On grey relation projection model based on projection pursuit
Wang Shuo; Yang Shanlin; Ma Xijun
2008-01-01
Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by u-sing projection pursuit model.The larger the projection value is,the better the model.Thus,according to the projection value,the best one can be chosen from the model aggregation.Because projection pursuit modeling based on accelera-ting genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.
Asymptotic distributions in the projection pursuit based canonical correlation analysis
无
2010-01-01
In this paper, associations between two sets of random variables based on the projection pursuit (PP) method are studied. The asymptotic normal distributions of estimators of the PP based canonical correlations and weighting vectors are derived.
Approach to Weighted Geometric Evaluation Based on Projection Pursuit
Yang Shanlin; Wang Shuo; Gong Daning
2006-01-01
Weighted geometric evaluation approach based on Projection pursuit (PP) model is presented in this paper to optimize the choice of schemes. By using PP model, the multi-dimension evaluation index values of schemes can be synthesized into projection value with one dimension. The scheme with a bigger projection value is much better, so the schemes sample can be an optimized choice according to the projection value of each scheme. The modeling of PP based on accelerating genetic algorithm can predigest the realized process of projection pursuit technique, can overcome the shortcomings of large computation amount and the difficulty of computer programming in traditional projection pursuit methods, and can give a new method for application of projection pursuit technique to optimize choice of schemes by using weighted geometric evaluation. The analysis of an applied sample shows that applying PP model driven directly by samples data to optimize choice of schemes is both simple and feasible, that its projection values are relatively decentralized and profit decision-making, that its applicability and maneuverability are high. It can avoid the shortcoming of subjective weighing method, and its results are scientific and objective.
Brian M. Wood
2015-12-01
Full Text Available This article describes a software tool called “Pursuit” that is intended to be used for both research and teaching on the topic of optimal foraging theory. The tool provides a dynamic graphical and auditory interface in which users encounter different prey animals and then must decide whether to pursue or ignore the encountered prey. Based on the characteristics of the prey in the foraging environment and the decisions of the player, each user harvests a set of prey per round and achieves a corresponding foraging return rate. Administrators of Pursuit specify the environmental parameters that determine what prey users will encounter. All environmental parameters and user decisions are tracked and logged for analysis. We created this tool for laboratory experiments, but we believe Pursuit could also be an engaging and effective teaching tool, whereby students adopt the role of forager, and through such play, experience a simulated foraging context and learn about foraging theory. Pursuit is freely available and can run on any platform that supports Java, including Mac OS, Windows, and Linux.
Seismic comprehensive forecast based on modified project pursuit regression
Anxu Wu; Xiangdong Lin; Changsheng Jiang; Yongxian Zhang; Xiaodong Zhang; Mingxiao Li; Pingan Li
2009-01-01
In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression (PPR) is one of most applicable methods. But generally, the algorithm structure of the PPR is very complicated. By partial smooth regressions for many times, it has a large amount of calculation and complicated extrapolation, so it is easily trapped in partial solution. On the basis of the algorithm features of the PPR method, some solutions are given as below to aim at some shortcomings in the PPR calculation: to optimize project direction by using particle swarm optimization instead of Gauss-Newton algorithm, to simplify the optimal process with fitting ridge function by using Hermitian polynomial instead of piecewise linear regression. The overall optimal ridge function can be obtained without grouping the parameter optimization. The modeling capability and calculating accuracy of projection pursuit method are tested by means of numerical emulation technique on the basis of particle swarm optimization and Hermitian polynomial, and then applied to the seismic comprehensive forecasting models of poly-dimensional seismic time series and general disorder seismic samples. The calculation and analysis show that the projection pursuit model in this paper is characterized by simplicity, celerity and effectiveness. And this model is approved to have satisfactory effects in the real seismic comprehensive forecasting, which can be regarded as a comprehensive analysis method in seismic comprehensive forecast.
Managing Performance Analysis with Dynamic Statistical Projection Pursuit
Vetter, J.S.; Reed, D.A.
2000-05-22
Computer systems and applications are growing more complex. Consequently, performance analysis has become more difficult due to the complex, transient interrelationships among runtime components. To diagnose these types of performance issues, developers must use detailed instrumentation to capture a large number of performance metrics. Unfortunately, this instrumentation may actually influence the performance analysis, leading the developer to an ambiguous conclusion. In this paper, we introduce a technique for focusing a performance analysis on interesting performance metrics. This technique, called dynamic statistical projection pursuit, identifies interesting performance metrics that the monitoring system should capture across some number of processors. By reducing the number of performance metrics, projection pursuit can limit the impact of instrumentation on the performance of the target system and can reduce the volume of performance data.
Projection Pursuit Through ϕ-Divergence Minimisation
Jacques Touboul
2010-06-01
Full Text Available In his 1985 article (“Projection pursuit”, Huber demonstrates the interest of his method to estimate a density from a data set in a simple given case. He considers the factorization of density through a Gaussian component and some residual density. Huber’s work is based on maximizing Kullback–Leibler divergence. Our proposal leads to a new algorithm. Furthermore, we will also consider the case when the density to be factorized is estimated from an i.i.d. sample. We will then propose a test for the factorization of the estimated density. Applications include a new test of fit pertaining to the elliptical copulas.
Procrustes rotation as a diagnostic tool for projection pursuit analysis.
Wentzell, Peter D; Hou, Siyuan; Silva, Carolina Santos; Wicks, Chelsi C; Pimentel, Maria Fernanda
2015-06-02
Projection pursuit (PP) is an effective exploratory data analysis tool because it optimizes the projection of high dimensional data using distributional characteristics rather than variance or distance metrics. The recent development of fast and simple PP algorithms based on minimization of kurtosis for clustering data has made this powerful tool more accessible, but under conditions where the sample-to-variable ratio is small, PP fails due to opportunistic overfitting of random correlations to limiting distributional targets. Therefore, some kind of variable compression or data regularization is required in these cases. However, this introduces an additional parameter whose optimization is manually time consuming and subject to bias. The present work describes the use of Procrustes analysis as diagnostic tool that can be used to evaluate the results of PP analysis in an efficient manner. Through Procrustes rotation, the similarity of different PP projections can be examined in an automated fashion with "Procrustes maps" to establish regions of stable projections as a function of the parameter to be optimized. The application of this diagnostic is demonstrated using principal components analysis to compress FTIR spectra from ink samples of ten different brands of pen, and also in conjunction with regularized PP for soybean disease classification. Copyright © 2015 Elsevier B.V. All rights reserved.
XU Zi-rong; ZHANG Yi-fei
2011-01-01
The paper studies on case-based reasoning of uncertain product attributes in configuration design of a product family. Interval numbers characterize uncertain product attributes. By interpolating a number of certain values randomly to replace interval numbers and making projection pursuit analysis on source cases and target cases of expanded numbers, we can get a projection value in the optimal projection direction. Based on projection value, we can construct a case retrieval model of projection pursuit that can handle coexisting certain and uncertain product attributes. The application examples of chainsaw configuration design show that case retrieval is highly sensitive to reliable results.
REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit
Fischer, Daniel; Berro, Alain; Nordhausen, Klaus; Ruiz-Gazen, Anne
2016-01-01
The R-package REPPlab is designed to explore multivariate data sets using one-dimensional unsupervised projection pursuit. It is useful in practice as a preprocessing step to find clusters or as an outlier detection tool for multivariate numerical data. Except from the package tourr that implements smooth sequences of projection matrices and rggobi that provides an interface to a dynamic graphics package called GGobi, there is no implementation of exploratory projection pursuit tools availabl...
Coal Calorific Value Prediction Based on Projection Pursuit Principle
QI Minfang
2012-10-01
Full Text Available The calorific value of coal is an important factor for the economic operation of coal-fired power plant. However, calorific value is tremendous difference between the different coal, and even if coal is from the same mine. Restricted by the coal market, most of coal fired power plants can not burn the designed-coal by now in China. The properties of coal as received are changing so frequently that pulverized coal firing is always with the unexpected condition. Therefore, the researches on the prediction of calorific value of coal have a profound significance for the economic operation of power plants. Aiming at the problem of uncertainty of coal calorific value, establish a soft measurement model for calorific value of coal based on projection pursuit principle combined with genetic algorithm to optimize parameters, and support vector machine algorithm. It is shown by an example that the model has a stronger objectivity, effective and feasible for avoiding the disadvantage of the artificially decided weights of feature indexes. The model could provide a good guidance for the calculation of the coal calorific value and optimization operation of coal-fired power plants.
Benefit Evaluation Model of Small Watershed Control Based on Projection Pursuit
无
2001-01-01
A projection pursuit model is presented in this paper for comprehensive evaluation of benefits of small watershed control. By using the model ,small watershed control samples with many benefit evaluation indexes can be synthesized projective values with one dimension. The samples can be naturally evaluated according to the projective values. The parameters of the model is optimized by using real coding beased accelerating genetic aglrothm,which overcomes the shortcomings of large computation amount and difficulty of computer programming in traditional projection prusuit methods,and provides a new way for wide applications of projection pursuit technique to different evaluation problems in agricultural systems engineering.
Research on evaluating water resource resilience based on projection pursuit classification model
Liu, Dong; Zhao, Dan; Liang, Xu; Wu, Qiuchen
2016-03-01
Water is a fundamental natural resource while agriculture water guarantees the grain output, which shows that the utilization and management of water resource have a significant practical meaning. Regional agricultural water resource system features with unpredictable, self-organization, and non-linear which lays a certain difficulty on the evaluation of regional agriculture water resource resilience. The current research on water resource resilience remains to focus on qualitative analysis and the quantitative analysis is still in the primary stage, thus, according to the above issues, projection pursuit classification model is brought forward. With the help of artificial fish-swarm algorithm (AFSA), it optimizes the projection index function, seeks for the optimal projection direction, and improves AFSA with the application of self-adaptive artificial fish step and crowding factor. Taking Hongxinglong Administration of Heilongjiang as the research base and on the basis of improving AFSA, it established the evaluation of projection pursuit classification model to agriculture water resource system resilience besides the proceeding analysis of projection pursuit classification model on accelerating genetic algorithm. The research shows that the water resource resilience of Hongxinglong is the best than Raohe Farm, and the last 597 Farm. And the further analysis shows that the key driving factors influencing agricultural water resource resilience are precipitation and agriculture water consumption. The research result reveals the restoring situation of the local water resource system, providing foundation for agriculture water resource management.
Lin Wei; Tian Zheng; Wen Xianbin
2003-01-01
The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.
PROJECTION-PURSUIT BASED PRINCIPAL COMPONENT ANALYSIS: A LARGE SAMPLE THEORY
Jian ZHANG
2006-01-01
The principal component analysis (PCA) is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit (PP), a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.
Projection pursuit cluster model and its application in water quality assessment
WANG Shun-jiu; YANG Zhi-feng; DING Jing
2004-01-01
One of the difficulties frequently encountered in water quality assessment is that there are many factors and they cannot be assessed according to one factor, all the effect factors associated with water quality must be used. In order to overcome this issues the projection pursuit principle is introduced into water quality assessment, and projection pursuit cluster(PPC) model is developed in this study. The PPC model makes the transition from high dimension to one-dimension. In other words, based on the PPC model, multifactor problem can be converted to one factor problem. The application of PPC model can be divided into four parts: (1) to estimate projection index function ; (2) to find the right projection direction; (3) to calculate projection characteristic value of the sample , and (4) to draw comprehensive analysis on the basis of . On the other hand, the empirical formula of cutoff radius is developed, which is benefit for the model to be used in practice. Finally, a case study of water quality assessment is proposed in this paper. The results showed that the PPC model is reasonable, and it is more objective and less subjective in water quality assessment. It is a new method for multivariate problem comprehensive analysis.
Goodness-of-Fit Tests For Elliptical and Independent Copulas through Projection Pursuit
Jacques Touboul
2011-04-01
Full Text Available Two goodness-of-fit tests for copulas are being investigated. The first one deals with the case of elliptical copulas and the second one deals with independent copulas. These tests result from the expansion of the projection pursuit methodology that we will introduce in the present article. This method enables us to determine on which axis system these copulas lie as well as the exact value of these very copulas in the basis formed by the axes previously determined irrespective of their value in their canonical basis. Simulations are also presented as well as an application to real datasets.
Souad Larabi Marie-Sainte
2017-01-01
Full Text Available This article consists of using biologically inspired algorithms in order to detect potentially interesting structures in large and multidimensional data sets. Data exploration and the detection of interesting structures are based on the use of Projection Pursuit that involves the definition and the optimization of an index associated with each direction or projection. The optimization of a projection index should provide a set of multiple optima that is expected to correspond to interesting graphical representations in low dimensional space. The implementation of the bio-inspired algorithms along with the projection pursuit develops a new software called EPP-Lab. Projection pursuit is widely used in different scientific domains (biology, pharmacy, bioinformatics, biometry, etc but not widely present in the well-known softwares. EPP-Lab is dedicated to recognize and visualize clusters and outlying observations on one dimension from high dimensional and multivariate data sets. It includes different statistical techniques for results analysis. It provides several features and gives the user the option to adjust the parameters of the selected bio-inspired methods or to use defaults values. EPP-Lab is a unique software for detection, visualization and analysis of non-linear structures. The performance of this tool has been validated by testing different real and simulated data sets.
Landscape ecological security assessment based on projection pursuit in Pearl River Delta.
Gao, Yang; Wu, Zhifeng; Lou, Quansheng; Huang, Huamei; Cheng, Jiong; Chen, Zhangli
2012-04-01
Regional landscape ecological security is an important issue for ecological security, and has a great influence on national security and social sustainable development. The Pearl River Delta (PRD) in southern China has experienced rapid economic development and intensive human activities in recent years. This study, based on landscape analysis, provides a method to discover the alteration of character among different landscape types and to understand the landscape ecological security status. Based on remotely sensed products of the Landsat 5 TM images in 1990 and the Landsat 7 ETM+ images in 2005, landscape classification maps of nine cities in the PRD were compiled by implementing Remote Sensing and Geographic Information System technology. Several indices, including aggregation, crush index, landscape shape index, Shannon's diversity index, landscape fragile index, and landscape security adjacent index, were applied to analyze spatial-temporal characteristics of landscape patterns in the PRD. A landscape ecological security index based on these outcomes was calculated by projection pursuit using genetic algorithm. The landscape ecological security of nine cities in the PRD was thus evaluated. The main results of this research are listed as follows: (1) from 1990 to 2005, the aggregation index, crush index, landscape shape index, and Shannon's diversity index of nine cities changed little in the PRD, while the landscape fragile index and landscape security adjacent index changed obviously. The landscape fragile index of nine cities showed a decreasing trend; however, the landscape security adjacent index has been increasing; (2) from 1990 to 2005, landscape ecology of the cities of Zhuhai and Huizhou maintained a good security situation. However, there was a relatively low value of ecological security in the cities of Dongguan and Foshan. Except for Foshan and Guangzhou, whose landscape ecological security situation were slightly improved, the cities had reduced
Jiahang Yuan
2017-01-01
Full Text Available In consideration of the interaction among attributes and the influence of decision makers’ risk attitude, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. With respect to a multiattribute group decision-making problem, the prospect value functions of intuitionistic trapezoidal fuzzy numbers are aggregated by the proposed operator; then a grey relation-projection pursuit dynamic cluster method is developed to obtain the ranking of alternatives; the firefly algorithm is used to optimize the objective function of projection for obtaining the best projection direction of grey correlation projection values, and the grey correlation projection values are evaluated, which are applied to classify, rank, and prefer the alternatives. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.
Pires, Carlos A. L.; Ribeiro, Andreia F. S.
2017-02-01
We develop an expansion of space-distributed time series into statistically independent uncorrelated subspaces (statistical sources) of low-dimension and exhibiting enhanced non-Gaussian probability distributions with geometrically simple chosen shapes (projection pursuit rationale). The method relies upon a generalization of the principal component analysis that is optimal for Gaussian mixed signals and of the independent component analysis (ICA), optimized to split non-Gaussian scalar sources. The proposed method, supported by information theory concepts and methods, is the independent subspace analysis (ISA) that looks for multi-dimensional, intrinsically synergetic subspaces such as dyads (2D) and triads (3D), not separable by ICA. Basically, we optimize rotated variables maximizing certain nonlinear correlations (contrast functions) coming from the non-Gaussianity of the joint distribution. As a by-product, it provides nonlinear variable changes `unfolding' the subspaces into nearly Gaussian scalars of easier post-processing. Moreover, the new variables still work as nonlinear data exploratory indices of the non-Gaussian variability of the analysed climatic and geophysical fields. The method (ISA, followed by nonlinear unfolding) is tested into three datasets. The first one comes from the Lorenz'63 three-dimensional chaotic model, showing a clear separation into a non-Gaussian dyad plus an independent scalar. The second one is a mixture of propagating waves of random correlated phases in which the emergence of triadic wave resonances imprints a statistical signature in terms of a non-Gaussian non-separable triad. Finally the method is applied to the monthly variability of a high-dimensional quasi-geostrophic (QG) atmospheric model, applied to the Northern Hemispheric winter. We find that quite enhanced non-Gaussian dyads of parabolic shape, perform much better than the unrotated variables in which concerns the separation of the four model's centroid regimes
Hongying Du
Full Text Available The epidermal growth factor receptor (EGFR protein tyrosine kinase (PTK is an important protein target for anti-tumor drug discovery. To identify potential EGFR inhibitors, we conducted a quantitative structure-activity relationship (QSAR study on the inhibitory activity of a series of quinazoline derivatives against EGFR tyrosine kinase. Two 2D-QSAR models were developed based on the best multi-linear regression (BMLR and grid-search assisted projection pursuit regression (GS-PPR methods. The results demonstrate that the inhibitory activity of quinazoline derivatives is strongly correlated with their polarizability, activation energy, mass distribution, connectivity, and branching information. Although the present investigation focused on EGFR, the approach provides a general avenue in the structure-based drug development of different protein receptor inhibitors.
FU QIANG; XIE YONGGANG; WEI ZIMIN
2003-01-01
A new technique of dimension reduction named projection pursuit is applied to model and evaluatewetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the im-proved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimized and multi-dimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluationof wetland soil quality variations are realized by pursuing optimum projection direction and projection func-tion value. Therefore, by adopting this new method, any possible human interference can be avoided andsound results can be achieved in researching quality changes and classification of wetland soils.
Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity
Blythe, Duncan A J
2011-01-01
This thesis derives, tests and applies two linear projection algorithms for machine learning under non-stationarity. The first finds a direction in a linear space upon which a data set is maximally non-stationary. The second aims to robustify two-way classification against non-stationarity. The algorithm is tested on a key application scenario, namely Brain Computer Interfacing.
Mabb, David; Parekh, Manisha; Dayanita, Dayanita Singh; Audiobombing Crew,
2011-01-01
Nature Morte is pleased to announce „Serial Pursuits,“ a group exhibition in which recent works in various media by David Mabb, Manisha Parekh, Dayanita Singh and Audiobombing Crew will be brought together to present an exploration of art works created as sets or in sequences. The highlight of the opening night will be a performance by Audiobombing Crew. Founded by Markus Zull and Stephan Ebersthäuser in 2003, the sound art collective creates serial sound loops, which are collaged togethe...
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Quantal Response: Nonparametric Modeling
2017-01-01
spline N−spline Fig. 3 Logistic regression 7 Approved for public release; distribution is unlimited. 5. Nonparametric QR Models Nonparametric linear ...stimulus and probability of response. The Generalized Linear Model approach does not make use of the limit distribution but allows arbitrary functional...7. Conclusions and Recommendations 18 8. References 19 Appendix A. The Linear Model 21 Appendix B. The Generalized Linear Model 33 Appendix C. B
Nonparametric statistical methods
Hollander, Myles; Chicken, Eric
2013-01-01
Praise for the Second Edition"This book should be an essential part of the personal library of every practicing statistician."-Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given sit
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Zhao, Y.; Su, X. H.; Wang, M. H.; Li, Z. Y.; Li, E. K.; Xu, X.
2017-08-01
Water resources vulnerability control management is essential because it is related to the benign evolution of socio-economic, environmental and water resources system. Research on water resources system vulnerability is helpful to realization of water resources sustainable utilization. In this study, the DPSIR framework of driving forces-pressure–state–impact-response was adopted to construct the evaluation index system of water resources system vulnerability. Then the co-evolutionary genetic algorithm and projection pursuit were used to establish evaluation model of water resources system vulnerability. Tengzhou City in Shandong Province was selected as a study area. The system vulnerability was analyzed in terms of driving forces, pressure, state, impact and response on the basis of the projection value calculated by the model. The results show that the five components all belong to vulnerability Grade II, the vulnerability degree of impact and state were higher than other components due to the fierce imbalance in supply-demand and the unsatisfied condition of water resources utilization. It is indicated that the influence of high speed socio-economic development and the overuse of the pesticides have already disturbed the benign development of water environment to some extents. While the indexes in response represented lower vulnerability degree than the other components. The results of the evaluation model are coincident with the status of water resources system in the study area, which indicates that the model is feasible and effective.
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
基于投影寻踪的Web软件复杂性度量%Web software complexity metrics based on projection pursuit
曾一; 胡小威; 李鹃
2012-01-01
传统的软件复杂性度量方法主要是针对C/C++、Ada等语言开发的非Web应用.以面向对象的基于Struts框架的Web软件为研究对象,提出了适合于Web-Struts软件的3个方面的复杂性度量指标,并提出了一种基于带交叉算子人工鱼群和投影寻踪(PP)算法的Web应用软件复杂性度量方法.把Web软件多个复杂性度量指标综合成一维综合投影值,利用样本数据求解最佳投影方向,确定评价等级的综合投影值区间,根据测试样本综合投影值与区间值比较,获得综合评价结果.实例评价结果表明,所提方法具有较强的适用性和应用性.%Web software complexity metrics does play a very important role in the software development. The traditional software complexity metrics method mainly targets on the non-Web applications which use language like C/C + + and Ada. This paper took object-oriented Web software based on Struts framework as research subject and put forward three complexity metrics suitable for the Web-Struts software. Besides, this paper also proposed a method for computing Web software complexity metrics based on Artificial Fish Swarm Algorithm ( AFSA) with cross operator and Projection Pursuit ( PP) algorithm. After integrating multiple complexity metrics into one-dimension comprehensive projection value, the optimized projection direction could be acquired through sample data. Then the comprehensive projection value of evaluation grades could also be determined. According to the comparison between the comprehensive projection values of the testing samples and the interval of level, the comprehensive metrics result could be finally obtained. The example evaluation results prove the feasibility and effectiveness of the proposed method.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2014-01-01
Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.
Min Ge
2017-01-01
Full Text Available Clarification of initial water rights is the basis and prerequisite for a water rights trade-off market and also an effective solution to the problem of water scarcity and water conflicts. According to the new requirements for the most stringent water resources management in China, an initial provincial water rights allocation model is proposed. Firstly, based on analysis of multiple principles for initial provincial water rights allocation including total water use, water use efficiency, water quality of water function zones, regional coordination and sharing, an index system of initial provincial water rights allocation is designed. Secondly, according to dynamic projection pursuit technique, an initial provincial water rights allocation model with the total water use control is set up. Moreover, the self-adaptive chaotic optimization algorithm is applied to tackle the model. Finally, a case study of Taihu Basin is adopted. Considering the multiple scenarios of three different water frequencies (50%, 75% and 90% and planning year 2030, the empirical results show Jiangsu Province always obtains the most initial water rights. When the developing situation of provinces are given more consideration, Shanghai should acquire more initial water rights than Zhejiang Province; but when the dynamic increment evolving trend of provinces is taken more into account, Shanghai should obtain less initial water rights than Zhejiang Province. The case about Taihu Lake further verifies the feasibility and effectiveness of the proposed model and provides a multiple-scenarios decision making support for entitling the initial water rights with the most stringent water resources management constrains in Taihu Basin.
基于投影寻踪分析的芯片硬件木马检测%Hardware Trojans detection based on projection pursuit
张鹏; 王新成; 周庆
2013-01-01
提出一种利用芯片旁路泄漏信息的硬件木马无损检测方法，通过基于绝对信息散度指标的投影寻踪技术，将芯片运行过程中产生的高维旁路信号投影变换到低维子空间，在信息损失尽量小的前提下发现原始数据中的分布特征，从而实现芯片旁路信号特征提取与识别。针对示例性高级加密标准(AES-128)木马电路的检测实验表明，该技术可以有效分辨基准芯片与硬件木马测试芯片之间的旁路信号特征差异，实现硬件木马检测。%A novel hardware Trojans detection technique using the side channel signals of chips was proposed. Based on the projection pursuit with absolute information divergence index, this technique could find out the data structure enables reflect high dimension special rules without obvious information loss, so as to attain the goal of feature abstraction and identification on side channel signals of IC chips. The detection experiment against an exemplary AES-128 hardware Trojan circuit showed that the technique could distinguish the difference of side channel signal’s feature between the ge-nuine chip and tested chip, and consequently could detect the existence of the hardware Trojan.
Nonparametric tests for censored data
Bagdonavicus, Vilijandas; Nikulin, Mikhail
2013-01-01
This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
基于混合蛙跳投影寻踪模型的水利水电规划方案优选%Hydropower Planning Scheme preferred Based on Projection Pursuit Model SFLA
张博; 许娇娇
2015-01-01
介绍了投影寻踪方法的基本原理, 并引入混合蛙跳算法求解投影寻踪模型中最佳投影方向优化问题,建立基于混合蛙跳算法的投影寻踪模型, 并将其应用于水利水电规划方案优选问题, 结果表明该方法不仅避免了常规方法主观赋权的任意性, 而且计算简单、 使用范围广, 具有良好的有效性与适用性.%The concept and postulate of the Projection Pursuit was introduced ,and Shuffled Frog Leaping Al-gorithm was introduced to solve the optimal projection direction optimization problem ,then Projection Pursuit model based on SFLA was established and applied for hydropower planning scheme optimization problems ,the evaluation results indicated that this method not only avoids arbitrariness of empowerment in traditional method,but also has a simple calculation and wide range ,as well as good validity and applicability.
CURRENT STATUS OF NONPARAMETRIC STATISTICS
Orlov A. I.
2015-02-01
Full Text Available Nonparametric statistics is one of the five points of growth of applied mathematical statistics. Despite the large number of publications on specific issues of nonparametric statistics, the internal structure of this research direction has remained undeveloped. The purpose of this article is to consider its division into regions based on the existing practice of scientific activity determination of nonparametric statistics and classify investigations on nonparametric statistical methods. Nonparametric statistics allows to make statistical inference, in particular, to estimate the characteristics of the distribution and testing statistical hypotheses without, as a rule, weakly proven assumptions about the distribution function of samples included in a particular parametric family. For example, the widespread belief that the statistical data are often have the normal distribution. Meanwhile, analysis of results of observations, in particular, measurement errors, always leads to the same conclusion - in most cases the actual distribution significantly different from normal. Uncritical use of the hypothesis of normality often leads to significant errors, in areas such as rejection of outlying observation results (emissions, the statistical quality control, and in other cases. Therefore, it is advisable to use nonparametric methods, in which the distribution functions of the results of observations are imposed only weak requirements. It is usually assumed only their continuity. On the basis of generalization of numerous studies it can be stated that to date, using nonparametric methods can solve almost the same number of tasks that previously used parametric methods. Certain statements in the literature are incorrect that nonparametric methods have less power, or require larger sample sizes than parametric methods. Note that in the nonparametric statistics, as in mathematical statistics in general, there remain a number of unresolved problems
Nonparametric statistical methods using R
Kloke, John
2014-01-01
A Practical Guide to Implementing Nonparametric and Rank-Based ProceduresNonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.The book first gives an overview of the R language and basic statistical c
Nonparametric Bayes analysis of social science data
Kunihama, Tsuyoshi
Social science data often contain complex characteristics that standard statistical methods fail to capture. Social surveys assign many questions to respondents, which often consist of mixed-scale variables. Each of the variables can follow a complex distribution outside parametric families and associations among variables may have more complicated structures than standard linear dependence. Therefore, it is not straightforward to develop a statistical model which can approximate structures well in the social science data. In addition, many social surveys have collected data over time and therefore we need to incorporate dynamic dependence into the models. Also, it is standard to observe massive number of missing values in the social science data. To address these challenging problems, this thesis develops flexible nonparametric Bayesian methods for the analysis of social science data. Chapter 1 briefly explains backgrounds and motivations of the projects in the following chapters. Chapter 2 develops a nonparametric Bayesian modeling of temporal dependence in large sparse contingency tables, relying on a probabilistic factorization of the joint pmf. Chapter 3 proposes nonparametric Bayes inference on conditional independence with conditional mutual information used as a measure of the strength of conditional dependence. Chapter 4 proposes a novel Bayesian density estimation method in social surveys with complex designs where there is a gap between sample and population. We correct for the bias by adjusting mixture weights in Bayesian mixture models. Chapter 5 develops a nonparametric model for mixed-scale longitudinal surveys, in which various types of variables can be induced through latent continuous variables and dynamic latent factors lead to flexibly time-varying associations among variables.
2009-12-18
analysis of the equilibria based on linearization of the shape dynamics. In [10], the authors extend their analysis to incorporate feedback control...differentiable curves in R2, deriving our dynamics from the natural Frenet frame equations (see, e.g., [5] for details). (A three- dimensional analysis of...cyclic pursuit formulated in terms of the natural Frenet frame equations is a topic of ongoing work.) As is depicted in figure 1, we let ri denote the
David P. Schmitt et al.
2017-05-01
Full Text Available Previous studies have documented links between sub-clinical narcissism and the active pursuit of short-term mating strategies (e.g., unrestricted sociosexuality, marital infidelity, mate poaching. Nearly all of these investigations have relied solely on samples from Western cultures. In the current study, responses from a cross-cultural survey of 30,470 people across 53 nations spanning 11 world regions (North America, Central/South America, Northern Europe, Western Europe, Eastern Europe, Southern Europe, Middle East, Africa, Oceania, Southeast Asia, and East Asia were used to evaluate whether narcissism (as measured by the Narcissistic Personality Inventory; NPI was universally associated with short-term mating. Results revealed narcissism scores (including two broad factors and seven traditional facets as measured by the NPI were functionally equivalent across cultures, reliably associating with key sexual outcomes (e.g., more active pursuit of short-term mating, intimate partner violence, and sexual aggression and sex-related personality traits (e.g., higher extraversion and openness to experience. Whereas some features of personality (e.g., subjective well-being were universally associated with socially adaptive facets of Narcissism (e.g., self-sufficiency, most indicators of short-term mating (e.g., unrestricted sociosexuality and marital infidelity were universally associated with the socially maladaptive facets of narcissism (e.g., exploitativeness. Discussion addresses limitations of these cross-culturally universal findings and presents suggestions for future research into revealing the precise psychological features of narcissism that facilitate the strategic pursuit of short-term mating.
田铮; 文奇; 金子
2001-01-01
The convergence property of the projection pursuit learning network (PPLN) that is used to approximate to non-linear autoregrcssion is studied in this paper. The authros prove that PPLN can approximate to non-linear autoregression at any given precision in Lk space, where k is integer. The learning strategy and calculative procedures of PPLN's, which are used to establishthe models of non-linear time series {Xt } and forecast the subsequent behavior of {Xt}, are also presented. Using PPLN, the Wolfer sunspont number(1749-1894), Canada lvnx data(1821-1924) and Xi'an data(0-360) are fitted. Furthermore, the predictors for the above three kinds of data are also pre sented, respectively. Finally, we compare the performance the projection pursuit learning network not only with that of backpropagation learning (BPLN) but also with that of the threshold model. It is shown that the projection pursuit learning networks perform well and compare favorably to BPLN and the threshold model.%本文研究非线性自回归模型投影寻踪学习网络逼近的收敛性，证明了在Lk(k为正整数)空间上，投影寻踪学习网络可以以任意精度逼近非线性自回归模型，给出基于投影寻踪学习网络的非线性时间序列模型建模和预报的计算方法和应用实例，对太阳黑子数据、山猫数据及西安数据进行了拟合和预报，将其结果与改进的BP网和门限自回归模型相应的结果进行比较，结果表明基于投影寻踪学习网络的非线性时间序列的建模和预报方法是一类行之有效的方法。
Semi- and Nonparametric ARCH Processes
Oliver B. Linton
2011-01-01
Full Text Available ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatility function, the relationship between mean and variance, long memory processes, locally stationary processes, continuous time processes and multivariate models. The second part of the paper is about the general properties of such processes, including stationary conditions, ergodic conditions and mixing conditions. The last part is on the estimation methods in ARCH/GARCH processes.
袁尧; 刘超
2013-01-01
针对泵站优化运行计算时最优解评价指标单一的问题,建立了包含机组开停机约束的泵站优化运行数学模型和运行方案选优的投影寻踪决策模型.提出了求解泵站多机组优化运行模型的蚁群算法,并通过分析模型的特性改进了算法中启发式信息和信息素更新方式.对江都四站多机组日优化运行计算的结果显示,变量同等离散的情况下,利用蚁群算法优化的结果比用动态规划逐次逼近法优化的结果节省了2.8％的电费,前者相比设计工况运行时节省了29.2％的电费,且蚁群算法优化结果对应的运行方案中叶片调节次数少,机组运行时间短；方案选优时投影寻踪决策模型能够兼顾多个评价指标的优选,得到的运行方案不仅运行成本低,且更合理,更贴切于日常运行,可见改进后的蚁群算法结合投影寻踪决策模型在泵站优化运行及相近的领域有较大的实用价值.%Usually the evaluation index of optimal pump operation solution is single. An optimal pump operation model which contained the constraint of start-stop pump unit was developed, and projection pursuit evaluation method for scheme optimization was proposed. The ant colony optimization algorithm was used to calculate the model. The heuristic information and the pheromone trail update method were improved by analyzing characters of the model for better performances. A calculation example for the No. 4 Jiangdu pumping station was conducted. The results from ant colony optimization algorithm showed that 29. 2% of energy fee could be saved under the designed operation condition, which was compared with the result from dynamic programming with successive approximation algorithm under the same discrete condition, and was better with 2. 8% of the result from dynamic programming. The results from ant colony optimization algorithm had less times of the blade adjusting, and shorter operating time of the pumps
Generalized Orthogonal Matching Pursuit
Wang, Jian; Shim, Byonghyo
2011-01-01
As a greedy algorithm to recover sparse signals from compressed measurements, the orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we introduce an extension of the orthogonal matching pursuit (gOMP) for pursuing efficiency in reconstructing sparse signals. Our approach, henceforth referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple indices are identified per iteration. Owing to the selection of multiple "correct" indices, the gOMP algorithm is finished with much smaller number of iterations compared to the OMP. We show that the gOMP can perfectly reconstruct any $K$-sparse signals ($K > 1$), provided that the sensing matrix satisfies the RIP with $\\delta_{NK} < \\frac{\\sqrt{N}}{\\sqrt{K} + 2 \\sqrt{N}}$. We also demonstrate by empirical simulations that the gOMP has excellent recovery performance comparable to $\\ell_1$-minimization technique with fast processing speed and competitive computational com...
Nonparametric estimation of ultrasound pulses
Jensen, Jørgen Arendt; Leeman, Sidney
1994-01-01
An algorithm for nonparametric estimation of 1D ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
张目; 周宗放
2011-01-01
提出一种基于投影寻踪和最优分割的企业信用评级模型.该模型运用投影寻踪对样本企业进行信用综合评分,将信用综合得分由大到小排序,生成有序样品序列；利用最优分割法对有序样品进行聚类,得出明确的聚类结果；将最优分割点对应的信用综合得分作为划分信用等级的阈值,从而实现对样本企业的信用评级.应用实例证明了该模型的可行性和有效性.%A new credit rating model for enterprises based on projection pursuit and optimal partition is presented in this paper. Using projection pursuit, the comprehensive credit score of each sample is obtained. After sorting the comprehensive credit score descending, the ordered sample series is generated. A clustering analysis of the ordered samples is carried out with the optimal partition method, so the clustering results are obtained definitely. And then, each optimal partition point is regarded as the threshold to divide the credit grades. Finally, the credit ratin'g for enterprises is achieved. Through a specific example, it is proved that the model proposed by this paper is feasible and effective.
Non-Parametric Inference in Astrophysics
Wasserman, L H; Nichol, R C; Genovese, C; Jang, W; Connolly, A J; Moore, A W; Schneider, J; Wasserman, Larry; Miller, Christopher J.; Nichol, Robert C.; Genovese, Chris; Jang, Woncheol; Connolly, Andrew J.; Moore, Andrew W.; Schneider, Jeff; group, the PICA
2001-01-01
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
董丽丽; 于苗; 徐淑琴
2015-01-01
针对粒子群算法局部搜索能力较弱和存在早熟收敛的问题，为了有效地控制粒子群算法的全局搜索和局部搜索，提出了将线性递减权重引入到粒子群优化算法中。该算法是从随机解出发，通过追随当前搜索到的最优值来寻找全局最优解，增加了粒子群算法的局部搜索能力。将其算法优化投影寻踪模型，以此构建了线性递减权重粒子群优化投影寻踪模型，将该模型应用到土坝护坡模式优化评价中，选取9个指标作为评判因子，提出适合该地区的土坝护坡优化模式。结果表明：线性递减权重粒子群优化投影寻踪模型可以有效地找到最佳投影方向，计算投影值，根据投影指标值的大小可对方案进行优选。利用该模型对土坝护坡模式进行综合评价是切实可行的。该算法以其实现容易、精度高、收敛快等优点，并且在解决实际问题中展示的优越性，在工程优化领域具有广泛的应用前景。%To overcome the lower local search ability and the problem of premature convergence in par-ticle swarm optimization,and efficiently control the global and local search of particle swarm optimiza-tion,particle swarm optimization with linearly decreasing weight was proposed.It started from random solutions,which followed the current search for the optimal value to find the global optimum,increa-sing the local search ability of the algorithm.By optimizing the algorithm with the projection pursuit model,particle swarm with linearly decreasing weight optimized projection pursuit model was estab-lished.The model was applied to optimize evaluation of dam slope protection,and 9 indicators were reselected as evaluation factors;as a result,an area-suitable optimization model of dam slope protection was proposed.The results show that using particle swarm with linearly decreasing weight optimized pro-jection pursuit model can effectively find out the best
Nonparametric Inference for Periodic Sequences
Sun, Ying
2012-02-01
This article proposes a nonparametric method for estimating the period and values of a periodic sequence when the data are evenly spaced in time. The period is estimated by a "leave-out-one-cycle" version of cross-validation (CV) and complements the periodogram, a widely used tool for period estimation. The CV method is computationally simple and implicitly penalizes multiples of the smallest period, leading to a "virtually" consistent estimator of integer periods. This estimator is investigated both theoretically and by simulation.We also propose a nonparametric test of the null hypothesis that the data have constantmean against the alternative that the sequence of means is periodic. Finally, our methodology is demonstrated on three well-known time series: the sunspots and lynx trapping data, and the El Niño series of sea surface temperatures. © 2012 American Statistical Association and the American Society for Quality.
Nonparametric Econometrics: The np Package
Tristen Hayﬁeld
2008-07-01
Full Text Available We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of signiﬁcance and consistent model speciﬁcation tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Stable Principal Component Pursuit
Zhou, Zihan; Wright, John; Candes, Emmanuel; Ma, Yi
2010-01-01
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a high-dimensional data matrix despite both small entry-wise noise and gross sparse errors. Recently, it has been shown that a convex program, named Principal Component Pursuit (PCP), can recover the low-rank matrix when the data matrix is corrupted by gross sparse errors. We further prove that the solution to a related convex program (a relaxed PCP) gives an estimate of the low-rank matrix that is simultaneously stable to small entrywise noise and robust to gross sparse errors. More precisely, our result shows that the proposed convex program recovers the low-rank matrix even though a positive fraction of its entries are arbitrarily corrupted, with an error bound proportional to the noise level. We present simulation results to support our result and demonstrate that the new convex program accurately recovers the principal components (the low-rank matrix) under quite broad conditions. To our knowledge, this is...
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
Nonparametric identification of copula structures
Li, Bo
2013-06-01
We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process.We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on the structure of copulas, particularly when the sample size is moderately large. We illustrate our testing approach on two datasets. © 2013 American Statistical Association.
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
A contingency table approach to nonparametric testing
Rayner, JCW
2000-01-01
Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables.This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more comp
Nonparametric statistics for social and behavioral sciences
Kraska-MIller, M
2013-01-01
Introduction to Research in Social and Behavioral SciencesBasic Principles of ResearchPlanning for ResearchTypes of Research Designs Sampling ProceduresValidity and Reliability of Measurement InstrumentsSteps of the Research Process Introduction to Nonparametric StatisticsData AnalysisOverview of Nonparametric Statistics and Parametric Statistics Overview of Parametric Statistics Overview of Nonparametric StatisticsImportance of Nonparametric MethodsMeasurement InstrumentsAnalysis of Data to Determine Association and Agreement Pearson Chi-Square Test of Association and IndependenceContingency
任永泰; 李丽
2011-01-01
利用基于极大熵准则赋权和基于实数加速遗传算法的投影寻踪方法相结合的组合附权法确定了各预警指标的权重；采用层次分析法计算水资源可持续利用复合系统中各子系统所占权重；利用综合评价模型计算出哈尔滨市水资源可持续发展指数；最终得到哈尔滨市水资源可持续利用预警结果.%The weights of each warning index are determined by combination enables law which is based on the maximum entropy criterion empowerment and projection pursuit method of real accelerating genetic algorithm; Using analytic hierarchy process to calculate the weights of each subsystem in composite system of water resources sustainable utilization; Sustainable development index of Harbin water resources is calculated by using comprehensive evaluation model; Warning results of Harbin water resources sustainable utilization are got eventually.
侯秀玲; 周益民; 周密; 王绍俊
2012-01-01
[Objective] The purpose of this project was to provide a scientific basis for preventing and controlling pollution by choosing different methods basing on particle swarm algorithm projection pursuit model analysis of farmland soil pollution. [Method] The sewage irrigation area from Shihezi city was chosen as the research object, surface soil was collected and various elements analysis was conducted. Through a number of indicators of soil contamination as projection parameters, the projection direction was determined. The projection index reflected the characteristics of pollutants in the soil of sewage irrigation areas. The method can avoid the artificial disturbance and acquire preferable effect. [Result] The results showed that soil pollution was mainly from nickel and chromium, and showed a tendency of soil sampling points reduction and soil environmental comprehensive quality deterioration. [ Conclusion ] This study determined the prevention and control of soil pollution in Shihezi River.%[目的]利用基于粒子群算法的投影寻踪模型分析农田土壤污染问题,为农田土壤污染选用不同的污染防治方法提供科学依据.[方法]以石河子总场城市污水灌溉区为研究对象,采集表层土壤样品,对多种元素进行分析.以多个土壤污染指标作为投影参数来寻求其投影方向,由投影指标函效来反映污灌区土壤污染物特征,避免人为赋予权重的干扰.[结果]土壤污染以镍和铬为主要污染因子,并呈现随土壤环境质量综合状况的变差,土壤采样点数减少的规律.[结论]石河子总场对土壤污染影响最大的是污染物是镍和铬,对土壤的污染顺序为:Ni ＞Cd ＞Zn＞ Cu ＞V＞ As＞ Pb＞ Mn＞F＞Se＞Hg ＞Cr.确定了石河子总场农田土壤污染防治方向和方法.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Nonparametric Regression with Common Shocks
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Nonparametric Bayesian Modeling of Complex Networks
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....
An asymptotically optimal nonparametric adaptive controller
郭雷; 谢亮亮
2000-01-01
For discrete-time nonlinear stochastic systems with unknown nonparametric structure, a kernel estimation-based nonparametric adaptive controller is constructed based on truncated certainty equivalence principle. Global stability and asymptotic optimality of the closed-loop systems are established without resorting to any external excitations.
李祚泳; 张正健; 余春雪
2012-01-01
Traditional projection pursuit regression represented with matrix, which is applied in water quality evaluation for multi-index, affects not only learning efficient of optimized parameter matrix element, but also optimal effects. The present work set the proper reference values and transformed forms for each index. Therefore, the different in the same grade standard values with different index could be weakened after the normal transformation, the normalized values of different indexes were e-quivalent to a certain normalized index. Therefore, it is only necessary to set up the models of NV-PPR (2) and NV-PPR(3) suited to 2 indexes and 3 indexes, respectively, for each normalized index values. Meanwhile, the optimization of the parameter matrix elements of model were iterated by monkey-king genetic algorithm. Furthermore, the multi-index NV-PPR model could be represented into the combinations of some NV-PPR (2) and (or) NV-PPR (3) models. The practicality of models was verified virtually. The results showed that the projection pursuit regression model of water quality evaluation based on normalized index transform exhibited the characteristics of simplicity in form, convenience during calculation, university as well as commonness.%传统的投影寻踪回归(PPR)的矩阵表示法用于水质评价,当指标较多时,不仅优化参数矩阵元的学习效率低,而且优化效果亦受到影响.若适当设置3类水体(地表水、地下水和富营养化水体)各指标的参照值及指标值的规范变换式,使不同指标的同级标准的规范值差异不大,从而可以认为用规范值表示的不同指标皆与某个规范指标“等效”.因此,只需构造并优化得出对各指标规范值都共同适用的2个指标变量的NV-PPR(2)和3个指标变量的NV-PPR(3)模型,对于指标变量较多的NV-PPR建模,只需将其分解为若干个NV-PPR(2)和(或)NV-PPR(3)的组合表示即可.对模型的实用性进行的效
Nonparametric instrumental regression with non-convex constraints
Grasmair, M.; Scherzer, O.; Vanhems, A.
2013-03-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.
Parametric and Non-Parametric System Modelling
Nielsen, Henrik Aalborg
1999-01-01
considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how....... For this purpose non-parametric methods together with additive models are suggested. Also, a new approach specifically designed to detect non-linearities is introduced. Confidence intervals are constructed by use of bootstrapping. As a link between non-parametric and parametric methods a paper dealing with neural...... the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...
Bayesian nonparametric duration model with censorship
Joseph Hakizamungu
2007-10-01
Full Text Available This paper is concerned with nonparametric i.i.d. durations models censored observations and we establish by a simple and unified approach the general structure of a bayesian nonparametric estimator for a survival function S. For Dirichlet prior distributions, we describe completely the structure of the posterior distribution of the survival function. These results are essentially supported by prior and posterior independence properties.
Bootstrap Estimation for Nonparametric Efficiency Estimates
1995-01-01
This paper develops a consistent bootstrap estimation procedure to obtain confidence intervals for nonparametric measures of productive efficiency. Although the methodology is illustrated in terms of technical efficiency measured by output distance functions, the technique can be easily extended to other consistent nonparametric frontier models. Variation in estimated efficiency scores is assumed to result from variation in empirical approximations to the true boundary of the production set. ...
Stevens, Jon Scott; Gleitman, Lila R; Trueswell, John C; Yang, Charles
2017-04-01
We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from naturalistic corpora of parent-child interactions, even though the model maintains far less information than global models. Moreover, Pursuit is found to best capture human experimental findings from several relevant cross-situational word-learning experiments, including those of Yu and Smith (), the paradigm example of a finding believed to support fully global cross-situational models. Implications and limitations of these results are discussed, most notably that the model characterizes only the earliest stages of word learning, when reliance on the co-occurring referent world is at its greatest. Copyright © 2016 Cognitive Science Society, Inc.
曹永强; 马静; 李香云; 柯丽娜; 伊吉美
2011-01-01
Drought can directly affect agricultural output and food shortages. Its continuous accumulation can result in significant land degradation, water resources depletion, and environmental damage, which can restrict the sustainable development of agriculture. The status of agricultural production is prominent in Dalian. It often suffers from drought disaster, which has restrained agricultural development in recent years. Unraveling characteristics of agricultural drought of Dalian is therefore critical. The authors synthetically considered mechanisms of various interactions of agricultural drought, introduced reduced-order thoughts of projection, and built an agricultural drought model by the Projection Pursuit Technique to evaluate agricultural drought vulnerability of Dalian. A vulnerability evaluation index system was built, including population density, agricultural output value proportion, wet area proportion, precipitation, sowing area ratio, the agricultural population proportion, food production per capita, irrigation index, net income of farmer average per capita, and agricultural fertilizer quantity per unit area. We then selected the best projection direction, took characteristic quantities of direction as the mete yard, and evaluated the agricultural drought vulnerability of seven agricultural areas in Dalian based on the panel dataset during the period 2000-2007. Characteristics of variations in spatial-temporal differences of vulnerability assessment were comprehensively analyzed. Then, the zonal map of agricultural drought vulnerability of the study area was drawn. The following conclusions were drawn. The average values of agricultural drought vulnerability assessment in descending order are Zhnanghe City, Pulandian City, Wafangdian City, Jinzhou District, Lvshunkou District, Changhai County and Ganjingzi District. The agricultural drought vulnerability of the north is higher than the south and central Dalian. Results show that the Projection Pursuit
王健; 张晓丽; 刘陶
2011-01-01
In view of the difficulty in nonlinear modeling for prediction of woven fabric permeability, a projection pursuit regression ( PPR) model for prediction of air permeability of woven fabrics was established using the structural parameters such as the total tightness, thickness, and weight per square meter and average float as factors affecting the prediction of woven fabric permeability. The fitted values of tested samples and the predicted values of trained samples were analyzed with the means and standard deviations of relative error as the indicators and were compared with the results of BP neural network and multiple linear regression model. The results showed that the PPR model fitting and prediction accuracy was better than those of BP neural network and multiple linear regression model. In the case of less trained samples, the PPR model still had relatively high prediction accuracy and good generalization ability, providing a novel approach to the prediction of woven fabric permeability.%针对机织物透气性预测中存在非线性建模困难的问题,选择机织物总紧度、厚度、面密度及平均浮长等结构参数作为机织物透气性预测的影响因素,建立机织物透气性预测的投影寻踪回归模型.对模型训练样本的拟合值及检验样本的预测值以相对误差的均值及标准差为指标进行分析,并与BP神经网络及多元线性回归模型进行对比.结果表明,投影寻踪回归模型的拟合及预测精度均优于BP神经网络及多元线性回归模型,且在训练样本较少的情况下,投影寻踪回归模型仍有较高的预测精度和较强的泛化能力,可为机织物透气性预测提供一种新的方法.
EMDR effects on pursuit eye movements.
Kapoula, Zoi; Yang, Qing; Bonnet, Audrey; Bourtoire, Pauline; Sandretto, Jean
2010-05-21
This study aimed to objectivize the quality of smooth pursuit eye movements in a standard laboratory task before and after an Eye Movement Desensitization and Reprocessing (EMDR) session run on seven healthy volunteers. EMDR was applied on autobiographic worries causing moderate distress. The EMDR session was complete in 5 out of the 7 cases; distress measured by SUDS (Subjective Units of Discomfort Scale) decreased to a near zero value. Smooth pursuit eye movements were recorded by an Eyelink II video system before and after EMDR. For the five complete sessions, pursuit eye movement improved after their EMDR session. Notably, the number of saccade intrusions-catch-up saccades (CUS)-decreased and, reciprocally, there was an increase in the smooth components of the pursuit. Such an increase in the smoothness of the pursuit presumably reflects an improvement in the use of visual attention needed to follow the target accurately. Perhaps EMDR reduces distress thereby activating a cholinergic effect known to improve ocular pursuit.
Highly Scalable Matching Pursuit Signal Decomposition Algorithm
National Aeronautics and Space Administration — In this research, we propose a variant of the classical Matching Pursuit Decomposition (MPD) algorithm with significantly improved scalability and computational...
Why preferring parametric forecasting to nonparametric methods?
Jabot, Franck
2015-05-07
A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.
Pursuit-evasion differential games
Yavin, Y
1987-01-01
Twenty papers are devoted to the treatment of a wide spectrum of problems in the theory and applications of dynamic games with the emphasis on pursuit-evasion differential games. The problem of capturability is thoroughly investigated, also the problem of noise-corrupted (state) measurements. Attention is given to aerial combat problems and their attendant modelling issues, such as variable speed of the combatants, the three-dimensionality of physical space, and the combat problem, i.e. problems related to 'role determination'.
Dynamics of aerial target pursuit
Pal, S.
2015-12-01
During pursuit and predation, aerial species engage in multitasking behavior that involve simultaneous target detection, tracking, decision-making, approach and capture. The mobility of the pursuer and the target in a three dimensional environment during predation makes the capture task highly complex. Many researchers have studied and analyzed prey capture dynamics in different aerial species such as insects and bats. This article focuses on reviewing the capture strategies adopted by these species while relying on different sensory variables (vision and acoustics) for navigation. In conclusion, the neural basis of these capture strategies and some applications of these strategies in bio-inspired navigation and control of engineered systems are discussed.
Nonparametric correlation models for portfolio allocation
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major......This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural...... currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis....
Orthogonal Matching Pursuit with Replacement
Jain, Prateek; Dhillon, Inderjit S
2011-01-01
In this paper, we consider the problem of compressed sensing where the goal is to recover almost all the sparse vectors using a small number of fixed linear measurements. For this problem, we propose a novel partial hard-thresholding operator that leads to a general family of iterative algorithms. While one extreme of the family yields well known hard thresholding algorithms like ITI (Iterative Thresholding with Inversion) and HTP (Hard Thresholding Pursuit), the other end of the spectrum leads to a novel algorithm that we call Orthogonal Matching Pursuit with Replacement (OMPR). OMPR, like the classic greedy algorithm OMP, adds exactly one coordinate to the support at each iteration, based on the correlation with the current residual. However, unlike OMP, OMPR also removes one coordinate from the support. This simple change allows us to prove that OMPR has the best known guarantees for sparse recovery in terms of the Restricted Isometry Property (a condition on the measurement matrix). In contrast, OMP is kn...
Recent Advances and Trends in Nonparametric Statistics
Akritas, MG
2003-01-01
The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection o
Correlated Non-Parametric Latent Feature Models
Doshi-Velez, Finale
2012-01-01
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.
A Censored Nonparametric Software Reliability Model
无
2006-01-01
This paper analyses the effct of censoring on the estimation of failure rate, and presents a framework of a censored nonparametric software reliability model. The model is based on nonparametric testing of failure rate monotonically decreasing and weighted kernel failure rate estimation under the constraint of failure rate monotonically decreasing. Not only does the model have the advantages of little assumptions and weak constraints, but also the residual defects number of the software system can be estimated. The numerical experiment and real data analysis show that the model performs well with censored data.
Nonparametric correlation models for portfolio allocation
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural...... breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major...
楼文高; 楼际通; 宋雷娟; 王浪庆
2015-01-01
从上海市某区386家中小企业申报的15项税收指标数据中筛选出对判定企业纳税情况具有重要影响的10个评价指标，并将全部386个样本分成性质相似的建模样本和测试样本（其中测试样本个数占45％），建立了基于投影寻踪分类（PPC）技术的税务稽查评价模型。与多元线性回归（MLR）、判别分析（MDA）、Logistic 和支持向量机（SVM）模型相比，PPC 模型的识别错误率最低，建模样本和测试样本的平均分类错误率低于6％，改进型 PPC 模型包含的评价指标少，两类错误率很接近，非常适用于实际企业的税务稽查评估研究和实践。对339家待判断企业纳税情况的判定结果研究表明，建立的改进型 PPC 模型具有很好的泛化能力和鲁棒性。%Based on the 15 variables’(indexes’)tax-reporting data of 386 wooden-furniture manufacturing small-and medium-sized enterprises (WFMSMEs)located in some districts of Shanghai city,the ten variables mainly influencing the tax-checking situation (tax evasion or compliance)of the 386 WFMSMEs were obtained by applying sensitivity analysis method (SAM)for selecting input variables.The modelling set data and testing set data (about taking up 45%)with similar character-istics-similar mean values and variance-were divided using self-organizing map (SOM)approach.The practical,feasible and effective projection pursuit clustering (PPC)model for tax-checking assessment was thus established.Compared with the mult-ivariate linear regression (MLR),the multivariate discriminant analysis (MDA),Logistic and the support vector machine (SVM),the established PPC model possesses the most accurate and the lowest classification-error percentage (CEP)of the models.The mean CEP of modelling set data and the testing set data is lower than 6%.The improved PPC model including fe-wer variables is thus suitable to tax-checking assessment and research.The tax-checking situation of
Thirty years of nonparametric item response theory
Molenaar, W.
2001-01-01
Relationships between a mathematical measurement model and its real-world applications are discussed. A distinction is made between large data matrices commonly found in educational measurement and smaller matrices found in attitude and personality measurement. Nonparametric methods are evaluated fo
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the trea
Decompounding random sums: A nonparametric approach
Hansen, Martin Bøgsted; Pitts, Susan M.
review a number of applications and consider the nonlinear inverse problem of inferring the cumulative distribution function of the components in the random sum. We review the existing literature on non-parametric approaches to the problem. The models amenable to the analysis are generalized considerably...
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the
A Nonparametric Analogy of Analysis of Covariance
Burnett, Thomas D.; Barr, Donald R.
1977-01-01
A nonparametric test of the hypothesis of no treatment effect is suggested for a situation where measures of the severity of the condition treated can be obtained and ranked both pre- and post-treatment. The test allows the pre-treatment rank to be used as a concomitant variable. (Author/JKS)
Panel data specifications in nonparametric kernel regression
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
崔东文; 梁廷报
2016-01-01
提出湖泊健康评价指标体系和分级标准，构建基于投影寻踪( PP)模型的湖泊健康评价模型，以云南省抚仙湖和星云湖健康评价为例进行实例研究。首先，利用层次分析法( AHP)从水文完整性、物理结构完整性、化学完整性、生物完整性和服务功能完整性5个方面遴选出12个指标，构建3个层次的湖泊健康评价指标体系和5个等级的分级标准；其次，针对PP模型在实际应用中最佳投影方向难以确定以及基本蝙蝠算法( BA)存在早熟收敛等不足，提出一种基于Lévy飞行策略改进的蝙蝠算法( LBA)，通过10个复杂测试函数对该算法进行仿真验证，并与基本BA和粒子群优化( PSO)算法进行对比。最后，利用LBA算法搜寻PP模型最佳投影方向，提出LBA-PP湖泊健康评价模型，并对实例进行评价分析。结果表明：①LBA算法具有较好的收敛精度和全局寻优能力，将LBA算法用于PP模型最佳投影方向的选取，可有效提高PP模型评价精度；②LBA-PP模型对抚仙湖2011—2012年3次调查的评价结果均为“健康”，对星云湖2012年2次调查的评价结果均为“亚健康”。%Taking Fuxian Lake and Xingyun Lake in Yunnan Province health evaluation as an example case, Lake Health Evaluation In-dex System and grading standards is put forward, and lake health evaluation model is built on the basis of projection pursuit ( PP) . First of all, using the analytic hierarchy process ( AHP) to select 12 indexes from 5 aspects of hydrological integrity, physical structure, chemical integrity integrity, integrity and service function of biological integrity, grading standard construction of Lake health evaluation index system of 3 level and 5 level; secondly, according to the optimal projection direction the PP model in the practical application is difficult to determine the basic algorithm and bats ( BA) such as premature convergence and other problems
The pursuit of perfect packing
Weaire, Denis
2000-01-01
In 1998 Thomas Hales dramatically announced the solution of a problem that has long teased eminent mathematicians: what is the densest possible arrangement of identical spheres? The Pursuit of Perfect Packing recounts the story of this problem and many others that have to do with packing things together. The examples are taken from mathematics, physics, biology, and engineering, including the arrangement of soap bubbles in foam, atoms in a crystal, the architecture of the bee''s honeycomb, and the structure of the Giant''s Causeway. Using an informal style and with key references, the book also includes brief accounts of the lives of many of the scientists who devoted themselves to problems of packing over many centuries, together with wry comments on their efforts. It is an entertaining introduction to the field for both specialists and the more general public.
Robust PCA via Outlier Pursuit
Xu, Huan; Sanghavi, Sujay
2010-01-01
Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it is nevertheless plagued by a well-known, well-documented sensitivity to outliers. Recent work has considered the setting where each point has a few arbitrarily corrupted components. Yet, in applications of SVD or PCA such as robust collaborative filtering or bioinformatics, malicious agents, defective genes, or simply corrupted or contaminated experiments may effectively yield entire points that are completely corrupted. We present an efficient convex optimization-based algorithm we call Outlier Pursuit, that under some mild assumptions on the uncorrupted points (satisfied, e.g., by the standard generative assumption in PCA problems) recovers the exact optimal low-dimensional subspace, and identifies the corrupted points. Such identification of corrupted points that do not conform to the low-dimensional approximation, is of paramount ...
The pursuit of perfect packing
Weaire, Denis
2008-01-01
Coauthored by one of the creators of the most efficient space packing solution, the Weaire-Phelan structure, The Pursuit of Perfect Packing, Second Edition explores a problem of importance in physics, mathematics, chemistry, biology, and engineering: the packing of structures. Maintaining its mathematical core, this edition continues and revises some of the stories from its predecessor while adding several new examples and applications. The book focuses on both scientific and everyday problems ranging from atoms to honeycombs. It describes packing models, such as the Kepler conjecture, Voronoï decomposition, and Delaunay decomposition, as well as actual structure models, such as the Kelvin cell and the Weaire-Phelan structure. The authors discuss numerous historical aspects and provide biographical details on influential contributors to the field, including emails from Thomas Hales and Ken Brakke. With examples from physics, crystallography, engineering, and biology, this accessible and whimsical bo...
Non-parametric Morphologies of Mergers in the Illustris Simulation
Bignone, Lucas A; Sillero, Emanuel; Pedrosa, Susana E; Pellizza, Leonardo J; Lambas, Diego G
2016-01-01
We study non-parametric morphologies of mergers events in a cosmological context, using the Illustris project. We produce mock g-band images comparable to observational surveys from the publicly available Illustris simulation idealized mock images at $z=0$. We then measure non parametric indicators: asymmetry, Gini, $M_{20}$, clumpiness and concentration for a set of galaxies with $M_* >10^{10}$ M$_\\odot$. We correlate these automatic statistics with the recent merger history of galaxies and with the presence of close companions. Our main contribution is to assess in a cosmological framework, the empirically derived non-parametric demarcation line and average time-scales used to determine the merger rate observationally. We found that 98 per cent of galaxies above the demarcation line have a close companion or have experienced a recent merger event. On average, merger signatures obtained from the $G-M_{20}$ criteria anticorrelate clearly with the elapsing time to the last merger event. We also find that the a...
Nonparametric tests for pathwise properties of semimartingales
Cont, Rama; 10.3150/10-BEJ293
2011-01-01
We propose two nonparametric tests for investigating the pathwise properties of a signal modeled as the sum of a L\\'{e}vy process and a Brownian semimartingale. Using a nonparametric threshold estimator for the continuous component of the quadratic variation, we design a test for the presence of a continuous martingale component in the process and a test for establishing whether the jumps have finite or infinite variation, based on observations on a discrete-time grid. We evaluate the performance of our tests using simulations of various stochastic models and use the tests to investigate the fine structure of the DM/USD exchange rate fluctuations and SPX futures prices. In both cases, our tests reveal the presence of a non-zero Brownian component and a finite variation jump component.
Nonparametric Transient Classification using Adaptive Wavelets
Varughese, Melvin M; Stephanou, Michael; Bassett, Bruce A
2015-01-01
Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind ...
A Bayesian nonparametric meta-analysis model.
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G
2015-03-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.
Bayesian nonparametric estimation for Quantum Homodyne Tomography
Naulet, Zacharie; Barat, Eric
2016-01-01
We estimate the quantum state of a light beam from results of quantum homodyne tomography noisy measurements performed on identically prepared quantum systems. We propose two Bayesian nonparametric approaches. The first approach is based on mixture models and is illustrated through simulation examples. The second approach is based on random basis expansions. We study the theoretical performance of the second approach by quantifying the rate of contraction of the posterior distribution around ...
NONPARAMETRIC ESTIMATION OF CHARACTERISTICS OF PROBABILITY DISTRIBUTIONS
Orlov A. I.
2015-10-01
Full Text Available The article is devoted to the nonparametric point and interval estimation of the characteristics of the probabilistic distribution (the expectation, median, variance, standard deviation, variation coefficient of the sample results. Sample values are regarded as the implementation of independent and identically distributed random variables with an arbitrary distribution function having the desired number of moments. Nonparametric analysis procedures are compared with the parametric procedures, based on the assumption that the sample values have a normal distribution. Point estimators are constructed in the obvious way - using sample analogs of the theoretical characteristics. Interval estimators are based on asymptotic normality of sample moments and functions from them. Nonparametric asymptotic confidence intervals are obtained through the use of special output technology of the asymptotic relations of Applied Statistics. In the first step this technology uses the multidimensional central limit theorem, applied to the sums of vectors whose coordinates are the degrees of initial random variables. The second step is the conversion limit multivariate normal vector to obtain the interest of researcher vector. At the same considerations we have used linearization and discarded infinitesimal quantities. The third step - a rigorous justification of the results on the asymptotic standard for mathematical and statistical reasoning level. It is usually necessary to use the necessary and sufficient conditions for the inheritance of convergence. This article contains 10 numerical examples. Initial data - information about an operating time of 50 cutting tools to the limit state. Using the methods developed on the assumption of normal distribution, it can lead to noticeably distorted conclusions in a situation where the normality hypothesis failed. Practical recommendations are: for the analysis of real data we should use nonparametric confidence limits
Pursuit on an Organized Crime Network
Marshak, Charles Z; Bertozzi, Andrea L; D'Orsogña, Maria R
2015-01-01
We model the hierarchical evolution of an organized criminal network via antagonistic recruitment and pursuit processes. Within the recruitment phase, a criminal kingpin enlists new members into the network, who in turn seek out other affiliates. New recruits are linked to established criminals according to a probability distribution that depends on the current network structure. At the same time, law enforcement agents attempt to dismantle the growing organization using pursuit strategies that initiate on the lower level nodes and that unfold as self-avoiding random walks. The global details of the organization are unknown to law enforcement, who must explore the hierarchy node by node. We halt the pursuit when certain local criteria of the network are uncovered, encoding if and when an arrest is made; the criminal network is assumed to be eradicated if the kingpin is arrested. We first analyze recruitment and study the large scale properties of the growing network; later we add pursuit and use numerical sim...
portfolio optimization based on nonparametric estimation methods
mahsa ghandehari
2017-03-01
Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.
Peacock, Thomas
2014-11-01
Orders of magnitude larger than surface waves, and so powerful that their generation impacts the lunar orbit, internal waves, propagating disturbances of a density-stratified fluid, are ubiquitous throughout the ocean and atmosphere. Following the discovery of the phenomenon of ``dead water'' by early Arctic explorers and the classic laboratory visualizations of the curious St. Andrew's Cross internal wave pattern, there has been a resurgence of interest in internal waves, inspired by their pivotal roles in local environmental and global climate processes, and their profound impact on ocean and aerospace engineering. We detail our widespread pursuit of internal waves through theoretical modeling, laboratory experiments and field studies, from the Pacific Ocean one thousand miles north and south of Hawaii, to the South China Sea, and on to the Arctic Ocean. We also describe our recent expedition to surf the most striking internal wave phenomenon of them all: the Morning Glory cloud in remote Northwest Australia. This work was supported by the National Science Foundation through a CAREER Grant OCE-064559 and through Grants OCE-1129757 and OCE-1357434, and by the Office of Naval Research through Grants N00014-09-1-0282, N00014-08-1-0390 and N00014-05-1-0575.
Doing smooth pursuit paradigms in Windows 7
Wilms, Inge Linda
Smooth pursuit eye movements are interesting to study as they reflect the subject’s ability to predict movement of external targets, keep focus and move the eyes appropriately. The process of smooth pursuit requires collaboration between several systems in the brain and the resulting action may p...... in Windows 7 with live capturing of eye movements using a Tobii TX300 eye tracker. In particular, the poster describes the challenges and limitations created by the hardware and the software...
EMDR Effects on Pursuit Eye Movements
Zoi Kapoula; Qing Yang; Audrey Bonnet; Pauline Bourtoire; Jean Sandretto
2010-01-01
This study aimed to objectivize the quality of smooth pursuit eye movements in a standard laboratory task before and after an Eye Movement Desensitization and Reprocessing (EMDR) session run on seven healthy volunteers. EMDR was applied on autobiographic worries causing moderate distress. The EMDR session was complete in 5 out of the 7 cases; distress measured by SUDS (Subjective Units of Discomfort Scale) decreased to a near zero value. Smooth pursuit eye movements were recorded by an Eyelin...
Introduction to nonparametric statistics for the biological sciences using R
MacFarland, Thomas W
2016-01-01
This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses a...
Goal Pursuit in Youth with Chronic Pain
Fisher, Emma; Palermo, Tonya M.
2016-01-01
Children and adolescents frequently experience chronic pain that can disrupt their usual activities and lead to poor physical and emotional functioning. The fear avoidance model of pain with an emphasis on the maladaptive behaviors that lead to activity avoidance has guided research and clinical practice. However, this model does not take into consideration variability in responses to pain, in particular the active pursuit of goals despite pain. This review aims to introduce a novel conceptualization of children’s activity engagement versus avoidance using the framework of goal pursuit. We propose a new model of Goal Pursuit in Pediatric Chronic Pain, which proposes that the child’s experience of pain is modified by child factors (e.g., goal salience, motivation/energy, pain-related anxiety/fear, and self-efficacy) and parent factors (e.g., parent expectations for pain, protectiveness behaviors, and parent anxiety), which lead to specific goal pursuit behaviors. Goal pursuit is framed as engagement or avoidance of valued goals when in pain. Next, we recommend that research in youth with chronic pain should be reframed to account for the pursuit of valued goals within the context of pain and suggest directions for future research. PMID:27879686
Goal Pursuit in Youth with Chronic Pain
Emma Fisher
2016-11-01
Full Text Available Children and adolescents frequently experience chronic pain that can disrupt their usual activities and lead to poor physical and emotional functioning. The fear avoidance model of pain with an emphasis on the maladaptive behaviors that lead to activity avoidance has guided research and clinical practice. However, this model does not take into consideration variability in responses to pain, in particular the active pursuit of goals despite pain. This review aims to introduce a novel conceptualization of children’s activity engagement versus avoidance using the framework of goal pursuit. We propose a new model of Goal Pursuit in Pediatric Chronic Pain, which proposes that the child’s experience of pain is modified by child factors (e.g., goal salience, motivation/energy, pain-related anxiety/fear, and self-efficacy and parent factors (e.g., parent expectations for pain, protectiveness behaviors, and parent anxiety, which lead to specific goal pursuit behaviors. Goal pursuit is framed as engagement or avoidance of valued goals when in pain. Next, we recommend that research in youth with chronic pain should be reframed to account for the pursuit of valued goals within the context of pain and suggest directions for future research.
Goal Pursuit in Youth with Chronic Pain.
Fisher, Emma; Palermo, Tonya M
2016-11-22
Children and adolescents frequently experience chronic pain that can disrupt their usual activities and lead to poor physical and emotional functioning. The fear avoidance model of pain with an emphasis on the maladaptive behaviors that lead to activity avoidance has guided research and clinical practice. However, this model does not take into consideration variability in responses to pain, in particular the active pursuit of goals despite pain. This review aims to introduce a novel conceptualization of children's activity engagement versus avoidance using the framework of goal pursuit. We propose a new model of Goal Pursuit in Pediatric Chronic Pain, which proposes that the child's experience of pain is modified by child factors (e.g., goal salience, motivation/energy, pain-related anxiety/fear, and self-efficacy) and parent factors (e.g., parent expectations for pain, protectiveness behaviors, and parent anxiety), which lead to specific goal pursuit behaviors. Goal pursuit is framed as engagement or avoidance of valued goals when in pain. Next, we recommend that research in youth with chronic pain should be reframed to account for the pursuit of valued goals within the context of pain and suggest directions for future research.
EMDR effects on pursuit eye movements.
Zoi Kapoula
Full Text Available This study aimed to objectivize the quality of smooth pursuit eye movements in a standard laboratory task before and after an Eye Movement Desensitization and Reprocessing (EMDR session run on seven healthy volunteers. EMDR was applied on autobiographic worries causing moderate distress. The EMDR session was complete in 5 out of the 7 cases; distress measured by SUDS (Subjective Units of Discomfort Scale decreased to a near zero value. Smooth pursuit eye movements were recorded by an Eyelink II video system before and after EMDR. For the five complete sessions, pursuit eye movement improved after their EMDR session. Notably, the number of saccade intrusions-catch-up saccades (CUS-decreased and, reciprocally, there was an increase in the smooth components of the pursuit. Such an increase in the smoothness of the pursuit presumably reflects an improvement in the use of visual attention needed to follow the target accurately. Perhaps EMDR reduces distress thereby activating a cholinergic effect known to improve ocular pursuit.
Nonparametric forecasting of low-dimensional dynamical systems.
Berry, Tyrus; Giannakis, Dimitrios; Harlim, John
2015-03-01
This paper presents a nonparametric modeling approach for forecasting stochastic dynamical systems on low-dimensional manifolds. The key idea is to represent the discrete shift maps on a smooth basis which can be obtained by the diffusion maps algorithm. In the limit of large data, this approach converges to a Galerkin projection of the semigroup solution to the underlying dynamics on a basis adapted to the invariant measure. This approach allows one to quantify uncertainties (in fact, evolve the probability distribution) for nontrivial dynamical systems with equation-free modeling. We verify our approach on various examples, ranging from an inhomogeneous anisotropic stochastic differential equation on a torus, the chaotic Lorenz three-dimensional model, and the Niño-3.4 data set which is used as a proxy of the El Niño Southern Oscillation.
A nonparametric and diversified portfolio model
Shirazi, Yasaman Izadparast; Sabiruzzaman, Md.; Hamzah, Nor Aishah
2014-07-01
Traditional portfolio models, like mean-variance (MV) suffer from estimation error and lack of diversity. Alternatives, like mean-entropy (ME) or mean-variance-entropy (MVE) portfolio models focus independently on the issue of either a proper risk measure or the diversity. In this paper, we propose an asset allocation model that compromise between risk of historical data and future uncertainty. In the new model, entropy is presented as a nonparametric risk measure as well as an index of diversity. Our empirical evaluation with a variety of performance measures shows that this model has better out-of-sample performances and lower portfolio turnover than its competitors.
Non-Parametric Estimation of Correlation Functions
Brincker, Rune; Rytter, Anders; Krenk, Steen
In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are pointed...... out, and methods to prevent bias are presented. The techniques are evaluated by comparing their speed and accuracy on the simple case of estimating auto-correlation functions for the response of a single degree-of-freedom system loaded with white noise....
Lottery spending: a non-parametric analysis.
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Lottery spending: a non-parametric analysis.
Skip Garibaldi
Full Text Available We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Nonparametric inferences for kurtosis and conditional kurtosis
XIE Xiao-heng; HE You-hua
2009-01-01
Under the assumption of strictly stationary process, this paper proposes a nonparametric model to test the kurtosis and conditional kurtosis for risk time series. We apply this method to the daily returns of S&P500 index and the Shanghai Composite Index, and simulate GARCH data for verifying the efficiency of the presented model. Our results indicate that the risk series distribution is heavily tailed, but the historical information can make its future distribution light-tailed. However the far future distribution's tails are little affected by the historical data.
Parametric versus non-parametric simulation
Dupeux, Bérénice; Buysse, Jeroen
2014-01-01
Most of ex-ante impact assessment policy models have been based on a parametric approach. We develop a novel non-parametric approach, called Inverse DEA. We use non parametric efficiency analysis for determining the farm’s technology and behaviour. Then, we compare the parametric approach and the Inverse DEA models to a known data generating process. We use a bio-economic model as a data generating process reflecting a real world situation where often non-linear relationships exist. Results s...
Preliminary results on nonparametric facial occlusion detection
Daniel LÓPEZ SÁNCHEZ
2016-10-01
Full Text Available The problem of face recognition has been extensively studied in the available literature, however, some aspects of this field require further research. The design and implementation of face recognition systems that can efficiently handle unconstrained conditions (e.g. pose variations, illumination, partial occlusion... is still an area under active research. This work focuses on the design of a new nonparametric occlusion detection technique. In addition, we present some preliminary results that indicate that the proposed technique might be useful to face recognition systems, allowing them to dynamically discard occluded face parts.
In pursuit of homoleptic actinide alkyl complexes.
Seaman, Lani A; Walensky, Justin R; Wu, Guang; Hayton, Trevor W
2013-04-01
This Forum Article describes the pursuit of isolable homoleptic actinide alkyl complexes, starting with the pioneering work of Gilman during the Manhattan project. The initial reports in this area suggested that homoleptic uranium alkyls were too unstable to be isolated, but Wilkinson demonstrated that tractable uranium alkyls could be generated by purposeful "ate" complex formation, which serves to saturate the uranium coordination sphere and provide the complexes with greater kinetic stability. More recently, we reported the solid-state molecular structures of several homoleptic uranium alkyl complexes, including [Li(THF)4][U(CH2(t)Bu)5], [Li(TMEDA)]2[UMe6], [K(THF)]3[K(THF)2][U(CH2Ph)6]2, and [Li(THF)4][U(CH2SiMe3)6], by employing Wilkinson's strategy. Herein, we describe our attempts to extend this chemistry to thorium. The treatment of ThCl4(DME)2 with 5 equiv of LiCH2(t)Bu or LiCH2SiMe3 at -25 °C in THF affords [Th(CH2(t)Bu)5] (1) and [Li(DME)2][Th(CH2SiMe3)5 (2), respectively, in moderate yields. Similarly, the treatment of ThCl4(DME)2 with 6 equiv of K(CH2Ph) produces [K(THF)]2[Th(CH2Ph)6] (3), in good yield. Complexes 1-3 have been fully characterized, while the structures of 1 and 3 were confirmed by X-ray crystallography. Additionally, the electronic properties of 1 and 3 were explored by density functional theory.
马峰; 王千; 蔺文静; 王贵玲
2012-01-01
石家庄市处于我国缺水严重的华北地区,水资源利用一直倍受人们的关注,水资源承载力评价可为今后的地区水资源利用及规划提供建议.文章采用了综合指标法,根据石家庄市水资源具体情况和指标体系结构特点,选取了18个指标建立水资源承载力评价指标体系及指标标准,运用投影寻踪方法建立评价模型,通过归一化处理、线性投影、构造投影指标函数和优化投影指标函数的方法,将选取的指标运用matlab数学软件进行投影寻踪分析,得到石家庄市水资源承载力等级水平为Ⅳ级,主要影响水资源承载力的指标为基本农田比例、节水灌溉率、灌溉用水有效利用系数、水资源可开发利用系数和万元工业增加值用水量等.%Shijiazhuang lies in the north of china which is seriously lack of water, and its water resources utilization always receives attention. The evaluation of water resources carrying capacity could provide advices for water resources utilization and planning in this area in future. Based on the specific conditions of water resources and the structural features of the index system in Shijiazhuang.18 indexes were selected to build the evaluation index system of water resources carrying capacity. The projection pursuit method was used to assess the model. Relying on the methods of normalization treatment, linear projection, constructing projection target function as well as optimizing projection target function, the selected indexes were analyzed using the projective pursuit method based on matlab platform. The results showed that the water resources carrying capacity level is grade IV in Shijiazhuang. The main indexes affecting the water resources carrying capacity included the basic farmland proportion, water-saving irrigation rate,effective utilization coefficient of irrigation water, utilization coefficient of available water resources, and water consumption of ten thousand yuan
Bayesian Nonparametric Clustering for Positive Definite Matrices.
Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos
2016-05-01
Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.
In Pursuit of Land Tenure Security
Dekker, Henri
2006-01-01
In Pursuit of Land Tenure Security is a unique book that takes the reader on an international tour of perceptions of land tenure security. It contains an anthology of essays based on contacts with people during assignments in various parts of the world over a period of several years. The essays describe the human pursuit for a higher level of land tenure security. Because land tenure security is a perception, the use of stories of human experience introduces the reader to an array of issues a...
Nonparametric dark energy reconstruction from supernova data.
Holsclaw, Tracy; Alam, Ujjaini; Sansó, Bruno; Lee, Herbert; Heitmann, Katrin; Habib, Salman; Higdon, David
2010-12-10
Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.
Local Component Analysis for Nonparametric Bayes Classifier
Khademi, Mahmoud; safayani, Meharn
2010-01-01
The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on nonparametric density estimation methods such as Parzen windows, the decision regions depend on the choice of parameters such as window width. Moreover, these methods suffer from curse of dimensionality of the feature space and small sample size problem which severely restricts their practical applications. In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis. In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information. The proposed method can classify the data with co...
Nonparametric k-nearest-neighbor entropy estimator.
Lombardi, Damiano; Pant, Sanjay
2016-01-01
A nonparametric k-nearest-neighbor-based entropy estimator is proposed. It improves on the classical Kozachenko-Leonenko estimator by considering nonuniform probability densities in the region of k-nearest neighbors around each sample point. It aims to improve the classical estimators in three situations: first, when the dimensionality of the random variable is large; second, when near-functional relationships leading to high correlation between components of the random variable are present; and third, when the marginal variances of random variable components vary significantly with respect to each other. Heuristics on the error of the proposed and classical estimators are presented. Finally, the proposed estimator is tested for a variety of distributions in successively increasing dimensions and in the presence of a near-functional relationship. Its performance is compared with a classical estimator, and a significant improvement is demonstrated.
Nonparametric estimation of location and scale parameters
Potgieter, C.J.
2012-12-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Nonparametric Maximum Entropy Estimation on Information Diagrams
Martin, Elliot A; Meinke, Alexander; Děchtěrenko, Filip; Davidsen, Jörn
2016-01-01
Maximum entropy estimation is of broad interest for inferring properties of systems across many different disciplines. In this work, we significantly extend a technique we previously introduced for estimating the maximum entropy of a set of random discrete variables when conditioning on bivariate mutual informations and univariate entropies. Specifically, we show how to apply the concept to continuous random variables and vastly expand the types of information-theoretic quantities one can condition on. This allows us to establish a number of significant advantages of our approach over existing ones. Not only does our method perform favorably in the undersampled regime, where existing methods fail, but it also can be dramatically less computationally expensive as the cardinality of the variables increases. In addition, we propose a nonparametric formulation of connected informations and give an illustrative example showing how this agrees with the existing parametric formulation in cases of interest. We furthe...
Nonparametric estimation of employee stock options
FU Qiang; LIU Li-an; LIU Qian
2006-01-01
We proposed a new model to price employee stock options (ESOs). The model is based on nonparametric statistical methods with market data. It incorporates the kernel estimator and employs a three-step method to modify BlackScholes formula. The model overcomes the limits of Black-Scholes formula in handling option prices with varied volatility. It disposes the effects of ESOs self-characteristics such as non-tradability, the longer term for expiration, the early exercise feature, the restriction on shorting selling and the employee's risk aversion on risk neutral pricing condition, and can be applied to ESOs valuation with the explanatory variable in no matter the certainty case or random case.
On Parametric (and Non-Parametric Variation
Neil Smith
2009-11-01
Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.
In Pursuit of Land Tenure Security
Dekker, Henri
2006-01-01
In Pursuit of Land Tenure Security is a unique book that takes the reader on an international tour of perceptions of land tenure security. It contains an anthology of essays based on contacts with people during assignments in various parts of the world over a period of several years. The essays desc
The Pursuit of Excellence through Education.
Ferrari, Michel, Ed.
In this book theorists and researchers present a range of perspectives on how to promote excellence in education, providing an opportunity for expression to those who stress transformation of educational practice and those who emphasize individual abilities. In part 1, The Individual Pursuit of Excellence, the chapters are: (1) Learning from…
Nonparametric inference of network structure and dynamics
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Neurophysiology and Neuroanatomy of Smooth Pursuit in Humans
Lencer, Rebekka; Trillenberg, Peter
2008-01-01
Smooth pursuit eye movements enable us to focus our eyes on moving objects by utilizing well-established mechanisms of visual motion processing, sensorimotor transformation and cognition. Novel smooth pursuit tasks and quantitative measurement techniques can help unravel the different smooth pursuit components and complex neural systems involved…
A nonparametric dynamic additive regression model for longitudinal data
Martinussen, Torben; Scheike, Thomas H.
2000-01-01
dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models......dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models...
Nonparametric Bayesian inference for multidimensional compound Poisson processes
S. Gugushvili; F. van der Meulen; P. Spreij
2015-01-01
Given a sample from a discretely observed multidimensional compound Poisson process, we study the problem of nonparametric estimation of its jump size density r0 and intensity λ0. We take a nonparametric Bayesian approach to the problem and determine posterior contraction rates in this context, whic
Asymptotic theory of nonparametric regression estimates with censored data
施沛德; 王海燕; 张利华
2000-01-01
For regression analysis, some useful Information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literat黵e, but the optimal rates of global convergence have not been obtained yet. Because of the possible Information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression f unction based on right-censored response data, and proves, under some regularity condi-tions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtai
2nd Conference of the International Society for Nonparametric Statistics
Manteiga, Wenceslao; Romo, Juan
2016-01-01
This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cádiz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid. The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference. The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers...
Independent histogram pursuit for segmentation of skin lesions
Gomez, D.D.; Butakoff, C.; Ersbøll, Bjarne Kjær;
2008-01-01
In this paper, an unsupervised algorithm, called the Independent Histogram Pursuit (HIP), for segmenting dermatological lesions is proposed. The algorithm estimates a set of linear combinations of image bands that enhance different structures embedded in the image. In particular, the first estima...... to deal with different types of dermatological lesions. The boundary detection precision using k-means segmentation was close to 97%. The proposed algorithm can be easily combined with the majority of classification algorithms.......In this paper, an unsupervised algorithm, called the Independent Histogram Pursuit (HIP), for segmenting dermatological lesions is proposed. The algorithm estimates a set of linear combinations of image bands that enhance different structures embedded in the image. In particular, the first...... estimated combination enhances the contrast of the lesion to facilitate its segmentation. Given an N-band image, this first combination corresponds to a line in N dimensions, such that the separation between the two main modes of the histogram obtained by projecting the pixels onto this line, is maximized...
Applications of non-parametric statistics and analysis of variance on sample variances
Myers, R. H.
1981-01-01
Nonparametric methods that are available for NASA-type applications are discussed. An attempt will be made here to survey what can be used, to attempt recommendations as to when each would be applicable, and to compare the methods, when possible, with the usual normal-theory procedures that are avavilable for the Gaussion analog. It is important here to point out the hypotheses that are being tested, the assumptions that are being made, and limitations of the nonparametric procedures. The appropriateness of doing analysis of variance on sample variances are also discussed and studied. This procedure is followed in several NASA simulation projects. On the surface this would appear to be reasonably sound procedure. However, difficulties involved center around the normality problem and the basic homogeneous variance assumption that is mase in usual analysis of variance problems. These difficulties discussed and guidelines given for using the methods.
Cursive writing with smooth pursuit eye movements.
Lorenceau, Jean
2012-08-21
The eyes never cease to move: ballistic saccades quickly turn the gaze toward peripheral targets, whereas smooth pursuit maintains moving targets on the fovea where visual acuity is best. Despite the oculomotor system being endowed with exquisite motor abilities, any attempt to generate smooth eye movements against a static background results in saccadic eye movements. Although exceptions to this rule have been reported, volitional control over smooth eye movements is at best rudimentary. Here, I introduce a novel, temporally modulated visual display, which, although static, sustains smooth eye movements in arbitrary directions. After brief training, participants gain volitional control over smooth pursuit eye movements and can generate digits, letters, words, or drawings at will. For persons deprived of limb movement, this offers a fast, creative, and personal means of linguistic and emotional expression. Copyright © 2012 Elsevier Ltd. All rights reserved.
Pursuit and Synchronization in Hydrodynamic Dipoles
Kanso, Eva
2015-01-01
We study theoretically the behavior of a class of hydrodynamic dipoles. This study is motivated by recent experiments on synthetic and biological swimmers in microfluidic \\textit{Hele-Shaw} type geometries. Under such confinement, a swimmer's hydrodynamic signature is that of a potential source dipole, and the long-range interactions among swimmers are obtained from the superposition of dipole singularities. Here, we recall the equations governing the positions and orientations of interacting asymmetric swimmers in doubly-periodic domains, and focus on the dynamics of swimmer pairs. We obtain two families of `relative equilibria'-type solutions that correspond to pursuit and synchronization of the two swimmers, respectively. Interestingly, the pursuit mode is stable for large tail swimmers whereas the synchronization mode is stable for large head swimmers. These results have profound implications on the collective behavior reported in several recent studies on populations of confined microswimmers.
Prey pursuit and interception in dragonflies.
Olberg, R M; Worthington, A H; Venator, K R
2000-02-01
Perching dragonflies (Libellulidae; Odonata) are sit-and-wait predators, which take off and pursue small flying insects. To investigate their prey pursuit strategy, we videotaped 36 prey-capture flights of male dragonflies, Erythemis simplicicollis and Leucorrhinia intacta, for frame-by-frame analysis. We found that dragonflies fly directly toward the point of prey interception by steering to minimize the movement of the prey's image on the retina. This behavior could be guided by target-selective descending interneurons which show directionally selective visual responses to small-object movement. We investigated how dragonflies discriminate distance of potential prey. We found a peak in angular velocity of the prey shortly before take-off which might cue the dragonfly to nearby flying targets. Parallax information from head movements was not required for successful prey pursuit.
Nonparametric methods in actigraphy: An update
Bruno S.B. Gonçalves
2014-09-01
Full Text Available Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm results for each time interval. Simulated data showed that (1 synchronization analysis depends on sample size, and (2 fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep–wake cycle fragmentation and synchronization.
Bayesian nonparametric adaptive control using Gaussian processes.
Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A
2015-03-01
Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.
Nonparametric methods in actigraphy: An update
Gonçalves, Bruno S.B.; Cavalcanti, Paula R.A.; Tavares, Gracilene R.; Campos, Tania F.; Araujo, John F.
2014-01-01
Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm) results for each time interval. Simulated data showed that (1) synchronization analysis depends on sample size, and (2) fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep–wake cycle fragmentation and synchronization. PMID:26483921
Nonparametric Detection of Geometric Structures Over Networks
Zou, Shaofeng; Liang, Yingbin; Poor, H. Vincent
2017-10-01
Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exist, all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary and unknown. The goal is to design statistically consistent tests with probability of errors converging to zero as the network size becomes asymptotically large. Kernel-based tests are proposed based on maximum mean discrepancy that measures the distance between mean embeddings of distributions into a reproducing kernel Hilbert space. Detection of an anomalous interval over a line network is first studied. Sufficient conditions on minimum and maximum sizes of candidate anomalous intervals are characterized in order to guarantee the proposed test to be consistent. It is also shown that certain necessary conditions must hold to guarantee any test to be universally consistent. Comparison of sufficient and necessary conditions yields that the proposed test is order-level optimal and nearly optimal respectively in terms of minimum and maximum sizes of candidate anomalous intervals. Generalization of the results to other networks is further developed. Numerical results are provided to demonstrate the performance of the proposed tests.
Sharpen customer service skills with PCRAFT Pursuit.
Dologite, Kimberly A; Willner, Kathleen C; Klepeiss, Debra J; York, Susan A; Cericola, Lisa M
2003-01-01
Traditional approaches to teaching customer service skills do not involve participant interaction, nor do they provide a fun and relaxed atmosphere for learning. This article describes the development of PCRAFT Pursuit, an innovative game used to teach customer service skills. The development process began with concerns identified through patient satisfaction surveys. The implementation of this game became an integral component of education to improve customer service skills of staff throughout the hospital network.
Reframing our pursuit of life balance.
Fuentes, David G; Ogden, Rachel R; Ryan-Haddad, Ann; Strang, Aimee F
2015-04-25
During our time in the 2013 Academic Leadership Fellows Program, we explored what it takes to achieve life balance through a framework presented in a Harvard Business Review article. In this Statement, we describe 5 different areas from the article that provide infrastructure for reflecting on how we have learned to approach life balance in academia. We also provide brief messages based on this reading and others to help academics' pursuit of life balance.
Nonparametric Bayesian drift estimation for multidimensional stochastic differential equations
Gugushvili, S.; Spreij, P.
2014-01-01
We consider nonparametric Bayesian estimation of the drift coefficient of a multidimensional stochastic differential equation from discrete-time observations on the solution of this equation. Under suitable regularity conditions, we establish posterior consistency in this context.
Homothetic Efficiency and Test Power: A Non-Parametric Approach
J. Heufer (Jan); P. Hjertstrand (Per)
2015-01-01
markdownabstract__Abstract__ We provide a nonparametric revealed preference approach to demand analysis based on homothetic efficiency. Homotheticity is a useful restriction but data rarely satisfies testable conditions. To overcome this we provide a way to estimate homothetic efficiency of
A non-parametric approach to investigating fish population dynamics
Cook, R.M; Fryer, R.J
2001-01-01
.... Using a non-parametric model for the stock-recruitment relationship it is possible to avoid defining specific functions relating recruitment to stock size while also providing a natural framework to model process error...
Non-parametric approach to the study of phenotypic stability.
Ferreira, D F; Fernandes, S B; Bruzi, A T; Ramalho, M A P
2016-02-19
The aim of this study was to undertake the theoretical derivations of non-parametric methods, which use linear regressions based on rank order, for stability analyses. These methods were extension different parametric methods used for stability analyses and the result was compared with a standard non-parametric method. Intensive computational methods (e.g., bootstrap and permutation) were applied, and data from the plant-breeding program of the Biology Department of UFLA (Minas Gerais, Brazil) were used to illustrate and compare the tests. The non-parametric stability methods were effective for the evaluation of phenotypic stability. In the presence of variance heterogeneity, the non-parametric methods exhibited greater power of discrimination when determining the phenotypic stability of genotypes.
Nonparametric Bayesian Modeling for Automated Database Schema Matching
Ferragut, Erik M [ORNL; Laska, Jason A [ORNL
2015-01-01
The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.
PV power forecast using a nonparametric PV model
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quant...
Dai, Wenlin
2017-09-01
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
Matching pursuit and source deflation for sparse EEG/MEG dipole moment estimation.
Wu, Shun Chi; Swindlehurst, A Lee
2013-08-01
In this paper, we propose novel matching pursuit (MP)-based algorithms for EEG/MEG dipole source localization and parameter estimation for multiple measurement vectors with constant sparsity. The algorithms combine the ideas of MP for sparse signal recovery and source deflation, as employed in estimation via alternating projections. The source-deflated matching pursuit (SDMP) approach mitigates the problem of residual interference inherent in sequential MP-based methods or recursively applied (RAP)-MUSIC. Furthermore, unlike prior methods based on alternating projection, SDMP allows one to efficiently estimate the dipole orientation in addition to its location. Simulations show that the proposed algorithms outperform existing techniques under various conditions, including those with highly correlated sources. Results using real EEG data from auditory experiments are also presented to illustrate the performance of these algorithms.
侯秀玲; 周益民; 王绍俊; 周密
2012-01-01
Soil heavy metals contamination has become one of the urgent problems faced by the development and internationalization of green food industry in China. Studies on these fields therefore are of great practical significance. This study emploies projection parameters to seek its projection direction, through projection index function to reflect the characteristics of soil heavy metals. This method possesses the advantages of objectivity and robust mathematical theoretical frameworks. More over, the subjective weight assignment might be avoided as a result. Soil samples from Toutun River area farmland were collected and tested as well based on the studied model to investigate the heavy metal distribution. The results show that chromium and vanadium present the dominant contaminants in studied areas, which display a decreasing tendency. Furthermore, it implies that the studied model can applicable in dealing with multiple factor problems, which can thus offer technical assistance in farmland soil heavy metal contaminants regulation.%重金属问题已成为我国开发绿色食品国际化过程中亟待解决的重要问题之一，对此问题展开研究具有重要的现实意义。通过将农田土壤重金属含量指标作为多个投影参数来寻求其投影方向，由投影指标函数来反映农田重金属含量的特征，避免了人为赋予权重的干扰，客观性强，数学概念清晰。利用投影寻踪模型分析了头屯河地区的农田土壤重金属污染的规律，结果表明头屯河农场土壤的重金属污染以铬和钒为主要污染因子，呈现逐渐减小的规律。应用结果表明，投影寻踪模型能够较好地处理多因素问题，为中国农田土壤污染治理和控制提供科学依据。
Smooth pursuit eye movements and schizophrenia: literature review.
Franco, J G; de Pablo, J; Gaviria, A M; Sepúlveda, E; Vilella, E
2014-09-01
To review the scientific literature about the relationship between impairment on smooth pursuit eye movements and schizophrenia. Narrative review that includes historical articles, reports about basic and clinical investigation, systematic reviews, and meta-analysis on the topic. Up to 80% of schizophrenic patients have impairment of smooth pursuit eye movements. Despite the diversity of test protocols, 65% of patients and controls are correctly classified by their overall performance during this pursuit. The smooth pursuit eye movements depend on the ability to anticipate the target's velocity and the visual feedback, as well as on learning and attention. The neuroanatomy implicated in smooth pursuit overlaps to some extent with certain frontal cortex zones associated with some clinical and neuropsychological characteristics of the schizophrenia, therefore some specific components of smooth pursuit anomalies could serve as biomarkers of the disease. Due to their sedative effect, antipsychotics have a deleterious effect on smooth pursuit eye movements, thus these movements cannot be used to evaluate the efficacy of the currently available treatments. Standardized evaluation of smooth pursuit eye movements on schizophrenia will allow to use specific aspects of that pursuit as biomarkers for the study of its genetics, psychopathology, or neuropsychology. Copyright © 2013 Sociedad Española de Oftalmología. Published by Elsevier Espana. All rights reserved.
A robust nonparametric method for quantifying undetected extinctions.
Chisholm, Ryan A; Giam, Xingli; Sadanandan, Keren R; Fung, Tak; Rheindt, Frank E
2016-06-01
How many species have gone extinct in modern times before being described by science? To answer this question, and thereby get a full assessment of humanity's impact on biodiversity, statistical methods that quantify undetected extinctions are required. Such methods have been developed recently, but they are limited by their reliance on parametric assumptions; specifically, they assume the pools of extant and undetected species decay exponentially, whereas real detection rates vary temporally with survey effort and real extinction rates vary with the waxing and waning of threatening processes. We devised a new, nonparametric method for estimating undetected extinctions. As inputs, the method requires only the first and last date at which each species in an ensemble was recorded. As outputs, the method provides estimates of the proportion of species that have gone extinct, detected, or undetected and, in the special case where the number of undetected extant species in the present day is assumed close to zero, of the absolute number of undetected extinct species. The main assumption of the method is that the per-species extinction rate is independent of whether a species has been detected or not. We applied the method to the resident native bird fauna of Singapore. Of 195 recorded species, 58 (29.7%) have gone extinct in the last 200 years. Our method projected that an additional 9.6 species (95% CI 3.4, 19.8) have gone extinct without first being recorded, implying a true extinction rate of 33.0% (95% CI 31.0%, 36.2%). We provide R code for implementing our method. Because our method does not depend on strong assumptions, we expect it to be broadly useful for quantifying undetected extinctions. © 2016 Society for Conservation Biology.
Asymptotic theory of nonparametric regression estimates with censored data
无
2000-01-01
For regression analysis, some useful information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literature, but the optimal rates of global convergence have not been obtained yet. Because of the possible information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression function based on right_censored response data, and proves, under some regularity conditions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtain the asymptotic optimality of AIC, AICC, GCV, Cp and FPE criteria in the process of selecting the parameters.
Rediscovery of Good-Turing estimators via Bayesian nonparametrics.
Favaro, Stefano; Nipoti, Bernardo; Teh, Yee Whye
2016-03-01
The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good-Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature. Specifically, under the assumption of a two parameter Poisson-Dirichlet prior, we show that Bayesian nonparametric estimators of discovery probabilities are asymptotically equivalent, for a large sample size, to suitably smoothed Good-Turing estimators. As a by-product of this result, we introduce and investigate a methodology for deriving exact and asymptotic credible intervals to be associated with the Bayesian nonparametric estimators of discovery probabilities. The proposed methodology is illustrated through a comprehensive simulation study and the analysis of Expressed Sequence Tags data generated by sequencing a benchmark complementary DNA library.
Comparing parametric and nonparametric regression methods for panel data
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb-Douglas and......We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs...... rejects both the Cobb-Douglas and the Translog functional form, while a recently developed nonparametric kernel regression method with a fully nonparametric panel data specification delivers plausible results. On average, the nonparametric regression results are similar to results that are obtained from...
Neurophysiology and Neuroanatomy of Smooth Pursuit: Lesion Studies
Sharpe, James A.
2008-01-01
Smooth pursuit impairment is recognized clinically by the presence of saccadic tracking of a small object and quantified by reduction in pursuit gain, the ratio of smooth eye movement velocity to the velocity of a foveal target. Correlation of the site of brain lesions, identified by imaging or neuropathological examination, with defective smooth…
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Saerom Park
Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Park, Saerom; Lee, Jaewook; Son, Youngdoo
2016-01-01
Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Comparing parametric and nonparametric regression methods for panel data
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...
CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles
Yunxiao Shan
2015-09-01
Full Text Available Simple and efficient geometric controllers, like Pure-Pursuit, have been widely used in various types of autonomous vehicles to solve tracking problems. In this paper, we have developed a new pursuit method, named CFPursuit, which has been based on Pure-Pursuit but with certain differences. In CF-Pursuit, in order to reduce fitting errors, we used a clothoid C1 curve to replace the circle employed in Pure-Pursuit. This improvement to the fitting method helps the Pursuit method to decrease tracking errors. As regards the selection of look-ahead distance, we employed a fuzzy system to directly consider the path’s curvature. There are three input variables in this fuzzy system, 6mcurvature, 9mcurvature and 12mcurvature, calculated from the clothoid fit with the current position and the goal position on the defined path. A Sugeno fuzzy model was adapted to output a reasonable look-ahead distance using the experiences of human drivers as well as our own tests. Compared with some other geometric controllers, CF-Pursuit performs better in robustness, cross track errors and stability. The results from field tests have proven the CF-Pursuit is a practical and efficient geometric method for the path tracking problems of autonomous vehicles.
Nonparametric estimation of a convex bathtub-shaped hazard function.
Jankowski, Hanna K; Wellner, Jon A
2009-11-01
In this paper, we study the nonparametric maximum likelihood estimator (MLE) of a convex hazard function. We show that the MLE is consistent and converges at a local rate of n(2/5) at points x(0) where the true hazard function is positive and strictly convex. Moreover, we establish the pointwise asymptotic distribution theory of our estimator under these same assumptions. One notable feature of the nonparametric MLE studied here is that no arbitrary choice of tuning parameter (or complicated data-adaptive selection of the tuning parameter) is required.
The Pursuit of Identity in Invisible Man
谭佳
2013-01-01
Invisible Man is a representative work of black literature in America. In this novel, the writer Ralph Ellison depicts the hero’s growth experience in the white dominated society with his unique narrative techniques. As an individual in a society, the hero in this novel gradually realizes that he is an invisible man in the white dominated society and he doesn ’t have the social sta-tus which can be recognized by the white at all. To change this situation, the hero in this novel suffers many difficulties and hard-ships with an attempt to prove his existence in front of the white and the numerous black fellows and obtain his own identity as a black man which will be recognized by others. This paper tries to explore African American ’s pursuit of identity in Invisible Man by interpreting Ellison’s Invisible Man.
Principal Component Pursuit with Reduced Linear Measurements
Ganesh, Arvind; Wright, John; Ma, Yi
2012-01-01
In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem arises in many data processing tasks such as aligning multiple images or rectifying regular texture, where the goal is to recover a low-rank matrix with a large fraction of corrupted entries in the presence of nonlinear domain transformation. We consider a natural convex heuristic to this problem which is a variant to the recently proposed Principal Component Pursuit. We prove that under suitable conditions, this convex program guarantees to recover the correct low-rank and sparse components despite reduced measurements. Our analysis covers both random and deterministic measurement models.
John Hejduk's Pursuit of an Architectural Ethos
Søberg, Martin
2012-01-01
of architectural drawing; a method which, when approached on a more general and conceptual level, might even have the potential to inform design-based architectural research today. The author argues that the conceptual framework of such a method is not a theoretical pursuit of logos, but more a matter of character......Reflected, artistic practices and design-based research are drastically expanding fields within architectural academia. However, the interest in uniting theory and practice is not entirely new. Just a few decades ago, before a ‘death of theory’ was proclaimed, questions of architectural...... epistemology, of the language(s) of architecture, were indeed of profound interest to the discipline. This essay returns to and examines the investigatory practices of John Hejduk in an attempt to identify a poetic method asserting difference through repetition and primarily grounded in the medium...
Multiparameter image visualization by projection pursuit (Proceedings Only)
Harikumar, G.; Bresler, Yoram
1992-09-01
This paper addresses the display of multi-parameter medical image data, such as arises in MRI or multimodality image fusion. MRI or multi modality studies produce several different images of a given cross-section of the body, each providing different levels of contrast sensitivity between different tissues. The question then arises as to how to present this wealth of data to the diagnostician. While each of the different images may be misleading (as illustrated later by an example), in combination they may contain the correct information. Unfortunately, a human observer is not likely to be able to extract this information when presented with a parallel display of the distinct images. Given the sequential nature of detailed visual examination of a picture, a human observer is quite ineffective at integrating complex visual data from parallel sources. The development of a display technology that overcomes this difficulty by synthesizing a display method matched to the capabilities of the human observer is the subject of this paper. The ultimate goal of diagnostic imaging is the detection, localization, and quantification of abnormality. An intermediate goal, which is the one we address, is to present the diagnostician with an image that will maximize his changes to classify correctly different regions in the image as belonging to different tissue types. Our premise is that the diagnostician is able to bring to bear all his knowledge and experience, which are difficult to capture in a computer program, on the final analysis process. This is often key to the detection of subtle and otherwise elusive features in the image. We therefore rule out the generation of an automatically segmented image, which not only fails to include this knowledge, but also would deprive the diagnostician of the opportunity to exercise it, by presenting him with a hard-labeled segmentation. Instead we concentrate on the fusion of the multiple images of the same cross-section into a single most informative grey-scale image.
Project Based Learning: In Pursuit of Androgogic Effectiveness
Ntombela, Berrington X. S.
2015-01-01
In an attempt to standardise Foundation Programmes for Oman higher education providers, the Oman Academic Standards for General Foundation Programmes stipulated that higher education providers should offer programmes that ensure androgogic effectiveness. In the light of that, this paper presents attempts by a University College in Oman to…
Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders
Nielsen, Morten Ørregaard
2009-01-01
In this paper a nonparametric variance ratio testing approach is proposed for determining the number of cointegrating relations in fractionally integrated systems. The test statistic is easily calculated without prior knowledge of the integration order of the data, the strength of the cointegrating...
Non-parametric analysis of rating transition and default data
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move b...... but that this dependence vanishes after 2-3 years....
A non-parametric model for the cosmic velocity field
Branchini, E; Teodoro, L; Frenk, CS; Schmoldt, [No Value; Efstathiou, G; White, SDM; Saunders, W; Sutherland, W; Rowan-Robinson, M; Keeble, O; Tadros, H; Maddox, S; Oliver, S
1999-01-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing.
Influence of test and person characteristics on nonparametric appropriateness measurement
Meijer, Rob R.; Molenaar, Ivo W.; Sijtsma, Klaas
1994-01-01
Appropriateness measurement in nonparametric item response theory modeling is affected by the reliability of the items, the test length, the type of aberrant response behavior, and the percentage of aberrant persons in the group. The percentage of simulees defined a priori as aberrant responders tha
Influence of Test and Person Characteristics on Nonparametric Appropriateness Measurement
Meijer, Rob R; Molenaar, Ivo W; Sijtsma, Klaas
1994-01-01
Appropriateness measurement in nonparametric item response theory modeling is affected by the reliability of the items, the test length, the type of aberrant response behavior, and the percentage of aberrant persons in the group. The percentage of simulees defined a priori as aberrant responders tha
Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects
Qian, Minghui; Hu, Ridong; Chen, Jianwei
2016-01-01
Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…
Uniform Consistency for Nonparametric Estimators in Null Recurrent Time Series
Gao, Jiti; Kanaya, Shin; Li, Degui
2015-01-01
This paper establishes uniform consistency results for nonparametric kernel density and regression estimators when time series regressors concerned are nonstationary null recurrent Markov chains. Under suitable regularity conditions, we derive uniform convergence rates of the estimators. Our...... results can be viewed as a nonstationary extension of some well-known uniform consistency results for stationary time series....
Non-parametric Bayesian inference for inhomogeneous Markov point processes
Berthelsen, Kasper Klitgaard; Møller, Jesper
With reference to a specific data set, we consider how to perform a flexible non-parametric Bayesian analysis of an inhomogeneous point pattern modelled by a Markov point process, with a location dependent first order term and pairwise interaction only. A priori we assume that the first order term...
Investigating the cultural patterns of corruption: A nonparametric analysis
Halkos, George; Tzeremes, Nickolaos
2011-01-01
By using a sample of 77 countries our analysis applies several nonparametric techniques in order to reveal the link between national culture and corruption. Based on Hofstede’s cultural dimensions and the corruption perception index, the results reveal that countries with higher levels of corruption tend to have higher power distance and collectivism values in their society.
Coverage Accuracy of Confidence Intervals in Nonparametric Regression
Song-xi Chen; Yong-song Qin
2003-01-01
Point-wise confidence intervals for a nonparametric regression function with random design points are considered. The confidence intervals are those based on the traditional normal approximation and the empirical likelihood. Their coverage accuracy is assessed by developing the Edgeworth expansions for the coverage probabilities. It is shown that the empirical likelihood confidence intervals are Bartlett correctable.
Homothetic Efficiency and Test Power: A Non-Parametric Approach
J. Heufer (Jan); P. Hjertstrand (Per)
2015-01-01
markdownabstract__Abstract__ We provide a nonparametric revealed preference approach to demand analysis based on homothetic efficiency. Homotheticity is a useful restriction but data rarely satisfies testable conditions. To overcome this we provide a way to estimate homothetic efficiency of consump
Non-parametric analysis of rating transition and default data
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move...
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
Nonparametric Inference for the Cosmic Microwave Background
Genovese, C R; Nichol, R C; Arjunwadkar, M; Wasserman, L; Genovese, Christopher R.; Miller, Christopher J.; Nichol, Robert C.; Arjunwadkar, Mihir; Wasserman, Larry
2004-01-01
The Cosmic Microwave Background (CMB), which permeates the entire Universe, is the radiation left over from just 380,000 years after the Big Bang. On very large scales, the CMB radiation field is smooth and isotropic, but the existence of structure in the Universe - stars, galaxies, clusters of galaxies - suggests that the field should fluctuate on smaller scales. Recent observations, from the Cosmic Microwave Background Explorer to the Wilkinson Microwave Anisotropy Project, have strikingly confirmed this prediction. CMB fluctuations provide clues to the Universe's structure and composition shortly after the Big Bang that are critical for testing cosmological models. For example, CMB data can be used to determine what portion of the Universe is composed of ordinary matter versus the mysterious dark matter and dark energy. To this end, cosmologists usually summarize the fluctuations by the power spectrum, which gives the variance as a function of angular frequency. The spectrum's shape, and in particular the ...
Hofmann, Wilhelm; Finkel, Eli J; Fitzsimons, Gráinne M
2015-09-01
In the new millennium, scholars have built a robust intersection between close-relationships research and self-regulation research. However, virtually no work has investigated how the most basic and broad indicator of relationship quality, relationship satisfaction, affects self-regulation and vice versa. In the present research, we show that higher relationship satisfaction promotes a motivational mind-set that is conducive for effective self-regulation, and thus for goal progress and performance. In Study 1-a large-scale, intensive experience sampling project of 115 couples (total N = 230)-we closely tracked fluctuations in state relationship satisfaction (SRS) and 4 parameters of effective self-regulation according to our conceptual model. Dyadic process analyses showed that individuals experiencing higher SRS than they typically do exhibited higher levels of (a) perceived control, (b) goal focus, (c) perceived partner support, and (d) positive affect during goal pursuit than they typically exhibit. Together, these 4 self-regulation-relevant variables translated into higher rates of daily progress on specific, idiographic goals. In Study 2 (N = 195), we employed a novel experimental manipulation of SRS, replicating the link between SRS and parameters of effective self-regulation. Taken together, these findings suggest that momentary increases in relationship satisfaction may benefit everyday goal pursuit through a combination of cognitive and affective mechanisms, thus further integrating relationship research with social-cognitive research on goal pursuit. (c) 2015 APA, all rights reserved).
Akhtar R. Siddique
2000-03-01
Full Text Available This paper develops a filtering-based framework of non-parametric estimation of parameters of a diffusion process from the conditional moments of discrete observations of the process. This method is implemented for interest rate data in the Eurodollar and long term bond markets. The resulting estimates are then used to form non-parametric univariate and bivariate interest rate models and compute prices for the short term Eurodollar interest rate futures options and long term discount bonds. The bivariate model produces prices substantially closer to the market prices. This paper develops a filtering-based framework of non-parametric estimation of parameters of a diffusion process from the conditional moments of discrete observations of the process. This method is implemented for interest rate data in the Eurodollar and long term bond markets. The resulting estimates are then used to form non-parametric univariate and bivariate interest rate models and compute prices for the short term Eurodollar interest rate futures options and long term discount bonds. The bivariate model produces prices substantially closer to the market prices.
Liability for Damage Caused at the Pursuit of Financial Advisory
Slezáková Andrea
2017-06-01
Full Text Available The Act No 186/2009 Coll. on financial intermediation and financial advisory and on amendments and supplements to certain laws is reflecting on the topic of liability. It is incorporating provisions about the liability for damage at the pursuit of financial advisory. The attention is being paid to the liability for damage caused to the professional or non-professional client at the pursuit of financial advisory. In accordance with the element of the liability legal relationship, the subjective aspect, the liability for damage caused at the pursuit of financial advisory represents a subjective liability, where fault is necessary. Our proposal de lege ferenda is the introduction of strict liability for damage caused at the pursuit of financial advisory.
Comparison of Rank Analysis of Covariance and Nonparametric Randomized Blocks Analysis.
Porter, Andrew C.; McSweeney, Maryellen
The relative power of three possible experimental designs under the condition that data is to be analyzed by nonparametric techniques; the comparison of the power of each nonparametric technique to its parametric analogue; and the comparison of relative powers using nonparametric and parametric techniques are discussed. The three nonparametric…
Bakst, Leah; Fleuriet, Jérome; Mustari, Michael J
2017-05-01
Neurons in the smooth eye movement subregion of the frontal eye field (FEFsem) are known to play an important role in voluntary smooth pursuit eye movements. Underlying this function are projections to parietal and prefrontal visual association areas and subcortical structures, all known to play vital but differing roles in the execution of smooth pursuit. Additionally, the FEFsem has been shown to carry a diverse array of signals (e.g., eye velocity, acceleration, gain control). We hypothesized that distinct subpopulations of FEFsem neurons subserve these diverse functions and projections, and that the relative weights of retinal and extraretinal signals could form the basis for categorization of units. To investigate this, we used a step-ramp tracking task with a target blink to determine the relative contributions of retinal and extraretinal signals in individual FEFsem neurons throughout pursuit. We found that the contributions of retinal and extraretinal signals to neuronal activity and behavior change throughout the time course of pursuit. A clustering algorithm revealed three distinct neuronal subpopulations: cluster 1 was defined by a higher sensitivity to eye velocity, acceleration, and retinal image motion; cluster 2 had greater activity during blinks; and cluster 3 had significantly greater eye position sensitivity. We also performed a comparison with a sample of medial superior temporal neurons to assess similarities and differences between the two areas. Our results indicate the utility of simple tests such as the target blink for parsing the complex and multifaceted roles of cortical areas in behavior.NEW & NOTEWORTHY The frontal eye field (FEF) is known to play a critical role in volitional smooth pursuit, carrying a variety of signals that are distributed throughout the brain. This study used a novel application of a target blink task during step ramp tracking to determine, in combination with a clustering algorithm, the relative contributions of
Nonparametric inference procedures for multistate life table analysis.
Dow, M M
1985-01-01
Recent generalizations of the classical single state life table procedures to the multistate case provide the means to analyze simultaneously the mobility and mortality experience of 1 or more cohorts. This paper examines fairly general nonparametric combinatorial matrix procedures, known as quadratic assignment, as an analysis technic of various transitional patterns commonly generated by cohorts over the life cycle course. To some degree, the output from a multistate life table analysis suggests inference procedures. In his discussion of multstate life table construction features, the author focuses on the matrix formulation of the problem. He then presents several examples of the proposed nonparametric procedures. Data for the mobility and life expectancies at birth matrices come from the 458 member Cayo Santiago rhesus monkey colony. The author's matrix combinatorial approach to hypotheses testing may prove to be a useful inferential strategy in several multidimensional demographic areas.
Non-parametric estimation of Fisher information from real data
Shemesh, Omri Har; Miñano, Borja; Hoekstra, Alfons G; Sloot, Peter M A
2015-01-01
The Fisher Information matrix is a widely used measure for applications ranging from statistical inference, information geometry, experiment design, to the study of criticality in biological systems. Yet there is no commonly accepted non-parametric algorithm to estimate it from real data. In this rapid communication we show how to accurately estimate the Fisher information in a nonparametric way. We also develop a numerical procedure to minimize the errors by choosing the interval of the finite difference scheme necessary to compute the derivatives in the definition of the Fisher information. Our method uses the recently published "Density Estimation using Field Theory" algorithm to compute the probability density functions for continuous densities. We use the Fisher information of the normal distribution to validate our method and as an example we compute the temperature component of the Fisher Information Matrix in the two dimensional Ising model and show that it obeys the expected relation to the heat capa...
International Conference on Robust Rank-Based and Nonparametric Methods
McKean, Joseph
2016-01-01
The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...
Combined parametric-nonparametric identification of block-oriented systems
Mzyk, Grzegorz
2014-01-01
This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.
Estimation of Stochastic Volatility Models by Nonparametric Filtering
Kanaya, Shin; Kristensen, Dennis
2016-01-01
/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases......A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Nonparametric Regression Estimation for Multivariate Null Recurrent Processes
Biqing Cai
2015-04-01
Full Text Available This paper discusses nonparametric kernel regression with the regressor being a \\(d\\-dimensional \\(\\beta\\-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate \\(\\sqrt{n(Th^{d}}\\, where \\(n(T\\ is the number of regenerations for a \\(\\beta\\-null recurrent process and the limiting distribution (with proper normalization is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.
Using non-parametric methods in econometric production analysis
Czekaj, Tomasz Gerard; Henningsen, Arne
-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
Right-Censored Nonparametric Regression: A Comparative Simulation Study
Dursun Aydın
2016-11-01
Full Text Available This paper introduces the operating of the selection criteria for right-censored nonparametric regression using smoothing spline. In order to transform the response variable into a variable that contains the right-censorship, we used the KaplanMeier weights proposed by [1], and [2]. The major problem in smoothing spline method is to determine a smoothing parameter to obtain nonparametric estimates of the regression function. In this study, the mentioned parameter is chosen based on censored data by means of the criteria such as improved Akaike information criterion (AICc, Bayesian (or Schwarz information criterion (BIC and generalized crossvalidation (GCV. For this purpose, a Monte-Carlo simulation study is carried out to illustrate which selection criterion gives the best estimation for censored data.
Using non-parametric methods in econometric production analysis
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
by investigating the relationship between the elasticity of scale and the farm size. We use a balanced panel data set of 371~specialised crop farms for the years 2004-2007. A non-parametric specification test shows that neither the Cobb-Douglas function nor the Translog function are consistent with the "true......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...
Poverty and life cycle effects: A nonparametric analysis for Germany
Stich, Andreas
1996-01-01
Most empirical studies on poverty consider the extent of poverty either for the entire society or for separate groups like elderly people.However, these papers do not show what the situation looks like for persons of a certain age. In this paper poverty measures depending on age are derived using the joint density of income and age. The density is nonparametrically estimated by weighted Gaussian kernel density estimation. Applying the conditional density of income to several poverty measures ...
Nonparametric estimation of Fisher information from real data
Har-Shemesh, Omri; Quax, Rick; Miñano, Borja; Hoekstra, Alfons G.; Sloot, Peter M. A.
2016-02-01
The Fisher information matrix (FIM) is a widely used measure for applications including statistical inference, information geometry, experiment design, and the study of criticality in biological systems. The FIM is defined for a parametric family of probability distributions and its estimation from data follows one of two paths: either the distribution is assumed to be known and the parameters are estimated from the data or the parameters are known and the distribution is estimated from the data. We consider the latter case which is applicable, for example, to experiments where the parameters are controlled by the experimenter and a complicated relation exists between the input parameters and the resulting distribution of the data. Since we assume that the distribution is unknown, we use a nonparametric density estimation on the data and then compute the FIM directly from that estimate using a finite-difference approximation to estimate the derivatives in its definition. The accuracy of the estimate depends on both the method of nonparametric estimation and the difference Δ θ between the densities used in the finite-difference formula. We develop an approach for choosing the optimal parameter difference Δ θ based on large deviations theory and compare two nonparametric density estimation methods, the Gaussian kernel density estimator and a novel density estimation using field theory method. We also compare these two methods to a recently published approach that circumvents the need for density estimation by estimating a nonparametric f divergence and using it to approximate the FIM. We use the Fisher information of the normal distribution to validate our method and as a more involved example we compute the temperature component of the FIM in the two-dimensional Ising model and show that it obeys the expected relation to the heat capacity and therefore peaks at the phase transition at the correct critical temperature.
ANALYSIS OF TIED DATA: AN ALTERNATIVE NON-PARAMETRIC APPROACH
I. C. A. OYEKA
2012-02-01
Full Text Available This paper presents a non-parametric statistical method of analyzing two-sample data that makes provision for the possibility of ties in the data. A test statistic is developed and shown to be free of the effect of any possible ties in the data. An illustrative example is provided and the method is shown to compare favourably with its competitor; the Mann-Whitney test and is more powerful than the latter when there are ties.
Nonparametric test for detecting change in distribution with panel data
Pommeret, Denys; Ghattas, Badih
2011-01-01
This paper considers the problem of comparing two processes with panel data. A nonparametric test is proposed for detecting a monotone change in the link between the two process distributions. The test statistic is of CUSUM type, based on the empirical distribution functions. The asymptotic distribution of the proposed statistic is derived and its finite sample property is examined by bootstrap procedures through Monte Carlo simulations.
A Bayesian nonparametric method for prediction in EST analysis
Prünster Igor
2007-09-01
Full Text Available Abstract Background Expressed sequence tags (ESTs analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library. Results In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b the number of new unique genes to be observed in a future sample; c the discovery rate of new genes as a function of the future sample size. The Bayesian nonparametric model we adopt conveys, in a statistically rigorous way, the available information into prediction. Our proposal has appealing properties over frequentist nonparametric methods, which become unstable when prediction is required for large future samples. EST libraries, previously studied with frequentist methods, are analyzed in detail. Conclusion The Bayesian nonparametric approach we undertake yields valuable tools for gene capture and prediction in EST libraries. The estimators we obtain do not feature the kind of drawbacks associated with frequentist estimators and are reliable for any size of the additional sample.
Fusion of Hard and Soft Information in Nonparametric Density Estimation
2015-06-10
estimation exploiting, in concert, hard and soft information. Although our development, theoretical and numerical, makes no distinction based on sample...Fusion of Hard and Soft Information in Nonparametric Density Estimation∗ Johannes O. Royset Roger J-B Wets Department of Operations Research...univariate density estimation in situations when the sample ( hard information) is supplemented by “soft” information about the random phenomenon. These
Nonparametric estimation for hazard rate monotonously decreasing system
Han Fengyan; Li Weisong
2005-01-01
Estimation of density and hazard rate is very important to the reliability analysis of a system. In order to estimate the density and hazard rate of a hazard rate monotonously decreasing system, a new nonparametric estimator is put forward. The estimator is based on the kernel function method and optimum algorithm. Numerical experiment shows that the method is accurate enough and can be used in many cases.
Non-parametric versus parametric methods in environmental sciences
Muhammad Riaz
2016-01-01
Full Text Available This current report intends to highlight the importance of considering background assumptions required for the analysis of real datasets in different disciplines. We will provide comparative discussion of parametric methods (that depends on distributional assumptions (like normality relative to non-parametric methods (that are free from many distributional assumptions. We have chosen a real dataset from environmental sciences (one of the application areas. The findings may be extended to the other disciplines following the same spirit.
The Initiation of Smooth Pursuit is Delayed in Anisometropic Amblyopia.
Raashid, Rana Arham; Liu, Ivy Ziqian; Blakeman, Alan; Goltz, Herbert C; Wong, Agnes M F
2016-04-01
Several behavioral studies have shown that the reaction times of visually guided movements are slower in people with amblyopia, particularly during amblyopic eye viewing. Here, we tested the hypothesis that the initiation of smooth pursuit eye movements, which are responsible for accurately keeping moving objects on the fovea, is delayed in people with anisometropic amblyopia. Eleven participants with anisometropic amblyopia and 14 visually normal observers were asked to track a step-ramp target moving at ±15°/s horizontally as quickly and as accurately as possible. The experiment was conducted under three viewing conditions: amblyopic/nondominant eye, binocular, and fellow/dominant eye viewing. Outcome measures were smooth pursuit latency, open-loop gain, steady state gain, and catch-up saccade frequency. Participants with anisometropic amblyopia initiated smooth pursuit significantly slower during amblyopic eye viewing (206 ± 20 ms) than visually normal observers viewing with their nondominant eye (183 ± 17 ms, P = 0.002). However, mean pursuit latency in the anisometropic amblyopia group during binocular and monocular fellow eye viewing was comparable to the visually normal group. Mean open-loop gain, steady state gain, and catch-up saccade frequency were similar between the two groups, but participants with anisometropic amblyopia exhibited more variable steady state gain (P = 0.045). This study provides evidence of temporally delayed smooth pursuit initiation in anisometropic amblyopia. After initiation, the smooth pursuit velocity profile in anisometropic amblyopia participants is similar to visually normal controls. This finding differs from what has been observed previously in participants with strabismic amblyopia who exhibit reduced smooth pursuit velocity gains with more catch-up saccades.
Experimental and computational analysis of monkey smooth pursuit eye movements.
Churchland, M M; Lisberger, S G
2001-08-01
Smooth pursuit eye movements are guided by visual feedback and are surprisingly accurate despite the time delay between visual input and motor output. Previous models have reproduced the accuracy of pursuit either by using elaborate visual signals or by adding sources of motor feedback. Our goal was to constrain what types of signals drive pursuit by obtaining data that would discriminate between these two modeling approaches, represented by the "image motion model" and the "tachometer feedback" model. Our first set of experiments probed the visual properties of pursuit with brief square-pulse and sine-wave perturbations of target velocity. Responses to pulse perturbations increased almost linearly with pulse amplitude, while responses to sine wave perturbations showed strong saturation with increasing stimulus amplitude. The response to sine wave perturbations was strongly dependent on the baseline image velocity at the time of the perturbation. Responses were much smaller if baseline image velocity was naturally large, or was artificially increased by superimposing sine waves on pulse perturbations. The image motion model, but not the tachometer feedback model, could reproduce these features of pursuit. We used a revision of the image motion model that was, like the original, sensitive to both image velocity and image acceleration. Due to a saturating nonlinearity, the sensitivity to image acceleration declined with increasing image velocity. Inclusion of this nonlinearity was motivated by our experimental results, was critical in accounting for the responses to perturbations, and provided an explanation for the unexpected stability of pursuit in the presence of perturbations near the resonant frequency. As an emergent property, the revised image motion model was able to reproduce the frequency and damping of oscillations recorded during artificial feedback delays. Our second set of experiments replicated prior recordings of pursuit responses to multiple
Context-specific adaptation of pursuit initiation in humans
Takagi, M.; Abe, H.; Hasegawa, S.; Usui, T.; Hasebe, H.; Miki, A.; Zee, D. S.; Shelhauser, M. (Principal Investigator)
2000-01-01
PURPOSE: To determine if multiple states for the initiation of pursuit, as assessed by acceleration in the "open-loop" period, can be learned and gated by context. METHODS: Four normal subjects were studied. A modified step-ramp paradigm for horizontal pursuit was used to induce adaptation. In an increasing paradigm, target velocity doubled 230 msec after onset; in a decreasing paradigm, it was halved. In the first experiment, vertical eye position (+/-5 degrees ) was used as the context cue, and the training paradigm (increasing or decreasing) changed with vertical eye position. In the second experiment, with vertical position constant, when the target was red, training was decreasing, and when green, increasing. The average eye acceleration in the first 100 msec of tracking was the index of open-loop pursuit performance. RESULTS: With vertical position as the cue, pursuit adaptation differed between up and down gaze. In some cases, the direction of adaptation was in exact accord with the training stimuli. In others, acceleration increased or decreased for both up and down gaze but always in correct relative proportion to the training stimuli. In contrast, multiple adaptive states were not induced with color as the cue. CONCLUSIONS: Multiple values for the relationship between the average eye acceleration during the initiation of pursuit and target velocity could be learned and gated by context. Vertical position was an effective contextual cue but not target color, implying that useful contextual cues must be similar to those occurring naturally, for example, orbital position with eye muscle weakness.
Nearly Cyclic Pursuit and its Hierarchical variant for Multi-agent Systems
Iqbal, Muhammad; Leth, John-Josef; Ngo, Trung Dung
2015-01-01
The rendezvous problem for multiple agents under nearly cyclic pursuit and hierarchical nearly cyclic pursuit is discussed in this paper. The control law designed under nearly cyclic pursuit strategy enables the agents to converge at a point dictated by a beacon. A hierarchical version of the nea......The rendezvous problem for multiple agents under nearly cyclic pursuit and hierarchical nearly cyclic pursuit is discussed in this paper. The control law designed under nearly cyclic pursuit strategy enables the agents to converge at a point dictated by a beacon. A hierarchical version...
Automatic Epileptic Seizure Onset Detection Using Matching Pursuit
Sorensen, Thomas Lynggaard; Olsen, Ulrich L.; Conradsen, Isa
2010-01-01
An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose....... The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial...... electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature xtractor for detection of epileptic seizures....
Relative Effects of Forward and Backward Planning on Goal Pursuit.
Park, Jooyoung; Lu, Fang-Chi; Hedgcock, William M
2017-09-01
Considerable research has shown that planning plays an important role in goal pursuit. But how does the way people plan affect goal pursuit? Research on this question is scarce. In the current research, we examined how planning the steps required for goal attainment in chronological order (i.e., forward planning) and reverse chronological order (i.e., backward planning) influences individuals' motivation for and perceptions of goal pursuit. Compared with forward planning, backward planning not only led to greater motivation, higher goal expectancy, and less time pressure but also resulted in better goal-relevant performance. We further demonstrated that this motivational effect occurred because backward planning allowed people to think of tasks required to reach their goals more clearly, especially when goals were complex to plan. These findings suggest that the way people plan matters just as much as whether or not they plan.
a Multivariate Downscaling Model for Nonparametric Simulation of Daily Flows
Molina, J. M.; Ramirez, J. A.; Raff, D. A.
2011-12-01
A multivariate, stochastic nonparametric framework for stepwise disaggregation of seasonal runoff volumes to daily streamflow is presented. The downscaling process is conditional on volumes of spring runoff and large-scale ocean-atmosphere teleconnections and includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. The non-parametric and assumption-free character of the framework allows consideration of the random nature and nonlinearities of daily flows, which parametric models are unable to account for adequately. This paper examines statistical links between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a periodicity analysis of climate patterns to detect coherences of their cyclic behavior in the frequency domain. We explore the use of such relationships and selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices, NPGO, SOI and PDO, respectively) in the proposed data-driven framework by means of a combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. In addition, we present a new and simple algorithm, based on nonparametric resampling, that overcomes the common limitation of lack of preservation of historical correlation between daily flows across months. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and produces synthetic series that are statistically indistinguishable from the observations. We present evidence showing that both
YAMPA: Yet Another Matching Pursuit Algorithm for compressive sensing
Lodhi, Muhammad A.; Voronin, Sergey; Bajwa, Waheed U.
2016-05-01
State-of-the-art sparse recovery methods often rely on the restricted isometry property for their theoretical guarantees. However, they cannot explicitly incorporate metrics such as restricted isometry constants within their recovery procedures due to the computational intractability of calculating such metrics. This paper formulates an iterative algorithm, termed yet another matching pursuit algorithm (YAMPA), for recovery of sparse signals from compressive measurements. YAMPA differs from other pursuit algorithms in that: (i) it adapts to the measurement matrix using a threshold that is explicitly dependent on two computable coherence metrics of the matrix, and (ii) it does not require knowledge of the signal sparsity. Performance comparisons of YAMPA against other matching pursuit and approximate message passing algorithms are made for several types of measurement matrices. These results show that while state-of-the-art approximate message passing algorithms outperform other algorithms (including YAMPA) in the case of well-conditioned random matrices, they completely break down in the case of ill-conditioned measurement matrices. On the other hand, YAMPA and comparable pursuit algorithms not only result in reasonable performance for well-conditioned matrices, but their performance also degrades gracefully for ill-conditioned matrices. The paper also shows that YAMPA uniformly outperforms other pursuit algorithms for the case of thresholding parameters chosen in a clairvoyant fashion. Further, when combined with a simple and fast technique for selecting thresholding parameters in the case of ill-conditioned matrices, YAMPA outperforms other pursuit algorithms in the regime of low undersampling, although some of these algorithms can outperform YAMPA in the regime of high undersampling in this setting.
Panel data nonparametric estimation of production risk and risk preferences
Czekaj, Tomasz Gerard; Henningsen, Arne
We apply nonparametric panel data kernel regression to investigate production risk, out-put price uncertainty, and risk attitudes of Polish dairy farms based on a firm-level unbalanced panel data set that covers the period 2004–2010. We compare different model specifications and different...... approaches for obtaining firm-specific measures of risk attitudes. We found that Polish dairy farmers are risk averse regarding production risk and price uncertainty. According to our results, Polish dairy farmers perceive the production risk as being more significant than the risk related to output price...
Digital spectral analysis parametric, non-parametric and advanced methods
Castanié, Francis
2013-01-01
Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a
Nonparametric statistics a step-by-step approach
Corder, Gregory W
2014-01-01
"…a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught."" -CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical powerSPSS® (Version 21) software and updated screen ca
Categorical and nonparametric data analysis choosing the best statistical technique
Nussbaum, E Michael
2014-01-01
Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain
Nonparametric statistical structuring of knowledge systems using binary feature matches
Mørup, Morten; Glückstad, Fumiko Kano; Herlau, Tue
2014-01-01
statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary......Structuring knowledge systems with binary features is often based on imposing a similarity measure and clustering objects according to this similarity. Unfortunately, such analyses can be heavily influenced by the choice of similarity measure. Furthermore, it is unclear at which level clusters have...
Testing for a constant coefficient of variation in nonparametric regression
Dette, Holger; Marchlewski, Mareen; Wagener, Jens
2010-01-01
In the common nonparametric regression model Y_i=m(X_i)+sigma(X_i)epsilon_i we consider the problem of testing the hypothesis that the coefficient of the scale and location function is constant. The test is based on a comparison of the observations Y_i=\\hat{sigma}(X_i) with their mean by a smoothed empirical process, where \\hat{sigma} denotes the local linear estimate of the scale function. We show weak convergence of a centered version of this process to a Gaussian process under the null ...
Generative Temporal Modelling of Neuroimaging - Decomposition and Nonparametric Testing
Hald, Ditte Høvenhoff
The goal of this thesis is to explore two improvements for functional magnetic resonance imaging (fMRI) analysis; namely our proposed decomposition method and an extension to the non-parametric testing framework. Analysis of fMRI allows researchers to investigate the functional processes...... of the brain, and provides insight into neuronal coupling during mental processes or tasks. The decomposition method is a Gaussian process-based independent components analysis (GPICA), which incorporates a temporal dependency in the sources. A hierarchical model specification is used, featuring both...
Cyclic Matching Pursuits with Multiscale Time-frequency Dictionaries
Sturm, Bob L.; Christensen, Mads Græsbøll
2010-01-01
We generalize cyclic matching pursuit (CMP), propose an orthogonal variant, and examine their performance using multiscale time-frequency dictionaries in the sparse approximation of signals. Overall, we find that the cyclic approach of CMP produces signal models that have a much lower approximation...... error than existing greedy iterative descent methods such as matching pursuit (MP), and are competitive with models found using orthogonal MP (OMP), and orthogonal least squares (OLS). This implies that CMP is a strong alternative to the more computationally complex approaches of OMP and OLS...... for modeling high-dimensional signals....
Evolving Pacing Strategies for Team Pursuit Track Cycling
Wagner, Markus; Jordan, Diora; Kroeger, Trent; Neumann, Frank
2011-01-01
Team pursuit track cycling is a bicycle racing sport held on velodromes and is part of the Summer Olympics. It involves the use of strategies to minimize the overall time that a team of cyclists needs to complete a race. We present an optimisation framework for team pursuit track cycling and show how to evolve strategies using metaheuristics for this interesting real-world problem. Our experimental results show that these heuristics lead to significantly better strategies than state-of-art strategies that are currently used by teams of cyclists.
Chases and escapes the mathematics of pursuit and evasion
Nahin, Paul J
2012-01-01
We all played tag when we were kids. What most of us don't realize is that this simple chase game is in fact an application of pursuit theory, and that the same principles of games like tag, dodgeball, and hide-and-seek are also at play in military strategy, high-seas chases by the Coast Guard, and even romantic pursuits. In Chases and Escapes, Paul Nahin gives us the first complete history of this fascinating area of mathematics, from its classical analytical beginnings to the present day. Drawing on game theory, geometry, linear algebra, target-tracking algorithms, and much
DOPPLERLET BASED TIME-FREQUENCY REPRESENTATION VIA MATCHING PURSUITS
Zou Hongxing; Zhou Xiaobo; Dai Qionghai; Li Yanda
2001-01-01
A new time-frequency representation called Dopplerlet transform, which uses the dilated, translated and modulated windowed Doppler signals as its basis functions, is proposed, and the Fourier transform, short-time Fourier transform (including Gabor transform), wavelet transform, and chirplet transform are formulated in one framework of Dopplerlet transform accordingly.It is proved that the matching pursuits based on Dopplerlet basis functions are convergent, and that the energy of residual signals yielded in the decomposition process decays exponentially. Simulation results show that the matching pursuits with Dopplerlet basis functions can characterize compactly a nonstationary signal.
Using Mathematica to build Non-parametric Statistical Tables
Gloria Perez Sainz de Rozas
2003-01-01
Full Text Available In this paper, I present computational procedures to obtian statistical tables. The tables of the asymptotic distribution and the exact distribution of Kolmogorov-Smirnov statistic Dn for one population, the table of the distribution of the runs R, the table of the distribution of Wilcoxon signed-rank statistic W+ and the table of the distribution of Mann-Whitney statistic Ux using Mathematica, Version 3.9 under Window98. I think that it is an interesting cuestion because many statistical packages give the asymptotic significance level in the statistical tests and with these porcedures one can easily calculate the exact significance levels and the left-tail and right-tail probabilities with non-parametric distributions. I have used mathematica to make these calculations because one can use symbolic language to solve recursion relations. It's very easy to generate the format of the tables, and it's possible to obtain any table of the mentioned non-parametric distributions with any precision, not only with the standard parameters more used in Statistics, and without transcription mistakes. Furthermore, using similar procedures, we can generate tables for the following distribution functions: Binomial, Poisson, Hypergeometric, Normal, x2 Chi-Square, T-Student, F-Snedecor, Geometric, Gamma and Beta.
1st Conference of the International Society for Nonparametric Statistics
Lahiri, S; Politis, Dimitris
2014-01-01
This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for NonParametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI, and other organizations. M.G. Akritas, S.N. Lahiri, and D.N. Politis are the first executive committee members of ISNPS, and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao, and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world. The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the wo...
Genomic breeding value estimation using nonparametric additive regression models
Solberg Trygve
2009-01-01
Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.
Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes
Zhou, Wei-Xing; Sornette, Didier
We apply two nonparametric methods to further test the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The term "parametric" refers here to the use of the log-periodic power law formula to fit the data; in contrast, "nonparametric" refers to the use of general tools such as Fourier transform, and in the present case the Hilbert transform and the so-called (H, q)-analysis. The analysis using the (H, q)-derivative is applied to seven time series ending with the October 1987 crash, the October 1997 correction and the April 2000 crash of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq indices. The Hilbert transform is applied to two detrended price time series in terms of the ln(tc-t) variable, where tc is the time of the crash. Taking all results together, we find strong evidence for a universal fundamental log-frequency f=1.02±0.05 corresponding to the scaling ratio λ=2.67±0.12. These values are in very good agreement with those obtained in earlier works with different parametric techniques. This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all relevant details so that the reader can judge for himself or herself the validity and robustness of the results.
Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization
Lee, Kyungbook; Song, Seok Goo
2016-10-01
Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events (M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.
A non-parametric framework for estimating threshold limit values
Ulm Kurt
2005-11-01
Full Text Available Abstract Background To estimate a threshold limit value for a compound known to have harmful health effects, an 'elbow' threshold model is usually applied. We are interested on non-parametric flexible alternatives. Methods We describe how a step function model fitted by isotonic regression can be used to estimate threshold limit values. This method returns a set of candidate locations, and we discuss two algorithms to select the threshold among them: the reduced isotonic regression and an algorithm considering the closed family of hypotheses. We assess the performance of these two alternative approaches under different scenarios in a simulation study. We illustrate the framework by analysing the data from a study conducted by the German Research Foundation aiming to set a threshold limit value in the exposure to total dust at workplace, as a causal agent for developing chronic bronchitis. Results In the paper we demonstrate the use and the properties of the proposed methodology along with the results from an application. The method appears to detect the threshold with satisfactory success. However, its performance can be compromised by the low power to reject the constant risk assumption when the true dose-response relationship is weak. Conclusion The estimation of thresholds based on isotonic framework is conceptually simple and sufficiently powerful. Given that in threshold value estimation context there is not a gold standard method, the proposed model provides a useful non-parametric alternative to the standard approaches and can corroborate or challenge their findings.
Using non-parametric methods in econometric production analysis
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb-Douglas a......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...... to estimate production functions without the specification of a functional form. Therefore, they avoid possible misspecification errors due to the use of an unsuitable functional form. In this paper, we use parametric and non-parametric methods to identify the optimal size of Polish crop farms...
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study
Anestis Antoniadis
2001-06-01
Full Text Available Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced.
Computing Economies of Scope Using Robust Partial Frontier Nonparametric Methods
Pedro Carvalho
2016-03-01
Full Text Available This paper proposes a methodology to examine economies of scope using the recent order-α nonparametric method. It allows us to investigate economies of scope by comparing the efficient order-α frontiers of firms that produce two or more goods with the efficient order-α frontiers of firms that produce only one good. To accomplish this, and because the order-α frontiers are irregular, we suggest to linearize them by the DEA estimator. The proposed methodology uses partial frontier nonparametric methods that are more robust than the traditional full frontier methods. By using a sample of 67 Portuguese water utilities for the period 2002–2008 and, also, a simulated sample, we prove the usefulness of the approach adopted and show that if only the full frontier methods were used, they would lead to different results. We found evidence of economies of scope in the provision of water supply and wastewater services simultaneously by water utilities in Portugal.
Bayesian nonparametric dictionary learning for compressed sensing MRI.
Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping
2014-12-01
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.
Effraimidis, Georgios; Dahl, Christian Møller
In this paper, we develop a fully nonparametric approach for the estimation of the cumulative incidence function with Missing At Random right-censored competing risks data. We obtain results on the pointwise asymptotic normality as well as the uniform convergence rate of the proposed nonparametric...... estimator. A simulation study that serves two purposes is provided. First, it illustrates in details how to implement our proposed nonparametric estimator. Secondly, it facilitates a comparison of the nonparametric estimator to a parametric counterpart based on the estimator of Lu and Liang (2008...
38 CFR 21.7654 - Pursuit and absences.
2010-07-01
...) VOCATIONAL REHABILITATION AND EDUCATION Educational Assistance for Members of the Selected Reserve Pursuit of...) Exceptions to the monthly verification requirement. A reservist does not have to submit a monthly... termination of training; (iii) Except as provided in § 21.7656(a), changes in the number of credit hours or in...
The Pursuit of Happiness, Stress and Temporomandibular Disorders
D. Marcus
2013-11-01
Full Text Available Mismanaging the pursuit of happiness causes negative psychological effects such as stress and disappointment. The resultant stress often manifests itself as psychological and physical health problems. We explore the problems of measuring happiness according to materialistic wealth and demonstrate that misinterpreting happiness can lead to a stress inducing pursuit. The happiness that human beings pursue is often material-based hedonism whereas eudaimonic happiness has been shown to be a by-product of the pursuit of meaningful activities. Pursuing a predefined happiness, the failure to achieve it and the resistance to it can create stress induced psychosomatic health problems; temporomandibular disorders (TMD are one such example. Masticatory myofascial pain syndrome is a form of TMD that has a strong association to psychological stress. In this paper the research on TMD associated facial pain across different socioeconomic status (SES groups is utilized to compare an objective, stress related physiological disorder with happiness data. We also discuss how the pressures of pursuing socially determined aesthetic happiness such as conforming to society’s expectations of smile and facial aesthetics can drive people to make surgical or orthodontic changes. This review proposes that pursuing happiness has the propensity to cause not only psychological stress but also negative behaviors. We aim to encourage further scientific research that will help to clarify this philosophical pursuit.
Cyclic Matching Pursuits with Multiscale Time-frequency Dictionaries
Sturm, Bob L.; Christensen, Mads Græsbøll
2010-01-01
We generalize cyclic matching pursuit (CMP), propose an orthogonal variant, and examine their performance using multiscale time-frequency dictionaries in the sparse approximation of signals. Overall, we find that the cyclic approach of CMP produces signal models that have a much lower approximati...
Measuring Experiences of Interest-Related Pursuits in Connected Learning
Maul, Andrew; Penuel, William R.; Dadey, Nathan; Gallagher, Lawrence P.; Podkul, Timothy; Price, Emily
2017-01-01
This paper describes an effort to develop a survey instrument capable of measuring important aspects of adolescents' experiences of interest-related pursuits that are supported by technology. The measure focuses on youths' experiences of "connected learning" (Ito et al. in Connected learning: an agenda for research and design. Digital…
Incremental principal component pursuit for video background modeling
Rodriquez-Valderrama, Paul A.; Wohlberg, Brendt
2017-03-14
An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.
Multi-target pursuit formation of multi-agent systems
Yan Jing; Guan Xin-Ping; Luo Xiao-Yuan
2011-01-01
The main goal of this paper is to design a team of agents that can accomplish multi-target pursuit formation using a developed leader-follower strategy. It is supposed that every target can accept a certain number of agents. First, each agent can automatically choose its target based on the distance from the agent to the target and the number of agents
Narcissism and the Strategic Pursuit of Short-Term Mating
Schmitt, David P.; Alcalay, Lidia; Allik, Jüri
2017-01-01
associating with key sexual outcomes (e.g., more active pursuit of short-term mating, intimate partner violence, and sexual aggression) and sex-related personality traits (e.g., higher extraversion and openness to experience). Whereas some features of personality (e.g., subjective well-being) were universally...
Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit
Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan
2012-01-01
We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures asym...
The Selfish Goal: Unintended Consequences of Intended Goal Pursuits.
Bargh, John A; Green, Michelle; Fitzsimons, Gráinne
2008-10-01
Three experiments tested the hypothesis that consciously intended goal pursuits have unintended consequences for social judgment and behavior. From evolutionary theory (Dawkins 1976/2006) and empirical evidence of a nonconscious mode of goal pursuit (Bargh, 2005) we derive the hypothesis that most human goal pursuits are open-ended in nature: Once active, goals will operate on goal-relevant content in the environment, even if that content is not the intended focus of the conscious goal. Experiments 1 and 2 demonstrate that goals to evaluate a job applicant for either a waiter or crime reporter position also shape impressions of incidental bystanders in the situation, such that the bystander is later liked or disliked not on his own merits, but on how well his behavior matches the criteria consciously applied in evaluating the job applicant. Experiment 3 finds that a goal to help a specific target person spills over to influence actions toward incidental bystanders, but only while active. Implications of these findings for goal pursuit in everyday life are discussed.
Nieuport-Delage Pursuit Airplane 48 C. 1. : "jockey" type
1927-01-01
This is a light single-seat pursuit airplane with a tractor propeller actuated by a 12 cylinder V-type Hispano-Suiza engine giving 400 HP at 2000 R.P.M. This is a single winged aircraft capable of 273 km/h.
Positive affect as informational feedback in goal pursuit
Orehek, Edward; Bessarabova, Elena; Chen, Xiaoyan; Kruglanski, Arie W.
2011-01-01
Two studies investigated the cognitive activation of a goal following a promise to complete it. Current theorizing about the impact of positive affect as informational feedback in goal pursuit suggests two contradictory conclusions: (1) positive affect can signal that sufficient progress towards a g
Is the Study of Happiness a Worthy Scientific Pursuit?
Norrish, Jacolyn M.; Vella-Brodrick, Dianne A.
2008-01-01
This paper critiques the view that the study of happiness is not a worthy scientific pursuit. The happiness set point and hedonic treadmill theories denote the complexity of increasing happiness levels due to genetic limitations and adaptation, however, there is mounting evidence to suggest that with the use of appropriate measures and specific…
Rapid Nonconjugate Adaptation of Vertical Voluntary Pursuit Eye Movements
1991-01-01
applied to the post-adaptation data from the left eye magnification condition: YRpost(Transformed) = (2 * YRpre) - YRPost (6) For example, if the pie ...nonconjugate adaptation with spectacle- mounted plano -cylindrical lenses, Lemij (1990) demonstrated that nonconjugate pursuit adaptation was
Sense of place in outdoor-pursuits trip groups
Sharon L. Todd; Anderson B. Young; Lynn S. Anderson; Timothy S. O' Connell; Mary Breunig
2009-01-01
Studies have revealed that sense of community and group cohesion increase significantly over time in outdoor-pursuits trip groups. This study sought to understand similar development of sense of place. Do people simultaneously become more attached to or dependent on the natural environment as they grow closer to each other? Results from a study of college students...
Smooth pursuit eye movements improve temporal resolution for color perception.
Masahiko Terao
Full Text Available Human observers see a single mixed color (yellow when different colors (red and green rapidly alternate. Accumulating evidence suggests that the critical temporal frequency beyond which chromatic fusion occurs does not simply reflect the temporal limit of peripheral encoding. However, it remains poorly understood how the central processing controls the fusion frequency. Here we show that the fusion frequency can be elevated by extra-retinal signals during smooth pursuit. This eye movement can keep the image of a moving target in the fovea, but it also introduces a backward retinal sweep of the stationary background pattern. We found that the fusion frequency was higher when retinal color changes were generated by pursuit-induced background motions than when the same retinal color changes were generated by object motions during eye fixation. This temporal improvement cannot be ascribed to a general increase in contrast gain of specific neural mechanisms during pursuit, since the improvement was not observed with a pattern flickering without changing position on the retina or with a pattern moving in the direction opposite to the background motion during pursuit. Our findings indicate that chromatic fusion is controlled by a cortical mechanism that suppresses motion blur. A plausible mechanism is that eye-movement signals change spatiotemporal trajectories along which color signals are integrated so as to reduce chromatic integration at the same locations (i.e., along stationary trajectories on the retina that normally causes retinal blur during fixation.
Influence of positive subliminal and supraliminal affective cues on goal pursuit in schizophrenia
Chaillou, Anne Clémence; Giersch, Anne; Bonnefond, Anne; Custers, Ruud; Capa, Rémi L.
2015-01-01
Goal pursuit is known to be impaired in schizophrenia, but nothing much is known in these patients about unconscious affective processes underlying goal pursuit. Evidence suggests that in healthy individuals positive subliminal cues are taken as a signal that goal pursuit is easy and therefore reduc
38 CFR 21.310 - Rate of pursuit of a rehabilitation program.
2010-07-01
... rehabilitation program. 21.310 Section 21.310 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS... 38 U.S.C. Chapter 31 Rate of Pursuit § 21.310 Rate of pursuit of a rehabilitation program. (a... and part-time rate of pursuit of a rehabilitation program by a veteran whose ability to pursue...
The Neural Basis of Smooth Pursuit Eye Movements in the Rhesus Monkey Brain
Ilg, Uwe J.; Thier, Peter
2008-01-01
Smooth pursuit eye movements are performed in order to prevent retinal image blur of a moving object. Rhesus monkeys are able to perform smooth pursuit eye movements quite similar as humans, even if the pursuit target does not consist in a simple moving dot. Therefore, the study of the neuronal responses as well as the consequences of…
Multiagent pursuit-evasion games: Algorithms and experiments
Kim, Hyounjin
Deployment of intelligent agents has been made possible through advances in control software, microprocessors, sensor/actuator technology, communication technology, and artificial intelligence. Intelligent agents now play important roles in many applications where human operation is too dangerous or inefficient. There is little doubt that the world of the future will be filled with intelligent robotic agents employed to autonomously perform tasks, or embedded in systems all around us, extending our capabilities to perceive, reason and act, and replacing human efforts. There are numerous real-world applications in which a single autonomous agent is not suitable and multiple agents are required. However, after years of active research in multi-agent systems, current technology is still far from achieving many of these real-world applications. Here, we consider the problem of deploying a team of unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV) to pursue a second team of UGV evaders while concurrently building a map in an unknown environment. This pursuit-evasion game encompasses many of the challenging issues that arise in operations using intelligent multi-agent systems. We cast the problem in a probabilistic game theoretic framework and consider two computationally feasible pursuit policies: greedy and global-max. We also formulate this probabilistic pursuit-evasion game as a partially observable Markov decision process and employ a policy search algorithm to obtain a good pursuit policy from a restricted class of policies. The estimated value of this policy is guaranteed to be uniformly close to the optimal value in the given policy class under mild conditions. To implement this scenario on real UAVs and UGVs, we propose a distributed hierarchical hybrid system architecture which emphasizes the autonomy of each agent yet allows for coordinated team efforts. We then describe our implementation on a fleet of UGVs and UAVs, detailing components such
Robust Depth-Weighted Wavelet for Nonparametric Regression Models
Lu LIN
2005-01-01
In the nonpaxametric regression models, the original regression estimators including kernel estimator, Fourier series estimator and wavelet estimator are always constructed by the weighted sum of data, and the weights depend only on the distance between the design points and estimation points. As a result these estimators are not robust to the perturbations in data. In order to avoid this problem, a new nonparametric regression model, called the depth-weighted regression model, is introduced and then the depth-weighted wavelet estimation is defined. The new estimation is robust to the perturbations in data, which attains very high breakdown value close to 1/2. On the other hand, some asymptotic behaviours such as asymptotic normality are obtained. Some simulations illustrate that the proposed wavelet estimator is more robust than the original wavelet estimator and, as a price to pay for the robustness, the new method is slightly less efficient than the original method.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P
2016-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or "communities", a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: 1. Deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, that not only remove limitations that seriously degrade the inference on large networks, but also reveal s...
A Non-Parametric Spatial Independence Test Using Symbolic Entropy
López Hernández, Fernando
2008-01-01
Full Text Available In the present paper, we construct a new, simple, consistent and powerful test forspatial independence, called the SG test, by using symbolic dynamics and symbolic entropyas a measure of spatial dependence. We also give a standard asymptotic distribution of anaffine transformation of the symbolic entropy under the null hypothesis of independencein the spatial process. The test statistic and its standard limit distribution, with theproposed symbolization, are invariant to any monotonuous transformation of the data.The test applies to discrete or continuous distributions. Given that the test is based onentropy measures, it avoids smoothed nonparametric estimation. We include a MonteCarlo study of our test, together with the well-known Moran’s I, the SBDS (de Graaffet al, 2001 and (Brett and Pinkse, 1997 non parametric test, in order to illustrate ourapproach.
Analyzing single-molecule time series via nonparametric Bayesian inference.
Hines, Keegan E; Bankston, John R; Aldrich, Richard W
2015-02-03
The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Analyzing multiple spike trains with nonparametric Granger causality.
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou
2009-08-01
Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.
Prior processes and their applications nonparametric Bayesian estimation
Phadia, Eswar G
2016-01-01
This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and P...
Using non-parametric methods in econometric production analysis
Czekaj, Tomasz Gerard; Henningsen, Arne
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb......-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
Nonparametric Estimation of Distributions in Random Effects Models
Hart, Jeffrey D.
2011-01-01
We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. © 2011 American Statistical Association.
Curve registration by nonparametric goodness-of-fit testing
Dalalyan, Arnak
2011-01-01
The problem of curve registration appears in many different areas of applications ranging from neuroscience to road traffic modeling. In the present work, we propose a nonparametric testing framework in which we develop a generalized likelihood ratio test to perform curve registration. We first prove that, under the null hypothesis, the resulting test statistic is asymptotically distributed as a chi-squared random variable. This result, often referred to as Wilks' phenomenon, provides a natural threshold for the test of a prescribed asymptotic significance level and a natural measure of lack-of-fit in terms of the p-value of the chi squared test. We also prove that the proposed test is consistent, i.e., its power is asymptotically equal to 1. Some numerical experiments on synthetic datasets are reported as well.
Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.
Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z
2017-03-01
A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.
Nonparametric estimation of stochastic differential equations with sparse Gaussian processes
García, Constantino A.; Otero, Abraham; Félix, Paulo; Presedo, Jesús; Márquez, David G.
2017-08-01
The application of stochastic differential equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we introduce a nonparametric method for estimating the drift and diffusion terms of SDEs from a densely observed discrete time series. The use of Gaussian processes as priors permits working directly in a function-space view and thus the inference takes place directly in this space. To cope with the computational complexity that requires the use of Gaussian processes, a sparse Gaussian process approximation is provided. This approximation permits the efficient computation of predictions for the drift and diffusion terms by using a distribution over a small subset of pseudosamples. The proposed method has been validated using both simulated data and real data from economy and paleoclimatology. The application of the method to real data demonstrates its ability to capture the behavior of complex systems.
Indoor Positioning Using Nonparametric Belief Propagation Based on Spanning Trees
Savic Vladimir
2010-01-01
Full Text Available Nonparametric belief propagation (NBP is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based on spanning trees (NBP-ST created by breadth first search (BFS method. In addition, we propose a reliable indoor model based on obtained measurements in our lab. According to our simulation results, NBP-ST performs better than NBP in terms of accuracy and communication cost in the networks with high connectivity (i.e., highly loopy networks. Furthermore, the computational and communication costs are nearly constant with respect to the transmission radius. However, the drawbacks of proposed method are a little bit higher computational cost and poor performance in low-connected networks.
Revealing components of the galaxy population through nonparametric techniques
Bamford, Steven P; Nichol, Robert C; Miller, Christopher J; Wasserman, Larry; Genovese, Christopher R; Freeman, Peter E
2008-01-01
The distributions of galaxy properties vary with environment, and are often multimodal, suggesting that the galaxy population may be a combination of multiple components. The behaviour of these components versus environment holds details about the processes of galaxy development. To release this information we apply a novel, nonparametric statistical technique, identifying four components present in the distribution of galaxy H$\\alpha$ emission-line equivalent-widths. We interpret these components as passive, star-forming, and two varieties of active galactic nuclei. Independent of this interpretation, the properties of each component are remarkably constant as a function of environment. Only their relative proportions display substantial variation. The galaxy population thus appears to comprise distinct components which are individually independent of environment, with galaxies rapidly transitioning between components as they move into denser environments.
Multi-Directional Non-Parametric Analysis of Agricultural Efficiency
Balezentis, Tomas
This thesis seeks to develop methodologies for assessment of agricultural efficiency and employ them to Lithuanian family farms. In particular, we focus on three particular objectives throughout the research: (i) to perform a fully non-parametric analysis of efficiency effects, (ii) to extend...... relative to labour, intermediate consumption and land (in some cases land was not treated as a discretionary input). These findings call for further research on relationships among financial structure, investment decisions, and efficiency in Lithuanian family farms. Application of different techniques...... of stochasticity associated with Lithuanian family farm performance. The former technique showed that the farms differed in terms of the mean values and variance of the efficiency scores over time with some clear patterns prevailing throughout the whole research period. The fuzzy Free Disposal Hull showed...
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Nonparametric reconstruction of the Om diagnostic to test LCDM
Escamilla-Rivera, Celia
2015-01-01
Cosmic acceleration is usually related with the unknown dark energy, which equation of state, w(z), is constrained and numerically confronted with independent astrophysical data. In order to make a diagnostic of w(z), the introduction of a null test of dark energy can be done using a diagnostic function of redshift, Om. In this work we present a nonparametric reconstruction of this diagnostic using the so-called Loess-Simex factory to test the concordance model with the advantage that this approach offers an alternative way to relax the use of priors and find a possible 'w' that reliably describe the data with no previous knowledge of a cosmological model. Our results demonstrate that the method applied to the dynamical Om diagnostic finds a preference for a dark energy model with equation of state w =-2/3, which correspond to a static domain wall network.
Evaluation of Nonparametric Probabilistic Forecasts of Wind Power
Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008;
likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...... of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind......Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...
Equity and efficiency in private and public education: a nonparametric comparison
L. Cherchye; K. de Witte; E. Ooghe; I. Nicaise
2007-01-01
We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the
Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W
2009-02-01
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.
Out-of-Sample Extensions for Non-Parametric Kernel Methods.
Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang
2017-02-01
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
Non-parametric tests of productive efficiency with errors-in-variables
Kuosmanen, T.K.; Post, T.; Scholtes, S.
2007-01-01
We develop a non-parametric test of productive efficiency that accounts for errors-in-variables, following the approach of Varian. [1985. Nonparametric analysis of optimizing behavior with measurement error. Journal of Econometrics 30(1/2), 445-458]. The test is based on the general Pareto-Koopmans
Equity and efficiency in private and public education: a nonparametric comparison
Cherchye, L.; de Witte, K.; Ooghe, E.; Nicaise, I.
2007-01-01
We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the
D. Das
2014-04-01
Full Text Available Climate projections simulated by Global Climate Models (GCM are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often precludes their application towards accurately assessing the effects of climate change on finer regional scale phenomena. Downscaling of climate variables from coarser to finer regional scales using statistical methods are often performed for regional climate projections. Statistical downscaling (SD is based on the understanding that the regional climate is influenced by two factors – the large scale climatic state and the regional or local features. A transfer function approach of SD involves learning a regression model which relates these features (predictors to a climatic variable of interest (predictand based on the past observations. However, often a single regression model is not sufficient to describe complex dynamic relationships between the predictors and predictand. We focus on the covariate selection part of the transfer function approach and propose a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP, for simultaneous clustering and discovery of covariates within the clusters while automatically finding the number of clusters. Sparse linear models are parsimonious and hence relatively more generalizable than non-sparse alternatives, and lends to domain relevant interpretation. Applications to synthetic data demonstrate the value of the new approach and preliminary results related to feature selection for statistical downscaling shows our method can lead to new insights.
Semi-parametric regression: Efficiency gains from modeling the nonparametric part
Yu, Kyusang; Park, Byeong U; 10.3150/10-BEJ296
2011-01-01
It is widely admitted that structured nonparametric modeling that circumvents the curse of dimensionality is important in nonparametric estimation. In this paper we show that the same holds for semi-parametric estimation. We argue that estimation of the parametric component of a semi-parametric model can be improved essentially when more structure is put into the nonparametric part of the model. We illustrate this for the partially linear model, and investigate efficiency gains when the nonparametric part of the model has an additive structure. We present the semi-parametric Fisher information bound for estimating the parametric part of the partially linear additive model and provide semi-parametric efficient estimators for which we use a smooth backfitting technique to deal with the additive nonparametric part. We also present the finite sample performances of the proposed estimators and analyze Boston housing data as an illustration.
Wesselink, Christiaan; Heeg, Govert P.; Jansonius, Nomdo M.
Objective: To compare prospectively 2 perimetric progression detection algorithms for glaucoma, the Early Manifest Glaucoma Trial algorithm (glaucoma progression analysis [GPA]) and a nonparametric algorithm applied to the mean deviation (MD) (nonparametric progression analysis [NPA]). Methods:
Landmine detection using two-tapped joint orthogonal matching pursuits
Goldberg, Sean; Glenn, Taylor; Wilson, Joseph N.; Gader, Paul D.
2012-06-01
Joint Orthogonal Matching Pursuits (JOMP) is used here in the context of landmine detection using data obtained from an electromagnetic induction (EMI) sensor. The response from an object containing metal can be decomposed into a discrete spectrum of relaxation frequencies (DSRF) from which we construct a dictionary. A greedy iterative algorithm is proposed for computing successive residuals of a signal by subtracting away the highest matching dictionary element at each step. The nal condence of a particular signal is a combination of the reciprocal of this residual and the mean of the complex component. A two-tap approach comparing signals on opposite sides of the geometric location of the sensor is examined and found to produce better classication. It is found that using only a single pursuit does a comparable job, reducing complexity and allowing for real-time implementation in automated target recognition systems. JOMP is particularly highlighted in comparison with a previous EMI detection algorithm known as String Match.
First-order optimality condition of basis pursuit denoise problem
朱玮; 舒适; 成礼智
2014-01-01
A new first-order optimality condition for the basis pursuit denoise (BPDN) problem is derived. This condition provides a new approach to choose the penalty param-eters adaptively for a fixed point iteration algorithm. Meanwhile, the result is extended to matrix completion which is a new field on the heel of the compressed sensing. The numerical experiments of sparse vector recovery and low-rank matrix completion show validity of the theoretic results.
Sparse Signals Recovery from Noisy Measurements by Orthogonal Matching Pursuit
Shen, Yi
2011-01-01
Recently, many practical algorithms have been proposed to recover the sparse signal from fewer measurements. Orthogonal matching pursuit (OMP) is one of the most effective algorithm. In this paper, we use the restricted isometry property to analysis the algorithm. We show that, under certain conditions based on the restricted isometry property and the signals, OMP will recover the support of the sparse signal when measurements are corrupted by additive noise.
RHIC AND THE PURSUIT OF THE QUARK-GLUON PLASMA.
MITCHELL,J.T.
2001-07-25
There is a fugitive on the loose. Its name is Quark-Gluon Plasma, alias the QGP. The QGP is a known informant with knowledge about the fundamental building blocks of nature that we wish to extract. This briefing will outline the status of the pursuit of the elusive QGP. We will cover what makes the QGP tick, its modus operandi, details on how we plan to hunt the fugitive down, and our level of success thus far.
Wavelet-based multicomponent matching pursuit trace interpolation
Choi, Jihun; Byun, Joongmoo; Seol, Soon Jee; Kim, Young
2016-09-01
Typically, seismic data are sparsely and irregularly sampled due to limitations in the survey environment and these cause problems for key seismic processing steps such as surface-related multiple elimination or wave-equation-based migration. Various interpolation techniques have been developed to alleviate the problems caused by sparse and irregular sampling. Among many interpolation techniques, matching pursuit interpolation is a robust tool to interpolate the regularly sampled data with large receiver separation such as crossline data in marine seismic acquisition when both pressure and particle velocity data are used. Multicomponent matching pursuit methods generally used the sinusoidal basis function, which have shown to be effective for interpolating multicomponent marine seismic data in the crossline direction. In this paper, we report the use of wavelet basis functions which further enhances the performance of matching pursuit methods for de-aliasing than sinusoidal basis functions. We also found that the range of the peak wavenumber of the wavelet is critical to the stability of the interpolation results and the de-aliasing performance and that the range should be determined based on Nyquist criteria. In addition, we reduced the computational cost by adopting the inner product of the wavelet and the input data to find the parameters of the wavelet basis function instead of using L-2 norm minimization. Using synthetic data, we illustrate that for aliased data, wavelet-based matching pursuit interpolation yields more stable results than sinusoidal function-based one when we use not only pressure data only but also both pressure and particle velocity together.
Hierarchical brain networks active in approach and avoidance goal pursuit
Jeffrey Martin Spielberg
2013-06-01
Full Text Available Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal pursuit processes (e.g., motivation has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity vital to goal pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.
Smooth ocular pursuit in Chiari type II malformation.
Salman, Michael S; Sharpe, James A; Lillakas, Linda; Steinbach, Martin J; Dennis, Maureen
2007-04-01
Chiari type II malformation (CII) is a congenital anomaly of the cerebellum and brainstem, both important structures for processing smooth ocular pursuit. CII is associated with myelomeningocele and hydrocephalus. We investigated the effects of CII on smooth pursuit (SP) eye movements, and determined the effects of spinal lesion level, number of shunt revisions, nystagmus, and brain dysmorphology on SP. SP was recorded using an infrared eye tracker in 21 participants with CII (11 males, 10 females; age range 8-19y, mean 14y 3mo [SD 3y 2mo]). Thirty-eight healthy children (21 males, 17 females) constituted the comparison group. Participants followed a visual target moving sinusoidally at +/- 10 degrees amplitude, horizontally and vertically at 0.25 or 0.5Hz. SP gains, the ratio of eye to target velocities, were abnormal in the CII group with nystagmus (n= 8). The number of shunt revisions (range 0-10), brain dysmorphology, or spinal lesion level (n= 15 for lower and n= 6 for upper spinal lesion level) did not correlate with SP gains. SP is impaired in children with CII and nystagmus. Abnormal pursuit might be related to the CII dysgenesis or to effects of hydrocephalus. The lack of effect of shunt revisions and abnormal tracking in participants with nystagmus provide evidence that it is related primarily to the cerebellar and brainstem malformation.
Langhinrichsen-Rohling, J; Palarea, R E; Cohen, J; Rohling, M L
2000-01-01
This study investigated the prevalence and predictors of unwanted pursuit behaviors among college students. Participants (n = 282) had experienced the termination of a meaningful romantic relationship. Two questionnaires were administered. One assessed unwanted pursuit behaviors that were perpetrated by individuals who had not initiated the relationship breakup (breakup sufferers; n = 120); the other assessed individuals who had initiated the relationship breakup (relationship dissolvers; n = 162). Results indicated that most breakup sufferers had engaged in at least one act of unwanted pursuit (i.e., unwanted phone calls, unwanted in-person conversations) after the breakup. Breakup sufferers were more likely than relationship dissolvers to perceive a positive impact from their unwanted pursuit behavior. Partner-specific attachment experiences and love styles emerged as significant predictors of unwanted pursuit behavior perpetration, according to both victims and perpetrators of unwanted pursuit. However, only victims of unwanted pursuit revealed an association between levels of relationship violence and unwanted pursuit behavior perpetration. Victims also reported that their unwanted pursuit was related to a lack of friendship between themselves and their expartners. In contrast, there was a positive association between feelings of friendship and unwanted pursuit for perpetrators. The implications of these findings and their application to the stalking literature are discussed.
The effects of smooth pursuit adaptation on the gain of visuomotor transmission in monkeys
Seiji eOno
2013-12-01
Full Text Available Smooth pursuit eye movements are supported by visual-motor systems, where visual motion information is transformed into eye movement commands. Adaptation of the visuomotor systems for smooth pursuit is an important factor to maintain pursuit accuracy and high acuity vision. Short-term adaptation of initial pursuit gain can be produced experimentally using by repeated trials of a step-ramp tracking with two different velocities (double-step paradigm that step-up (10–30 °/s or step-down (20–5 °/s. It is also known that visuomotor gain during smooth pursuit is regulated by a dynamic gain control mechanism by showing that eye velocity evoked by a target perturbation during pursuit increases bidirectionally when ongoing pursuit velocity is higher. However, it remains uncertain how smooth pursuit adaptation alters the gain of visuomotor transmission. Therefore, a single cycle of sinusoidal motion (2.5 Hz, ± 10 °/s was introduced during step-ramp tracking pre- and post-adaptation to determine whether smooth pursuit adaptation affects the perturbation response. The results showed that pursuit adaptation had a significant effect on the perturbation response that was specific to the adapted direction. These results indicate that there might be different visuomotor mechanisms between adaptation and dynamic gain control. Furthermore, smooth pursuit adaptation altered not only the gain of the perturbation response, but also the gain slope (regression curve at different target velocities (5, 10 and 15 °/s. Therefore, pursuit adaptation could affect the dynamic regulation of the visuomotor gain at different pursuit velocities.
Nonparametric predictive inference for combining diagnostic tests with parametric copula
Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.
2017-09-01
Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
Alejandro Jara
2011-04-01
Full Text Available Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.
Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.
Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A
2017-01-18
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.
The Utility of Nonparametric Transformations for Imputation of Survey Data
Robbins Michael W.
2014-12-01
Full Text Available Missing values present a prevalent problem in the analysis of establishment survey data. Multivariate imputation algorithms (which are used to fill in missing observations tend to have the common limitation that imputations for continuous variables are sampled from Gaussian distributions. This limitation is addressed here through the use of robust marginal transformations. Specifically, kernel-density and empirical distribution-type transformations are discussed and are shown to have favorable properties when used for imputation of complex survey data. Although such techniques have wide applicability (i.e., they may be easily applied in conjunction with a wide array of imputation techniques, the proposed methodology is applied here with an algorithm for imputation in the USDA’s Agricultural Resource Management Survey. Data analysis and simulation results are used to illustrate the specific advantages of the robust methods when compared to the fully parametric techniques and to other relevant techniques such as predictive mean matching. To summarize, transformations based upon parametric densities are shown to distort several data characteristics in circumstances where the parametric model is ill fit; however, no circumstances are found in which the transformations based upon parametric models outperform the nonparametric transformations. As a result, the transformation based upon the empirical distribution (which is the most computationally efficient is recommended over the other transformation procedures in practice.
Nonparametric identification of structural modifications in Laplace domain
Suwała, G.; Jankowski, Ł.
2017-02-01
This paper proposes and experimentally verifies a Laplace-domain method for identification of structural modifications, which (1) unlike time-domain formulations, allows the identification to be focused on these parts of the frequency spectrum that have a high signal-to-noise ratio, and (2) unlike frequency-domain formulations, decreases the influence of numerical artifacts related to the particular choice of the FFT exponential window decay. In comparison to the time-domain approach proposed earlier, advantages of the proposed method are smaller computational cost and higher accuracy, which leads to reliable performance in more difficult identification cases. Analytical formulas for the first- and second-order sensitivity analysis are derived. The approach is based on a reduced nonparametric model, which has the form of a set of selected structural impulse responses. Such a model can be collected purely experimentally, which obviates the need for design and laborious updating of a parametric model, such as a finite element model. The approach is verified experimentally using a 26-node lab 3D truss structure and 30 identification cases of a single mass modification or two concurrent mass modifications.
A New Non-Parametric Approach to Galaxy Morphological Classification
Lotz, J M; Madau, P; Lotz, Jennifer M.; Primack, Joel; Madau, Piero
2003-01-01
We present two new non-parametric methods for quantifying galaxy morphology: the relative distribution of the galaxy pixel flux values (the Gini coefficient or G) and the second-order moment of the brightest 20% of the galaxy's flux (M20). We test the robustness of G and M20 to decreasing signal-to-noise and spatial resolution, and find that both measures are reliable to within 10% at average signal-to-noise per pixel greater than 3 and resolutions better than 1000 pc and 500 pc, respectively. We have measured G and M20, as well as concentration (C), asymmetry (A), and clumpiness (S) in the rest-frame near-ultraviolet/optical wavelengths for 150 bright local "normal" Hubble type galaxies (E-Sd) galaxies and 104 0.05 < z < 0.25 ultra-luminous infrared galaxies (ULIRGs).We find that most local galaxies follow a tight sequence in G-M20-C, where early-types have high G and C and low M20 and late-type spirals have lower G and C and higher M20. The majority of ULIRGs lie above the normal galaxy G-M20 sequence...
Nonparametric Bayes modeling for case control studies with many predictors.
Zhou, Jing; Herring, Amy H; Bhattacharya, Anirban; Olshan, Andrew F; Dunson, David B
2016-03-01
It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects.
Biological parametric mapping with robust and non-parametric statistics.
Yang, Xue; Beason-Held, Lori; Resnick, Susan M; Landman, Bennett A
2011-07-15
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Copyright © 2011 Elsevier Inc. All rights reserved.
Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.
Chairez, Isaac
2016-04-05
This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.
Nonparametric estimation of quantum states, processes and measurements
Lougovski, Pavel; Bennink, Ryan
Quantum state, process, and measurement estimation methods traditionally use parametric models, in which the number and role of relevant parameters is assumed to be known. When such an assumption cannot be justified, a common approach in many disciplines is to fit the experimental data to multiple models with different sets of parameters and utilize an information criterion to select the best fitting model. However, it is not always possible to assume a model with a finite (countable) number of parameters. This typically happens when there are unobserved variables that stem from hidden correlations that can only be unveiled after collecting experimental data. How does one perform quantum characterization in this situation? We present a novel nonparametric method of experimental quantum system characterization based on the Dirichlet Process (DP) that addresses this problem. Using DP as a prior in conjunction with Bayesian estimation methods allows us to increase model complexity (number of parameters) adaptively as the number of experimental observations grows. We illustrate our approach for the one-qubit case and show how a probability density function for an unknown quantum process can be estimated.
Bayesian nonparametric meta-analysis using Polya tree mixture models.
Branscum, Adam J; Hanson, Timothy E
2008-09-01
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
Non-parametric and least squares Langley plot methods
P. W. Kiedron
2015-04-01
Full Text Available Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer–Lambert–Beer law V=V>/i>0e−τ ·m, where a plot of ln (V voltage vs. m air mass yields a straight line with intercept ln (V0. This ln (V0 subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The eleven techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln (V0's are smoothed and interpolated with median and mean moving window filters.
Pivotal Estimation of Nonparametric Functions via Square-root Lasso
Belloni, Alexandre; Wang, Lie
2011-01-01
In a nonparametric linear regression model we study a variant of LASSO, called square-root LASSO, which does not require the knowledge of the scaling parameter $\\sigma$ of the noise or bounds for it. This work derives new finite sample upper bounds for prediction norm rate of convergence, $\\ell_1$-rate of converge, $\\ell_\\infty$-rate of convergence, and sparsity of the square-root LASSO estimator. A lower bound for the prediction norm rate of convergence is also established. In many non-Gaussian noise cases, we rely on moderate deviation theory for self-normalized sums and on new data-dependent empirical process inequalities to achieve Gaussian-like results provided log p = o(n^{1/3}) improving upon results derived in the parametric case that required log p = O(log n). In addition, we derive finite sample bounds on the performance of ordinary least square (OLS) applied tom the model selected by square-root LASSO accounting for possible misspecification of the selected model. In particular, we provide mild con...
Non-parametric reconstruction of the galaxy-lens in PG1115+080
Saha, P; Saha, Prasenjit; Williams, Liliya L. R.
1997-01-01
We describe a new, non-parametric, method for reconstructing lensing mass distributions in multiple-image systems, and apply it to PG1115, for which time delays have recently been measured. It turns out that the image positions and the ratio of time delays between different pairs of images constrain the mass distribution in a linear fashion. Since observational errors on image positions and time delay ratios are constantly improving, we use these data as a rigid constraint in our modelling. In addition, we require the projected mass distributions to be inversion-symmetric and to have inward-pointing density gradients. With these realistic yet non-restrictive conditions it is very easy to produce mass distributions that fit the data precisely. We then present models, for $H_0=42$, 63 and 84 \\kmsmpc, that in each case minimize mass-to-light variations while strictly obeying the lensing constraints. (Only a very rough light distribution is available at present.) All three values of $H_0$ are consistent with the ...
Non-parametric Deprojection of Surface Brightness Profiles of Galaxies in Generalised Geometries
Chakrabarty, Dalia
2009-01-01
We present a new Bayesian non-parametric deprojection algorithm DOPING (Deprojection of Observed Photometry using and INverse Gambit), that is designed to extract 3-D luminosity density distributions $\\rho$ from observed surface brightness maps $I$, in generalised geometries, while taking into account changes in intrinsic shape with radius, using a penalised likelihood approach and an MCMC optimiser. We provide the most likely solution to the integral equation that represents deprojection of the measured $I$ to $\\rho$. In order to keep the solution modular, we choose to express $\\rho$ as a function of the line-of-sight (LOS) coordinate $z$. We calculate the extent of the system along the ${\\bf z}$-axis, for a given point on the image that lies within an identified isophotal annulus. The extent along the LOS is binned and density is held a constant over each such $z$-bin. The code begins with a seed density and at the beginning of an iterative step, the trial $\\rho$ is updated. Comparison of the projection of ...
Nonparametric Single-Trial EEG Feature Extraction and Classification of Driver's Cognitive Responses
I-Fang Chung
2008-05-01
Full Text Available We proposed an electroencephalographic (EEG signal analysis approach to investigate the driver's cognitive response to traffic-light experiments in a virtual-reality-(VR- based simulated driving environment. EEG signals are digitally sampled and then transformed by three different feature extraction methods including nonparametric weighted feature extraction (NWFE, principal component analysis (PCA, and linear discriminant analysis (LDA, which were also used to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data could be classified with fewer features and their classification results were compared by utilizing two different classifiers including k nearest neighbor classification (KNNC and naive bayes classifier (NBC. Experimental data were collected from 6 subjects and the results show that NWFE+NBC gives the best classification accuracy ranging from 71%Ã¢ÂˆÂ¼77%, which is over 10%Ã¢ÂˆÂ¼24% higher than LDA+KNN1. It also demonstrates the feasibility of detecting and analyzing single-trial EEG signals that represent operators' cognitive states and responses to task events.
Nonparametric Single-Trial EEG Feature Extraction and Classification of Driver's Cognitive Responses
Lin, Chin-Teng; Lin, Ken-Li; Ko, Li-Wei; Liang, Sheng-Fu; Kuo, Bor-Chen; Chung, I.-Fang
2008-12-01
We proposed an electroencephalographic (EEG) signal analysis approach to investigate the driver's cognitive response to traffic-light experiments in a virtual-reality-(VR-) based simulated driving environment. EEG signals are digitally sampled and then transformed by three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), and linear discriminant analysis (LDA), which were also used to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data could be classified with fewer features and their classification results were compared by utilizing two different classifiers including [InlineEquation not available: see fulltext.] nearest neighbor classification (KNNC) and naive bayes classifier (NBC). Experimental data were collected from 6 subjects and the results show that NWFE+NBC gives the best classification accuracy ranging from [InlineEquation not available: see fulltext.], which is over [InlineEquation not available: see fulltext.] higher than LDA+KNN1. It also demonstrates the feasibility of detecting and analyzing single-trial EEG signals that represent operators' cognitive states and responses to task events.
Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts
AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.
2014-12-01
Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using
Kikuro eFukushima
2011-12-01
Full Text Available Smooth-pursuit eye movements are voluntary responses to small slow-moving objects in the fronto-parallel plane. They evolved in primates, who possess high-acuity foveae, to ensure clear vision about the moving target. The primate frontal cortex contains two smooth-pursuit related areas; the caudal part of the frontal eye fields (FEF and the supplementary eye fields (SEF. Both areas receive vestibular inputs. We review functional differences between the two areas in smooth-pursuit. Most FEF pursuit neurons signal pursuit parameters such as eye velocity and gaze-velocity, and are involved in cancelling the vestibulo-ocular reflex by linear addition of vestibular and smooth-pursuit responses. In contrast, gaze-velocity signals are rarely represented in the SEF. Most FEF pursuit neurons receive neck velocity inputs, while discharge modulation during pursuit and trunk-on-head rotation adds linearly. Linear addition also occurs between neck velocity responses and vestibular responses during head-on-trunk rotation in a task-dependent manner. During cross-axis pursuit-vestibular interactions, vestibular signals effectively initiate predictive pursuit eye movements. Most FEF pursuit neurons discharge during the interaction training after the onset of pursuit eye velocity, making their involvement unlikely in the initial stages of generating predictive pursuit. Comparison of representative signals in the two areas and the results of chemical inactivation during a memory-based smooth-pursuit task indicate they have different roles; the SEF plans smooth-pursuit including working memory of motion-direction, whereas the caudal FEF generates motor commands for pursuit eye movements. Patients with idiopathic Parkinson’s disease were asked to perform this task, since impaired smooth-pursuit and visual working memory deficit during cognitive tasks have been reported in most patients. Preliminary results suggested specific roles of the basal ganglia in memory
LINGNeng-xiang; DUXue-qiao
2005-01-01
In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.
Kernel bandwidth estimation for non-parametric density estimation: a comparative study
Van der Walt, CM
2013-12-01
Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...
Bayesian nonparametric estimation and consistency of mixed multinomial logit choice models
De Blasi, Pierpaolo; Lau, John W; 10.3150/09-BEJ233
2011-01-01
This paper develops nonparametric estimation for discrete choice models based on the mixed multinomial logit (MMNL) model. It has been shown that MMNL models encompass all discrete choice models derived under the assumption of random utility maximization, subject to the identification of an unknown distribution $G$. Noting the mixture model description of the MMNL, we employ a Bayesian nonparametric approach, using nonparametric priors on the unknown mixing distribution $G$, to estimate choice probabilities. We provide an important theoretical support for the use of the proposed methodology by investigating consistency of the posterior distribution for a general nonparametric prior on the mixing distribution. Consistency is defined according to an $L_1$-type distance on the space of choice probabilities and is achieved by extending to a regression model framework a recent approach to strong consistency based on the summability of square roots of prior probabilities. Moving to estimation, slightly different te...
Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change
Hae-Bum Yun
2011-01-01
Full Text Available A nonparametric, data-driven methodology of monitoring for geotechnical structures subject to long-term environmental change is discussed. Avoiding physical assumptions or excessive simplification of the monitored structures, the nonparametric monitoring methodology presented in this paper provides reliable performance-related information particularly when the collection of sensor data is limited. For the validation of the nonparametric methodology, a field case study was performed using a full-scale retaining wall, which had been monitored for three years using three tilt gauges. Using the very limited sensor data, it is demonstrated that important performance-related information, such as drainage performance and sensor damage, could be disentangled from significant daily, seasonal and multiyear environmental variations. Extensive literature review on recent developments of parametric and nonparametric data processing techniques for geotechnical applications is also presented.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models
Fan, Jianqing; Song, Rui
2011-01-01
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under the nonparametric additive models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, an iterative nonparametric independence screening (INIS) is also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data a...
Nonparametric TOA estimators for low-resolution IR-UWB digital receiver
Yanlong Zhang; Weidong Chen
2015-01-01
Nonparametric time-of-arrival (TOA) estimators for im-pulse radio ultra-wideband (IR-UWB) signals are proposed. Non-parametric detection is obviously useful in situations where de-tailed information about the statistics of the noise is unavailable or not accurate. Such TOA estimators are obtained based on condi-tional statistical tests with only a symmetry distribution assumption on the noise probability density function. The nonparametric es-timators are attractive choices for low-resolution IR-UWB digital receivers which can be implemented by fast comparators or high sampling rate low resolution analog-to-digital converters (ADCs), in place of high sampling rate high resolution ADCs which may not be available in practice. Simulation results demonstrate that nonparametric TOA estimators provide more effective and robust performance than typical energy detection (ED) based estimators.
Nonparametric statistical tests for the continuous data: the basic concept and the practical use.
Nahm, Francis Sahngun
2016-02-01
Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of sample means is normally distributed. However, parametric test can be misleading when this assumption is not satisfied. In this circumstance, nonparametric tests are the alternative methods available, because they do not required the normality assumption. Nonparametric tests are the statistical methods based on signs and ranks. In this article, we will discuss about the basic concepts and practical use of nonparametric tests for the guide to the proper use.
Visual crowding is anisotropic along the horizontal meridian during smooth pursuit.
Harrison, W J; Remington, R W; Mattingley, J B
2014-01-22
Humans make smooth pursuit eye movements to foveate moving objects of interest. It is known that smooth pursuit alters visual processing, but there is currently no consensus on whether changes in vision are contingent on the direction the eyes are moving. We recently showed that visual crowding can be used as a sensitive measure of changes in visual processing, resulting from involvement of the saccadic eye movement system. The present paper extends these results by examining the effect of smooth pursuit eye movements on the spatial extent of visual crowding-the area over which visual stimuli are integrated. We found systematic changes in crowding that depended on the direction of pursuit and the distance of stimuli from the pursuit target. Relative to when no eye movement was made, the spatial extent of crowding increased for objects located contraversive to the direction of pursuit at an eccentricity of approximately 3°. By contrast, crowding for objects located ipsiversive to the direction of pursuit remained unchanged. There was no change in crowding during smooth pursuit for objects located approximately 7° from the fovea. The increased size of the crowding zone for the contraversive direction may be related to the distance that the fovea lags behind the pursuit target during smooth eye movements. Overall, our results reveal that visual perception is altered dynamically according to the intended destination of oculomotor commands.
Predictors of pursuit of physician-assisted death.
Smith, Kathryn A; Harvath, Theresa A; Goy, Elizabeth R; Ganzini, Linda
2015-03-01
Physician-assisted death (PAD) was legalized in 1997 by Oregon's Death with Dignity Act. The States of Washington, Montana, Vermont, and New Mexico have since provided legal sanction for PAD. Through 2013, 1173 Oregonians have received a prescription under the Death with Dignity Act and 752 have died after taking the prescribed medication in Oregon. To determine the predictive value of personal and interpersonal variables in the pursuit of PAD. Fifty-five Oregonians who either requested PAD or contacted a PAD advocacy organization were compared with 39 individuals with advanced disease who did not pursue PAD. We compared the two groups on responses to standardized measures of depression, hopelessness, spirituality, social support, and pain. We also compared the two groups on style of attachment to intimate others and caregivers as understood through attachment theory. We found that PAD requesters had higher levels of depression, hopelessness, and dismissive attachment (attachment to others characterized by independence and self-reliance), and lower levels of spirituality. There were moderate correlations among the variables of spirituality, hopelessness, depression, social support, and dismissive attachment. There was a strong correlation between depression and hopelessness. Low spirituality emerged as the strongest predictor of pursuit of PAD in the regression analysis. Although some factors motivating pursuit of PAD, such as depression, may be ameliorated by medical interventions, other factors, such as style of attachment and sense of spirituality, are long-standing aspects of the individual that should be supported at the end of life. Practitioners must develop respectful awareness and understanding of the interpersonal and spiritual perspectives of their patients to provide such support. Published by Elsevier Inc.
Dash, Suryadeep; Thier, Peter
2013-01-01
Smooth-pursuit adaptation (SPA) refers to the fact that pursuit gain in the early, still open-loop response phase of the pursuit eye movement can be adjusted based on experience. For instance, if the target moves initially at a constant velocity for ~100-200 ms and then steps to a higher velocity, subjects learn to up-regulate the pursuit gain associated with the initial target velocity (gain-increase SPA) in order to reduce the retinal error resulting from the velocity step. Correspondingly, a step to a lower target velocity leads to a decrease in gain (gain-decrease SPA). In this study we demonstrate that the increase in peak eye velocity during gain-increase SPA is a consequence of expanding the duration of the eye acceleration profile while the decrease in peak velocity during gain-decrease SPA results from reduced peak eye acceleration but unaltered duration. Furthermore, we show that carrying out stereotypical smooth pursuit eye movements elicited by constant velocity target ramps for several hundred trials (=test of pursuit resilience) leads to a clear drop in initial peak acceleration, a reflection of oculomotor and/or cognitive fatigue. However, this drop in acceleration gets compensated by an increase in the duration of the acceleration profile, thereby keeping initial pursuit gain constant. The compensatory expansion of the acceleration profile in the pursuit resilience experiment is reminiscent of the one leading to gain-increase SPA, suggesting that both processes tap one and the same neuronal mechanism warranting a precise acceleration-duration trade-off. Finally, we show that the ability to adjust acceleration duration during pursuit resilience depends on the integrity of the oculomotor vermis (OMV) as indicated by the complete loss of the duration adjustment following a surgical lesion of the OMV in one rhesus monkey we could study.
Suryadeep eDash
2013-10-01
Full Text Available Smooth-pursuit adaptation (SPA refers to the fact that pursuit gain in the early, still open-loop response phase of the pursuit eye movement can be adjusted based on experience. For instance, if the target moves initially at a constant velocity for approximately 100-200ms and then steps to a higher velocity, subjects learn to up-regulate the pursuit gain associated with the initial target velocity (gain-increase SPA in order to reduce the retinal error resulting from the velocity step. Correspondingly, a step to a lower target velocity leads to a decrease in gain (gain-decrease SPA. In this study we demonstrate that the increase in peak eye velocity during gain-increase SPA is a consequence of expanding the duration of the eye acceleration profile while the decrease in peak velocity during gain-decrease SPA results from reduced peak eye acceleration but unaltered duration. Furthermore, we show that carrying out stereotypical smooth pursuit eye movements elicited by constant velocity target ramps for several hundred trials (= test of pursuit resilience leads to a clear drop in initial peak acceleration, a reflection of oculomotor and/ or cognitive fatigue. However, this drop in acceleration gets compensated by an increase in the duration of the acceleration profile, thereby keeping initial pursuit gain constant. The compensatory expansion of the acceleration profile in the pursuit resilience experiment is reminiscent of the one leading to gain-increase SPA, suggesting that both processes tap one and the same neuronal mechanism warranting a precise acceleration/ duration trade-off. Finally, we show that the ability to adjust acceleration duration during pursuit resilience depends on the integrity of the oculomotor vermis (OMV as indicated by the complete loss of the duration adjustment following a surgical lesion of the OMV in one rhesus monkey we could study.
Examples of the Application of Nonparametric Information Geometry to Statistical Physics
Giovanni Pistone
2013-09-01
Full Text Available We review a nonparametric version of Amari’s information geometry in which the set of positive probability densities on a given sample space is endowed with an atlas of charts to form a differentiable manifold modeled on Orlicz Banach spaces. This nonparametric setting is used to discuss the setting of typical problems in machine learning and statistical physics, such as black-box optimization, Kullback-Leibler divergence, Boltzmann-Gibbs entropy and the Boltzmann equation.
Economic decision making and the application of nonparametric prediction models
Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.
2008-01-01
Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P.
2017-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
Akhtar, Naveed; Mian, Ajmal
2017-10-03
We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.
Non-parametric combination and related permutation tests for neuroimaging.
Winkler, Anderson M; Webster, Matthew A; Brooks, Jonathan C; Tracey, Irene; Smith, Stephen M; Nichols, Thomas E
2016-04-01
In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.
The relationshipbetween haze and the pursuit of wealth
刘贝赟
2016-01-01
In recent years,China’s economic construction has made unprecedented achievements in development.People’s living standards have been effectively improved,however,along with a wealth of material life,the quality of our environment has significantly decreased,especially in winter in north China,where the haze phenomenon is particularly serious,not only affecting people’s travel and health,but also reducing people’s happiness index.Focusing on the relationship between the haze and the pursuit of wealth,this article will try to explore how to coordinate the relationship between the development of the socio-economy and natural environment.
Effects of priming goal pursuit on implicit sequence learning
Gamble, Katherine R.; Lee, Joanna M.; Howard, James H.; Howard, Darlene V.
2014-01-01
Implicit learning, the type of learning that occurs without intent to learn or awareness of what has been learned, has been thought to be insensitive to the effects of priming, but recent studies suggest this is not the case. One study found that learning in the Serial Reaction Time (SRT) task was improved by nonconscious goal pursuit, primed via a word search task (Eitam et al., 2008). In two studies, we used the goal priming word search task from Eitam et al., but with a different version o...
Qualitative Criterion for Interception in a Pursuit/Evasion Game
Morgan, John A
2009-01-01
A qualitative account is given of a differential pursuit/evasion game. A criterion for the existence of an intercept solution is obtained using future cones that contain all attainable trajectories of target or interceptor originating from an initial position. A sufficient and necessary conditon that an opportunity to intercept always exist is that, after some initial time, the future cone of the target be contained within the future cone of the interceptor. The sufficient condition may be regarded as a kind of Nash equillibrium.
The pursuit of happiness: time, money, and social connection.
Mogilner, Cassie
2010-09-01
Does thinking about time, rather than money, influence how effectively individuals pursue personal happiness? Laboratory and field experiments revealed that implicitly activating the construct of time motivates individuals to spend more time with friends and family and less time working-behaviors that are associated with greater happiness. In contrast, implicitly activating money motivates individuals to work more and socialize less, which (although productive) does not increase happiness. Implications for the relative roles of time versus money in the pursuit of happiness are discussed.
Nonparametric VSS-APA based on precise background noise power estimate
昊翔; 赖晓翰; 陈隆道; 蔡忠法
2015-01-01
The adaptive algorithm used for echo cancellation (EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm (APA) is a better alternative than normalized least mean square (NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA (VSS-APA) provides a more reliable solution. A nonparametric VSS-APA (NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power (BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.
LIU Yong-jian; DUAN Chuan; TIAN Meng-liang; HU Er-liang; HUANG Yu-bi
2010-01-01
Analysis of multi-environment trials (METs) of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yield is essential in MET analyses. The objective of the present investigation was to compare 11 nonparametric stability statistics and apply nonparametric tests for genotype-by-environment interaction (GEI) to 14 maize (Zea mays L.) genotypes grown at 25 locations in southwestern China during 2005. Results of nonparametric tests of GEI and a combined ANOVA across locations showed that both crossover and noncrossover GEI, and genotypes varied highly significantly for yield. The results of principal component analysis, correlation analysis of nonparametric statistics, and yield indicated the nonparametric statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability.Furthermore, high values of TOP and low values of rank-sum were associated with high mean yield, but the other nonparametric statistics were not positively correlated with mean yield. Therefore, only rank-sum and TOP methods would be useful for simultaneously selection for high yield and stability. These two statistics recommended JY686 and HX 168 as desirable and ND 108, CM 12, CN36, and NK6661 as undesirable genotypes.
A novel nonparametric confidence interval for differences of proportions for correlated binary data.
Duan, Chongyang; Cao, Yingshu; Zhou, Lizhi; Tan, Ming T; Chen, Pingyan
2016-11-16
Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n - 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance. © The Author(s) 2016.
Explaining Factors of Job Pursuit Intention in Indonesian Military Institution
Muhammad Irfan Syaebani
2015-09-01
Full Text Available Reformation brought many changes in public sectors in Indonesia, one of them is Military institution. Reformation required Military to become professional in every organizational aspects, including human resources as a part of resource that need to be manage strategically. Proficient and competent human resource will help organization reach its vision, missons, and strategic goals. One of the strategy to attract competent human resource is to design the recruitment and selection process in talent management corridor, where organization must identify factors which attracting a candidate to join into organization or simply called job pursuit intention. To find out what factors lead to job pursuit intention into military institution in Indonesia, data was collected using qualitative approach from middle-rank military officer. Their past experiences concerning motives/factors which lead them joined into military were explored. From analysis, it reveals that there are 5 factors which make them joined military; employer familiarity, subjective fit, hiring expectation, economic motive, and nationalism/patriotism motive.
Low-bit-rate subband image coding with matching pursuits
Rabiee, Hamid; Safavian, S. R.; Gardos, Thomas R.; Mirani, A. J.
1998-01-01
In this paper, a novel multiresolution algorithm for low bit-rate image compression is presented. High quality low bit-rate image compression is achieved by first decomposing the image into approximation and detail subimages with a shift-orthogonal multiresolution analysis. Then, at the coarsest resolution level, the coefficients of the transformation are encoded by an orthogonal matching pursuit algorithm with a wavelet packet dictionary. Our dictionary consists of convolutional splines of up to order two for the detail and approximation subbands. The intercorrelation between the various resolutions is then exploited by using the same bases from the dictionary to encode the coefficients of the finer resolution bands at the corresponding spatial locations. To further exploit the spatial correlation of the coefficients, the zero trees of wavelets (EZW) algorithm was used to identify the potential zero trees. The coefficients of the presentation are then quantized and arithmetic encoded at each resolution, and packed into a scalable bit stream structure. Our new algorithm is highly bit-rate scalable, and performs better than the segmentation based matching pursuit and EZW encoders at lower bit rates, based on subjective image quality and peak signal-to-noise ratio.
DECISION UTILITY, THE BRAIN, AND PURSUIT OF HEDONIC GOALS
Berridge, Kent C.; Aldridge, J. Wayne
2009-01-01
How do brain representations of the utility of a hedonic goal guide decisions about whether to pursue it? Our focus here will be on brain mechanisms of reward utility operating at particular decision moments in life. Moments such as when you encounter an image, sound, scent or other cue associated in your past with a particular reward; or perhaps just vividly imagine that cue. Such a cue can often trigger a sudden motivational urge to pursue that goal, and sometimes a decision to do so. In drug addicts trying to quit, a cue for the addicted drug might trigger urges that rise to compulsive levels of intensity, despite prior commitments to abstain, leading to the decision to relapse into taking the drug again. Normal or addicted, the urge and decision may well have been lacking immediately before the cue was encountered. The decision to pursue the cued reward might never have happened if the cue had not been encountered. Why can such cues momentarily dominate decision making? The answer involves brain mesolimbic dopamine mechanisms that amplify the incentive salience of reward cues, selectively elevating decision utility to trigger “wanting” for the goal. We describe affective neuroscience studies of brain limbic generators of “wanting” that shed light on how cues trigger pursuit of their goals, both normally and even under intense conditions of irrational goal pursuit. PMID:20198128
MATCHING PURSUITS AMONG SHIFTED CAUCHY KERNELS IN HIGHER-DIMENSIONAL SPACES
钱涛; 王晋勋; 杨燕
2014-01-01
Appealing to the Clifford analysis and matching pursuits, we study the adaptive decompositions of functions of several variables of finite energy under the dictionaries con-sisting of shifted Cauchy kernels. This is a realization of matching pursuits among shifted Cauchy kernels in higher-dimensional spaces. It offers a method to process signals in arbitrary dimensions.
Sparse spikes super-resolution on thin grids II: the continuous basis pursuit
Duval, Vincent; Peyré, Gabriel
2017-09-01
This article analyzes the performance of the continuous basis pursuit (C-BP) method for sparse super-resolution. The C-BP has been recently proposed by Ekanadham, Tranchina and Simoncelli as a refined discretization scheme for the recovery of spikes in inverse problems regularization. One of the most well known discretization scheme, the basis pursuit (BP, also known as \
COMT val(158)met genotype and smooth pursuit eye movements in schizophrenia
Haraldsson, H Magnus; Ettinger, Ulrich; Magnusdottir, Brynja B;
2009-01-01
The association between the catechol-O-methyltransferase (COMT) val(158)met polymorphism (rs4680) and smooth pursuit eye movements (SPEM) was investigated in 110 schizophrenia patients and 96 controls. Patients had lower steady-state pursuit gain and made more frequent saccades than controls...
Velocity scaling of cue-induced smooth pursuit acceleration obeys constraints of natural motion.
Ladda, Jennifer; Eggert, Thomas; Glasauer, Stefan; Straube, Andreas
2007-09-01
Information about the future trajectory of a visual target is contained not only in the history of target motion but also in static visual cues, e.g., the street provides information about the car's future trajectory. For most natural moving targets, this information imposes strong constraints on the relation between velocity and acceleration which can be exploited by predictive smooth pursuit mechanisms. We questioned how cue-induced predictive changes in pursuit direction depend on target speed and how cue- and target-induced pursuit interact. Subjects pursued a target entering a +/-90 degrees curve and moving on either a homogeneous background or on a low contrast static band indicating the future trajectory. The cue induced a predictive change of pursuit direction, which occurred before curve onset of the target. The predictive velocity component orthogonal to the initial pursuit direction started later and became faster with increasing target velocity. The predictive eye acceleration increased quadratically with target velocity and was independent of the initial target direction. After curve onset, cue- and target-induced pursuit velocity components were not linearly superimposed. The quadratic increase of eye acceleration with target velocity is consistent with the natural velocity scaling implied by the two-thirds power law, which is a characteristic of biological controlled movements. Comparison with linear pursuit models reveals that the ratio between eye acceleration and actual or expected retinal slip cannot be considered a constant gain factor. To obey a natural velocity scaling, this acceleration gain must linearly increase with target or pursuit velocity. We suggest that gain control mechanisms, which affect target-induced changes of pursuit velocity, act similarly on predictive changes of pursuit induced by static visual cues.
A Hybrid Index for Characterizing Drought Based on a Nonparametric Kernel Estimator
Huang, Shengzhi; Huang, Qiang; Leng, Guoyong; Chang, Jianxia
2016-06-01
This study develops a nonparametric multivariate drought index, namely, the Nonparametric Multivariate Standardized Drought Index (NMSDI), by considering the variations of both precipitation and streamflow. Building upon previous efforts in constructing Nonparametric Multivariate Drought Index, we use the nonparametric kernel estimator to derive the joint distribution of precipitation and streamflow, thus providing additional insights in drought index development. The proposed NMSDI are applied in the Wei River Basin (WRB), based on which the drought evolution characteristics are investigated. Results indicate: (1) generally, NMSDI captures the drought onset similar to Standardized Precipitation Index (SPI) and drought termination and persistence similar to Standardized Streamflow Index (SSFI). The drought events identified by NMSDI match well with historical drought records in the WRB. The performances are also consistent with that by an existing Multivariate Standardized Drought Index (MSDI) at various timescales, confirming the validity of the newly constructed NMSDI in drought detections (2) An increasing risk of drought has been detected for the past decades, and will be persistent to a certain extent in future in most areas of the WRB; (3) the identified change points of annual NMSDI are mainly concentrated in the early 1970s and middle 1990s, coincident with extensive water use and soil reservation practices. This study highlights the nonparametric multivariable drought index, which can be used for drought detections and predictions efficiently and comprehensively.
Support agnostic Bayesian matching pursuit for block sparse signals
Masood, Mudassir
2013-05-01
A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Stuck in the middle: the psychophysics of goal pursuit.
Bonezzi, Andrea; Brendl, C Miguel; De Angelis, Matteo
2011-05-01
The classic goal-gradient hypothesis posits that motivation to reach a goal increases monotonically with proximity to the desired end state. However, we argue that this is not always the case. In this article, we show that motivation to engage in goal-consistent behavior can be higher when people are either far from or close to the end state and lower when they are about halfway to the end state. We propose a psychophysical explanation for this tendency to get "stuck in the middle." Building on the assumption that motivation is influenced by the perceived marginal value of progress toward the goal, we show that the shape of the goal gradient varies depending on whether an individual monitors progress in terms of distance from the initial state or from the desired end state. Our psychophysical model of goal pursuit predicts a previously undiscovered nonmonotonic gradient, as well as two monotonic gradients.
Deficit of pursuit ocular movements in early Alzheimer's disease
Francesco Cordici; Pietro Lanzafame; Silvia Marino; Alessandro Celona; Lilla Bonanno; Annalisa Baglieri; Alessia Bramanti; Placido Bramanti
2010-01-01
Previous studies have demonstrated that advanced Alzheimer's disease(AD)patients have deficiency of eye movements.However,there have been no reports on eye movement in the early stages of AD.The aim of this study was to evaluate pursuit ocular movements(POM)provided by a vision-based non-intrusive eye tracker in patients with early AD.POM values were significantly lower in AD patients than in normal controls(P < 0.01).In AD patients,POM values were not closely correlated with the Mini-Mental State Examination scores(P = 0.3).There was no significant difference in POM values among patients treated with or without anticholinesterase therapy.We used a vision-based method,for non-intrusive eye tracking,which can be proposed as a possible tool for supporting the diagnosis of early AD.
Detection of neonatal EEG seizure using multichannel matching pursuit.
Khlif, M S; Mesbah, M; Boashash, B; Colditz, P
2008-01-01
It is unusual for a newborn to have the classic "tonic-clonic" seizure experienced by adults and older children. Signs of seizure in newborns are either subtle or may become clinically silent. Therefore, the electroencephalogram (EEG) is becoming the most reliable tool for detecting neonatal seizure. Being non-stationary and multicomponent, EEG signals are suitably analyzed using time-frequency (TF) based methods. In this paper, we present a seizure detection method using a new measure based on the matching pursuit (MP) decomposition of EEG data. Signals are represented in the TF domain where seizure structural characteristics are extracted to form a new coherent TF dictionary to be used in the MP decomposition. A new approach to set data-dependent thresholds, used in the seizure detection process, is proposed. To enhance the performance of the detector, the concept of areas of incidence is utilized to determine the geometrical correlation between EEG recording channels.
Sparse reconstruction using distribution agnostic bayesian matching pursuit
Masood, Mudassir
2013-11-01
A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Radar Signal Recovery using Compressive Sampling Matching Pursuit Algorithm
M Sreenivasa Rao
2016-12-01
Full Text Available In this study, we propose compressive sampling matching pursuit (CoSaMP algorithm for sub-Nyquist based electronic warfare (EW receiver system. In compressed sensing (CS theory time-frequency plane localisation and discretisation into a N×N grid in union of subspaces is established. The train of radar signals are sparse in time and frequency can be under sampled with almost no information loss. The CS theory may be applied to EW digital receivers to reduce sampling rate of analog to digital converter; to improve radar parameter resolution and increase input bandwidth. Simulated an efficient approach for radar signal recovery by CoSaMP algorithm by using a set of various sample and different sparsity level with various radar signals. This approach allows a scalable and flexible recovery process. The method has been satisfied with data in a wide frequency range up to 40 GHz. The simulation shows the feasibility of our method.
Ramirez, José Rangel; Sørensen, John Dalsgaard
2011-01-01
This work illustrates the updating and incorporation of information in the assessment of fatigue reliability for offshore wind turbine. The new information, coming from external and condition monitoring can be used to direct updating of the stochastic variables through a non-parametric Bayesian...... updating approach and be integrated in the reliability analysis by a third-order polynomial chaos expansion approximation. Although Classical Bayesian updating approaches are often used because of its parametric formulation, non-parametric approaches are better alternatives for multi-parametric updating...... with a non-conjugating formulation. The results in this paper show the influence on the time dependent updated reliability when non-parametric and classical Bayesian approaches are used. Further, the influence on the reliability of the number of updated parameters is illustrated....
Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures
Li, Quanbao; Wei, Fajie; Zhou, Shenghan
2017-05-01
The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.
Non-parametric seismic hazard analysis in the presence of incomplete data
Yazdani, Azad; Mirzaei, Sajjad; Dadkhah, Koroush
2017-01-01
The distribution of earthquake magnitudes plays a crucial role in the estimation of seismic hazard parameters. Due to the complexity of earthquake magnitude distribution, non-parametric approaches are recommended over classical parametric methods. The main deficiency of the non-parametric approach is the lack of complete magnitude data in almost all cases. This study aims to introduce an imputation procedure for completing earthquake catalog data that will allow the catalog to be used for non-parametric density estimation. Using a Monte Carlo simulation, the efficiency of introduced approach is investigated. This study indicates that when a magnitude catalog is incomplete, the imputation procedure can provide an appropriate tool for seismic hazard assessment. As an illustration, the imputation procedure was applied to estimate earthquake magnitude distribution in Tehran, the capital city of Iran.
Kalman filtering naturally accounts for visually guided and predictive smooth pursuit dynamics.
Orban de Xivry, Jean-Jacques; Coppe, Sébastien; Blohm, Gunnar; Lefèvre, Philippe
2013-10-30
The brain makes use of noisy sensory inputs to produce eye, head, or arm motion. In most instances, the brain combines this sensory information with predictions about future events. Here, we propose that Kalman filtering can account for the dynamics of both visually guided and predictive motor behaviors within one simple unifying mechanism. Our model relies on two Kalman filters: (1) one processing visual information about retinal input; and (2) one maintaining a dynamic internal memory of target motion. The outputs of both Kalman filters are then combined in a statistically optimal manner, i.e., weighted with respect to their reliability. The model was tested on data from several smooth pursuit experiments and reproduced all major characteristics of visually guided and predictive smooth pursuit. This contrasts with the common belief that anticipatory pursuit, pursuit maintenance during target blanking, and zero-lag pursuit of sinusoidally moving targets all result from different control systems. This is the first instance of a model integrating all aspects of pursuit dynamics within one coherent and simple model and without switching between different parallel mechanisms. Our model suggests that the brain circuitry generating a pursuit command might be simpler than previously believed and only implement the functional equivalents of two Kalman filters whose outputs are optimally combined. It provides a general framework of how the brain can combine continuous sensory information with a dynamic internal memory and transform it into motor commands.
Wannez, Sarah; Hoyoux, Thomas; Langohr, Thomas; Bodart, Olivier; Martial, Charlotte; Wertz, Jérôme; Chatelle, Camille; Verly, Jacques G; Laureys, Steven
2017-03-31
Visual pursuit is a key marker of residual consciousness in patients with disorders of consciousness (DOC). Currently, its assessment relies on subjective clinical decisions. In this study, we explore the variability of such clinical assessments, and present an easy-to-use device composed of cameras and video processing algorithms that could help the clinician to improve the detection of visual pursuit in a clinical context. Visual pursuit was assessed by an experienced research neuropsychologist on 31 patients with DOC and on 23 healthy subjects, while the device was used to simultaneously record videos of both one eye and the mirror. These videos were then scored by three researchers: the experienced research neuropsychologist who did the clinical assessment, another experienced research neuropsychologist, and a neurologist. For each video, a consensus was decided between the three persons, and used as the gold standard of the presence or absence of visual pursuit. Almost 10% of the patients were misclassified at the bedside according to their consensus. An automatic classifier analyzed eye and mirror trajectories, and was able to identify patients and healthy subjects with visual pursuit, in total agreement with the consensus on video. In conclusion, our device can be used easily in patients with DOC while respecting the current guidelines of visual pursuit assessment. Our results suggest that our material and our classification method can identify patients with visual pursuit, as well as the three researchers based on video recordings can.
Modern nonparametric, robust and multivariate methods festschrift in honour of Hannu Oja
Taskinen, Sara
2015-01-01
Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the occasion of his 65th birthday and is intended for researchers as well as PhD students with a good knowledge of statistics.
Multivariate nonparametric regression and visualization with R and applications to finance
Klemelä, Jussi
2014-01-01
A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functio
Rabia Ece OMAY
2013-06-01
Full Text Available In this study, relationship between gross domestic product (GDP per capita and sulfur dioxide (SO2 and particulate matter (PM10 per capita is modeled for Turkey. Nonparametric fixed effect panel data analysis is used for the modeling. The panel data covers 12 territories, in first level of Nomenclature of Territorial Units for Statistics (NUTS, for period of 1990-2001. Modeling of the relationship between GDP and SO2 and PM10 for Turkey, the non-parametric models have given good results.
Zhao, Zhibiao
2011-06-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
Davenport, Clemontina A; Maity, Arnab; Wu, Yichao
2015-04-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
Non-Parametric, Closed-Loop Testing of Autonomy in Unmanned Aircraft Systems Project
National Aeronautics and Space Administration — The proposed Phase I program aims to develop new methods to support safety testing for integration of Unmanned Aircraft Systems into the National Airspace (NAS) with...
Validation of two (parametric vs non-parametric) daily weather generators
Dubrovsky, M.; Skalak, P.
2015-12-01
As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series
Patterned fabric defect detection via convolutional matching pursuit dual-dictionary
Jing, Junfeng; Fan, Xiaoting; Li, Pengfei
2016-05-01
Automatic patterned fabric defect detection is a promising technique for textile manufacturing due to its low cost and high efficiency. The applicability of most existing algorithms, however, is limited by their intensive computation. To overcome or alleviate the problem, this paper presents a convolutional matching pursuit (CMP) dual-dictionary algorithm for patterned fabric defect detection. A preprocessing with mean sampling is performed to eliminate the influence of background texture of fabric defects. Subsequently, a set of defect-free image blocks are selected as a sample set by sliding window. Dual-dictionary and sparse coefficiencies of the defect-free sample set are obtained via CMP and the K-singular value decomposition (K-SVD) based on a Gabor filter. Then we employ the defect-free and defective fabric image's projections onto the dual-dictionary as features for defect detection. Finally, the test results are determined by comparing the distance between the features to be measured. Experimental results reveal that the proposed algorithm is effective for patterned fabric defect detection and an acceptable average detection rate reaches by 94.2%.
Applying matching pursuit decomposition time-frequency processing to UGS footstep classification
Larsen, Brett W.; Chung, Hugh; Dominguez, Alfonso; Sciacca, Jacob; Kovvali, Narayan; Papandreou-Suppappola, Antonia; Allee, David R.
2013-06-01
The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.
Abnormalities of fixation, saccade and pursuit in posterior cortical atrophy.
Shakespeare, Timothy J; Kaski, Diego; Yong, Keir X X; Paterson, Ross W; Slattery, Catherine F; Ryan, Natalie S; Schott, Jonathan M; Crutch, Sebastian J
2015-07-01
The clinico-neuroradiological syndrome posterior cortical atrophy is the cardinal 'visual dementia' and most common atypical Alzheimer's disease phenotype, offering insights into mechanisms underlying clinical heterogeneity, pathological propagation and basic visual phenomena (e.g. visual crowding). Given the extensive attention paid to patients' (higher order) perceptual function, it is surprising that there have been no systematic analyses of basic oculomotor function in this population. Here 20 patients with posterior cortical atrophy, 17 patients with typical Alzheimer's disease and 22 healthy controls completed tests of fixation, saccade (including fixation/target gap and overlap conditions) and smooth pursuit eye movements using an infrared pupil-tracking system. Participants underwent detailed neuropsychological and neurological examinations, with a proportion also undertaking brain imaging and analysis of molecular pathology. In contrast to informal clinical evaluations of oculomotor dysfunction frequency (previous studies: 38%, current clinical examination: 33%), detailed eyetracking investigations revealed eye movement abnormalities in 80% of patients with posterior cortical atrophy (compared to 17% typical Alzheimer's disease, 5% controls). The greatest differences between posterior cortical atrophy and typical Alzheimer's disease were seen in saccadic performance. Patients with posterior cortical atrophy made significantly shorter saccades especially for distant targets. They also exhibited a significant exacerbation of the normal gap/overlap effect, consistent with 'sticky fixation'. Time to reach saccadic targets was significantly associated with parietal and occipital cortical thickness measures. On fixation stability tasks, patients with typical Alzheimer's disease showed more square wave jerks whose frequency was associated with lower cerebellar grey matter volume, while patients with posterior cortical atrophy showed large saccadic intrusions
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any func
Nonparametric estimation of population density for line transect sampling using FOURIER series
Crain, B.R.; Burnham, K.P.; Anderson, D.R.; Lake, J.L.
1979-01-01
A nonparametric, robust density estimation method is explored for the analysis of right-angle distances from a transect line to the objects sighted. The method is based on the FOURIER series expansion of a probability density function over an interval. With only mild assumptions, a general population density estimator of wide applicability is obtained.
A non-parametric peak finder algorithm and its application in searches for new physics
Chekanov, S
2011-01-01
We have developed an algorithm for non-parametric fitting and extraction of statistically significant peaks in the presence of statistical and systematic uncertainties. Applications of this algorithm for analysis of high-energy collision data are discussed. In particular, we illustrate how to use this algorithm in general searches for new physics in invariant-mass spectra using pp Monte Carlo simulations.
Nonparametric estimation of the stationary M/G/1 workload distribution function
Hansen, Martin Bøgsted
2005-01-01
In this paper it is demonstrated how a nonparametric estimator of the stationary workload distribution function of the M/G/1-queue can be obtained by systematic sampling the workload process. Weak convergence results and bootstrap methods for empirical distribution functions for stationary associ...
Testing a parametric function against a nonparametric alternative in IV and GMM settings
Gørgens, Tue; Wurtz, Allan
This paper develops a specification test for functional form for models identified by moment restrictions, including IV and GMM settings. The general framework is one where the moment restrictions are specified as functions of data, a finite-dimensional parameter vector, and a nonparametric real...
Non-parametric Bayesian graph models reveal community structure in resting state fMRI
Andersen, Kasper Winther; Madsen, Kristoffer H.; Siebner, Hartwig Roman
2014-01-01
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian...
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-parametric system identification from non-linear stochastic response
Rüdinger, Finn; Krenk, Steen
2001-01-01
An estimation method is proposed for identification of non-linear stiffness and damping of single-degree-of-freedom systems under stationary white noise excitation. Non-parametric estimates of the stiffness and damping along with an estimate of the white noise intensity are obtained by suitable p...
The Probability of Exceedance as a Nonparametric Person-Fit Statistic for Tests of Moderate Length
Tendeiro, Jorge N.; Meijer, Rob R.
2013-01-01
To classify an item score pattern as not fitting a nonparametric item response theory (NIRT) model, the probability of exceedance (PE) of an observed response vector x can be determined as the sum of the probabilities of all response vectors that are, at most, as likely as x, conditional on the test
Ramirez, José Rangel; Sørensen, John Dalsgaard
2011-01-01
This work illustrates the updating and incorporation of information in the assessment of fatigue reliability for offshore wind turbine. The new information, coming from external and condition monitoring can be used to direct updating of the stochastic variables through a non-parametric Bayesian u...
Jang, Eunice Eunhee; Roussos, Louis
2007-01-01
This article reports two studies to illustrate methodologies for conducting a conditional covariance-based nonparametric dimensionality assessment using data from two forms of the Test of English as a Foreign Language (TOEFL). Study 1 illustrates how to assess overall dimensionality of the TOEFL including all three subtests. Study 2 is aimed at…
Wei, Jiawei
2011-07-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee, et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey one-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity, et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.
Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Comparison of reliability techniques of parametric and non-parametric method
C. Kalaiselvan
2016-06-01
Full Text Available Reliability of a product or system is the probability that the product performs adequately its intended function for the stated period of time under stated operating conditions. It is function of time. The most widely used nano ceramic capacitor C0G and X7R is used in this reliability study to generate the Time-to failure (TTF data. The time to failure data are identified by Accelerated Life Test (ALT and Highly Accelerated Life Testing (HALT. The test is conducted at high stress level to generate more failure rate within the short interval of time. The reliability method used to convert accelerated to actual condition is Parametric method and Non-Parametric method. In this paper, comparative study has been done for Parametric and Non-Parametric methods to identify the failure data. The Weibull distribution is identified for parametric method; Kaplan–Meier and Simple Actuarial Method are identified for non-parametric method. The time taken to identify the mean time to failure (MTTF in accelerating condition is the same for parametric and non-parametric method with relative deviation.
Non-parametric Tuning of PID Controllers A Modified Relay-Feedback-Test Approach
Boiko, Igor
2013-01-01
The relay feedback test (RFT) has become a popular and efficient tool used in process identification and automatic controller tuning. Non-parametric Tuning of PID Controllers couples new modifications of classical RFT with application-specific optimal tuning rules to form a non-parametric method of test-and-tuning. Test and tuning are coordinated through a set of common parameters so that a PID controller can obtain the desired gain or phase margins in a system exactly, even with unknown process dynamics. The concept of process-specific optimal tuning rules in the nonparametric setup, with corresponding tuning rules for flow, level pressure, and temperature control loops is presented in the text. Common problems of tuning accuracy based on parametric and non-parametric approaches are addressed. In addition, the text treats the parametric approach to tuning based on the modified RFT approach and the exact model of oscillations in the system under test using the locus of a perturbedrelay system (LPRS) meth...
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
A non-parametric method for correction of global radiation observations
Bacher, Peder; Madsen, Henrik; Perers, Bengt;
2013-01-01
in the observations are corrected. These are errors such as: tilt in the leveling of the sensor, shadowing from surrounding objects, clipping and saturation in the signal processing, and errors from dirt and wear. The method is based on a statistical non-parametric clear-sky model which is applied to both...
Nonparametric estimation in an "illness-death" model when all transition times are interval censored
Frydman, Halina; Gerds, Thomas; Grøn, Randi
2013-01-01
We develop nonparametric maximum likelihood estimation for the parameters of an irreversible Markov chain on states {0,1,2} from the observations with interval censored times of 0 → 1, 0 → 2 and 1 → 2 transitions. The distinguishing aspect of the data is that, in addition to all transition times ...
A Comparison of Shewhart Control Charts based on Normality, Nonparametrics, and Extreme-Value Theory
Ion, R.A.; Does, R.J.M.M.; Klaassen, C.A.J.
2000-01-01
Several control charts for individual observations are compared. The traditional ones are the well-known Shewhart control charts with estimators for the spread based on the sample standard deviation and the average of the moving ranges. The alternatives are nonparametric control charts, based on emp
Non-parametric production analysis of pesticides use in the Netherlands
Oude Lansink, A.G.J.M.; Silva, E.
2004-01-01
Many previous empirical studies on the productivity of pesticides suggest that pesticides are under-utilized in agriculture despite the general held believe that these inputs are substantially over-utilized. This paper uses data envelopment analysis (DEA) to calculate non-parametric measures of the
An Assessment of the Nonparametric Approach for Evaluating the Fit of Item Response Models
Liang, Tie; Wells, Craig S.; Hambleton, Ronald K.
2014-01-01
As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting…
Agasisti, Tommaso
2011-01-01
The objective of this paper is an efficiency analysis concerning higher education systems in European countries. Data have been extracted from OECD data-sets (Education at a Glance, several years), using a non-parametric technique--data envelopment analysis--to calculate efficiency scores. This paper represents the first attempt to conduct such an…
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models.
Fan, Jianqing; Feng, Yang; Song, Rui
2011-06-01
A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under general nonparametric models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, a data-driven thresholding and an iterative nonparametric independence screening (INIS) are also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data analysis demonstrate that the proposed procedure works well with moderate sample size and large dimension and performs better than competing methods.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models.
Fan, Jianqing; Ma, Yunbei; Dai, Wei
2014-01-01
The varying-coefficient model is an important class of nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this paper, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high dimensional varying-coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance the practical utility and finite sample performance, two data-driven iterative NIS methods are proposed for selecting thresholding parameters and variables: conditional permutation and greedy methods, resulting in Conditional-INIS and Greedy-INIS. The effectiveness and flexibility of the proposed methods are further illustrated by simulation studies and real data applications.
Low default credit scoring using two-class non-parametric kernel density estimation
Rademeyer, E
2016-12-01
Full Text Available This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes’ rule, to include either...
Measuring the influence of networks on transaction costs using a non-parametric regression technique
Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Do Former College Athletes Earn More at Work? A Nonparametric Assessment
Henderson, Daniel J.; Olbrecht, Alexandre; Polachek, Solomon W.
2006-01-01
This paper investigates how students' collegiate athletic participation affects their subsequent labor market success. By using newly developed techniques in nonparametric regression, it shows that on average former college athletes earn a wage premium. However, the premium is not uniform, but skewed so that more than half the athletes actually…
Nonparametric Tests of Collectively Rational Consumption Behavior : An Integer Programming Procedure
Cherchye, L.J.H.; de Rock, B.; Sabbe, J.; Vermeulen, F.M.P.
2008-01-01
We present an IP-based nonparametric (revealed preference) testing proce- dure for rational consumption behavior in terms of general collective models, which include consumption externalities and public consumption. An empiri- cal application to data drawn from the Russia Longitudinal Monitoring
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Wei, Jiawei; Carroll, Raymond J; Maity, Arnab
2011-07-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee, et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey one-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity, et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.
Linear Pursuit Differential Game under Phase Constraint on the State of Evader
Askar Rakhmanov
2016-01-01
Full Text Available We consider a linear pursuit differential game of one pursuer and one evader. Controls of the pursuer and evader are subjected to integral and geometric constraints, respectively. In addition, phase constraint is imposed on the state of evader, whereas pursuer moves throughout the space. We say that pursuit is completed, if inclusion y(t1-x(t1∈M is satisfied at some t1>0, where x(t and y(t are states of pursuer and evader, respectively, and M is terminal set. Conditions of completion of pursuit in the game from all initial points of players are obtained. Strategy of the pursuer is constructed so that the phase vector of the pursuer first is brought to a given set, and then pursuit is completed.
In Pursuit of Perspective: Does Linear Perspective Disambiguate Depth from Motion Parallax?
George, Jonathon M.; Johnson, Joshua I.; Nawrot, Mark
2014-01-01
Motion parallax provides a dynamic, unambiguous, monocular visual depth cue. However, the lateral image motion in computer-generated motion parallax displays is depth-sign ambiguous. While mounting evidence indicates that the visual system uses an extra-retinal signal from the pursuit system to disambiguate depth, vertical perspective is a potential confound because it co-varies with the stimulus translation that produces the pursuit signal. Here the role of an extra-retinal pursuit signal and the role of vertical perspective in disambiguating depth from motion parallax were investigated. Through the careful isolation of each cue, the results indicate that observers have excellent depth discrimination with an extra-retinal pursuit cue alone, but have poor discrimination with vertical perspective alone. The conclusion is that vertical perspective does not play a role in the disambiguation of depth in small computer-generated motion parallax displays. PMID:24422245
In pursuit of perspective: does vertical perspective disambiguate depth from motion parallax?
George, Jonathon M; Johnson, Joshua I; Nawrot, Mark
2013-01-01
Motion parallax provides a dynamic, unambiguous, monocular visual depth cue. However, the lateral image motion in computer-generated motion parallax displays is depth-sign ambiguous. While mounting evidence indicates that the visual system uses an extra-retinal signal from the pursuit system to disambiguate depth, vertical perspective is a potential confound because it co-varies with the stimulus translation that produces the pursuit signal. Here the role of an extra-retinal pursuit signal and the role of vertical perspective in disambiguating depth from motion parallax were investigated. Through the careful isolation of each cue, the results indicate that observers have excellent depth discrimination with an extra-retinal pursuit cue alone, but have poor discrimination with vertical perspective alone. The conclusion is that vertical perspective does not play a role in the disambiguation of depth in small computer-generated motion parallax displays.
Isaacs, Rufus
1999-01-01
Definitive work draws on game theory, calculus of variations, and control theory to solve an array of problems: military, pursuit and evasion, athletic contests, many more. Detailed examples, formal calculations. 1965 edition.
Research on Multirobot Pursuit Task Allocation Algorithm Based on Emotional Cooperation Factor
Baofu Fang
2014-01-01
Full Text Available Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots’ individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and team collaborators, set up pursuit teams, and finally use certain strategies to complete the pursuit task. In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm.
[The comparison of characteristics of smooth pursuit in left-handed and right-handed persons].
Bozhkova, V P; Surovicheva, N S; Nikolaev, D P
2010-01-01
The estimation of the smooth pursuit efficiency in healthy young adults by method based on stroboscopic stimulation is given. The influence of manual function asymmetry on smooth pursuit was tested. Subjects were classified as left-handed or right-handed under a well known handedness questionnaire of Annett supplemented by Luria's tests. It was shown that the strong right-handed persons have a high quality of smooth pursuit of stimuli moving horizontally in rightward and leftward directions with the velocities 20 degrees/s and 25 degrees/s. Left-handed persons track similar stimuli, on the average, worse than the strong right-handed ones. It haven't been observed the influence of manual function asymmetry on the dependence of the smooth pursuit efficiency from the moving stimuli direction (left to right or right to left).
Research on multirobot pursuit task allocation algorithm based on emotional cooperation factor.
Fang, Baofu; Chen, Lu; Wang, Hao; Dai, Shuanglu; Zhong, Qiubo
2014-01-01
Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots' individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and team collaborators, set up pursuit teams, and finally use certain strategies to complete the pursuit task. In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm.
- Wave Spectrum of Carbonyl Diazide in Pursuit of Diazirinone
Amberger, Brent K.; Esselman, Brian J.; Woods, R. Claude; McMahon, Robert J.
2013-06-01
Pyrolysis of carbonyl diazide (CO(N_3)_2) has been shown to give diazirinone (CON_2). While diazirione decomposes over the course of a few hours under terrestrial conditions, there is the possibility for it to exist in space. In the pursuit of obtaining a rotational spectrum for diazirinone, we have started with the rotational spectroscopy of its immediate precursor, carbonyl diazide. Carbonyl diazide is highly explosive, and requires careful synthesis. Spectra in the range of 260-360 GHz were collected at room temperature and at -60°C. Ab initio calculations at the CCSD/cc-pVDZ level predict that the conformation where both azide groups are syn to the carbonyl is preferred. A second conformation, where one azide is syn and one is anti, is calculated to lie about 2 kcal/ mol higher in energy. Pure rotational transitions for the ground state and multiple low-lying excited vibrational states of the syn- syn conformation are readily observed and assigned. X. Zeng, H. Beckers, H. Willner and J. F. Stanton, Angew. Chem. Int. Ed. 50 (2011), 1720-1723 A. M. Nolan, B. K. Amberger, B. J. Esselman, V. S. Thimmakondu, J. F. Stanton, R. C. Woods, and R. J. McMahon, Inorg. Chem. 51 (2012), 9846-9851
Fast Component Pursuit for Large-Scale Inverse Covariance Estimation.
Han, Lei; Zhang, Yu; Zhang, Tong
2016-08-01
The maximum likelihood estimation (MLE) for the Gaussian graphical model, which is also known as the inverse covariance estimation problem, has gained increasing interest recently. Most existing works assume that inverse covariance estimators contain sparse structure and then construct models with the ℓ1 regularization. In this paper, different from existing works, we study the inverse covariance estimation problem from another perspective by efficiently modeling the low-rank structure in the inverse covariance, which is assumed to be a combination of a low-rank part and a diagonal matrix. One motivation for this assumption is that the low-rank structure is common in many applications including the climate and financial analysis, and another one is that such assumption can reduce the computational complexity when computing its inverse. Specifically, we propose an efficient COmponent Pursuit (COP) method to obtain the low-rank part, where each component can be sparse. For optimization, the COP method greedily learns a rank-one component in each iteration by maximizing the log-likelihood. Moreover, the COP algorithm enjoys several appealing properties including the existence of an efficient solution in each iteration and the theoretical guarantee on the convergence of this greedy approach. Experiments on large-scale synthetic and real-world datasets including thousands of millions variables show that the COP method is faster than the state-of-the-art techniques for the inverse covariance estimation problem when achieving comparable log-likelihood on test data.
Effects of priming goal pursuit on implicit sequence learning.
Gamble, Katherine R; Lee, Joanna M; Howard, James H; Howard, Darlene V
2014-11-01
Implicit learning, the type of learning that occurs without intent to learn or awareness of what has been learned, has been thought to be insensitive to the effects of priming, but recent studies suggest this is not the case. One study found that learning in the serial reaction time (SRT) task was improved by nonconscious goal pursuit, primed via a word search task (Eitam et al. in Psychol Sci 19:261-267, 2008). In two studies, we used the goal priming word search task from Eitam et al., but with a different version of the SRT, the alternating serial reaction time task (ASRT). Unlike the SRT, which often results in explicit knowledge and assesses sequence learning at one point in time, the ASRT has been shown to be implicit through sensitive measures of judgment, and it enables sequence learning to be measured continuously. In both studies, we found that implicit learning was superior in the groups that were primed for goal achievement compared to control groups, but the effect was transient. We discuss possible reasons for the observed time course of the positive effects of goal priming, as well as some future areas of investigation to better understand the mechanisms that underlie this effect, which could lead to methods to prolong the positive effects.
Crack growth sparse pursuit for wind turbine blade
Li, Xiang; Yang, Zhibo; Zhang, Han; Du, Zhaohui; Chen, Xuefeng
2015-01-01
One critical challenge to achieving reliable wind turbine blade structural health monitoring (SHM) is mainly caused by composite laminates with an anisotropy nature and a hard-to-access property. The typical pitch-catch PZTs approach generally detects structural damage with both measured and baseline signals. However, the accuracy of imaging or tomography by delay-and-sum approaches based on these signals requires improvement in practice. Via the model of Lamb wave propagation and the establishment of a dictionary that corresponds to scatters, a robust sparse reconstruction approach for structural health monitoring comes into view for its promising performance. This paper proposes a neighbor dictionary that identifies the first crack location through sparse reconstruction and then presents a growth sparse pursuit algorithm that can precisely pursue the extension of the crack. An experiment with the goal of diagnosing a composite wind turbine blade with an artificial crack is performed, and it validates the proposed approach. The results give competitively accurate crack detection with the correct locations and extension length.
Contingencies of self-worth, academic failure, and goal pursuit.
Park, Lora E; Crocker, Jennifer; Kiefer, Amy K
2007-11-01
Two studies examine the effects of failure on explicit and implicit self-esteem, affect, and self-presentation goals as a function of people's trait self-esteem and academic contingency of self-worth. Study 1 shows that participants with low self-esteem (LSE) who receive failure feedback experience lower state self-esteem, less positive affect, and less desire to be perceived as competent the more they base self-worth on academics. In contrast, participants with high self-esteem (HSE) who strongly base self-worth on academics show a slight boost in state self-esteem and desire to be perceived as competent following failure. Study 2 shows that following failure, academically contingent LSE participants downplay the importance of appearing competent to others and associate themselves with failure on an implicit level. Taken together, these findings suggest that academically contingent HSE people show resilience following failure, whereas academically contingent LSE people experience negative outcomes and disengage from the pursuit of competence self-presentation goals.
Newtonized Orthogonal Matching Pursuit: Frequency Estimation Over the Continuum
Mamandipoor, Babak; Ramasamy, Dinesh; Madhow, Upamanyu
2016-10-01
We propose a fast sequential algorithm for the fundamental problem of estimating frequencies and amplitudes of a noisy mixture of sinusoids. The algorithm is a natural generalization of Orthogonal Matching Pursuit (OMP) to the continuum using Newton refinements, and hence is termed Newtonized OMP (NOMP). Each iteration consists of two phases: detection of a new sinusoid, and sequential Newton refinements of the parameters of already detected sinusoids. The refinements play a critical role in two ways: (1) sidestepping the potential basis mismatch from discretizing a continuous parameter space, (2) providing feedback for locally refining parameters estimated in previous iterations. We characterize convergence, and provide a Constant False Alarm Rate (CFAR) based termination criterion. By benchmarking against the Cramer Rao Bound, we show that NOMP achieves near-optimal performance under a variety of conditions. We compare the performance of NOMP with classical algorithms such as MUSIC and more recent Atomic norm Soft Thresholding (AST) and Lasso algorithms, both in terms of frequency estimation accuracy and run time.
Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning
Indah Yulia Prafitaning Tiyas
2014-06-01
Full Text Available Today, information technology is growing rapidly,all information can be obtainedmuch easier. It raises some new problems; one of them is unauthorized access to the system. We need a reliable network security system that is resistant to a variety of attacks against the system. Therefore, Intrusion Detection System (IDS required to overcome the problems of intrusions. Many researches have been done on intrusion detection using classification methods. Classification methodshave high precision, but it takes efforts to determine an appropriate classification model to the classification problem. In this paper, we propose a new reinforced approach to detect intrusion with On-line Clustering using Reinforcement Learning. Reinforcement Learning is a new paradigm in machine learning which involves interaction with the environment.It works with reward and punishment mechanism to achieve solution. We apply the Reinforcement Learning to the intrusion detection problem with considering competitive learning using Pursuit Reinforcement Competitive Learning (PRCL. Based on the experimental result, PRCL can detect intrusions in real time with high accuracy (99.816% for DoS, 95.015% for Probe, 94.731% for R2L and 99.373% for U2R and high speed (44 ms.The proposed approach can help network administrators to detect intrusion, so the computer network security systembecome reliable. Keywords: Intrusion Detection System, On-Line Clustering, Reinforcement Learning, Unsupervised Learning.
Toward Simulating Realistic Pursuit-Evasion Using a Roadmap-Based Approach
Rodriguez, Samuel
2010-01-01
In this work, we describe an approach for modeling and simulating group behaviors for pursuit-evasion that uses a graph-based representation of the environment and integrates multi-agent simulation with roadmap-based path planning. We demonstrate the utility of this approach for a variety of scenarios including pursuit-evasion on terrains, in multi-level buildings, and in crowds. © 2010 Springer-Verlag Berlin Heidelberg.
Spatial contexts can inhibit a mislocalization of visual stimuli during smooth pursuit
Noguchi, Yasuki; Shimojo, Shinsuke; Kakigi, Ryusuke; Hoshiyama, Minoru
2007-01-01
The position of a flash presented during pursuit is mislocalized in the direction of the pursuit. Although this has been explained by a temporal mismatch between the slow visual processing of flash and fast efferent signals on eye positions, here we show that spatial contexts also play an important role in determining the flash position. We put various continuously lit objects (walls) between veridical and to-be-mislocalized positions of flash. Consequently, these walls significantly reduced ...
Single-channel and multi-channel orthogonal matching pursuit for seismic trace decomposition
Feng, Xuan; Zhang, Xuebing; Liu, Cai; Lu, Qi
2017-02-01
The conventional matching pursuit (MP) algorithm can decompose a 1D signal into a set of wavelet atoms adaptively. As to reflection seismic data, some applicable algorithms based on the MP decomposition has been developed, such as single-channel matching pursuit (SCMP) and multi-channel matching pursuit (MCMP). However, these algorithms cannot always select the optimal atoms, which results in less meaningful decompositions. To overcome this limitation, we introduce the idea of orthogonal matching pursuit into a multi-channel decomposition scheme, which we refer to as the multi-channel orthogonal matching pursuit (MCOMP). Each iteration of the proposed MCOMP might extract a more reasonable atom among a redundant Morlet wavelet dictionary, like the MCMP decomposition does, and estimate the corresponding amplitude more accurately by solving a least-squares problem. In order to correspond to SCMP, we also simplified the MCOMP decomposition to single-channel orthogonal matching pursuit (SCOMP) for decompositions of an individual seismic trace. We tested the proposed SCOMP algorithm on a synthetic signal and a field seismic trace. Then a field marine dataset example showed relative high resolution of the proposed MCOMP method with applications to the detection of low-frequency anomalies. These application examples all demonstrate more meaningful decomposition results and relative high convergence speed of the proposed algorithms.
Linnet, Kristian
2005-01-01
Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors......Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors...
Spline Nonparametric Regression Analysis of Stress-Strain Curve of Confined Concrete
Tavio Tavio
2008-01-01
Full Text Available Due to enormous uncertainties in confinement models associated with the maximum compressive strength and ductility of concrete confined by rectilinear ties, the implementation of spline nonparametric regression analysis is proposed herein as an alternative approach. The statistical evaluation is carried out based on 128 large-scale column specimens of either normal-or high-strength concrete tested under uniaxial compression. The main advantage of this kind of analysis is that it can be applied when the trend of relation between predictor and response variables are not obvious. The error in the analysis can, therefore, be minimized so that it does not depend on the assumption of a particular shape of the curve. This provides higher flexibility in the application. The results of the statistical analysis indicates that the stress-strain curves of confined concrete obtained from the spline nonparametric regression analysis proves to be in good agreement with the experimental curves available in literatures
Non-parametric Bayesian human motion recognition using a single MEMS tri-axial accelerometer.
Ahmed, M Ejaz; Song, Ju Bin
2012-09-27
In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer
M. Ejaz Ahmed
2012-09-01
Full Text Available In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Testing the Non-Parametric Conditional CAPM in the Brazilian Stock Market
Daniel Reed Bergmann
2014-04-01
Full Text Available This paper seeks to analyze if the variations of returns and systematic risks from Brazilian portfolios could be explained by the nonparametric conditional Capital Asset Pricing Model (CAPM by Wang (2002. There are four informational variables available to the investors: (i the Brazilian industrial production level; (ii the broad money supply M4; (iii the inflation represented by the Índice de Preços ao Consumidor Amplo (IPCA; and (iv the real-dollar exchange rate, obtained by PTAX dollar quotation.This study comprised the shares listed in the BOVESPA throughout January 2002 to December 2009. The test methodology developed by Wang (2002 and retorted to the Mexican context by Castillo-Spíndola (2006 was used. The observed results indicate that the nonparametric conditional model is relevant in explaining the portfolios’ returns of the sample considered for two among the four tested variables, M4 and PTAX dollar at 5% level of significance.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
Czekaj, Tomasz Gerard
This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...... to avoid this problem. The main objective is to investigate the applicability of the nonparametric kernel regression method in applied production analysis. The focus of the empirical analyses included in this thesis is the agricultural sector in Poland. Data on Polish farms are used to investigate...... practically and politically relevant problems and to illustrate how nonparametric regression methods can be used in applied microeconomic production analysis both in panel data and cross-section data settings. The thesis consists of four papers. The first paper addresses problems of parametric...
On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests
Aaditya Ramdas
2017-01-01
Full Text Available Nonparametric two-sample or homogeneity testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. The literature is old and rich, with a wide variety of statistics having being designed and analyzed, both for the unidimensional and the multivariate setting. Inthisshortsurvey,wefocusonteststatisticsthatinvolvetheWassersteindistance. Usingan entropic smoothing of the Wasserstein distance, we connect these to very different tests including multivariate methods involving energy statistics and kernel based maximum mean discrepancy and univariate methods like the Kolmogorov–Smirnov test, probability or quantile (PP/QQ plots and receiver operating characteristic or ordinal dominance (ROC/ODC curves. Some observations are implicit in the literature, while others seem to have not been noticed thus far. Given nonparametric two-sample testing’s classical and continued importance, we aim to provide useful connections for theorists and practitioners familiar with one subset of methods but not others.
Stahel-Donoho kernel estimation for fixed design nonparametric regression models
LIN; Lu
2006-01-01
This paper reports a robust kernel estimation for fixed design nonparametric regression models.A Stahel-Donoho kernel estimation is introduced,in which the weight functions depend on both the depths of data and the distances between the design points and the estimation points.Based on a local approximation,a computational technique is given to approximate to the incomputable depths of the errors.As a result the new estimator is computationally efficient.The proposed estimator attains a high breakdown point and has perfect asymptotic behaviors such as the asymptotic normality and convergence in the mean squared error.Unlike the depth-weighted estimator for parametric regression models,this depth-weighted nonparametric estimator has a simple variance structure and then we can compare its efficiency with the original one.Some simulations show that the new method can smooth the regression estimation and achieve some desirable balances between robustness and efficiency.
Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Xibin Zhang
2016-04-01
Full Text Available This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP growth rates among the organisation for economic co-operation and development (OECD and non-OECD countries.
Mustafa Koroglu
2016-02-01
Full Text Available This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown functional coefficients by a local linear regression approach. Some Monte Carlo simulation results are reported to assess the finite sample performance of our proposed estimators. We then apply the proposed model to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries’ openness to trade, respectively.
Saad, Walid; Poor, H Vincent; Başar, Tamer; Song, Ju Bin
2012-01-01
This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users(PUs), to cooperate and use \\emph{Bayesian nonparametric} techniques to estimate the distributions of the PUs' activity pattern, assumed to be completely unknown. In the proposed model, each cognitive node may have its own individual view on each PU's distribution, and, hence, seeks to find partners having a correlated perception. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use a combination of Bayesian nonparametric models such as the Dirichlet process and statistical goodness of fit techniques ...
非参数判别模型%Nonparametric discriminant model
谢斌锋; 梁飞豹
2011-01-01
提出了一类新的判别分析方法,主要思想是将非参数回归模型推广到判别分析中,形成相应的非参数判别模型.通过实例与传统判别法相比较,表明非参数判别法具有更广泛的适用性和较高的回代正确率.%In this paper, the author puts forth a new class of discriminant method, which the main idea is applied non- parametric regression model to discriminant analysis and forms the corresponding nonparametric discriminant model. Compared with the traditional discriminant methods by citing an example, the nonparametric discriminant method has more comprehensive adaptability and higher correct rate of back subsitution.
Non-Parametric Tests of Structure for High Angular Resolution Diffusion Imaging in Q-Space
Olhede, Sofia C
2010-01-01
High angular resolution diffusion imaging data is the observed characteristic function for the local diffusion of water molecules in tissue. This data is used to infer structural information in brain imaging. Non-parametric scalar measures are proposed to summarize such data, and to locally characterize spatial features of the diffusion probability density function (PDF), relying on the geometry of the characteristic function. Summary statistics are defined so that their distributions are, to first order, both independent of nuisance parameters and also analytically tractable. The dominant direction of the diffusion at a spatial location (voxel) is determined, and a new set of axes are introduced in Fourier space. Variation quantified in these axes determines the local spatial properties of the diffusion density. Non-parametric hypothesis tests for determining whether the diffusion is unimodal, isotropic or multi-modal are proposed. More subtle characteristics of white-matter microstructure, such as the degre...
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
Czekaj, Tomasz Gerard
This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...... function. However, the a priori specification of a functional form involves the risk of choosing one that is not similar to the “true” but unknown relationship between the regressors and the dependent variable. This problem, known as parametric misspecification, can result in biased parameter estimates...... and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...
A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems
Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J.
2017-06-01
We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.
Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction
WENG Jian-cheng; HU Zhong-wei; YU Quan; REN Fu-tian
2007-01-01
A K-nearest neighbor (K-NN) based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series,collected from the floating car data (FCD) system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.
Variable selection in identification of a high dimensional nonlinear non-parametric system
Er-Wei BAI; Wenxiao ZHAO; Weixing ZHENG
2015-01-01
The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.
Estimating Financial Risk Measures for Futures Positions:A Non-Parametric Approach
Cotter, John; dowd, kevin
2011-01-01
This paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned on the confidence level. Our findings indicate that all risk measures increase dramatically and the...
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access to no...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Asymmetry Effects in Chinese Stock Markets Volatility: A Generalized Additive Nonparametric Approach
Hou, Ai Jun
2007-01-01
The unique characteristics of the Chinese stock markets make it difficult to assume a particular distribution for innovations in returns and the specification form of the volatility process when modeling return volatility with the parametric GARCH family models. This paper therefore applies a generalized additive nonparametric smoothing technique to examine the volatility of the Chinese stock markets. The empirical results indicate that an asymmetric effect of negative news exists in the Chin...
Using a nonparametric PV model to forecast AC power output of PV plants
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
In this paper, a methodology using a nonparametric model is used to forecast AC power output of PV plants using as inputs several forecasts of meteorological variables from a Numerical Weather Prediction (NWP) model and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast the AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, an...
An exact predictive recursion for Bayesian nonparametric analysis of incomplete data
Garibaldi, Ubaldo; Viarengo, Paolo
2010-01-01
This paper presents a new derivation of nonparametric distribution estimation with right-censored data. It is based on an extension of the predictive inferences to compound evidence. The estimate is recursive and exact, and no stochastic approximation is needed: it simply requires that the censored data are processed in decreasing order. Only in this case the recursion provides exact posterior predictive distributions for subsequent samples under a Dirichlet process prior. The resulting estim...
Atamurat Kuchkarov
2016-01-01
Full Text Available We consider pursuit and evasion differential games of a group of m pursuers and one evader on manifolds with Euclidean metric. The motions of all players are simple, and maximal speeds of all players are equal. If the state of a pursuer coincides with that of the evader at some time, we say that pursuit is completed. We establish that each of the differential games (pursuit or evasion is equivalent to a differential game of m groups of countably many pursuers and one group of countably many evaders in Euclidean space. All the players in any of these groups are controlled by one controlled parameter. We find a condition under which pursuit can be completed, and if this condition is not satisfied, then evasion is possible. We construct strategies for the pursuers in pursuit game which ensure completion the game for a finite time and give a formula for this time. In the case of evasion game, we construct a strategy for the evader.
A Pursuit Theory Account for the Perception of Common Motion in Motion Parallax.
Ratzlaff, Michael; Nawrot, Mark
2016-09-01
The visual system uses an extraretinal pursuit eye movement signal to disambiguate the perception of depth from motion parallax. Visual motion in the same direction as the pursuit is perceived nearer in depth while visual motion in the opposite direction as pursuit is perceived farther in depth. This explanation of depth sign applies to either an allocentric frame of reference centered on the fixation point or an egocentric frame of reference centered on the observer. A related problem is that of depth order when two stimuli have a common direction of motion. The first psychophysical study determined whether perception of egocentric depth order is adequately explained by a model employing an allocentric framework, especially when the motion parallax stimuli have common rather than divergent motion. A second study determined whether a reversal in perceived depth order, produced by a reduction in pursuit velocity, is also explained by this model employing this allocentric framework. The results show than an allocentric model can explain both the egocentric perception of depth order with common motion and the perceptual depth order reversal created by a reduction in pursuit velocity. We conclude that an egocentric model is not the only explanation for perceived depth order in these common motion conditions.
Larsson, Linnéa; Nyström, Marcus; Ardö, Håkan; Åström, Kalle; Stridh, Martin
2016-12-01
An increasing number of researchers record binocular eye-tracking signals from participants viewing moving stimuli, but the majority of event-detection algorithms are monocular and do not consider smooth pursuit movements. The purposes of the present study are to develop an algorithm that discriminates between fixations and smooth pursuit movements in binocular eye-tracking signals and to evaluate its performance using an automated video-based strategy. The proposed algorithm uses a clustering approach that takes both spatial and temporal aspects of the binocular eye-tracking signal into account, and is evaluated using a novel video-based evaluation strategy based on automatically detected moving objects in the video stimuli. The binocular algorithm detects 98% of fixations in image stimuli compared to 95% when only one eye is used, while for video stimuli, both the binocular and monocular algorithms detect around 40% of smooth pursuit movements. The present article shows that using binocular information for discrimination of fixations and smooth pursuit movements is advantageous in static stimuli, without impairing the algorithm's ability to detect smooth pursuit movements in video and moving-dot stimuli. With an automated evaluation strategy, time-consuming manual annotations are avoided and a larger amount of data can be used in the evaluation process.
t-tests, non-parametric tests, and large studies—a paradox of statistical practice?
Fagerland Morten W
2012-06-01
Full Text Available Abstract Background During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences. Methods A simulation study is used to compare the rejection rates of the Wilcoxon-Mann-Whitney (WMW test and the two-sample t-test for increasing sample size. Samples are drawn from skewed distributions with equal means and medians but with a small difference in spread. A hypothetical case study is used for illustration and motivation. Results The WMW test produces, on average, smaller p-values than the t-test. This discrepancy increases with increasing sample size, skewness, and difference in spread. For heavily skewed data, the proportion of p Conclusions Non-parametric tests are most useful for small studies. Using non-parametric tests in large studies may provide answers to the wrong question, thus confusing readers. For studies with a large sample size, t-tests and their corresponding confidence intervals can and should be used even for heavily skewed data.
Nonparametric Kernel Smoothing Methods. The sm library in Xlisp-Stat
Luca Scrucca
2001-06-01
Full Text Available In this paper we describe the Xlisp-Stat version of the sm library, a software for applying nonparametric kernel smoothing methods. The original version of the sm library was written by Bowman and Azzalini in S-Plus, and it is documented in their book Applied Smoothing Techniques for Data Analysis (1997. This is also the main reference for a complete description of the statistical methods implemented. The sm library provides kernel smoothing methods for obtaining nonparametric estimates of density functions and regression curves for different data structures. Smoothing techniques may be employed as a descriptive graphical tool for exploratory data analysis. Furthermore, they can also serve for inferential purposes as, for instance, when a nonparametric estimate is used for checking a proposed parametric model. The Xlisp-Stat version includes some extensions to the original sm library, mainly in the area of local likelihood estimation for generalized linear models. The Xlisp-Stat version of the sm library has been written following an object-oriented approach. This should allow experienced Xlisp-Stat users to implement easily their own methods and new research ideas into the built-in prototypes.
Kianisarkaleh, Azadeh; Ghassemian, Hassan
2016-09-01
Feature extraction plays a crucial role in improvement of hyperspectral images classification. Nonparametric feature extraction methods show better performance compared to parametric ones when distribution of classes is non normal-like. Moreover, they can extract more features than parametric methods do. In this paper, a new nonparametric linear feature extraction method is introduced for classification of hyperspectral images. The proposed method has no free parameter and its novelty can be discussed in two parts. First, neighbor samples are specified by using Parzen window idea for determining local mean. Second, two new weighting functions are used. Samples close to class boundaries will have more weight in the between-class scatter matrix formation and samples close to class mean will have more weight in the within-class scatter matrix formation. The experimental results on three real hyperspectral data sets, Indian Pines, Salinas and Pavia University, demonstrate that the proposed method has better performance in comparison with some other nonparametric and parametric feature extraction methods.
A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale.
Mircioiu, Constantin; Atkinson, Jeffrey
2017-05-10
A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple "yes" or "no" but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with "large" (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 "very important" and 4 "essential"), a "score analysis" based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses.
Non-parametric foreground subtraction for 21cm epoch of reionization experiments
Harker, Geraint; Bernardi, Gianni; Brentjens, Michiel A; De Bruyn, A G; Ciardi, Benedetta; Jelic, Vibor; Koopmans, Leon V E; Labropoulos, Panagiotis; Mellema, Garrelt; Offringa, Andre; Pandey, V N; Schaye, Joop; Thomas, Rajat M; Yatawatta, Sarod
2009-01-01
An obstacle to the detection of redshifted 21cm emission from the epoch of reionization (EoR) is the presence of foregrounds which exceed the cosmological signal in intensity by orders of magnitude. We argue that in principle it would be better to fit the foregrounds non-parametrically - allowing the data to determine their shape - rather than selecting some functional form in advance and then fitting its parameters. Non-parametric fits often suffer from other problems, however. We discuss these before suggesting a non-parametric method, Wp smoothing, which seems to avoid some of them. After outlining the principles of Wp smoothing we describe an algorithm used to implement it. We then apply Wp smoothing to a synthetic data cube for the LOFAR EoR experiment. The performance of Wp smoothing, measured by the extent to which it is able to recover the variance of the cosmological signal and to which it avoids leakage of power from the foregrounds, is compared to that of a parametric fit, and to another non-parame...
The properties and mechanism of long-term memory in nonparametric volatility
Li, Handong; Cao, Shi-Nan; Wang, Yan
2010-08-01
Recent empirical literature documents the presence of long-term memory in return volatility. But the mechanism of the existence of long-term memory is still unclear. In this paper, we investigate the origin and properties of long-term memory with nonparametric volatility, using high-frequency time series data of the Chinese Shanghai Composite Stock Price Index. We perform Detrended Fluctuation Analysis (DFA) on three different nonparametric volatility estimators with different sampling frequencies. For the same volatility series, the Hurst exponents reduce as the sampling time interval increases, but they are still larger than 1/2, which means that no matter how the interval changes, it still cannot change the existence of long memory. RRV presents a relatively stable property on long-term memory and is less influenced by sampling frequency. RV and RBV have some evolutionary trends depending on time intervals, which indicating that the jump component has no significant impact on the long-term memory property. This suggests that the presence of long-term memory in nonparametric volatility can be contributed to the integrated variance component. Considering the impact of microstructure noise, RBV and RRV still present long-term memory under various time intervals. We can infer that the presence of long-term memory in realized volatility is not affected by market microstructure noise. Our findings imply that the long-term memory phenomenon is an inherent characteristic of the data generating process, not a result of microstructure noise or volatility clustering.
Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.
Xu, Yanxun; Müller, Peter; Wahed, Abdus S; Thall, Peter F
2016-01-01
We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out. To evaluate the regimes, mean overall survival time is expressed as a weighted average of the means of all possible sums of successive transitions times. We assume a Bayesian nonparametric survival regression model for each transition time, with a dependent Dirichlet process prior and Gaussian process base measure (DDP-GP). Posterior simulation is implemented by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. The proposed approach is compared with inverse probability of treatment weighting, including a doubly robust augmented version of this approach, for both single-stage and multi-stage regimes with treatment assignment depending on baseline covariates. The simulations show that the proposed nonparametric Bayesian approach can substantially improve inference compared to existing methods. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at https://www.ma.utexas.edu/users/yxu/.
Optimized Projection Matrix for Compressive Sensing
Jianping Xu
2010-01-01
Full Text Available Compressive sensing (CS is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame (ETF design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore, an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit.
A Model of the Smooth Pursuit Eye Movement with Prediction and Learning
Davide Zambrano
2010-01-01
Full Text Available Smooth pursuit is one of the five main eye movements in humans, consisting of tracking a steadily moving visual target. Smooth pursuit is a good example of a sensory-motor task that is deeply based on prediction: tracking a visual target is not possible by correcting the error between the eye and the target position or velocity with a feedback loop, but it is only possible by predicting the trajectory of the target. This paper presents a model of smooth pursuit based on prediction and learning. It starts from amodel of the neuro-physiological system proposed by Shibata and Schaal (Shibata et al., Neural Networks, vol. 18, pp. 213-224, 2005. The learning component added here decreases the prediction time in the case of target dynamics already experienced by the system. In the implementation described here, the convergence time is, after the learning phase, 0.8 s.
A New Method of Using Sensor Network for Solving Pursuit-Evasion Problem
Peng Zhuang
2007-02-01
Full Text Available Wireless sensor networks offer the potential to significantly improve the performance of pursuers in pursuit-evasion games. In this paper, we study several sensor network systems, their interaction with the pursuers, and the effect on pursuer performance. We propose a general framework to solve the pursuit-evasion problem and present new centralized as well as distributed methods. Specifically, we address three issues in the design of pursuers based on data provided by the sensor network : a how to identify evader moving patterns, (b how to predict the evader locations using different evader moving models, and c how to choose the most efficient pursuit strategies. We propose efficient algorithms to solve these problems and show that they are effective in reducing the capturing time in our simulations. We also compare the distributed and centralized methods. Experimental results show that the distributed method is efficient and produces solutions close to the centralized method.
Eye movements and hazard perception in police pursuit and emergency response driving.
Crundall, David; Chapman, Peter; Phelps, Nicola; Underwood, Geoffrey
2003-09-01
How do police cope with the visual demands placed on them during pursuit driving? This study compared the hazard ratings, eye movements, and physiological responses of police drivers with novice and with age-matched control drivers while viewing video clips of driving taken from police vehicles. The clips included pursuits, emergency responses, and control drives. Although police drivers did not report more hazards than the other participants reported, they had an increased frequency of electrodermal responses while viewing dangerous clips and a greater visual sampling rate and spread of search. However, despite an overall police advantage in oculomotor and physiological measures, all drivers had a reduced spread of search in nighttime pursuits because of the focusing of overt attention.
Saccadic and smooth-pursuit eye movements during reading of drifting texts.
Valsecchi, Matteo; Gegenfurtner, Karl R; Schütz, Alexander C
2013-08-16
Reading is a complex visuomotor behavior characterized by an alternation of fixations and saccadic eye movements. Despite the widespread use of drifting texts in various settings, very little is known about eye movements under these conditions. Here we investigated oculomotor behavior during reading of texts which were drifting horizontally or vertically at different speeds. Consistent with previous reports, drifting texts were read by an alternation of smooth-pursuit and saccadic eye movements. Detailed analysis revealed several interactions between smooth pursuit and saccades. On one side, the gain of smooth pursuit was increased after the execution of a saccade. On the other side, the peak velocity of saccades was reduced for the horizontally drifting text, in which saccades and pursuit were executed in opposite directions. In addition, we show that well-known findings from the reading of static texts extend to drifting text, such as the preferred viewing location, the inverted optimal viewing position, and the correlation between saccade amplitude and subsequent pursuit/fixation duration. In general, individual eye-movement parameters such as saccade amplitude and fixation/pursuit durations were correlated across self-paced reading of static text and time-constrained reading of static and drifting texts. These results show that findings from basic oculomotor research also apply to the reading of drifting texts. Similarly, basic reading principles apply to the reading of static and drifting texts in a similar way. This exemplifies the reading of drifting text as a visuomotor behavior which is influenced by low-level eye-movement control as well as by cognitive and linguistic processing.
Reduction of snapshots for MIMO radar detection by block/group orthogonal matching pursuit
Ali, Hussain El Hosiny
2014-10-01
Multiple-input multiple-output (MIMO) radar works on the principle of transmission of independent waveforms at each element of its antenna array and is widely used for surveillance purposes. In this work, we investigate MIMO radar target localization problem with compressive sensing. Specifically, we try to solve the problem of estimation of target location in MIMO radar by group and block sparsity algorithms. It will lead us to a reduced number of snapshots required and also we can achieve better radar resolution. We will use group orthogonal matching pursuit (GOMP) and block orthogonal matching pursuit (BOMP) for our problem. © 2014 IEEE.
Zhao Ruizhen; Ren Xiaoxin; Han Xuelian; Hu Shaohai
2012-01-01
Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown.In order to match the sparsity more accurately,we presented an improved SAMP algorithm based on Regularized Backtracking (SAMP-RB).By adapting a regularized backtracking step to SAMP algorithm in each iteration stage,the proposed algorithm can flexibly remove the inappropriate atoms.The experimental results show that SAMP-RB reconstruction algorithm greatly improves SAMP algorithm both in reconstruction quality and computational time.It has better reconstruction efficiency than most of the available matching pursuit algorithms.
The relationship between disordered pursuit and vestibulo-ocular reflex suppression
Chambers, Br; Gresty, MA
1983-01-01
The performance of the smooth pursuit reflex and the ability to suppress the vestibulo-ocular reflex were assessed in 10 normal subjects and in patients with a variety of diseases of the central nervous system. Pursuit was measured as the maximum velocity of slow phase eye movement in response to a laser target moving sinusoidally at various frequencies up to 1 Hz and with amplitudes stepped up to 35° peak. Suppression of the vestibulo-ocular reflex was assessed with subjects seated in a Bara...
Multi-Objective Genetic Programming Projection Pursuit for Exploratory Data Modeling
Icke, Ilknur
2010-01-01
For classification problems, feature extraction is a crucial process which aims to find a suitable data representation that increases the performance of the machine learning algorithm. According to the curse of dimensionality theorem, the number of samples needed for a classification task increases exponentially as the number of dimensions (variables, features) increases. On the other hand, it is costly to collect, store and process data. Moreover, irrelevant and redundant features might hinder classifier performance. In exploratory analysis settings, high dimensionality prevents the users from exploring the data visually. Feature extraction is a two-step process: feature construction and feature selection. Feature construction creates new features based on the original features and feature selection is the process of selecting the best features as in filter, wrapper and embedded methods. In this work, we focus on feature construction methods that aim to decrease data dimensionality for visualization tasks. V...
Hyvonen, Katriina; Feldt, Taru; Tolvanen, Asko; Kinnunen, Ulla
2010-01-01
The relation of the core components of the Effort-Reward Imbalance model (ERI; Siegrist, 1996) to goal pursuit was investigated. Goal pursuit was studied through categories of goal contents--competency, progression, well-being, job change, job security, organization, finance, or no work goal--based on the personal work goals of managers (Hyvonen,…
Kongsted, Alice; Jørgensen, L V; Bendix, T
2007-01-01
To evaluate whether smooth pursuit eye movements differed between patients with long-lasting whiplash-associated disorders and controls when using a purely computerized method for the eye movement analysis.......To evaluate whether smooth pursuit eye movements differed between patients with long-lasting whiplash-associated disorders and controls when using a purely computerized method for the eye movement analysis....
C Pallus, Adam; G Freedman, Edward
2016-08-01
Gaze pursuit is the coordinated movement of the eyes and head that allows humans and other foveate animals to track moving objects. The control of smooth pursuit eye movements when the head is restrained is relatively well understood, but how the eyes coordinate with concurrent head movements when the head is free remains unresolved. In this study, we describe behavioral tasks that dissociate head and gaze velocity during head-free pursuit in monkeys. Existing models of gaze pursuit propose that both eye and head movements are driven only by the perceived velocity of the visual target and are therefore unable to account for these data. We show that in addition to target velocity, the positions of the eyes in the orbits and the retinal position of the target are important factors for predicting head movement during pursuit. When the eyes are already near their limits, further pursuit in that direction will be accompanied by more head movement than when the eyes are centered in the orbits, even when target velocity is the same. The step-ramp paradigm, often used in pursuit tasks, produces larger or smaller head movements, depending on the direction of the position step, while gaze pursuit velocity is insensitive to this manipulation. Using these tasks, we can reliably evoke head movements with peak velocities much faster than the target's velocity. Under these circumstances, the compensatory eye movements, which are often called counterproductive since they rotate the eyes in the opposite direction, are essential to maintaining accurate gaze velocity.
Environmentalism and Science: Politics and the Pursuit of Knowledge.
Rycroft, Robert W.
1991-01-01
Examination of the relationship between environmentalists and scientists concludes that environmentalism has had little impact on science. Topics discussed include the degree to which scientific research has become more applied; efforts to integrate and coordinate research projects; the synthesis of scientific information for policy purposes; and…
Non-parametric change-point method for differential gene expression detection.
Yao Wang
Full Text Available BACKGROUND: We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short, by using a single equation for detecting differential gene expression (DGE in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability. METHODOLOGY: NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods. CONCLUSIONS: Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods.
Applications of Parametric and Nonparametric Tests for Event Studies on ISE
Handan YOLSAL
2011-01-01
In this study, we conducted a research as to whether splits in shares on the ISE-ON Index at the Istanbul Stock Exchange have had an impact on returns generated from shares between 2005 and 2011 or not using event study method. This study is based on parametric tests, as well as on nonparametric tests developed as an alternative to them. It has been observed that, when cross-sectional variance adjustment is applied to data set, such null hypothesis as “there is no average abnormal return at d...
Ohkubo, Jun
2011-12-01
A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.
Blundell, Richard; Horowitz, Joel L.; Parey, Matthias
2011-01-01
This paper develops a new method for estimating a demand function and the welfare consequences of price changes. The method is applied to gasoline demand in the U.S. and is applicable to other goods. The method uses shape restrictions derived from economic theory to improve the precision of a nonparametric estimate of the demand function. Using data from the U.S. National Household Travel Survey, we show that the restrictions are consistent with the data on gasoline demand and remove the anom...
Two new non-parametric tests to the distance duality relation with galaxy clusters
Costa, S S; Holanda, R F L
2015-01-01
The cosmic distance duality relation is a milestone of cosmology involving the luminosity and angular diameter distances. Any departure of the relation points to new physics or systematic errors in the observations, therefore tests of the relation are extremely important to build a consistent cosmological framework. Here, two new tests are proposed based on galaxy clusters observations (angular diameter distance and gas mass fraction) and $H(z)$ measurements. By applying Gaussian Processes, a non-parametric method, we are able to derive constraints on departures of the relation where no evidence of deviation is found in both methods, reinforcing the cosmological and astrophysical hypotheses adopted so far.
Nonparametric variance estimation in the analysis of microarray data: a measurement error approach.
Carroll, Raymond J; Wang, Yuedong
2008-01-01
This article investigates the effects of measurement error on the estimation of nonparametric variance functions. We show that either ignoring measurement error or direct application of the simulation extrapolation, SIMEX, method leads to inconsistent estimators. Nevertheless, the direct SIMEX method can reduce bias relative to a naive estimator. We further propose a permutation SIMEX method which leads to consistent estimators in theory. The performance of both SIMEX methods depends on approximations to the exact extrapolants. Simulations show that both SIMEX methods perform better than ignoring measurement error. The methodology is illustrated using microarray data from colon cancer patients.
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.
Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben
2017-06-06
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
López Fontán, J L; Costa, J; Ruso, J M; Prieto, G; Sarmiento, F
2004-02-01
The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found.
Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)
2004-02-01
The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)
Poage, J. L.
1975-01-01
A sequential nonparametric pattern classification procedure is presented. The method presented is an estimated version of the Wald sequential probability ratio test (SPRT). This method utilizes density function estimates, and the density estimate used is discussed, including a proof of convergence in probability of the estimate to the true density function. The classification procedure proposed makes use of the theory of order statistics, and estimates of the probabilities of misclassification are given. The procedure was tested on discriminating between two classes of Gaussian samples and on discriminating between two kinds of electroencephalogram (EEG) responses.
Nonparametric bootstrap analysis with applications to demographic effects in demand functions.
Gozalo, P L
1997-12-01
"A new bootstrap proposal, labeled smooth conditional moment (SCM) bootstrap, is introduced for independent but not necessarily identically distributed data, where the classical bootstrap procedure fails.... A good example of the benefits of using nonparametric and bootstrap methods is the area of empirical demand analysis. In particular, we will be concerned with their application to the study of two important topics: what are the most relevant effects of household demographic variables on demand behavior, and to what extent present parametric specifications capture these effects." excerpt
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
Tan, Qihua; Thomassen, Mads; Burton, Mark
2017-01-01
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering...... the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...... time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health....
Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling
Knowles, David
2010-01-01
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on G to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated datasets based on a known sparse connectivity matrix for E. Coli, and on three biological datasets of increasing complexity.
Non-parametric trend analysis of water quality data of rivers in Kansas
Yu, Y.-S.; Zou, S.; Whittemore, D.
1993-01-01
Surface water quality data for 15 sampling stations in the Arkansas, Verdigris, Neosho, and Walnut river basins inside the state of Kansas were analyzed to detect trends (or lack of trends) in 17 major constituents by using four different non-parametric methods. The results show that concentrations of specific conductance, total dissolved solids, calcium, total hardness, sodium, potassium, alkalinity, sulfate, chloride, total phosphorus, ammonia plus organic nitrogen, and suspended sediment generally have downward trends. Some of the downward trends are related to increases in discharge, while others could be caused by decreases in pollution sources. Homogeneity tests show that both station-wide trends and basinwide trends are non-homogeneous. ?? 1993.
Noise and speckle reduction in synthetic aperture radar imagery by nonparametric Wiener filtering.
Caprari, R S; Goh, A S; Moffatt, E K
2000-12-10
We present a Wiener filter that is especially suitable for speckle and noise reduction in multilook synthetic aperture radar (SAR) imagery. The proposed filter is nonparametric, not being based on parametrized analytical models of signal statistics. Instead, the Wiener-Hopf equation is expressed entirely in terms of observed signal statistics, with no reference to the possibly unobservable pure signal and noise. This Wiener filter is simple in concept and implementation, exactly minimum mean-square error, and directly applicable to signal-dependent and multiplicative noise. We demonstrate the filtering of a genuine two-look SAR image and show how a nonnegatively constrained version of the filter substantially reduces ringing.
Rotondi, R.
2009-04-01
According to the unified scaling theory the probability distribution function of the recurrence time T is a scaled version of a base function and the average value of T can be used as a scale parameter for the distribution. The base function must belong to the scale family of distributions: tested on different catalogues and for different scale levels, for Corral (2005) the (truncated) generalized gamma distribution is the best model, for German (2006) the Weibull distribution. The scaling approach should overcome the difficulty of estimating distribution functions over small areas but theorical limitations and partial instability of the estimated distributions have been pointed out in the literature. Our aim is to analyze the recurrence time of strong earthquakes that occurred in the Italian territory. To satisfy the hypotheses of independence and identical distribution we have evaluated the times between events that occurred in each area of the Database of Individual Seismogenic Sources and then we have gathered them by eight tectonically coherent regions, each of them dominated by a well characterized geodynamic process. To solve problems like: paucity of data, presence of outliers and uncertainty in the choice of the functional expression for the distribution of t, we have followed a nonparametric approach (Rotondi (2009)) in which: (a) the maximum flexibility is obtained by assuming that the probability distribution is a random function belonging to a large function space, distributed as a stochastic process; (b) nonparametric estimation method is robust when the data contain outliers; (c) Bayesian methodology allows to exploit different information sources so that the model fitting may be good also to scarce samples. We have compared the hazard rates evaluated through the parametric and nonparametric approach. References Corral A. (2005). Mixing of rescaled data and Bayesian inference for earthquake recurrence times, Nonlin. Proces. Geophys., 12, 89
A NONPARAMETRIC PROCEDURE OF THE SAMPLE SIZE DETERMINATION FOR SURVIVAL RATE TEST
无
2000-01-01
Objective This paper proposes a nonparametric procedure of the sample size determination for survival rate test. Methods Using the classical asymptotic normal procedure yields the required homogenetic effective sample size and using the inverse operation with the prespecified value of the survival function of censoring times yields the required sample size. Results It is matched with the rate test for censored data, does not involve survival distributions, and reduces to its classical counterpart when there is no censoring. The observed power of the test coincides with the prescribed power under usual clinical conditions. Conclusion It can be used for planning survival studies of chronic diseases.
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
Tan, Qihua; Thomassen, Mads; Burton, Mark
2017-01-01
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering...... the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...
Kerschbamer, Rudolf
2015-05-01
This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context. It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants. As a by-product the test yields a two-dimensional index of preference intensity.
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images
2010-04-01
OF EACH CELL ARE RESULTS OF KSVD AND BPFA, RESPECTIVELY. σ C.man House Peppers Lena Barbara Boats F.print Couple Hill 5 37.87 39.37 37.78 38.60 38.08...INTERPOLATION PSNR RESULTS, USING PATCH SIZE 8× 8. BOTTOM: BPFA RGB IMAGE INTERPOLATION PSNR RESULTS, USING PATCH SIZE 7× 7. data ratio C.man House Peppers Lena...of subspaces. IEEE Trans. Inform. Theory, 2009. [16] T. Ferguson . A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1:209–230
BOOTSTRAP WAVELET IN THE NONPARAMETRIC REGRESSION MODEL WITH WEAKLY DEPENDENT PROCESSES
林路; 张润楚
2004-01-01
This paper introduces a method of bootstrap wavelet estimation in a nonparametric regression model with weakly dependent processes for both fixed and random designs. The asymptotic bounds for the bias and variance of the bootstrap wavelet estimators are given in the fixed design model. The conditional normality for a modified version of the bootstrap wavelet estimators is obtained in the fixed model. The consistency for the bootstrap wavelet estimator is also proved in the random design model. These results show that the bootstrap wavelet method is valid for the model with weakly dependent processes.
An adaptive nonparametric method in benchmark analysis for bioassay and environmental studies.
Bhattacharya, Rabi; Lin, Lizhen
2010-12-01
We present a novel nonparametric method for bioassay and benchmark analysis in risk assessment, which averages isotonic MLEs based on disjoint subgroups of dosages. The asymptotic theory for the methodology is derived, showing that the MISEs (mean integrated squared error) of the estimates of both the dose-response curve F and its inverse F(-1) achieve the optimal rate O(N(-4/5)). Also, we compute the asymptotic distribution of the estimate ζ~p of the effective dosage ζ(p) = F(-1) (p) which is shown to have an optimally small asymptotic variance.
Curry, Marnie W.
2012-01-01
In the ideal, reciprocity in qualitative inquiry occurs when there is give-and-take between researchers and the researched; however, the demands of the academy and resource constraints often make the pursuit of reciprocity difficult. Drawing on two video-based, qualitative studies in which researchers utilized video records as resources to enhance…
Modeling depth from motion parallax with the motion/pursuit ratio
Mark eNawrot
2014-10-01
Full Text Available The perception of unambiguous scaled depth from motion parallax relies on both retinal image motion and an extra-retinal pursuit eye movement signal. The motion/pursuit ratio represents a dynamic geometric model linking these two proximal cues to the ratio of depth to viewing distance. An important step in understanding the visual mechanisms serving the perception of depth from motion parallax is to determine the relationship between these stimulus parameters and empirically determined perceived depth magnitude. Observers compared perceived depth magnitude of dynamic motion parallax stimuli to static binocular disparity comparison stimuli at three different viewing distances, in both head-moving and head-stationary conditions. A stereo-viewing system provided ocular separation for stereo stimuli and monocular viewing of parallax stimuli. For each motion parallax stimulus, a point of subjective equality was estimated for the amount of binocular disparity that generates the equivalent magnitude of perceived depth from motion parallax. Similar to previous results, perceived depth from motion parallax had significant foreshortening. Head-moving conditions produced even greater foreshortening due to the differences in the compensatory eye movement signal. An empirical version of motion/pursuit law, termed the empirical motion/pursuit ratio, which models perceived depth magnitude from these stimulus parameters, is proposed.
Modeling depth from motion parallax with the motion/pursuit ratio.
Nawrot, Mark; Ratzlaff, Michael; Leonard, Zachary; Stroyan, Keith
2014-01-01
The perception of unambiguous scaled depth from motion parallax relies on both retinal image motion and an extra-retinal pursuit eye movement signal. The motion/pursuit ratio represents a dynamic geometric model linking these two proximal cues to the ratio of depth to viewing distance. An important step in understanding the visual mechanisms serving the perception of depth from motion parallax is to determine the relationship between these stimulus parameters and empirically determined perceived depth magnitude. Observers compared perceived depth magnitude of dynamic motion parallax stimuli to static binocular disparity comparison stimuli at three different viewing distances, in both head-moving and head-stationary conditions. A stereo-viewing system provided ocular separation for stereo stimuli and monocular viewing of parallax stimuli. For each motion parallax stimulus, a point of subjective equality (PSE) was estimated for the amount of binocular disparity that generates the equivalent magnitude of perceived depth from motion parallax. Similar to previous results, perceived depth from motion parallax had significant foreshortening. Head-moving conditions produced even greater foreshortening due to the differences in the compensatory eye movement signal. An empirical version of the motion/pursuit law, termed the empirical motion/pursuit ratio, which models perceived depth magnitude from these stimulus parameters, is proposed.
Information fusion control with time delay for smooth pursuit eye movement.
Zhang, Menghua; Ma, Xin; Qin, Bin; Wang, Guangmao; Guo, Yanan; Xu, Zhigang; Wang, Yafang; Li, Yibin
2016-05-01
Smooth pursuit eye movement depends on prediction and learning, and is subject to time delays in the visual pathways. In this paper, an information fusion control method with time delay is presented, implementing smooth pursuit eye movement with prediction and learning as well as solving the problem of time delays in the visual pathways. By fusing the soft constraint information of the target trajectory of eyes and the ideal control strategy, and the hard constraint information of the eye system state equation and the output equation, optimal estimations of the co-state sequence and the control variable are obtained. The proposed control method can track not only constant velocity, sinusoidal target motion, but also arbitrary moving targets. Moreover, the absolute value of the retinal slip reaches steady state after 0.1 sec. Information fusion control method elegantly describes in a function manner how the brain may deal with arbitrary target velocities, how it implements the smooth pursuit eye movement with prediction, learning, and time delays. These two principles allowed us to accurately describe visually guided, predictive and learning smooth pursuit dynamics observed in a wide variety of tasks within a single theoretical framework. The tracking control performance of the proposed information fusion control with time delays is verified by numerical simulation results.
Physical Activities and Sedentary Pursuits in African American and Caucasian Girls
Dowda, Marsha; Pate, Russell R.; Felton, Gwen M.; Saunders, Ruth; Ward, Dianne S.; Dishman, Rod K.; Trost, Stewart G.
2004-01-01
The purposes of this study were to describe and compare the specific physical activity choices and sedentary pursuits of African American and Caucasian American girls. Participants were 1,124 African American and 1,068 Caucasian American eighth-grade students from 31 middle schools. The 3-Day Physical Activity Recall (3DPAR) was used to measure…
The Role of an Epistemology of Inclusivity on the Pursuit of Social Justice: A Case Study
Scanlan, Martin
2012-01-01
Social justice education emphasizes how schools can better serve traditionally marginalized students. This case study examines the pursuit of social justice education in an unlikely setting: a Catholic elementary school that both espouses inclusion of all children and effectively includes children with a wide range of disabilities. The article…
38 CFR 21.314 - Pursuit of training under special conditions.
2010-07-01
... is required to pursue a rehabilitation program at a rate which meets the requirement for full- or... rehabilitation program at a lesser rate, if such pursuit is a part of the veteran's plan. Subsistence allowance... AFFAIRS (CONTINUED) VOCATIONAL REHABILITATION AND EDUCATION Vocational Rehabilitation and Employment...
Raghavan, Ramanujan T; Joshua, Mati
2017-07-19
We investigated the composition of preparatory activity of frontal eye field (FEF) neurons in monkeys performing a pursuit target selection task. In response to the orthogonal motion of a large and a small reward target, monkeys initiated pursuit biased towards the direction of large reward target motion. FEF neurons exhibited robust preparatory activity preceding movement initiation in this task. Preparatory activity consisted of two components, ramping activity that was constant across target selection conditions and a flat offset in firing rates that signaled the target selection condition. Ramping activity accounted for 50% of the variance in the preparatory activity and was linked most strongly, on a trial-by-trial basis, to pursuit eye movement latency rather than to its direction or gain. The offset in firing rates that discriminated target selection conditions accounted for 25% of the variance in the preparatory activity, and was commensurate with a winner-take-all representation signaling the direction of large reward target motion rather than a representation that matched the parameters of the upcoming movement. These offer new insights into the role the frontal eye fields play in target selection and pursuit control. They show that preparatory activity in the FEF signals more strongly when to move rather than where or how to move, and suggest that structures outside the FEF augment its contributions to the target selection process. Copyright © 2017, Journal of Neurophysiology.
Coping with spinal cord injury: Tenacious goal pursuit and flexible goal adjustment.
van Lankveld, Wim; van Diemen, Tijn; van Nes, Ilse J. W.
2011-01-01
Objective: To investigate the correlation of higher-order coping strategies of tenacious goal pursuit and flexible goal adjustment with adjustment after rehabilitation in spinal cordinjury. Design: Cross-sectional correlational study.Subjects/patients: All 397 eligible patients entered for spinal
Kong, Xiaoqing; Chakraverty, Devasmita; Jeffe, Donna B.; Andriole, Dorothy A.; Wathington, Heather D.; Tai, Robert H.
2013-01-01
This exploratory qualitative study investigated how doctoral students reported their personal and professional interaction experiences that they believed might facilitate or impede their academic pursuits in biomedical research. We collected 19 in-depth interviews with doctoral students in biomedical research from eight universities, and we based…
When goal pursuit fails: The functions of counterfactual thought in intention formation
Epstude, K.; Roese, N.J.
2011-01-01
Counterfactual thoughts predominantly occur in response to failed goal pursuit. The primary function of self-related counterfactuals seems to be correction of specific behaviors and preparation for future successful goal attainment. In the present article we describe a model that outlines this view
The Role of Intrinsic Motivation in the Academic Pursuits of Nontraditional Students
Shillingford, Shani; Karlin, Nancy J.
2013-01-01
This article examines the role of intrinsic motivation in the academic pursuits of nontraditional students. The Academic Motivational Scale (AMS) was administered to 35 undergraduate students, 6 males and 29 females, aged 25 to 49 to explore their motivational orientations in choosing to attend college. The results of the study show that…
On Discrete-Time Pursuit-Evasion Games with Sensing Limitations
2008-01-01
different number of pursuers. Index Terms Pursuit-evasion games, sensing limitations, cooperative control. Paper type: Technical Report Number CCDC -08-0313...Cover Note: Shaunak D. Bopardikar, Francesco Bullo and João P. Hespanha are with the Center for Control, Dynamical systems and Computation ( CCDC
Dynamics of the echolocation beam during prey pursuit in aerial hawking bats.
Jakobsen, Lasse; Olsen, Mads Nedergaard; Surlykke, Annemarie
2015-06-30
In the evolutionary arms race between prey and predator, measures and countermeasures continuously evolve to increase survival on both sides. Bats and moths are prime examples. When exposed to intense ultrasound, eared moths perform dramatic escape behaviors. Vespertilionid and rhinolophid bats broaden their echolocation beam in the final stage of pursuit, presumably as a countermeasure to keep evading moths within their "acoustic field of view." In this study, we investigated if dynamic beam broadening is a general property of echolocation when catching moving prey. We recorded three species of emballonurid bats, Saccopteryx bilineata, Saccopteryx leptura, and Rhynchonycteris naso, catching airborne insects in the field. The study shows that S. bilineata and S. leptura maintain a constant beam shape during the entire prey pursuit, whereas R. naso broadens the beam by lowering the peak call frequency from 100 kHz during search and approach to 67 kHz in the buzz. Surprisingly, both Saccopteryx bats emit calls with very high energy throughout the pursuit, up to 60 times more than R. naso and Myotis daubentonii (a similar sized vespertilionid), providing them with as much, or more, peripheral "vision" than the vespertilionids, but ensonifying objects far ahead suggesting more clutter. Thus, beam broadening is not a fundamental property of the echolocation system. However, based on the results, we hypothesize that increased peripheral detection is crucial to all aerial hawking bats in the final stages of prey pursuit and speculate that beam broadening is a feature characterizing more advanced echolocation.