Relevant bibliographies by topics / Partially linear single index

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Partially linear single index'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Partially linear single index"

1

Carroll, R. J., Jianqing Fan, Irène Gijbels, and M. P. Wand. "Generalized Partially Linear Single-Index Models." Journal of the American Statistical Association 92, no. 438 (June 1997): 477–89. http://dx.doi.org/10.1080/01621459.1997.10474001.

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Lu, Xuewen, and Tsung-Lin Cheng. "Randomly censored partially linear single-index models." Journal of Multivariate Analysis 98, no. 10 (November 2007): 1895–922. http://dx.doi.org/10.1016/j.jmva.2006.11.008.

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Lian, Heng, Hua Liang, and Raymond J. Carroll. "Variance function partially linear single-index models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77, no. 1 (April 8, 2014): 171–94. http://dx.doi.org/10.1111/rssb.12066.

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Xia, Y. "On extended partially linear single-index models." Biometrika 86, no. 4 (December 1, 1999): 831–42. http://dx.doi.org/10.1093/biomet/86.4.831.

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Yu, Ping, Jiang Du, and Zhongzhan Zhang. "Single-index partially functional linear regression model." Statistical Papers 61, no. 3 (January 18, 2018): 1107–23. http://dx.doi.org/10.1007/s00362-018-0980-6.

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Dong, Chaohua, Jiti Gao, and Dag Tjøstheim. "Estimation for single-index and partially linear single-index integrated models." Annals of Statistics 44, no. 1 (February 2016): 425–53. http://dx.doi.org/10.1214/15-aos1372.

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Lian, Heng, and Hua Liang. "Separation of linear and index covariates in partially linear single-index models." Journal of Multivariate Analysis 143 (January 2016): 56–70. http://dx.doi.org/10.1016/j.jmva.2015.08.017.

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Wang, Qin, and Rongning Wu. "Shrinkage estimation of partially linear single-index models." Statistics & Probability Letters 83, no. 10 (October 2013): 2324–31. http://dx.doi.org/10.1016/j.spl.2013.06.019.

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Ma, Shujie, Hua Liang, and Chih-Ling Tsai. "Partially linear single index models for repeated measurements." Journal of Multivariate Analysis 130 (September 2014): 354–75. http://dx.doi.org/10.1016/j.jmva.2014.06.011.

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Poon, Wai-Yin, and Hai-Bin Wang. "Multivariate partially linear single-index models: Bayesian analysis." Journal of Nonparametric Statistics 26, no. 4 (October 2, 2014): 755–68. http://dx.doi.org/10.1080/10485252.2014.965706.

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Dissertations / Theses on the topic "Partially linear single index"

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Zhang, Yuankun. "(Ultra-)High Dimensional Partially Linear Single Index Models for Quantile Regression." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535703962712806.

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Apanasovich, Tatiyana Vladimirovna. "Testing for spatial correlation and semiparametric spatial modeling of binary outcomes with application to aberrant crypt foci in colon carcinogenesis experiments." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/2674.

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In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.
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Petry, Sebastian. "Regularization approaches for generalized linear models and single index models." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-143983.

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Petry, Sebastian [Verfasser]. "Regularization Approaches for Generalized Linear Models and Single Index Models / Sebastian Petry." München : Verlag Dr. Hut, 2012. http://d-nb.info/1023435241/34.

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Petry, Sebastian Verfasser], and Gerhard [Akademischer Betreuer] [Tutz. "Regularization approaches for generalized linear models and single index models / Sebastian Petry. Betreuer: Gerhard Tutz." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2011. http://d-nb.info/1023435586/34.

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Chen, Jinsong. "Semiparametric Methods for the Generalized Linear Model." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28012.

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The generalized linear model (GLM) is a popular model in many research areas. In the GLM, each outcome of the dependent variable is assumed to be generated from a particular distribution function in the exponential family. The mean of the distribution depends on the independent variables. The link function provides the relationship between the linear predictor and the mean of the distribution function. In this dissertation, two semiparametric extensions of the GLM will be developed. In the first part of this dissertation, we have proposed a new model, called a semiparametric generalized linear model with a log-concave random component (SGLM-L). In this model, the estimate of the distribution of the random component has a nonparametric form while the estimate of the systematic part has a parametric form. In the second part of this dissertation, we have proposed a model, called a generalized semiparametric single-index mixed model (GSSIMM). A nonparametric component with a single index is incorporated into the mean function in the generalized linear mixed model (GLMM) assuming that the random component is following a parametric distribution. In the first part of this dissertation, since most of the literature on the GLM deals with the parametric random component, we relax the parametric distribution assumption for the random component of the GLM and impose a log-concave constraint on the distribution. An iterative numerical algorithm for computing the estimators in the SGLM-L is developed. We construct a log-likelihood ratio test for inference. In the second part of this dissertation, we use a single index model to generalize the GLMM to have a linear combination of covariates enter the model via a nonparametric mean function, because the linear model in the GLMM is not complex enough to capture the underlying relationship between the response and its associated covariates. The marginal likelihood is approximated using the Laplace method. A penalized quasi-likelihood approach is proposed to estimate the nonparametric function and parameters including single-index coe±cients in the GSSIMM. We estimate variance components using marginal quasi-likelihood. Asymptotic properties of the estimators are developed using a similar idea by Yu (2008). A simulation example is carried out to compare the performance of the GSSIMM with that of the GLMM. We demonstrate the advantage of my approach using a study of the association between daily air pollutants and daily mortality adjusted for temperature and wind speed in various counties of North Carolina.
Ph. D.
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Mahmoud, Hamdy Fayez Farahat. "Some Advanced Semiparametric Single-index Modeling for Spatially-Temporally Correlated Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/76744.

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Semiparametric modeling is a hybrid of the parametric and nonparametric modelings where some function forms are known and others are unknown. In this dissertation, we have made several contributions to semiparametric modeling based on the single index model related to the following three topics: the first is to propose a model for detecting change points simultaneously with estimating the unknown function; the second is to develop two models for spatially correlated data; and the third is to further develop two models for spatially-temporally correlated data. To address the first topic, we propose a unified approach in its ability to simultaneously estimate the nonlinear relationship and change points. We propose a single index change point model as our unified approach by adjusting for several other covariates. We nonparametrically estimate the unknown function using kernel smoothing and also provide a permutation based testing procedure to detect multiple change points. We show the asymptotic properties of the permutation testing based procedure. The advantage of our approach is demonstrated using the mortality data of Seoul, Korea from January, 2000 to December, 2007. On the second topic, we propose two semiparametric single index models for spatially correlated data. One additively separates the nonparametric function and spatially correlated random effects, while the other does not separate the nonparametric function and spatially correlated random effects. We estimate these two models using two algorithms based on Markov Chain Expectation Maximization algorithm. Our approaches are compared using simulations, suggesting that the semiparametric single index nonadditive model provides more accurate estimates of spatial correlation. The advantage of our approach is demonstrated using the mortality data of six cities, Korea from January, 2000 to December, 2007. The third topic involves proposing two semiparametric single index models for spatially and temporally correlated data. Our first model has the nonparametric function which can separate from spatially and temporally correlated random effects. We refer it to "semiparametric spatio-temporal separable single index model (SSTS-SIM)", while the second model does not separate the nonparametric function from spatially correlated random effects but separates the time random effects. We refer our second model to "semiparametric nonseparable single index model (SSTN-SIM)". Two algorithms based on Markov Chain Expectation Maximization algorithm are introduced to simultaneously estimate parameters, spatial effects, and times effects. The proposed models are then applied to the mortality data of six major cities in Korea. Our results suggest that SSTN-SIM is more flexible than SSTS-SIM because it can estimate various nonparametric functions while SSTS-SIM enforces the similar nonparametric curves. SSTN-SIM also provides better estimation and prediction.
Ph. D.
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Ohinata, Ren Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan [Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] [Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1043515593/34.

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Ohinata, Ren [Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] [Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1043515593/34.

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Ohinata, Ren [Verfasser], Stefan [Akademischer Betreuer] Sperlich, Stephan [Akademischer Betreuer] Klasen, and Thomas [Akademischer Betreuer] Kneib. "Three Essays on Application of Semiparametric Regression: Partially Linear Mixed Effects Model and Index Model / Ren Ohinata. Gutachter: Stefan Sperlich ; Stephan Klasen ; Thomas Kneib. Betreuer: Stefan Sperlich." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://nbn-resolving.de/urn:nbn:de:gbv:7-webdoc-3831-5.

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Books on the topic "Partially linear single index"

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Ferraty, Frédéric, and Philippe Vieu. Kernel Regression Estimation for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.4.

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This article provides an overview of recent nonparametric and semiparametric advances in kernel regression estimation for functional data. In particular, it considers the various statistical techniques based on kernel smoothing ideas that have recently been developed for functional regression estimation problems. The article first examines nonparametric functional regression modelling before discussing three popular functional regression estimates constructed by means of kernel ideas, namely: the Nadaraya-Watson convolution kernel estimate, the kNN functional estimate, and the local linear functional estimate. Uniform asymptotic results are then presented. The article proceeds by reviewing kernel methods in semiparametric functional regression such as single functional index regression and partial linear functional regression. It also looks at the use of kernels for additive functional regression and concludes by assessing the impact of kernel methods on practical real-data analysis involving functional (curves) datasets.
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Ferraty, Frédéric, and Philippe Vieu. A Unifying Classification for Functional Regression Modeling. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.1.

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This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.
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Book chapters on the topic "Partially linear single index"

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Rachdi, Mustapha, Mohamed Alahiane, Idir Ouassou, and Philippe Vieu. "Generalized Functional Partially Linear Single-index Models." In Functional and High-Dimensional Statistics and Related Fields, 221–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47756-1_29.

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Habyarimana, Ephrem, and Sofia Michailidou. "Genomic Prediction and Selection in Support of Sorghum Value Chains." In Big Data in Bioeconomy, 207–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_16.

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AbstractGenomic prediction and selection models (GS) were deployed as part of DataBio project infrastructure and solutions. The work addressed end-user requirements, i.e., the need for cost-effectiveness of the implemented technologies, simplified breeding schemes, and shortening the time to cultivar development by selecting for genetic merit. Our solutions applied genomic modelling in order to sustainably improve productivity and profits. GS models were implemented in sorghum crop for several breeding scenarios. We fitted the best linear unbiased predictions data using Bayesian ridge regression, genomic best linear unbiased predictions, Bayesian least absolute shrinkage and selection operator, and BayesB algorithms. The performance of the models was evaluated using Monte Carlo cross-validation with 70% and 30%, respectively, as training and validation sets. Our results show that genomic models perform comparably with traditional methods under single environments. Under multiple environments, predicting non-field evaluated lines benefits from borrowing information from lines that were evaluated in other environments. Accounting for environmental noise and other factors, also this model gave comparable accuracy with traditional methods, but higher compared to the single environment model. The GS accuracy was comparable in genomic selection index, aboveground dry biomass yield and plant height, while it was lower for the dry mass fraction of the fresh weight. The genomic selection model performances obtained in our pilots are high enough to sustain sorghum breeding for several traits including antioxidants production and allow important genetic gains per unit of time and cost.
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"Optimal control of linear stochastic systems with an exponential-of-integral performance index." In Stochastic Control of Partially Observable Systems, 53–73. Cambridge University Press, 1992. http://dx.doi.org/10.1017/cbo9780511526503.004.

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Panja, Soma. "Heuristic Optimization of Portfolio Considering Sharpe's Single Index Model." In Metaheuristic Approaches to Portfolio Optimization, 179–97. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8103-1.ch008.

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Selection of weights of the selected securities in the portfolio is a cumbersome job for any investor. The famous nonlinear Sharpe's single index model has been simplified with a linear solution and the risk-taking propensity of the investors have been taken into consideration in the simplified formulation. The coefficient of optimism is included to observe the effect of risk-taking propensity in the portfolio selection. After the empirical analysis it is found that heuristically an investor can reach near to the optimum solution. For empirical analysis 126 months data have been considered of NSE Bank Index. To reduce the volatility of the data the whole period again has been divided into two parts each of 63 months duration, and separately the data pertaining to the three periods have been considered for calculation. The city block distance is used to calculate the nearness between the optimum solutions and the heuristic solutions.
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Scano, Alessandro, Marco Caimmi, Andrea Chiavenna, Matteo Malosio, and Lorenzo Molinari Tosatti. "A Kinect-Based Biomechanical Assessment of Neurological Patients' Motor Performances for Domestic Rehabilitation." In Advances in Medical Technologies and Clinical Practice, 252–79. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9740-9.ch013.

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Stroke is one of the main causes of disability in Western countries. Damaged brain areas are not able to provide the fine-tuned muscular control typical of human upper-limbs, resulting in many symptoms that affect consistently patients' daily-life activities. Neurological rehabilitation is a multifactorial process that aims at partially restoring the functional properties of the impaired limbs, taking advantage of neuroplasticity, i.e. the capability of re-aggregating neural networks in order to repair and substitute the damaged neural circuits. Recently, many virtual reality-based, robotic and exoskeleton approaches have been developed to exploit neuroplasticity and help conventional therapies in clinic. The effectiveness of such methods is only partly demonstrated. Patients' performances and clinical courses are assessed via a variety of complex and expensive sensors and time-consuming techniques: motion capture systems, EMG, EEG, MRI, interaction forces with the devices, clinical scales. Evidences show that benefits are proportional to treatment duration and intensity. Clinics can provide intensive assistance just for a limited amount of time. Thus, in order to preserve the benefits and increase them in time, the rehabilitative process should be continued at home. Simplicity, easiness of use, affordability, reliability and capability of storing logs of the rehabilitative sessions are the most important requirements in developing devices to allow and facilitate domestic rehabilitation. Tracking systems are the primary sources of information to assess patients' motor performances. While expensive and sophisticated techniques can investigate neuroplasticity, neural activation (fMRI) and muscle stimulation patterns (EMG), the kinematic assessment is fundamental to provide basic but essential quantitative evaluations as range of motion, motor control quality and measurements of motion abilities. Microsoft Kinect and Kinect One are programmable and affordable tracking sensors enabling the measurement of the positions of human articular centers. They are widely used in rehabilitation, mainly for interacting with virtual environments and videogames, or training motor primitives and single joints. In this paper, the authors propose a novel use of the Kinect and Kinect One sensors in a medical protocol specifically developed to assess the motor control quality of neurologically impaired people. It is based on the evaluation of clinically meaningful synthetic performance indexes, derived from previously developed experiences in upper-limb robotic treatments. The protocol provides evaluations taking into account kinematics (articular clinical angles, velocities, accelerations), dynamics (shoulder torque and shoulder effort index), motor and postural control quantities (normalized jerk of the wrist, coefficient of periodicity, center of mass displacement). The Kinect-based platform performance evaluation was off-line compared with the measurements obtained with a marker-based motion tracking system during the execution of reaching tasks against gravity. Preliminary results based on the Kinect sensor suggest its efficacy in clustering healthy subjects and patients according to their motor performances, despite the less sensibility in respect to the marker-based system used for comparison. A software library to evaluate motor performances has been developed by the authors, implemented in different programming languages and is available for on-line use during training/evaluation sessions (Figure 1). The Kinect sensor coupled with the developed computational library is proposed as an assessment technology during domestic rehabilitation therapies with on-line feedback, enabled by an application featuring tracking, graphical representation and data logging. An experimental campaign is under development on post-stroke patients with the Kinect-One sensor. Preliminary results on patients with different residual functioning and level of impairment indicate the capability of the whole system in discriminating motor performances.
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Scano, Alessandro, Marco Caimmi, Andrea Chiavenna, Matteo Malosio, and Lorenzo Molinari Tosatti. "A Kinect-Based Biomechanical Assessment of Neurological Patients' Motor Performances for Domestic Rehabilitation." In Robotic Systems, 811–37. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch042.

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Stroke is one of the main causes of disability in Western countries. Damaged brain areas are not able to provide the fine-tuned muscular control typical of human upper-limbs, resulting in many symptoms that affect consistently patients' daily-life activities. Neurological rehabilitation is a multifactorial process that aims at partially restoring the functional properties of the impaired limbs, taking advantage of neuroplasticity, i.e. the capability of re-aggregating neural networks in order to repair and substitute the damaged neural circuits. Recently, many virtual reality-based, robotic and exoskeleton approaches have been developed to exploit neuroplasticity and help conventional therapies in clinic. The effectiveness of such methods is only partly demonstrated. Patients' performances and clinical courses are assessed via a variety of complex and expensive sensors and time-consuming techniques: motion capture systems, EMG, EEG, MRI, interaction forces with the devices, clinical scales. Evidences show that benefits are proportional to treatment duration and intensity. Clinics can provide intensive assistance just for a limited amount of time. Thus, in order to preserve the benefits and increase them in time, the rehabilitative process should be continued at home. Simplicity, easiness of use, affordability, reliability and capability of storing logs of the rehabilitative sessions are the most important requirements in developing devices to allow and facilitate domestic rehabilitation. Tracking systems are the primary sources of information to assess patients' motor performances. While expensive and sophisticated techniques can investigate neuroplasticity, neural activation (fMRI) and muscle stimulation patterns (EMG), the kinematic assessment is fundamental to provide basic but essential quantitative evaluations as range of motion, motor control quality and measurements of motion abilities. Microsoft Kinect and Kinect One are programmable and affordable tracking sensors enabling the measurement of the positions of human articular centers. They are widely used in rehabilitation, mainly for interacting with virtual environments and videogames, or training motor primitives and single joints. In this paper, the authors propose a novel use of the Kinect and Kinect One sensors in a medical protocol specifically developed to assess the motor control quality of neurologically impaired people. It is based on the evaluation of clinically meaningful synthetic performance indexes, derived from previously developed experiences in upper-limb robotic treatments. The protocol provides evaluations taking into account kinematics (articular clinical angles, velocities, accelerations), dynamics (shoulder torque and shoulder effort index), motor and postural control quantities (normalized jerk of the wrist, coefficient of periodicity, center of mass displacement). The Kinect-based platform performance evaluation was off-line compared with the measurements obtained with a marker-based motion tracking system during the execution of reaching tasks against gravity. Preliminary results based on the Kinect sensor suggest its efficacy in clustering healthy subjects and patients according to their motor performances, despite the less sensibility in respect to the marker-based system used for comparison. A software library to evaluate motor performances has been developed by the authors, implemented in different programming languages and is available for on-line use during training/evaluation sessions (Figure 1). The Kinect sensor coupled with the developed computational library is proposed as an assessment technology during domestic rehabilitation therapies with on-line feedback, enabled by an application featuring tracking, graphical representation and data logging. An experimental campaign is under development on post-stroke patients with the Kinect-One sensor. Preliminary results on patients with different residual functioning and level of impairment indicate the capability of the whole system in discriminating motor performances.
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Mondal, Shyamal Kumar. "Some Studies in Multi-Storage Inventory System." In Advances in Logistics, Operations, and Management Science, 176–200. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9888-8.ch009.

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In this chapter, a multi-storage inventory system has been considered to develop a deterministic inventory model in finite planning horizon. Realistically, it is shown that due to large stock and insufficient space of existing own warehouse (OW); excess items are stored in single rented warehouse (RW). Due to different preserving facilities and storage environment, inventory holding cost is considered to be different in different warehouses. Here, the replenishment cycle lengths are of equal length, the demand rate is a continuous linear increasing function of time and partially backlogged shortages are allowed in all cycles. In each cycle, the replenishment cost is assumed to be dependent linearly on lot size and the stocks of RW are also transported to OW in continuous release pattern. The model is formulated as a constrained non-linear mixed integer cost objective function under single management. Finally, results with a sensitivity analysis have been shown with the help of a real coded GA.
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Teel, A. R. "USING SATURATION TO STABILIZE A CLASS OF SINGLE-INPUT PARTIALLY LINEAR COMPOSITE SYSTEMS**Research supported in part by the Army under grants ARO DAAL-88-K-0106 and DAAL-91-G-0191, and NASA under grant NAG2-243." In Nonlinear Control Systems Design 1992, 379–84. Elsevier, 1993. http://dx.doi.org/10.1016/b978-0-08-041901-5.50067-1.

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Conference papers on the topic "Partially linear single index"

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Xia, Yafeng, and Erzhong Chang. "Variable Selection for Partial Linear Single-Index Model with M-Estimation." In 2016 International Conference on Applied Mathematics, Simulation and Modelling. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/amsm-16.2016.40.

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Samuel, Simon, and B. Sundar Rajan. "Optimal Linear Error-Correcting Index Codes for Single-Prior Index-Coding with Side Information." In 2017 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2017. http://dx.doi.org/10.1109/wcnc.2017.7925745.

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Ambadi, Niranjana, and B. Sundar Rajan. "Optimal scalar linear codes for single unicast neighboring interference symmetric index coding." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7997269.

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Husseien Al-Nuaimy, Louay A. "Muscat securities market index (MSM30) prediction using Single Layer LInear Counterpropagation (SLLIC) neural network." In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, 2016. http://dx.doi.org/10.1109/icbdsc.2016.7460366.

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Bandyopadhyay, Pratap Kumar, and S. N. Sarkar. "Propagation characteristics of single mode step index linear and non-linear optical fiber involving improved Lorentzian approximation for the fundamental mode." In 2012 International Conference on Computers and Devices for Communication (CODEC). IEEE, 2012. http://dx.doi.org/10.1109/codec.2012.6509310.

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Ramachandran, Gopakumar, Ankit Kumar Dutta, Harish Durairaj, and Swetaprovo Chaudhuri. "On the Interaction of Swirling Flames in a Lean Premixed Combustor." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90679.

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Abstract Premixed or partially premixed swirling flames are widely used in gas turbine applications because of their compactness, high ignition efficiency, low NOx emissions and flame stability. A typical annular combustor consists of about eighteen to twenty-two swirling flames which interact (directly or indirectly) with their immediate neighbors even during stable operation. These interactions significantly alter the flow and flame topologies thereby bringing in some discrepancies between the single nozzle (SN) and multi nozzle (MN), ignition, emission, pattern factor and Flame Transfer Functions (FTF) characteristics. For example, in MN configurations, application of a model based on SN FTF data could lead to erroneous conclusions. Due to the complexities involved in this problem in terms of size, thermal power, cost, optical accessibility etc., a limited amount of experimental studies has been reported, that too on scaled down models with reduced number of nozzles. Here, we present a detailed experimental study on the behavior of three interacting swirl premixed flames, arranged in-line in an optically accessible hollow cuboid test section, which closely resembles a three-cup sector of an annular gas turbine combustor with very large radius. Multiple configurations with various combinations of swirl levels between the adjacent nozzles and the associated flame and flow topologies have been studied. Spatio-temporal information of the heat release rate obtained from OH* chemiluminescence imaging was used along with the acoustic pressure signatures to compute the Rayleigh index so as to identify the regions within the flame that pumps energy into the self-excited thermoacoustic instability modes. It was found that the structure of the flame-flame interaction regions plays a dominant role in the resulting thermoacoustic instability. To resolve the flow and reactive species field distributions in the interacting flames, two-dimensional, three component Stereoscopic Particle Image Velocimetry (SPIV) and Planar Laser Induced Fluorescence (PLIF) of hydroxyl radical was applied to all the test conditions. Significant differences in the flow structures among the different configurations were observed. Simultaneous OH-PLIF and SPIV techniques were also utilized to track the flame front, from which the curvature and stretch rates were computed. Flame surface density which is defined as the mean surface area of the reaction zone per unit volume is also computed for all the test cases. These measurements and analyses elucidate the structure of the interaction regions, their unique characteristics and possible role in thermoacoustic instability.
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Cano-Andrade, Sergio, Michael R. von Spakovsky, Alejandro Fuentes, Chiara Lo Prete, Benjamin F. Hobbs, and Lamine Mili. "Multi-Objective Optimization for the Sustainable-Resilient Synthesis/Design/Operation of a Power Network Coupled to Distributed Power Producers via Microgrids." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87950.

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In this paper, multiobjective optimization is proposed for evaluating the sustainable synthesis/design and operation of sets of small renewable and non-renewable energy production technologies coupled to power production/transmission/distribution networks via microgrids. The optimization is conducted over a quasi-stationary twenty four hour, winter period. Partial load behavior of the generators is included by introducing non-linear functions for efficiency, costs and emissions as a function of the electricity generated by each technology. A new index for resiliency is included in the multiobjective optimization model in order to account for the capacity of the power network system to self-recover to a new normal state after experiencing an unanticipated catastrophic event. Since sustainability/resiliency indices are typically not expressed in the same units, fuzzy logic and an explicit set of weighting factor methods are employed to calculate a composite sustainability-resiliency index. Results indicate for the particular problem posed that the inclusion of microgrids into the network leads to a better overall network efficiency, a reduction in life cycle costs, and an improved network resiliency. On the other hand, total life SO2 emissions and network reliability are not improved for this particular case when microgrids are included.
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Weber, Emanuel, Dietmar Puchberger-Enengl, and Michael J. Vellekoop. "In-Line Characterization of Micro-Droplets Based on Partial Light Reflection at the Solid-Liquid Interface." In ASME 2012 10th International Conference on Nanochannels, Microchannels, and Minichannels collocated with the ASME 2012 Heat Transfer Summer Conference and the ASME 2012 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icnmm2012-73155.

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In this paper a novel optofluidic setup, fabricated on a single layer device for in-line droplet characterization yielding droplet-size, droplet-frequency, and optical properties with compatibility for full on-chip integration is presented. Chips were fabricated using a simple, fast, and cost effective technology. A T-junction arrangement on the device is used for droplet generation. The optical part of the setup consists of an external light source, external silicon photodetectors, integrated air micro-lenses, and an integrated waveguide. The design makes use of partial light reflection/transmission at the solid-liquid interface to count, size, and discriminate droplets based on their optical properties. When passing the interrogation point, droplets having a lower refractive index as the continuous phase result in light deflections. Both, reflected and transmitted light, are detected simultaneously. A relation of those two signals is then used for the analysis resulting in a continuously stable signal. The generated pattern is unique for different droplets and can be exploited for droplet characterization. Using this arrangement, droplets of de-ionized water (DI) were counted at frequencies of up to 320 droplets per second. In addition, information about the droplet sizes and their variations could be obtained. Finally, 5 mol/L CaCl2 and DI droplets, having different indices of refraction were examined and could clearly be discriminated based on their unique reflected and transmitted light signals. This principle can be applied for the detection of dissolved molecules in droplets as long as they influence the index of refraction. Examples could be the determination of DNA or protein content in the droplet.
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Sander, Aljoscha, Andreas F. Haselsteiner, Kader Barat, Michael Janssen, Stephan Oelker, Jan-Hendrik Ohlendorf, and Klaus-Dieter Thoben. "Relative Motion During Single Blade Installation: Measurements From the North Sea." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18935.

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Abstract During single blade installation in offshore wind farms, relative motion between nacelle and blade root due to wind and wave excitation pose a significant challenge. Wave excitation can be modelled considerably well by employing state-of-the-art simulation tools and can, therefore, be included in installation planning. Other phenomena, such as flow-induced vibrations are hard to capture and hence challenging to account for when defining installation procedures and limitations. Here, we present measurements conducted during the installation of an offshore wind farm consisting of multi-megawatt turbines installed on monopile foundations in the North Sea. A custom-built sensor capturing linear & angular acceleration and GPS-data was deployed atop the nacelle. Both partially and fully assembled turbines displayed complex oscillation orbits, swiftly changing amplitude and direction. Mean nacelle deflection correlated strongly with significant wave height as well as mean wind speed. As wind speed and significant wave height showed a strong correlation as well, it is difficult to discern which load drives the observed relative motions. While wind loads are significantly smaller than wave loads on partially assembled turbines under installation conditions, additional momentum induced by vortex shedding may prove sufficient to cause the observed effects.
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Alshaikh, Rana, Zied Bouraoui, and Steven Schockaert. "Hierarchical Linear Disentanglement of Data-Driven Conceptual Spaces." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/494.

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Conceptual spaces are geometric meaning representations in which similar entities are represented by similar vectors. They are widely used in cognitive science, but there has been relatively little work on learning such representations from data. In particular, while standard representation learning methods can be used to induce vector space embeddings from text corpora, these differ from conceptual spaces in two crucial ways. First, the dimensions of a conceptual space correspond to salient semantic features, known as quality dimensions, whereas the dimensions of learned vector space embeddings typically lack any clear interpretation. This has been partially addressed in previous work, which has shown that it is possible to identify directions in learned vector spaces which capture semantic features. Second, conceptual spaces are normally organised into a set of domains, each of which is associated with a separate vector space. In contrast, learned embeddings represent all entities in a single vector space. Our hypothesis in this paper is that such single-space representations are sub-optimal for learning quality dimensions, due to the fact that semantic features are often only relevant to a subset of the entities. We show that this issue can be mitigated by identifying features in a hierarchical fashion. Intuitively, the top-level features split the vector space into different domains, making it possible to subsequently identify domain-specific quality dimensions.
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Reports on the topic "Partially linear single index"

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Manninen, Terhikki, and Pauline Stenberg. Influence of forest floor vegetation on the total forest reflectance and its implications for LAI estimation using vegetation indices. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361379.

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Recently a simple analytic canopy bidirectional reflectance factor (BRF) model based on the spectral invariants theory was presented. The model takes into account that the recollision probability in the forest canopy is different for the first scattering than the later ones. Here this model is extended to include the forest floor contribution to the total forest BRF. The effect of the understory vegetation on the total forest BRF as well as on the simple ratio (SR) and the normalized difference (NDVI) vegetation indices is demonstrated for typical cases of boreal forest. The relative contribution of the forest floor to the total BRF was up to 69 % in the red wavelength range and up to 54 % in the NIR wavelength range. Values of SR and NDVI for the forest and the canopy differed within 10 % and 30 % in red and within 1 % and 10 % in the NIR wavelength range. The relative variation of the BRF with the azimuth and view zenith angles was not very sensitive to the forest floor vegetation. Hence, linear correlation of the modelled total BRF and the Ross-thick kernel was strong for dense forests (R2 > 0.9). The agreement between modelled BRF and satellite-based reflectance values was good when measured LAI, clumping index and leaf single scattering albedo values for a boreal forest were used as input to the model.
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