Relevant bibliographies by topics / MOMENTS METHOD

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

Academic literature on the topic 'MOMENTS METHOD'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "MOMENTS METHOD"

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Jurczek, E. "Orthogonalized-moments method." Physical Review B 32, no. 6 (1985): 4208–11. http://dx.doi.org/10.1103/physrevb.32.4208.

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Morrison, Hugh, Matthew R. Kumjian, Charlotte P. Martinkus, Olivier P. Prat, and Marcus van Lier-Walqui. "A General N-Moment Normalization Method for Deriving Raindrop Size Distribution Scaling Relationships." Journal of Applied Meteorology and Climatology 58, no. 2 (2019): 247–67. http://dx.doi.org/10.1175/jamc-d-18-0060.1.

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AbstractA general drop size distribution (DSD) normalization method is formulated in terms of generalized power series relating any DSD moment to any number and combination of reference moments. This provides a consistent framework for comparing the variability of normalized DSD moments using different sets of reference moments, with no explicit assumptions about the DSD functional form (e.g., gamma). It also provides a method to derive any unknown moment plus an estimate of its uncertainty from one or more known moments, which is relevant to remote sensing retrievals and bulk microphysics schemes in weather and climate models. The approach is applied to a large dataset of disdrometer-observed and bin microphysics-modeled DSDs. As expected, the spread of normalized moments decreases as the number of reference moments is increased, quantified by the logarithmic standard deviation of the normalized moments, σ. Averaging σ for all combinations of reference moments and normalized moments of integer order 0–10, 42.9%, 81.3%, 93.7%, and 96.9% of spread are accounted for applying one-, two-, three-, and four-moment normalizations, respectively. Thus, DSDs can be well characterized overall using three reference moments, whereas adding a fourth reference moment contributes little independent information. The spread of disdrometer-observed DSD moments from uncertainty associated with drop count statistics generally lies between values of σ using two- and three-moment normalizations. However, this uncertainty has little impact on the derived DSD scaling relationships or σ when considered.
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Kliche, Donna V., Paul L. Smith, and Roger W. Johnson. "L-Moment Estimators as Applied to Gamma Drop Size Distributions." Journal of Applied Meteorology and Climatology 47, no. 12 (2008): 3117–30. http://dx.doi.org/10.1175/2008jamc1936.1.

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Abstract The traditional approach with experimental raindrop size data has been to use the method of moments in the fitting procedure to estimate the parameters for the raindrop size distribution function. However, the moment method is known to be biased and can have substantial errors. Therefore, the L-moment method, which is widely used by hydrologists, was investigated as an alternative. The L-moment method was applied, along with the moment and maximum likelihood methods, to samples taken from simulated gamma raindrop populations. A comparison of the bias and the errors involved in the L-moments, moments, and maximum likelihood procedures shows that, with samples covering the full range of drop sizes, L-moments and maximum likelihood outperform the method of moments. For small sample sizes the moment method gives a large bias and large error while the L-moment method gives results close to the true population values, outperforming even maximum likelihood results. Because the goal of this work is to understand the properties of the various fitting procedures, the investigation was expanded to include the effects of the absence of small drops in the samples (typical disdrometer minimum size thresholds are 0.3–0.5 mm). The results show that missing small drops (due to the instrumental constraint) can result in a large bias in the case of the L-moment and maximum likelihood fitting methods; this bias does not decrease much with increasing sample size. Because the very small drops have a negligible contribution to moments of order 2 or higher, the bias in the moment methods seems to be about the same as in the case of full samples. However, when moments of order less than 2 are needed (as in the case of modelers using moments 0 and 3), the moment method gives much larger bias. Therefore a modification of these methods is needed to handle the truncated-data situation.
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Headrick, Todd C. "A Characterization of Power Method Transformations throughL-Moments." Journal of Probability and Statistics 2011 (2011): 1–22. http://dx.doi.org/10.1155/2011/497463.

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Power method polynomial transformations are commonly used for simulating continuous nonnormal distributions with specified moments. However, conventional moment-based estimators can (a) be substantially biased, (b) have high variance, or (c) be influenced by outliers. In view of these concerns, a characterization of power method transformations byL-moments is introduced. Specifically, systems of equations are derived for determining coefficients for specifiedL-moment ratios, which are associated with standard normal and standard logistic-based polynomials of order five and three. Boundaries forL-moment ratios are also derived, and closed-formed formulae are provided for determining if a power method distribution has a valid probability density function. It is demonstrated thatL-moment estimators are nearly unbiased and have relatively small variance in the context of the power method. Examples of fitting power method distributions to theoretical and empirical distributions based on the method ofL-moments are also provided.
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Jiade Yuan, Changqing Gu, and Guodong Han. "Efficient Generation of Method of Moments Matrices Using Equivalent Dipole-Moment Method." IEEE Antennas and Wireless Propagation Letters 8 (2009): 716–19. http://dx.doi.org/10.1109/lawp.2009.2024337.

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Gholizadeh, Mahdieh, Mohammad Hossein Gholizadeh, Hossein Ghayoumi Zadeh, and Mostafa Danaeian. "The noise reduction of medical radiography images using fractional moments." Medical Journal of Tabriz University of Medical Sciences and Health Services 42, no. 6 (2021): 649–58. http://dx.doi.org/10.34172/mj.2021.005.

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Background: This paper presents a method to improve medical radiography images based on the use of statistical signal moments. Methods: In this paper, the image with noise is considered as a statistical signal, and the noise reduction is performed by using fractional moments. The fractional moment’s method, on the one hand, has a speed similar to the moment method, and, on the other hand, has not the limitations of the moment method, which sometimes achieves inaccurate results. The proposed method is ultimately examined on radiographic images (CT). Results: The information obtained from the fractional moments of the received signal is a criterion to estimate the noise parameters and the gray scales of the main image. One of the limitations of the proposed method is that the image should be sent several times, because in statistical discussions, we cannot make a decision with only one sample. The error of the proposed noise reduction method in terms of the number of times the original image was sent, is about 0.009, 0.0009, 0.0002, and 0.0001, for n = 3, n = 6, n = 9 and n = 14, respectively. Conclusion: The simulation results show that the proposed method is more effective than the most conventional noise reduction methods, both in the low signal to noise ratio and in terms of image quality, and is more powerful than the most notable noise removal methods in restoring the subtleties and image details.
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Dell'Aquila, Rosario. "Generalized Method of Moments." Journal of the American Statistical Association 101, no. 475 (2006): 1309–10. http://dx.doi.org/10.1198/jasa.2006.s120.

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Benoit, C., E. Royer, and G. Poussigue. "The spectral moments method." Journal of Physics: Condensed Matter 4, no. 12 (1992): 3125–52. http://dx.doi.org/10.1088/0953-8984/4/12/010.

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Mat Jan, Nur Amalina, Ani Shabri, and Ruhaidah Samsudin. "Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter." Journal of Water and Climate Change 11, no. 4 (2019): 966–79. http://dx.doi.org/10.2166/wcc.2019.055.

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Abstract Non-stationary flood frequency analysis (NFFA) plays an important role in addressing the issue of the stationary assumption (independent and identically distributed flood series) that is no longer valid in infrastructure-designed methods. This confirms the necessity of developing new statistical models in order to identify the change of probability functions over time and obtain a consistent flood estimation method in NFFA. The method of Trimmed L-moments (TL-moments) with time covariate is confronted with the L-moment method for the stationary and non-stationary generalized extreme value (GEV) models. The aims of the study are to investigate the behavior of the proposed TL-moments method in the presence of NFFA and applying the method along with GEV distribution. Comparisons of the methods are made by Monte Carlo simulations and bootstrap-based method. The simulation study showed the better performance of most levels of TL-moments method, which is TL(η,0), (η = 2, 3, 4) than the L-moment method for all models (GEV1, GEV2, and GEV3). The TL-moment method provides more efficient quantile estimates than other methods in flood quantiles estimated at higher return periods. Thus, the TL-moments method can produce better estimation results since the L-moment eliminates lowest value and gives more weight to the largest value which provides important information.
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HOSNY, KHALID M. "EFFICIENT COMPUTATION OF LEGENDRE MOMENTS FOR GRAY LEVEL IMAGES." International Journal of Image and Graphics 07, no. 04 (2007): 735–47. http://dx.doi.org/10.1142/s021946780700288x.

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Direct computation of Legendre orthogonal moments requires huge arithmetic operations, which is very time consuming. Many works have described methods for reducing the computations involved in evaluating Legendre moments. Nevertheless, reduction computational complexity is still an open problem and needs more investigation. Existing algorithms mainly focused on binary images and compute Legendre moments using a set of geometric moments. We propose a fast and efficient method for computation of Legendre moments for binary and gray level images. A recurrence formula of one-dimensional Legendre moments will be established using the recursive property of Legendre polynomials; then the method will be extended to calculate the two-dimensional Legendre moments. This method is completely independent on geometric moment. The complexity analysis shows that the proposed method computes Legendre moments more efficiently than the direct method and the other conventional methods.
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Dissertations / Theses on the topic "MOMENTS METHOD"

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Shin, Changmock. "Entropy Based Moment Selection in Generalized Method of Moments." NCSU, 2005. http://www.lib.ncsu.edu/theses/available/etd-06072005-112026/.

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GMM provides a computationally convenient estimation method and the resulting estimator can be shown to be consistent and asymptotically normal under the fairly moderate regularity conditions. It is widely known that the information content in the population moment condition has impacts on the quality of the asymptotic approximation to finite sample behavior. This dissertation focuses on a moment selection procedure that leads us to choose relevant (asymptotically efficient and non-redundant) moment conditions in the presence of weak identification. The contributions of this dissertation can be characterized as follows: in the framework of linear model, (i) the concept of nearly redundant moment conditions is introduced and the connection between near redundancy and weak identification is explored; (ii) performance of RMSC(c) is evaluated when weak identification is a possibility but the parameter vector to be estimated is not weakly identified by the candidate set of moment conditions; (iii) performance of RMSC(c) is also evaluated when the parameter vector is weakly identified by the candidate set; (iv) a combined strategy of Stock and Yogo's (2002) test for weak identification and RMSC(c) is introduced and evaluated; (v) (i) and (ii) are extended to allow for nonlinear dynamic models. The subsequent simulation results support the analytical findings: when only a part of instruments in the set of possible candidates for instruments are relevant and the others are redundant given all or some of the relevant ones, RMSC(c) chooses all the relevant instruments with high probabilities and improves the quality of the post-selection inferences; when the candidates are in order of their importance, a combined strategy of Stock and Yogo's (2002) pretest and RMSC(c) improves the post-selection inferences, however it tends to select parsimonious models; when all the possible candidates are equally important, it seems that RMSC(c) does not provide any merits. However, in the last case, asymptotic efficiency and non-redundancy can be achieved by basing the estimation and inference on all the possible candidates.
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Lai, Yanzhao. "Generalized method of moments exponential distribution family." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-2/laiy/yanzhaolai.pdf.

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Virk, Bikram. "Implementing method of moments on a GPGPU using Nvidia CUDA." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33980.

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This thesis concentrates on the algorithmic aspects of Method of Moments (MoM) and Locally Corrected Nyström (LCN) numerical methods in electromagnetics. The data dependency in each step of the algorithm is analyzed to implement a parallel version that can harness the powerful processing power of a General Purpose Graphics Processing Unit (GPGPU). The GPGPU programming model provided by NVIDIA's Compute Unified Device Architecture (CUDA) is described to learn the software tools at hand enabling us to implement C code on the GPGPU. Various optimizations such as the partial update at every iteration, inter-block synchronization and using shared memory enable us to achieve an overall speedup of approximately 10. The study also brings out the strengths and weaknesses in implementing different methods such as Crout's LU decomposition and triangular matrix inversion on a GPGPU architecture. The results suggest future directions of study in different algorithms and their effectiveness on a parallel processor environment. The performance data collected show how different features of the GPGPU architecture can be enhanced to yield higher speedup.
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Li, Tao. "3D Capacitance Extraction With the Method of Moments." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/86.

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In this thesis, the Method of Moments has been applied to calculate capacitance between two arbitrary 3D metal conductors or a capacitance matrix for a 3D multi-conductor system. Capacitance extraction has found extensive use for systems involving sets of long par- allel transmission lines in multi-dielectric environment as well as integrated circuit package including three-dimensional conductors located on parallel planes. This paper starts by reviewing fundamental aspects of transient electro-magnetics followed by the governing dif- ferential and integral equations to motivate the application of numerical methods as Method of Moments(MoM), Finite Element Method(FEM), etc. Among these numerical tools, the surface-based integral-equation methodology - MoM is ideally suited to address the prob- lem. It leads to a well-conditioned system with reduced size, as compared to volumetric methods. In this dissertation, the MoM Surface Integral Equation (SIE)-based modeling approach is developed to realize electrostatic capacitance extraction for 3D geometry. MAT- LAB is employed to validate its e?ciency and e?ectiveness along with design of a friendly GUI. As a base example, a parallel-plate capacitor is considered. We evaluate the accu- racy of the method by comparison with FEM simulations as well as the corresponding quasi-analytical solution. We apply this method to the parallel-plate square capacitor and demonstrate how far could the undergraduate result 0C = A ? "=d' be from reality. For the completion of the solver, the same method is applied to the calculation of line capacitance for two- and multi-conductor 2D transmission lines.
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Liang, Yitian. "Generalized method of moments : theoretical, econometric and simulation studies." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/36866.

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The GMM estimator is widely used in the econometrics literature. This thesis mainly focus on three aspects of the GMM technique. First, I derive the prooves to study the asymptotic properties of the GMM estimator under certain conditions. To my best knowledge, the original complete prooves proposed by Hansen (1982) is not easily available. In this thesis, I provide complete prooves of consistency and asymptotic normality of the GMM estimator under some stronger assumptions than those in Hansen (1982). Second, I illustrate the application of GMM estimator in linear models. Specifically, I emphasize the economic reasons underneath the linear statistical models where GMM estimator (also referred to the Instrumental Variable estimator) is widely used. Third, I perform several simulation studies to investigate the performance of GMM estimator under different situations.
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McLeod, James William. "An investigation of the CDF-based method of moments." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0009/MQ34121.pdf.

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Strydom, Willem Jacobus. "Recovery based error estimation for the Method of Moments." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96881.

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Thesis (MEng)--Stellenbosch University, 2015.<br>ENGLISH ABSTRACT: The Method of Moments (MoM) is routinely used for the numerical solution of electromagnetic surface integral equations. Solution errors are inherent to any numerical computational method, and error estimators can be effectively employed to reduce and control these errors. In this thesis, gradient recovery techniques of the Finite Element Method (FEM) are formulated within the MoM context, in order to recover a higher-order charge of a Rao-Wilton-Glisson (RWG) MoM solution. Furthermore, a new recovery procedure, based specifically on the properties of the RWG basis functions, is introduced by the author. These recovered charge distributions are used for a posteriori error estimation of the charge. It was found that the newly proposed charge recovery method has the highest accuracy of the considered recovery methods, and is the most suited for applications within recovery based error estimation. In addition to charge recovery, the possibility of recovery procedures for the MoM solution current are also investigated. A technique is explored whereby a recovered charge is used to find a higher-order divergent current representation. Two newly developed methods for the subsequent recovery of the solenoidal current component, as contained in the RWG solution current, are also introduced by the author. A posteriori error estimation of the MoM current is accomplished through the use of the recovered current distributions. A mixed second-order recovered current, based on a vector recovery procedure, was found to produce the most accurate results. The error estimation techniques developed in this thesis could be incorporated into an adaptive solver scheme to optimise the solution accuracy relative to the computational cost.<br>AFRIKAANSE OPSOMMING: Die Moment Metode (MoM) vind algemene toepassing in die numeriese oplossing van elektromagnetiese oppervlak integraalvergelykings. Numeriese foute is inherent tot die prosedure: foutberamingstegnieke is dus nodig om die betrokke foute te analiseer en te reduseer. Gradiënt verhalingstegnieke van die Eindige Element Metode word in hierdie tesis in die MoM konteks geformuleer. Hierdie tegnieke word ingespan om die oppervlaklading van 'n Rao-Wilton-Glisson (RWG) MoM oplossing na 'n verbeterde hoër-orde voorstelling te neem. Verder is 'n nuwe lading verhalingstegniek deur die outeur voorgestel wat spesifiek op die eienskappe van die RWG basis funksies gebaseer is. Die verhaalde ladingsverspreidings is geïmplementeer in a posteriori fout beraming van die lading. Die nuut voorgestelde tegniek het die akkuraatste resultate gelewer, uit die groep verhalingstegnieke wat ondersoek is. Addisioneel tot ladingsverhaling, is die moontlikheid van MoM-stroom verhalingstegnieke ook ondersoek. 'n Metode vir die verhaling van 'n hoër-orde divergente stroom komponent, gebaseer op die verhaalde lading, is geïmplementeer. Verder is twee nuwe metodes vir die verhaling van die solenodiale komponent van die RWG stroom deur die outeur voorgestel. A posteriori foutberaming van die MoM-stroom is met behulp van die verhaalde stroom verspreidings gerealiseer, en daar is gevind dat 'n gemengde tweede-orde verhaalde stroom, gebaseer op 'n vektor metode, die beste resultate lewer. Die foutberamingstegnieke wat in hierdie tesis ondersoek is, kan in 'n aanpasbare skema opgeneem word om die akkuraatheid van 'n numeriese oplossing, relatief tot die berekeningskoste, te optimeer.
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Arvas, Serhend. "A method of moments analysis of microstructured optical fibers." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2009. http://wwwlib.umi.com/cr/syr/main.

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CUNHA, JOAO MARCO BRAGA DA. "ESTIMATING ARTIFICIAL NEURAL NETWORKS WITH GENERALIZED METHOD OF MOMENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26922@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>As Redes Neurais Artificiais (RNAs) começaram a ser desenvolvidas nos anos 1940. Porém, foi a partir dos anos 1980, com a popularização e o aumento de capacidade dos computadores, que as RNAs passaram a ter grande relevância. Também nos anos 1980, houve dois outros acontecimentos acadêmicos relacionados ao presente trabalho: (i) um grande crescimento do interesse de econometristas por modelos não lineares, que culminou nas abordagens econométricas para RNAs, no final desta década; e (ii) a introdução do Método Generalizado dos Momentos (MGM) para estimação de parâmetros, em 1982. Nas abordagens econométricas de RNAs, sempre predominou a estimação por Quasi Máxima Verossimilhança (QMV). Apesar de possuir boas propriedades assintóticas, a QMV é muito suscetível a um problema nas estimações em amostra finita, conhecido como sobreajuste. O presente trabalho estende o estado da arte em abordagens econométricas de RNAs, apresentando uma proposta alternativa à estimação por QMV que preserva as suas boas propriedades assintóticas e é menos suscetível ao sobreajuste. A proposta utiliza a estimação pelo MGM. Como subproduto, a estimação pelo MGM possibilita a utilização do chamado Teste J para verifificar a existência de não linearidade negligenciada. Os estudos de Monte Carlo realizados indicaram que as estimações pelo MGM são mais precisas que as geradas pela QMV em situações com alto ruído, especialmente em pequenas amostras. Este resultado é compatível com a hipótese de que o MGM é menos suscetível ao sobreajuste. Experimentos de previsão de taxas de câmbio reforçaram estes resultados. Um segundo estudo de Monte Carlo apontou boas propriedades em amostra finita para o Teste J aplicado à não linearidade negligenciada, comparado a um teste de referência amplamente conhecido e utilizado. No geral, os resultados apontaram que a estimação pelo MGM é uma alternativa recomendável, em especial no caso de dados com alto nível de ruído.<br>Artificial Neural Networks (ANN) started being developed in the decade of 1940. However, it was during the 1980 s that the ANNs became relevant, pushed by the popularization and increasing power of computers. Also in the 1980 s, there were two other two other academic events closely related to the present work: (i) a large increase of interest in nonlinear models from econometricians, culminating in the econometric approaches for ANN by the end of that decade; and (ii) the introduction of the Generalized Method of Moments (GMM) for parameter estimation in 1982. In econometric approaches for ANNs, the estimation by Quasi Maximum Likelihood (QML) always prevailed. Despite its good asymptotic properties, QML is very prone to an issue in finite sample estimations, known as overfiting. This thesis expands the state of the art in econometric approaches for ANNs by presenting an alternative to QML estimation that keeps its good asymptotic properties and has reduced leaning to overfiting. The presented approach relies on GMM estimation. As a byproduct, GMM estimation allows the use of the so-called J Test to verify the existence of neglected nonlinearity. The performed Monte Carlo studies indicate that the estimates from GMM are more accurate than those generated by QML in situations with high noise, especially in small samples. This result supports the hypothesis that GMM is susceptible to overfiting. Exchange rate forecasting experiments reinforced these findings. A second Monte Carlo study revealed satisfactory finite sample properties of the J Test applied to the neglected nonlinearity, compared with a reference test widely known and used. Overall, the results indicated that the estimation by GMM is a better alternative, especially for data with high noise level.
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Kluskens, Michael S. "Method of moments analysis of scattering by chiral media /." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487688507504775.

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Books on the topic "MOMENTS METHOD"

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Generalized method of moments. Oxford University Press, 2005.

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The Method of moments in electromagnetics. CRC Press/Taylor & Francis, 2014.

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Gibson, Walton C. The method of moments in electromagnetics. Chapman & Hall/CRC, 2008.

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Shaeffer, John F. MOM3D method of moments code: Theory manual. Lockheed Advanced Development Co., 1992.

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Bourlier, Christophe, Nicolas Pinel, and Gildas Kubické. Method of Moments for 2D Scattering Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118648674.

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Deshpande, Manohar D. Analysis of electromagnetic scattering from irregularly shaped, thin, metallic flat plates. Langley Research Center, 1993.

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Reddy, C. J. Analysis of three-dimensional-cavity-backed aperture antennas using a combined finite element method/method of moments/geometrical theory of diffraction technique. Langley Research Center, 1995.

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Bossaerts, Peter. "Method of moments tests of contingent claims asset pricing models". INSEAD, 1986.

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Stark, A. F. Coupled cluster theory, sum rules and the method of moments. UMIST, 1995.

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Bourlier, Christophe. Method of moments for 2D scattering problems: Basic concepts and applications. ISTE Ltd, 2013.

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Book chapters on the topic "MOMENTS METHOD"

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Archambeault, Bruce, Colin Brench, and Omar M. Ramahi. "Method of Moments." In EMI/EMC Computational Modeling Handbook. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1557-9_4.

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Haas, Fernando. "The Moments Method." In Quantum Plasmas. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8201-8_9.

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Karnovsky, Igor A., and Evgeniy Lebed. "Krein Moments Method." In Theory of Vibration Protection. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28020-2_11.

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Archambeault, Bruce, Omar M. Ramahi, and Colin Brench. "Method of Moments." In EMI/EMC Computational Modeling Handbook. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-5124-6_4.

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Cercignani, Carlo, and Gilberto Medeiros Kremer. "Method of Moments." In The Relativistic Boltzmann Equation: Theory and Applications. Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8165-4_6.

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Barkeshli, Kasra, and Sina Khorasani. "Method of Moments." In Advanced Electromagnetics and Scattering Theory. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11547-4_9.

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Qin, Jing. "Generalized Method of Moments." In Biased Sampling, Over-identified Parameter Problems and Beyond. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4856-2_7.

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Rylander, Thomas, Pär Ingelström, and Anders Bondeson. "The Method of Moments." In Computational Electromagnetics. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5351-2_7.

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Wansbeek, Tom J. "Generalized Method of Moments." In International Series in Quantitative Marketing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53469-5_15.

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Gibson, Walton C. "The Method of Moments." In The Method of Moments in Electromagnetics, 3rd ed. CRC Press, 2021. http://dx.doi.org/10.1201/9780429355509-2.

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Conference papers on the topic "MOMENTS METHOD"

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Hu, Hongchang. "Method of weighted moments." In 2012 IEEE Symposium on Robotics and Applications (ISRA). IEEE, 2012. http://dx.doi.org/10.1109/isra.2012.6219223.

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Song, J. M., W. W. Shu, and W. C. Chew. "Numerical resonances in method of moments." In 2007 IEEE Antennas and Propagation Society International Symposium. IEEE, 2007. http://dx.doi.org/10.1109/aps.2007.4396633.

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Nair, N. V., and B. Shanker. "Implementation of Generalized Method of Moments." In 2009 IEEE Antennas and Propagation Society International Symposium (APSURSI). IEEE, 2009. http://dx.doi.org/10.1109/aps.2009.5171540.

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Dault, D., and B. Shanker. "A penalty method for the Generalized Method of Moments." In 2014 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. IEEE, 2014. http://dx.doi.org/10.1109/aps.2014.6905408.

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Brench, Colin E. "Method of moments use, validation and limitations." In 2008 IEEE International Symposium on Electromagnetic Compatibility - EMC 2008. IEEE, 2008. http://dx.doi.org/10.1109/isemc.2008.4652201.

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Masek, Michal, Miloslav Capek, Lukas Jelinek, and Kurt Schab. "Utilization of Symmetries in Method of Moments." In 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. IEEE, 2019. http://dx.doi.org/10.1109/apusncursinrsm.2019.8888654.

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D'Ambrosio, K., R. Pirich, K. Petkov, and A. Kaufman. "Method of Moments software for GPU hardware." In Expo on Emerging Technologies for a Smarter World (CEWIT 2011). IEEE, 2011. http://dx.doi.org/10.1109/cewit.2011.6135878.

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Bose, Arup, Rajat Subhra Hazra, and Koushik Saha. "Patterned Random Matrices and Method of Moments." In Proceedings of the International Congress of Mathematicians 2010 (ICM 2010). Published by Hindustan Book Agency (HBA), India. WSPC Distribute for All Markets Except in India, 2011. http://dx.doi.org/10.1142/9789814324359_0142.

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KURYLEV, Y. "MOMENTS' METHOD FOR INVERSE BOUNDARY VALUE PROBLEMS." In Proceedings of the Sixth International Workshop. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702593_0034.

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Lee, Ikjin, Kyung K. Choi, and Liu Du. "Alternative Methods for Reliability-Based Robust Design Optimization Including Dimension Reduction Method." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99732.

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Abstract:
The objective of reliability-based robust design optimization (RBRDO) is to minimize the product quality loss function subject to probabilistic constraints. Since the quality loss function is usually expressed in terms of the first two statistical moments, mean and variance, many methods have been proposed to accurately and efficiently estimate the moments. Among the methods, the univariate dimension reduction method (DRM), performance moment integration (PMI), and percentile difference method (PDM) are recently proposed methods. In this paper, estimation of statistical moments and their sensitivities are carried out using DRM and compared with results obtained using PMI and PDM. In addition, PMI and DRM are also compared in terms of how accurately and efficiently they estimate the statistical moments and their sensitivities of a performance function. In this comparison, PDM is excluded since PDM could not even accurately estimate the statistical moments of the performance function. Also, robust design optimization using DRM is developed and then compared with the results of RBRDO using PMI and PDM. Several numerical examples are used for the two comparisons. The comparisons show that DRM is efficient when the number of design variables is small and PMI is efficient when the number of design variables is relatively large. For the inverse reliability analysis of reliability-based design, the enriched performance measure approach (PMA+) is used.
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Reports on the topic "MOMENTS METHOD"

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Gallant, Ron, Raffaella Giacomini, and Giuseppe Ragusa. Generalized method of moments with latent variables. Institute for Fiscal Studies, 2013. http://dx.doi.org/10.1920/wp.cem.2013.5013.

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Taylor, Douglas. Hybrid Version of Method of Moments Computer Code: IBC3D. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada363040.

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Wilhelm, Daniel. Optimal bandwidth selection for robust generalized method of moments estimation. Cemmap, 2014. http://dx.doi.org/10.1920/wp.cem.2014.1514.

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Burnside, Craig, and Martin Eichenbaum. Small Sample Properties of Generalized Method of Moments Based Wald Tests. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/t0155.

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Lynch, Anthony, and Jessica Wachter. Using Samples of Unequal Length in Generalized Method of Moments Estimation. National Bureau of Economic Research, 2008. http://dx.doi.org/10.3386/w14411.

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Glynn, Peter W., and Donald L. Iglehart. Estimation of Steady-State Central Moments by the Regenerative Method of Simulation. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada161435.

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Clark, W. Multiple coil pulsed magnetic resonance method to measure the SSC bending magnet multipole moments. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7107279.

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Clark, W. G. (Multiple coil pulsed magnetic resonance method to measure the SSC bending magnet multipole moments). Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6233086.

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Clarke, Paul S., Tom M. Palmer, and Frank Windmeijer. Estimating structural mean models with multiple instrumental variables using the generalised method of moments. Institute for Fiscal Studies, 2011. http://dx.doi.org/10.1920/wp.cem.2011.2811.

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Neely, Christopher J. A Reconsideration of the Properties of the Generalized Method of Moments in Asset Pricing Models. Federal Reserve Bank of St. Louis, 1994. http://dx.doi.org/10.20955/wp.1994.010.

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