Relevant bibliographies by topics / Quadratic Loss Function

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

Academic literature on the topic 'Quadratic Loss Function'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Quadratic Loss Function"

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Shim, Jooyong, Malsuk Kim, and Kyungha Seok. "SVQR with asymmetric quadratic loss function." Journal of the Korean Data and Information Science Society 26, no. 6 (November 30, 2015): 1537–45. http://dx.doi.org/10.7465/jkdi.2015.26.6.1537.

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Moskowitz, Herbert, and Kwei Tang. "Bayesian Variables Acceptance-Sampling Plans: Quadratic Loss Function and Step-Loss Function." Technometrics 34, no. 3 (August 1992): 340. http://dx.doi.org/10.2307/1270040.

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Jin, Qiu, and Shao Gang Liu. "Research of Asymmetric Quality Loss Function with Triangular Distribution." Advanced Materials Research 655-657 (January 2013): 2331–34. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.2331.

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The asymmetric quality loss functions with the triangular distribution for determining the optimum process mean are studied. The condition of using the linear and quadratic asymmetric quality loss function in the model is considered. The eight mathematical models under an asymmetric quality loss function with the triangular distribution based on the analysis of the linear and quadratic asymmetric quality loss function are presented. Finally, the validity of models is verified by the examples.
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KARAL, Omer. "ECG Data Compression Using ε-insensitive Quadratic Loss Function." Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 22, no. 2 (April 18, 2018): 380. http://dx.doi.org/10.19113/sdufbed.82260.

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Shim, Joo-Yong, and Chang-Ha Hwang. "Support Vector Quantile Regression with Weighted Quadratic Loss Function." Communications for Statistical Applications and Methods 17, no. 2 (March 31, 2010): 183–91. http://dx.doi.org/10.5351/ckss.2010.17.2.183.

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Lin, Shao-Bo, Jinshan Zeng, and Xiangyu Chang. "Learning Rates for Classification with Gaussian Kernels." Neural Computation 29, no. 12 (December 2017): 3353–80. http://dx.doi.org/10.1162/neco_a_00968.

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This letter aims at refined error analysis for binary classification using support vector machine (SVM) with gaussian kernel and convex loss. Our first result shows that for some loss functions, such as the truncated quadratic loss and quadratic loss, SVM with gaussian kernel can reach the almost optimal learning rate provided the regression function is smooth. Our second result shows that for a large number of loss functions, under some Tsybakov noise assumption, if the regression function is infinitely smooth, then SVM with gaussian kernel can achieve the learning rate of order [Formula: see text], where [Formula: see text] is the number of samples.
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Choi, Hoo-Gon R., Man-Hee Park, and Erik Salisbury. "Optimal Tolerance Allocation With Loss Functions." Journal of Manufacturing Science and Engineering 122, no. 3 (September 1, 1999): 529–35. http://dx.doi.org/10.1115/1.1285918.

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The tolerance allocation problem is formulated as a nonlinear integer model under the constraints of process capability. The problem is to minimize the sum of machining cost and quality loss. When the statistical tolerance limits are used and Taguchi’s quadratic loss function is defined, the total cost function becomes a convex function for a given feature and process. A complex search method is used to solve the model and ensure the optimal tolerance allocation. Numerical examples are presented demonstrating successful model implementation for both linear and nonlinear design functions. [S1087-1357(00)02602-2]
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Weinberger, Nir, and Ofer Shayevitz. "On the Optimal Boolean Function for Prediction Under Quadratic Loss." IEEE Transactions on Information Theory 63, no. 7 (July 2017): 4202–17. http://dx.doi.org/10.1109/tit.2017.2686437.

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Reyad, Hesham, and Soha Othman Ahmed. "E-Bayesian analysis of the Gumbel type-ii distribution under type-ii censored scheme." International Journal of Advanced Mathematical Sciences 3, no. 2 (September 5, 2015): 108. http://dx.doi.org/10.14419/ijams.v3i2.5093.

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<p>This paper seeks to focus on Bayesian and E-Bayesian estimation for the unknown shape parameter of the Gumbel type-II distribution based on type-II censored samples. These estimators are obtained under symmetric loss function [squared error loss (SELF))] and various asymmetric loss functions [LINEX loss function (LLF), Degroot loss function (DLF), Quadratic loss function (QLF) and minimum expected loss function (MELF)]. Comparisons between the E-Bayesian estimators with the associated Bayesian estimators are investigated through a simulation study.</p>
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Reyad, Hesham, and Soha Othman Ahmed. "Bayesian and E-Bayesian estimation for the Kumaraswamy distribution based on type-ii censoring." International Journal of Advanced Mathematical Sciences 4, no. 1 (March 5, 2016): 10. http://dx.doi.org/10.14419/ijams.v4i1.5750.

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<p>This paper introduces the Bayesian and E-Bayesian estimation for the shape parameter of the Kumaraswamy distribution based on type-II censored schemes. These estimators are derived under symmetric loss function [squared error loss (SELF))] and three asymmetric loss functions [LINEX loss function (LLF), Degroot loss function (DLF) and Quadratic loss function (QLF)]. Monte Carlo simulation is performed to compare the E-Bayesian estimators with the associated Bayesian estimators in terms of Mean Square Error (MSE).</p>
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Dissertations / Theses on the topic "Quadratic Loss Function"

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Guo, Mengmeng. "Generalized quantile regression." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2012. http://dx.doi.org/10.18452/16569.

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Die generalisierte Quantilregression, einschließlich der Sonderfälle bedingter Quantile und Expektile, ist insbesondere dann eine nützliche Alternative zum bedingten Mittel bei der Charakterisierung einer bedingten Wahrscheinlichkeitsverteilung, wenn das Hauptinteresse in den Tails der Verteilung liegt. Wir bezeichnen mit v_n(x) den Kerndichteschätzer der Expektilkurve und zeigen die stark gleichmßige Konsistenzrate von v-n(x) unter allgemeinen Bedingungen. Unter Zuhilfenahme von Extremwerttheorie und starken Approximationen der empirischen Prozesse betrachten wir die asymptotischen maximalen Abweichungen sup06x61 |v_n(x) − v(x)|. Nach Vorbild der asymptotischen Theorie konstruieren wir simultane Konfidenzb änder um die geschätzte Expektilfunktion. Wir entwickeln einen funktionalen Datenanalyseansatz um eine Familie von generalisierten Quantilregressionen gemeinsam zu schätzen. Dabei gehen wir in unserem Ansatz davon aus, dass die generalisierten Quantile einige gemeinsame Merkmale teilen, welche durch eine geringe Anzahl von Hauptkomponenten zusammengefasst werden können. Die Hauptkomponenten sind als Splinefunktionen modelliert und werden durch Minimierung eines penalisierten asymmetrischen Verlustmaßes gesch¨atzt. Zur Berechnung wird ein iterativ gewichteter Kleinste-Quadrate-Algorithmus entwickelt. Während die separate Schätzung von individuell generalisierten Quantilregressionen normalerweise unter großer Variablit¨at durch fehlende Daten leidet, verbessert unser Ansatz der gemeinsamen Schätzung die Effizienz signifikant. Dies haben wir in einer Simulationsstudie demonstriert. Unsere vorgeschlagene Methode haben wir auf einen Datensatz von 150 Wetterstationen in China angewendet, um die generalisierten Quantilkurven der Volatilität der Temperatur von diesen Stationen zu erhalten
Generalized quantile regressions, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We denote $v_n(x)$ as the kernel smoothing estimator of the expectile curves. We prove the strong uniform consistency rate of $v_{n}(x)$ under general conditions. Moreover, using strong approximations of the empirical process and extreme value theory, we consider the asymptotic maximal deviation $\sup_{ 0 \leqslant x \leqslant 1 }|v_n(x)-v(x)|$. According to the asymptotic theory, we construct simultaneous confidence bands around the estimated expectile function. We develop a functional data analysis approach to jointly estimate a family of generalized quantile regressions. Our approach assumes that the generalized quantiles share some common features that can be summarized by a small number of principal components functions. The principal components are modeled as spline functions and are estimated by minimizing a penalized asymmetric loss measure. An iteratively reweighted least squares algorithm is developed for computation. While separate estimation of individual generalized quantile regressions usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 150 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations
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吳豐文. "The Optimal Process Mean with Truncated Asymmetrical Quadratic Loss Function." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/94294666497854349605.

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碩士
逢甲大學
工業工程學系
88
For the unbalanced tolerance design with asymmetrical quality loss function, the expected quality loss could not be minimized if the process mean is set at the target value. The process mean should be shifted a little from the target value such that expected quality loss is minimized. The purpose of this thesis is to determine the optimal process mean for the truncated quadratic asymmetrical loss function. The sensitivity analyses of the parameters are also discussed. Several tables are provided for the optimal process mean of this model.
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Chang, Chung-Chi, and 陳仲麒. "The Economic Design of Asymmetric Cumulative Sum Charts —An Application of Quadratic Loss Function." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/42702887571276841320.

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碩士
東海大學
工業工程學系
87
The control chart is an important tool in process monitering, and it helps operators to evaluate the process is in control or not. Cumulative Sum Chart which is one of the modification of Shewhart control charts are sensitive in detecting the variations of process and are suitable for the high-technology industry nowadays. In this research, the Cumulative Sum Chart is used for supervising the process. An economic design of asymmetric cumulative sum chart which is based on Duncan’s[7] model have been developed. The Quadratic loss function which was defined by Taguchi[35] is utilized in the model developed as well. Thus, and it can distinguish "equivalent standard" from "equivalent quality" and is more appropriate to modern quality of evaluations and consumers oriented. It is assumed that there are two assignable causes exist in a process. These two assignable causes either enlarge or relieve the effect of shifting in a process, and are consistent with realities. When apply the same process parameters the single assignable cause model is a special example of the model developed.
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Fun, Yu Pen, and 方友平. "A second thought of the quadratic loss function of taguchi's quality engineering and its application." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/53413473513891718647.

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詹瑞伶. "Minimax estimation of the parameter for the Pareto distribution using progressively type II right censored sample under Quadratic and MLINEX loss function." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/81020110566080356251.

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Stroukalová, Marika. "Modelování systémů bonus - malus." Master's thesis, 2013. http://www.nusl.cz/ntk/nusl-324095.

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Title: Modelling Bonus - Malus Systems Author: Marika Stroukalová Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Lucie Mazurová, Ph.D., KPMS MFF UK Abstract: In this thesis we deal with bonus-malus tariff systems commonly used to adjust the a priori set premiums according to the individual claims during mo- tor third party liability insurance. The main aim of this thesis is to describe the standard model based on the Markov chain. For each bonus-malus class we also determine the relative premium ("relativity"). Another objective of this thesis is to find optimal values for the relativities taking into account the a priori set premiums. We apply the theoretical model based on the stationary distribu- tion of bonus-malus classes on real-world data and a particular real bonus-malus system used in the Czech Republic. The empirical part of this thesis compares the optimal and the real relativities and assesses the suitability of the chosen theoretical model for the particular bonus-malus system. Keywords: bonus-malus system, a priori segmentation, stationary distribution, relativity, quadratic loss function 1
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Craciun, Geanina. "Fonctions de perte en actuariat." Thèse, 2009. http://hdl.handle.net/1866/7878.

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Wang, Kai. "Heuristics for Inventory Systems Based on Quadratic Approximation of L-Natural-Convex Value Functions." Diss., 2014. http://hdl.handle.net/10161/8777.

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We propose an approximation scheme for single-product periodic-review inventory systems with L-natural-convex structure. We lay out three well-studied inventory models, namely the lost-sales system, the perishable inventory system, and the joint inventory-pricing problem. We approximate the value functions for these models by the class of L-natural-convex quadratic functions, through the technique of linear programming approach to approximate dynamic programming. A series of heuristics are derived based on the quadratic approximation, and their performances are evaluated by comparison with existing heuristics. We present the numerical results and show that our heuristics outperform the benchmarks for majority of cases and scale well with long lead times. In this dissertation we also discuss the alternative strategies we have tried but with unsatisfactory result.


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Books on the topic "Quadratic Loss Function"

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Chadha, Jagjit S. Monetary policy loss functions: Two cheers for the quadratic. London: Bank of England, 1999.

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Chadha, Jagjit S. Monetary policy loss functions: Two cheers for the quadratic. London: Bank of England, 1999.

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Chadha, Jagjit S. Monetary policy loss functions: Two cheers for the quadratic. Cambridge: Department of Applied Economics, University of Cambridge, 1999.

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Book chapters on the topic "Quadratic Loss Function"

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Zhang, Defei, Xiangzhao Cui, Chun Li, and Jianxin Pan. "Estimation of Covariance Matrix with ARMA Structure Through Quadratic Loss Function." In Contemporary Experimental Design, Multivariate Analysis and Data Mining, 227–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46161-4_15.

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Marti, Kurt. "Stochastic Structural Optimization with Quadratic Loss Functions." In Stochastic Optimization Methods, 289–322. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46214-0_7.

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Cudney, Elizabeth, and Bonnie Paris. "Applying the Quality Loss Function in Healthcare." In Encyclopedia of Healthcare Information Systems, 102–7. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-889-5.ch014.

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Using the quadratic loss function is one way to quantify a fundamental value in the provision of health care services: we must provide the best care and best service to every patient, every time. Sole reliance on specification limits leads to a focus on “acceptable” performance rather than “ideal” performance. This paper presents the application of the quadratic loss function to quantify improvement opportunities in the healthcare industry.
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Lu, Zhao, and Jing Sun. "Soft-constrained Linear Programming Support Vector Regression for Nonlinear Black-box Systems Identification." In Artificial Intelligence for Advanced Problem Solving Techniques, 137–47. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-705-8.ch005.

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As an innovative sparse kernel modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In the support vector regression, Vapnik developed the -insensitive loss function as a trade-off between the robust loss function of Huber and one that enables sparsity within the support vectors. The use of support vector kernel expansion provides us a potential avenue to represent nonlinear dynamical systems and underpin advanced analysis. However, in the standard quadratic programming support vector regression (QP-SVR), its implementation is more computationally expensive and enough model sparsity can not be guaranteed. In an attempt to surmount these drawbacks, this article focus on the application of soft-constrained linear programming support vector regression (LP-SVR) in nonlinear black-box systems identification, and the simulation results demonstrates that the LP-SVR is superior to QP-SVR in model sparsity and computational efficiency
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Conference papers on the topic "Quadratic Loss Function"

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Weinberger, Nir, and Ofer Shayevitz. "On the optimal boolean function for prediction under quadratic loss." In 2016 IEEE International Symposium on Information Theory (ISIT). IEEE, 2016. http://dx.doi.org/10.1109/isit.2016.7541348.

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Zhang, Jian, Lionel Fillatre, and Igor Nikiforov. "Better Bounds for Bayesian Multiple Test with Quadratic Loss Function." In 2015 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII). IEEE, 2015. http://dx.doi.org/10.1109/iciicii.2015.140.

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Huili, Ma, and Wen Ping. "Determining the optimal parameters under quadratic asymmetric loss function with general distribution." In 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018. http://dx.doi.org/10.1109/ccdc.2018.8408191.

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Kaining Yang, Kunbo Wang, and Chen Feng. "Finite-sample analysis of iterate averaging method for stochastic approximation with quadratic loss function." In 2017 51st Annual Conference on Information Sciences and Systems (CISS). IEEE, 2017. http://dx.doi.org/10.1109/ciss.2017.7926146.

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Stockman, Mel, Randa S. El Ramli, Mariette Awad, and Rabih Jabr. "An asymmetrical and quadratic Support Vector Regression loss function for Beirut short term load forecast." In 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2012. http://dx.doi.org/10.1109/icsmc.2012.6377800.

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Al-Me'raj, Ismail, Yahya Cinar, and S. O. Duffuaa. "Determining economic manufacturing quantity, the optimum process parameters based on Taguchi quadratic quality loss function under rectifying inspection plan." In 2011 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2011. http://dx.doi.org/10.1109/ieem.2011.6118136.

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Al-Me'raj, Ismail, Yahya Cinar, and S. O. Duffuaa. "Determining economic manufacturing quantity, the optimum process parameters based on Taguchi quadratic quality loss function under rectifying inspection plan." In 2011 IEEE MTT-S International Microwave Workshop Series on Innovative Wireless Power Transmission: Technologies, Systems, and Applications (IMWS 2011). IEEE, 2011. http://dx.doi.org/10.1109/imws.2011.6115391.

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Kurdi, Mohammad, Shahin Nudehi, and Gregory Scott Duncan. "Tailoring Plate Thickness of a Helmholtz Resonator for Improved Sound Attenuation." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59302.

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A Helmholtz resonator with flexible plate attenuates noise in exhaust ducts, and the transmission loss function quantifies the amount of filtered noise at a desired frequency. In this work the transmission loss is maximized (optimized) by allowing the resonator end plate thickness to vary for two cases: 1) a non-optimized baseline resonator, and 2) a resonator with a uniform flexible endplate that was previously optimized for transmission loss and resonator size. To accomplish this, receptance coupling techniques were used to couple a finite element model of a varying thickness resonator end plate to a mass-spring-damper model of the vibrating air mass in the resonator. Sequential quadratic programming was employed to complete a gradient based optimization search. By allowing the end plate thickness to vary, the transmission loss of the non-optimized baseline resonator was improved significantly, 28 percent. However, the transmission loss of the previously optimized resonator for transmission loss and resonator size showed minimal improvement.
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Tang, Ziying, Lei Wang, Fan Yi, and Huacheng He. "An Optimized Thrust Allocation Algorithm for Dynamic Positioning System Based on RBF Neural Network." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18267.

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Abstract The thrust allocation of Dynamic Positioning System (DPS) equipped with multiple thrusters is usually formulated as an optimization problem. Hydrodynamic interaction effects such as thruster-thruster interaction results in thrust loss. This interaction is generally avoided by defining forbidden zones for some azimuth angles. However, it leads to a higher power consumption and stuck thrust angles. For the purpose of improving the traditional Forbidden Zone (FZ) method, this paper proposes an optimized thrust allocation algorithm based on Radial Basis Function (RBF) neural network and Sequential Quadratic Programming (SQP) algorithm, named RBF-SQP. The thrust coefficient is introduced to express the thrust loss which is then incorporated into the mathematical model to remove forbidden zones. Specifically, the RBF neural network is constructed to approximate the thrust efficiency function, and the SQP algorithm is selected to solve the nonlinear optimization problem. The training dataset of RBF neural network is obtained from the model test of thrust-thrust interaction. Numerical simulations for the dynamic positioning of a semi-submersible platform are conducted under typical operating conditions. The simulation results demonstrate that the demanded forces can be correctly distributed among available thrusters. Compared with the traditional methods, the proposed thrust allocation algorithm can achieve a lower power consumption. Moreover, the advantages of considering hydrodynamic interaction effects and utilizing a neural network for function fitting are also highlighted, indicating a practical application prospect of the optimized algorithm.
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Kody, A. A., and J. T. Scruggs. "Optimal Energy Harvesting From Impulse Trains Using Piezoelectric Transduction." In ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/smasis2014-7576.

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In applications of vibration energy harvesting to embedded wireless sensing, the available power and energy can be very low. This poses interesting challenges for technological feasibility if the parasitic losses in the electronics used to harvest this energy are prohibitive. In this study, we present a theory for the active control of power generation in energy harvesters in a manner which addresses and compensates for parasitic loss. We conduct the analysis in the context of a single-transducer piezoelectric bimorph cantilever beam subjected to a low-frequency impulse train. The power generation of the vibration energy harvester is maximized while considering mechanical losses, electrical losses, and the static power required to activate control intelligence and facilitate power-electronic conversion. It is shown that the optimal harvesting current can be determined through the use of linear quadratic optimal control techniques. The optimal harvesting time over which energy should be generated, following an impulse, is determined concurrently with the optimal feedback law. We show that this optimal harvesting time exhibits bifurcations as a function of the parameters characterizing the losses in the system.
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