agents
DEFF Research Database (Denmark)
Andersen, Klaus Scheldrup
Many attempts to find a non-invasive procedure to measure the blood pressure locally in the body have been made. This dissertation focuses on the approaches which utilize highly compressible ultrasound contrast agents as ambient pressure sensors. The literature within the topic has been reviewed...
Muralidhar, K R; Komanduri, Krishna; Rout, Birendra Kumar; Ramesh, K K D
2013-07-01
Four dimensional (4D) target localization system (Calypso System) was installed at our hospital, which is equipped with Beacon Transponders, Console, Electromagnetic Array, Optical System, Tracking Station, Treatment table overlay, and Calypso kVue Couch top. The objective of this presentation is to describe the results of commissioning measurements carried out on the Calypso System to verify the manufacturer specifications and also to evolve a quality assurance (QA) procedure which can be used to test its performance routinely. The QA program consists of a series of tests (QA for checking the calibration or system accuracy, Camera Calibration with L-frame fixture, Camera Calibration with T-frame fixture, System calibration Fixture targets test, Localization, and Tracking). These tests were found to be useful to assess the performance of the Calypso System.
Non-linear non-local molecular electrodynamics with nano-optical fields.
Chernyak, Vladimir Y; Saurabh, Prasoon; Mukamel, Shaul
2015-10-28
The interaction of optical fields sculpted on the nano-scale with matter may not be described by the dipole approximation since the fields may vary appreciably across the molecular length scale. Rather than incrementally adding higher multipoles, it is advantageous and more physically transparent to describe the optical process using non-local response functions that intrinsically include all multipoles. We present a semi-classical approach for calculating non-local response functions based on the minimal coupling Hamiltonian. The first, second, and third order response functions are expressed in terms of correlation functions of the charge and the current densities. This approach is based on the gauge invariant current rather than the polarization, and on the vector potential rather than the electric and magnetic fields.
Directory of Open Access Journals (Sweden)
Eusebio Eduardo Hernández Martinez
2013-01-01
Full Text Available In robotics, solving the direct kinematics problem (DKP for parallel robots is very often more difficult and time consuming than for their serial counterparts. The problem is stated as follows: given the joint variables, the Cartesian variables should be computed, namely the pose of the mobile platform. Most of the time, the DKP requires solving a non‐linear system of equations. In addition, given that the system could be non‐convex, Newton or Quasi‐Newton (Dogleg based solvers get trapped on local minima. The capacity of such kinds of solvers to find an adequate solution strongly depends on the starting point. A well‐known problem is the selection of such a starting point, which requires a priori information about the neighbouring region of the solution. In order to circumvent this issue, this article proposes an efficient method to select and to generate the starting point based on probabilistic learning. Experiments and discussion are presented to show the method performance. The method successfully avoids getting trapped on local minima without the need for human intervention, which increases its robustness when compared with a single Dogleg approach. This proposal can be extended to other structures, to any non‐linear system of equations, and of course, to non‐linear optimization problems.