Relevant bibliographies by topics / Estimation by Method of Moments

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Academic literature on the topic 'Estimation by Method of Moments'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Estimation by Method of Moments"

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Kuersteiner, Guido M., and Laszlo Matyas. "Generalized Method of Moments Estimation." Journal of the American Statistical Association 95, no. 451 (September 2000): 1014. http://dx.doi.org/10.2307/2669498.

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Rao, K. Srinivasa. "Estimation of Parameters of Pert Distribution by Using Method of Moments." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1621–29. http://dx.doi.org/10.22214/ijraset.2021.38239.

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Abstract: The method of moments has been widely used for estimating the parameters of a distribution. Usually lower order moments are wont to find the parameter estimates as they're known to possess less sampling variability. The method of moments may be a technique for estimating the parameters of a statistical model. It works by finding values of the parameters that end in a match between the sample moments and therefore the population moments (as implied by the model). the Method of moment Estimator is used to find out Estimates the parameters of PERT Distribution. We also compare equispaced and unequispaced Optimally Constructed Grouped data by the method of an Asymptotically Relative Efficiency. We also computed Average Estimate (AE), Variance (VAR), Standard Deviation (STD), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Simulated Error (SE) and Relative Absolute Bias (RAB) for both the parameters under grouped sample supported 1000 simulations to assess the performance of the estimators. Keywords: Method of Moments, PERT Distribution, equispaced and unequipped Optimal Grouped sample
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Andrews, Donald W. K. "Consistent Moment Selection Procedures for Generalized Method of Moments Estimation." Econometrica 67, no. 3 (May 1999): 543–63. http://dx.doi.org/10.1111/1468-0262.00036.

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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 (August 16, 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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Nghiem, Linh H., Michael C. Byrd, and Cornelis J. Potgieter. "Estimation in linear errors-in-variables models with unknown error distribution." Biometrika 107, no. 4 (May 21, 2020): 841–56. http://dx.doi.org/10.1093/biomet/asaa025.

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Summary Parameter estimation in linear errors-in-variables models typically requires that the measurement error distribution be known or estimable from replicate data. A generalized method of moments approach can be used to estimate model parameters in the absence of knowledge of the error distributions, but it requires the existence of a large number of model moments. In this paper, parameter estimation based on the phase function, a normalized version of the characteristic function, is considered. This approach requires the model covariates to have asymmetric distributions, while the error distributions are symmetric. Parameters are estimated by minimizing a distance function between the empirical phase functions of the noisy covariates and the outcome variable. No knowledge of the measurement error distribution is needed to calculate this estimator. Both asymptotic and finite-sample properties of the estimator are studied. The connection between the phase function approach and method of moments is also discussed. The estimation of standard errors is considered and a modified bootstrap algorithm for fast computation is proposed. The newly proposed estimator is competitive with the generalized method of moments, despite making fewer model assumptions about the moment structure of the measurement error. Finally, the proposed method is applied to a real dataset containing measurements of air pollution levels.
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Frazier, David, and Eric Renault. "Indirect Inference: Which Moments to Match?" Econometrics 7, no. 1 (March 19, 2019): 14. http://dx.doi.org/10.3390/econometrics7010014.

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The standard approach to indirect inference estimation considers that the auxiliary parameters, which carry the identifying information about the structural parameters of interest, are obtained from some recently identified vector of estimating equations. In contrast to this standard interpretation, we demonstrate that the case of overidentified auxiliary parameters is both possible, and, indeed, more commonly encountered than one may initially realize. We then revisit the “moment matching” and “parameter matching” versions of indirect inference in this context and devise efficient estimation strategies in this more general framework. Perhaps surprisingly, we demonstrate that if one were to consider the naive choice of an efficient Generalized Method of Moments (GMM)-based estimator for the auxiliary parameters, the resulting indirect inference estimators would be inefficient. In this general context, we demonstrate that efficient indirect inference estimation actually requires a two-step estimation procedure, whereby the goal of the first step is to obtain an efficient version of the auxiliary model. These two-step estimators are presented both within the context of moment matching and parameter matching.
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Chatelain, Jean-Bernard. "Improving consistent moment selection procedures for generalized method of moments estimation." Economics Letters 95, no. 3 (June 2007): 380–85. http://dx.doi.org/10.1016/j.econlet.2006.11.011.

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Wilhelm, Daniel. "OPTIMAL BANDWIDTH SELECTION FOR ROBUST GENERALIZED METHOD OF MOMENTS ESTIMATION." Econometric Theory 31, no. 5 (October 2, 2014): 1054–77. http://dx.doi.org/10.1017/s026646661400067x.

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A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or bandwidth) so that the resulting point estimate is optimal in a certain sense. We derive an asymptotically optimal bandwidth that minimizes a higher-order approximation to the asymptotic mean-squared error of the estimator of interest. We show that the optimal bandwidth is of the same order as the one minimizing the mean-squared error of the nonparametric plugin estimator, but the constants of proportionality are significantly different. Finally, we develop a data-driven bandwidth selection rule and show, in a simulation experiment, that it may substantially reduce the estimator’s mean-squared error relative to existing bandwidth choices, especially when the number of moment conditions is large.
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Hu, Yi, Xiaohua Xia, Ying Deng, and Dongmei Guo. "Higher Order Mean Squared Error of Generalized Method of Moments Estimators for Nonlinear Models." Discrete Dynamics in Nature and Society 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/324904.

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Generalized method of moments (GMM) has been widely applied for estimation of nonlinear models in economics and finance. Although generalized method of moments has good asymptotic properties under fairly moderate regularity conditions, its finite sample performance is not very well. In order to improve the finite sample performance of generalized method of moments estimators, this paper studies higher-order mean squared error of two-step efficient generalized method of moments estimators for nonlinear models. Specially, we consider a general nonlinear regression model with endogeneity and derive the higher-order asymptotic mean square error for two-step efficient generalized method of moments estimator for this model using iterative techniques and higher-order asymptotic theories. Our theoretical results allow the number of moments to grow with sample size, and are suitable for general moment restriction models, which contains conditional moment restriction models as special cases. The higher-order mean square error can be used to compare different estimators and to construct the selection criteria for improving estimator’s finite sample performance.
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Wooldridge, Jeffrey M. "Applications of Generalized Method of Moments Estimation." Journal of Economic Perspectives 15, no. 4 (November 1, 2001): 87–100. http://dx.doi.org/10.1257/jep.15.4.87.

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I describe how the method of moments approach to estimation, including the more recent generalized method of moments (GMM) theory, can be applied to problems using cross section, time series, and panel data. Method of moments estimators can be attractive because in many circumstances they are robust to failures of auxiliary distributional assumptions that are not needed to identify key parameters. I conclude that while sophisticated GMM estimators are indispensable for complicated estimation problems, it seems unlikely that GMM will provide convincing improvements over ordinary least squares and two-stage least squares--by far the most common method of moments estimators used in econometrics--in settings faced most often by empirical researchers.
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Dissertations / Theses on the topic "Estimation by Method of Moments"

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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.
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.
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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Menshikova, M. "Uncertainty estimation using the moments method facilitated by automatic differentiation in Matlab." Thesis, Department of Engineering Systems and Management, 2010. http://hdl.handle.net/1826/4328.

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Computational models have long been used to predict the performance of some baseline design given its design parameters. Given inconsistencies in manufacturing, the manufactured product always deviates from the baseline design. There is currently much interest in both evaluating the effects of variability in design parameters on a design’s performance (uncertainty estimation), and robust optimization of the baseline design such that near optimal performance is obtained despite variability in design parameters. Traditionally, uncertainty analysis is performed by expensive Monte-Carlo methods. This work considers the alternative moments method for uncertainty propagation and its implementation in Matlab. In computational design it is assumed a computational model gives a sufficiently accurate approximation to a design’s performance. As such it can be used for estimating statistical moments (expectation, variance, etc.) of the design due to known statistical variation of the model’s parameters, e.g., by the Monte Carlo approach. In the moments method we further assume the model is sufficiently differentiable that a Taylor series approximation to a model may be constructed, and the moments of the Taylor series may be taken analytically to yield approximations to the model’s moments. In this thesis we generalise techniques considered within the engineering community and design and document associated software to generate arbitrary order Taylor series approximations to arbitrary order statistical moments of computational models implemented in Matlab; Taylor series coefficients are calculated using automatic differentiation. This approach is found to be more efficient than a standard Monte Carlo method for the small-scale model test problems we consider. Previously Christianson and Cox (2005) have indicated that the moments method will be non-convergent in the presence of complex poles of the computational model and suggested a partitioning method to overcome this problem. We implement a version of the partitioning method and demonstrate that it does result in convergence of the moments method. Additionally, we consider, what we term, the branch detection problem in order to ascertain if our Taylor series approximation might only be valid piecewise.
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Ginos, Brenda Faith. "Parameter Estimation for the Lognormal Distribution." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3205.pdf.

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Owen, Claire Elayne Bangerter. "Parameter Estimation for the Beta Distribution." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2670.pdf.

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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
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
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.
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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Pant, Mohan Dev. "Simulating Univariate and Multivariate Burr Type III and Type XII Distributions Through the Method of L-Moments." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/401.

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The Burr families (Type III and Type XII) of distributions are traditionally used in the context of statistical modeling and for simulating non-normal distributions with moment-based parameters (e.g., Skew and Kurtosis). In educational and psychological studies, the Burr families of distributions can be used to simulate extremely asymmetrical and heavy-tailed non-normal distributions. Conventional moment-based estimators (i.e., the mean, variance, skew, and kurtosis) are traditionally used to characterize the distribution of a random variable or in the context of fitting data. 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 the Burr Type III and Type XII distributions through the method of L-moments is introduced. Specifically, systems of equations are derived for determining the shape parameters associated with user specified L-moment ratios (e.g., L-Skew and L-Kurtosis). A procedure is also developed for the purpose of generating non-normal Burr Type III and Type XII distributions with arbitrary L-correlation matrices. Numerical examples are provided to demonstrate that L-moment based Burr distributions are superior to their conventional moment based counterparts in the context of estimation, distribution fitting, and robustness to outliers. Monte Carlo simulation results are provided to demonstrate that L-moment-based estimators are nearly unbiased, have relatively small variance, and are robust in the presence of outliers for any sample size. Simulation results are also provided to show that the methodology used for generating correlated non-normal Burr Type III and Type XII distributions is valid and efficient. Specifically, Monte Carlo simulation results are provided to show that the empirical values of L-correlations among simulated Burr Type III (and Type XII) distributions are in close agreement with the specified L-correlation matrices.
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Ragusa, Giuseppe. "Essays on moment conditions models econometrics /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3170252.

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Katyal, Bhavana. "Multiple current dipole estimation in a realistic head model using signal subspace methods." Online access for everyone, 2004. http://www.dissertations.wsu.edu/Thesis/Summer2004/b%5Fkatyal%5F072904.pdf.

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Babichev, Dmitry. "On efficient methods for high-dimensional statistical estimation." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE032.

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Dans cette thèse, nous examinons plusieurs aspects de l'estimation des paramètres pour les statistiques et les techniques d'apprentissage automatique, aussi que les méthodes d'optimisation applicables à ces problèmes. Le but de l'estimation des paramètres est de trouver les paramètres cachés inconnus qui régissent les données, par exemple les paramètres dont la densité de probabilité est inconnue. La construction d'estimateurs par le biais de problèmes d'optimisation n'est qu'une partie du problème, trouver la valeur optimale du paramètre est souvent un problème d'optimisation qui doit être résolu, en utilisant diverses techniques. Ces problèmes d'optimisation sont souvent convexes pour une large classe de problèmes, et nous pouvons exploiter leur structure pour obtenir des taux de convergence rapides. La première contribution principale de la thèse est de développer des techniques d'appariement de moments pour des problèmes de régression non linéaire multi-index. Nous considérons le problème classique de régression non linéaire, qui est irréalisable dans des dimensions élevées en raison de la malédiction de la dimensionnalité. Nous combinons deux techniques existantes : ADE et SIR pour développer la méthode hybride sans certain des aspects faibles de ses parents. Dans la deuxième contribution principale, nous utilisons un type particulier de calcul de la moyenne pour la descente stochastique du gradient. Nous considérons les familles exponentielles conditionnelles (comme la régression logistique), où l'objectif est de trouver la valeur inconnue du paramètre. Nous proposons le calcul de la moyenne des paramètres de moments, que nous appelons fonctions de prédiction. Pour les modèles à dimensions finies, ce type de calcul de la moyenne peut entraîner une erreur négative, c'est-à-dire que cette approche nous fournit un estimateur meilleur que tout estimateur linéaire ne peut jamais le faire. La troisième contribution principale de cette thèse porte sur les pertes de Fenchel-Young. Nous considérons des classificateurs linéaires multi-classes avec les pertes d'un certain type, de sorte que leur double conjugué a un produit direct de simplices comme support. La formulation convexe-concave à point-selle correspondante a une forme spéciale avec un terme de matrice bilinéaire et les approches classiques souffrent de la multiplication des matrices qui prend beaucoup de temps. Nous montrons que pour les pertes SVM multi-classes avec des techniques d'échantillonnage efficaces, notre approche a une complexité d'itération sous-linéaire, c'est-à-dire que nous devons payer seulement trois fois O(n+d+k) : pour le nombre de classes k, le nombre de caractéristiques d et le nombre d'échantillons n, alors que toutes les techniques existantes sont plus complexes
In this thesis we consider several aspects of parameter estimation for statistics and machine learning and optimization techniques applicable to these problems. The goal of parameter estimation is to find the unknown hidden parameters, which govern the data, for example parameters of an unknown probability density. The construction of estimators through optimization problems is only one side of the coin, finding the optimal value of the parameter often is an optimization problem that needs to be solved, using various optimization techniques. Hopefully these optimization problems are convex for a wide class of problems, and we can exploit their structure to get fast convergence rates. The first main contribution of the thesis is to develop moment-matching techniques for multi-index non-linear regression problems. We consider the classical non-linear regression problem, which is unfeasible in high dimensions due to the curse of dimensionality. We combine two existing techniques: ADE and SIR to develop the hybrid method without some of the weak sides of its parents. In the second main contribution we use a special type of averaging for stochastic gradient descent. We consider conditional exponential families (such as logistic regression), where the goal is to find the unknown value of the parameter. Classical approaches, such as SGD with constant step-size are known to converge only to some neighborhood of the optimal value of the parameter, even with averaging. We propose the averaging of moment parameters, which we call prediction functions. For finite-dimensional models this type of averaging can lead to negative error, i.e., this approach provides us with the estimator better than any linear estimator can ever achieve. The third main contribution of this thesis deals with Fenchel-Young losses. We consider multi-class linear classifiers with the losses of a certain type, such that their dual conjugate has a direct product of simplices as a support. We show, that for multi-class SVM losses with smart matrix-multiplication sampling techniques, our approach has an iteration complexity which is sublinear, i.e., we need to pay only trice O(n+d+k): for number of classes k, number of features d and number of samples n, whereas all existing techniques have higher complexity
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MENICHINI, AMILCAR ARMANDO. "Financial Frictions and Capital Structure Choice: A Structural Dynamic Estimation." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145397.

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This thesis studies different aspects of firm decisions by using a dynamic model. I estimate a dynamic model of the firm based on the trade-off theory of capital structure that endogenizes investment, leverage, and payout decisions. For the estimation of the model I use Efficient Method of Moments (EMM), which allows me to recover the structural parameters that best replicate the characteristics of the data. I start analyzing the question of whether target leverage varies over time. While this is a central issue in finance, there is no consensus in the literature on this point. I propose an explanation that reconciles some of the seemingly contradictory empirical evidence. The dynamic model generates a target leverage that changes over time and consistently reproduces the results of Lemmon, Roberts, and Zender (2008). These findings suggest that the time-varying target leverage assumption of the big bulk of the previous literature is not incompatible with the evidence presented by Lemmon, Roberts, and Zender (2008). Then I study how corporate income tax payments vary with the corporate income tax rate. The dynamic model produces a bell-shaped relationship between tax revenue and the tax rate that is consistent with the notion of the Laffer curve. The dynamic model generates the maximum tax revenue for a tax rate between 36% and 41%. Finally, I investigate the impact of financial constraints on investment decisions by firms. Model results show that investment-cash flow sensitivity is higher for less financially constrained firms. This result is consistent with Kaplan and Zingales (1997). The dynamic model also rationalizes why large and mature firms have a positive and significant investment-cash flow sensitivity.
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Books on the topic "Estimation by Method of Moments"

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Generalized method of moments. Oxford: Oxford University Press, 2005.

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Lee, Myoung-jae. Methods of moments and semiparametric econometrics for limited dependent and variable models. New York: Springer, 1996.

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McFadden, Daniel. A method of simulated moments for estimation of discrete response models without numerical integration. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1987.

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

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

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D, Retherford Robert, and Choe Minja Kim 1941-, eds. The own-children method of fertility estimation. Honolulu, HI: Population Institute, 1986.

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

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

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Lucas, James R. A variance component estimation method for sparse matrix applications. Rockville, Md: National Oceanic and Atmospheric Administration, National Ocean Service, Office of Charting and Geodetic Services, 1985.

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Lucas, James Raymond. A variance component estimation method for sparse matrix applications. Rockville, Md: National Oceanic and Atmospheric Administration, National Ocean Service, Office of Charting and Geodetic Services, 1985.

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Book chapters on the topic "Estimation by Method of Moments"

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Hansen, Lars Peter. "Generalized Method of Moments Estimation." In The New Palgrave Dictionary of Economics, 1–10. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2486-1.

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Hansen, Lars Peter. "Generalized Method of Moments Estimation." In The New Palgrave Dictionary of Economics, 1–10. London: Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-349-95121-5_2486-2.

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Hansen, Lars Peter. "Generalized Method of Moments Estimation." In The New Palgrave Dictionary of Economics, 5201–11. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2486.

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Hansen, Lars Peter. "Generalized method of moments estimation." In Macroeconometrics and Time Series Analysis, 105–18. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_13.

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Prucha, Ingmar R. "Instrumental Variables/Method of Moments Estimation." In Handbook of Regional Science, 1–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-36203-3_90-1.

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Prucha, Ingmar R. "Instrumental Variables/Method of Moments Estimation." In Handbook of Regional Science, 1597–617. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-23430-9_90.

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Ortelli, C., and F. Trojani. "Robust Efficient Method of Moments Estimation." In Theory and Applications of Recent Robust Methods, 271–82. Basel: Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_24.

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Prucha, Ingmar R. "Instrumental Variables/Method of Moments Estimation." In Handbook of Regional Science, 2097–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-60723-7_90.

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Hazelton, Martin L. "Methods of Moments Estimation." In International Encyclopedia of Statistical Science, 816–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_364.

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Ziegler, Andreas. "Generalized method of moment estimation." In Generalized Estimating Equations, 119–31. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6_8.

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Conference papers on the topic "Estimation by Method of Moments"

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Sheets, Alison L., Stefano Corazza, and Thomas Andriacchi. "An Automated Image-Based Method of 3D Subject Specific Body Segment Parameter Estimation." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-193068.

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Recent studies have suggested that limb kinetics during swing or float phase movements are important for ACL injury analysis and injury prevention [1]. Kinetic (moment and force) calculations during swing phase can be sensitive to the accuracy of subject-specific body segment parameters (BSP) including mass and inertial properties. While numerous methods for estimating BSP have been implemented including regression equations [2,3], geometric body shape estimations, medical imaging and optimization approaches, they all have application specific limitations. Almost all of these BSP estimation approaches are limited by assumptions that: the mass center (CM) lies on the axis connecting the segment’s proximal and distal joint center, the body principle moments of inertia are aligned with the segment axes [4], and the right and left limbs are symmetric. These assumptions could introduce errors in 3D kinematic analysis. Non-invasive methods of measuring the exact geometry and volume of body segments have the potential to reduce most sources of error.
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Du, Yao, Omar Tahri, and Hicham Hadj-Abdelkader. "An improved method for Rotation Estimation Using Photometric Spherical Moments." In 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2020. http://dx.doi.org/10.1109/icarcv50220.2020.9305374.

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Du, Yao, Omar Tahri, and Hicham Hadj-Abdelkader. "An improved method for Rotation Estimation Using Photometric Spherical Moments." In 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2020. http://dx.doi.org/10.1109/icarcv50220.2020.9305374.

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Rajan, Arvind, Ye Chow Kuang, Melanie Po-Leen Ooi, and Serge N. Demidenko. "Moments and maximum entropy method for expanded uncertainty estimation in measurements." In 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2017. http://dx.doi.org/10.1109/i2mtc.2017.7969851.

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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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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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Wang, Zihan, and Yaojun Qiao. "Intra-Channel Nonlinearity Estimation Based on Statistical Moments Method and Correlation Function." In the 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3291842.3291881.

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Wang, Liping, Don Beeson, and Gene Wiggs. "Efficient and Accurate Point Estimate Method for Moments and Probability Distribution Estimation." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4359.

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Andriulli, F., G. Vecchi, F. Vipiana, and P. Pirinoli. "A priori clipping threshold estimation for wavelet-based method of moments matrices." In IEEE Antennas and Propagation Society Symposium, 2004. IEEE, 2004. http://dx.doi.org/10.1109/aps.2004.1330467.

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Li, Yinsheng, Kunio Hasegawa, Naoki Miura, and Katsuaki Hoshino. "Experimental Study on Failure Estimation Method for Circumferentially Cracked Pipes Subjected to Multi-Axial Loads." In ASME 2015 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/pvp2015-45524.

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When a crack is detected in a piping line during in-service inspections, failure estimation method provided in ASME Boiler and Pressure Vessel Code Section XI or JSME Rules on Fitness-for-Service for Nuclear Power Plants can be applied to evaluate the structural integrity of the cracked pipe. The failure estimation method in the current codes accounts for the bending moment and axial force due to pressure. The torsion moment is not considered. Recently, analytical investigations have been carried out by several authors on the limit load of cracked pipes considering multi-axial loads including torsion and two failure estimation methods for multi-axial loads including torsion moment with different ranges of values have been proposed. In this study, to investigate the failure behavior of cracked pipes subjected to multi-axial loads including the torsion moment and to provide experimental support for the failure estimation methods, failure experiments were performed on 20 mm diameter stainless steel pipes with a circumferential surface crack or a through-wall crack under combined axial force and bending and torsion moments. Based on the experimental results, the proposed failure estimation methods were confirmed to be applicable to cracked pipes subjected to multi-axial loads.
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Li, Yinsheng, Kunio Hasegawa, Phuong H. Hoang, and Bostjan Bezensek. "Prediction Method for Plastic Collapse of Pipes Subjected to Combined Bending and Torsion Moments." In ASME 2010 Pressure Vessels and Piping Division/K-PVP Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/pvp2010-25101.

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When a crack is detected in a pipe during in-service inspection, the failure estimation method given in the codes such as ASME Boiler and Pressure Vessel Code Section XI non-mandatory Appendix C or JSME S NA-1-2008 Appendix E-8 can be applied to assess the integrity of the pipe. In the current editions of these codes, the failure estimation method is provided for bending moment and pressure. Torsion load is assumed to be relatively small and is not considered in the method. In this paper, finite element analyses are conducted for 24-inch stainless steel pipe with a circumferential surface crack subjected to the combined bending and torsion moments, focusing on large and pure torsion moments. Based on the analysis results, a prediction method for plastic collapse under the combined loading conditions of bending and torsion is proposed for the entire range of torsion moments.
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Reports on the topic "Estimation by Method of Moments"

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

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Lynch, Anthony, and Jessica Wachter. Using Samples of Unequal Length in Generalized Method of Moments Estimation. Cambridge, MA: National Bureau of Economic Research, October 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. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada161435.

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Eisenhauer, Phillipp, James Heckman, and Stefano Mosso. Estimation of Dynamic Discrete Choice Models by Maximum Likelihood and the Simulated Method of Moments. Cambridge, MA: National Bureau of Economic Research, October 2014. http://dx.doi.org/10.3386/w20622.

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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, August 2011. http://dx.doi.org/10.1920/wp.cem.2011.2811.

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Goncalves, Paulo, and Rudolf Riedi. Diverging Moments and Parameter Estimation. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada486761.

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de Paula, Áureo, Thomas Jorgensen, and Bo E. Honoré. The Informativeness of Estimation Moments. The IFS, January 2020. http://dx.doi.org/10.1920/wp.cem.2020320.

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Druska, Viliam, and William Horrace. Generalized Moments Estimation for Panel Data. Cambridge, MA: National Bureau of Economic Research, March 2003. http://dx.doi.org/10.3386/t0291.

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

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Andrews, Isaiah, Matthew Gentzkow, and Jesse Shapiro. Measuring the Sensitivity of Parameter Estimates to Estimation Moments. Cambridge, MA: National Bureau of Economic Research, November 2014. http://dx.doi.org/10.3386/w20673.

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