The Experts below are selected from a list of 123726 Experts worldwide ranked by ideXlab platform
Michael Jansson - One of the best experts on this subject based on the ideXlab platform.
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small bandwidth asymptotics for density Weighted Average derivatives
Econometric Theory, 2014Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:This paper proposes (apparently) novel standard error formulas for the density-Weighted Average derivative estimator of Powell, Stock, and Stoker (Econometrica 57, 1989). Asymptotic validity of the standard errors developed in this paper does not require the use of higher-order kernels, and the standard errors are robust in the sense that they accommodate (but do not require) bandwidths that are smaller than those for which conventional standard errors are valid. Moreover, the results of a Monte Carlo experiment suggest that the finite sample coverage rates of confidence intervals constructed using the standard errors developed in this papercoincide (approximately) with the nominal coverage rates across a nontrivial range of bandwidths. Copyright © Cambridge University Press 2013 A .
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generalized jackknife estimators of Weighted Average derivatives
Journal of the American Statistical Association, 2013Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:With the aim of improving the quality of asymptotic distributional approximations for nonlinear functionals of nonparametric estimators, this article revisits the large-sample properties of an important member of that class, namely a kernel-based Weighted Average derivative estimator. Asymptotic linearity of the estimator is established under weak conditions. Indeed, we show that the bandwidth conditions employed are necessary in some cases. A bias-corrected version of the estimator is proposed and shown to be asymptotically linear under yet weaker bandwidth conditions. Implementational details of the estimators are discussed, including bandwidth selection procedures. Consistency of an analog estimator of the asymptotic variance is also established. Numerical results from a simulation study and an empirical illustration are reported. To establish the results, a novel result on uniform convergence rates for kernel estimators is obtained. The online supplemental material to this article includes details on ...
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generalized jackknife estimators of Weighted Average derivatives
CREATES Research Papers, 2011Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:With the aim of improving the quality of asymptotic distributional approximations for nonlinear functionals of nonparametric estimators, this paper revisits the large-sample properties of an important member of that class, namely a kernel-based Weighted Average derivative estimator. Asymptotic linearity of the estimator is established under weak conditions. Indeed, we show that the bandwidth conditions employed are necessary in some cases. A bias-corrected version of the estimator is proposed and shown to be asymptotically linear under yet weaker bandwidth conditions. Consistency of an analog estimator of the asymptotic variance is also established. To establish the results, a novel result on uniform convergence rates for kernel estimators is obtained.
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robust data driven inference for density Weighted Average derivatives
Journal of the American Statistical Association, 2010Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:This paper presents a novel data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-Weighted Average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedures proposed by Cattaneo, Crump, and Jansson (2009) are coupled with this new data-driven bandwidth selector. The resulting robust data-driven confidence intervals compare favorably to the alternative procedures available in the literature. The online supplemental material to this paper contains further results from the simulation study.
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robust data driven inference for density Weighted Average derivatives
2009Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:This paper presents a new data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-Weighted Average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedure proposed by Cattaneo, Crump, and Jansson (2009) is coupled with this new data-driven bandwidth selector. The resulting robust data-driven confidence intervals compare favorably to the alternative procedures available in the literature.
K.r. Butcher - One of the best experts on this subject based on the ideXlab platform.
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Herd composite somatic cell counts: Average linear score and Weighted Average somatic cell count score and milk production.
Journal of Dairy Science, 2010Co-Authors: John Fetrow, Kevin L. Anderson, Susan Sexton, K.r. ButcherAbstract:Abstract The relationship between herd production Weighted Average somatic cell count (Average of somatic cell count Weighted by individual cow milk production) and Average linear score (Average of individual cow linear score somatic cell counts) was investigated using three models. The model with the highest R 2 was log Weighted Average somatic cell count versus Average linear score. Use of the regression model results to convert an Average linear score to Weighted Average somatic cell count results in a bulk tank somatic cell count approximately twice that of conversion of a linear score somatic cell count when done on an individual cow basis. Rolling herd Average milk decreased 190kg as Average linear score increased by 1.
José M. Merigó - One of the best experts on this subject based on the ideXlab platform.
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Using Ordered Weighted Average for Weighted Averages Inflation
International Journal of Information Technology & Decision Making, 2020Co-Authors: Luis F. Espinoza-audelo, Ernesto León-castro, José M. Merigó, Marycruz Olazabal-lugo, Anna M. Gil-lafuenteAbstract:This paper presents the ordered Weighted Average Weighted Average inflation (OWAWAI) and some extensions using induced and heavy aggregation operators and presents the generalized operators and some of their families. The main advantage of these new formulations is that they can use two different sets of weighting vectors and generate new scenarios based on the reordering of the arguments with the weights. With this idea, it is possible to generate new approaches that under- or overestimate the results according to the knowledge and expertise of the decision-maker. The work presents an application of these new approaches in the analysis of the inflation in Chile, Colombia, and Argentina during 2017.
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The Weighted Average multiexperton
Information Sciences, 2020Co-Authors: Salvador Linares-mustarós, Joan Carles Ferrer-comalat, Dolors Corominas-coll, José M. MerigóAbstract:Abstract Experton theory, a generalization of probabilistic set theory, that is of great usefulness to group decision analysis, was first proposed as a means of improving the processing and analysis of opinions issued by experts. This theory produces an information-fusion mathematical object, the experton, which can be used in predictive problems to justify decisions based on well-constructed reasoning. The aim of this paper is to present an aggregative method of several expertons, with the idea that some of the groups of experts involved in producing these expertons may have more influence than others in the decision-making process. In this article, we carry out an aggregation analysis of expertons, not experts, which culminates in the creation of a new mathematical object. This object, which is called the Weighted Average multiexperton, is coherent with an experton-type object created from a weighting of the initial data provided by all experts. Since the aggregation method presented has been devised to represent the decision-maker’s attitude regarding the importance of different groups of experts, this approach represents a new tool for dealing with group decision-making problems. Additionally, the study presents some properties of the new object. Finally, the paper ends with an application for business decision-making.
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Weighted Averages in the Ordered Weighted Average Inflation
Advances in Intelligent Systems and Computing, 2019Co-Authors: Ernesto León-castro, Fabio Blanco-mesa, José M. MerigóAbstract:This paper presents the ordered Weighted Average Weighted Average inflation (OWAWAI). The OWAWAI operator is a new formulation for calculating inflation that provides different criteria for the association between the arguments and weights. OWAWAI presents the possibility to generate new approaches that under- or overestimate the results according to the knowledge and expertise of the decision maker. The works present an approach in Chile inflation.
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The Ordered Weighted Average in the Variance and the Covariance
International Journal of Intelligent Systems, 2015Co-Authors: José M. Merigó, Montserrat Guillén, José M. SarabiaAbstract:Artículo de publicación ISIThe ordered Weighted Average (OWA) is an aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper analyzes the use of the OWA in the variance and the covariance. It presents several extensions by using a unified framework between the Weighted Average and the OWA. Furthermore, it also develops other generalizations with induced aggregation operators and by using quasi-arithmetic means. Several measures of correlation by using the OWA are introduced including a new type of Pearson coefficient. The paper ends with some numerical examples focused on the construction of interval and fuzzy numbers with the variance and the covariance.European Commission through Spanish Government PIEF-GA-2011-300062 ECO2010-21787-C03-01 ECO2013-48326-C2-1-P ECO2013-48326-C2-2-
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Induced intuitionistic fuzzy ordered Weighted averaging: Weighted Average operator and its application to business decision-making
Computer Science and Information Systems, 2014Co-Authors: Zeng Shou-zhen, José M. Merigó, Wang Qifeng, Pan TiejunAbstract:We present the induced intuitionistic fuzzy ordered Weighted averaging-Weighted Average (I-IFOWAWA) operator. It is a new aggregation operator that uses the intuitionistic fuzzy Weighted Average (IFWA) and the induced intuitionistic fuzzy ordered Weighted averaging (I-IFOWA) operator in the same formulation. We study some of its main properties and we have seen that it has a lot of particular cases such as the IFWA and the intuitionistic fuzzy ordered Weighted averaging (IFOWA) operator. We also study its applicability in a decision-making problem concerning strategic selection of investments. We see that depending on the particular type of I-IFOWAWA operator used, the results may lead to different decisions.
Matias D Cattaneo - One of the best experts on this subject based on the ideXlab platform.
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small bandwidth asymptotics for density Weighted Average derivatives
Econometric Theory, 2014Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:This paper proposes (apparently) novel standard error formulas for the density-Weighted Average derivative estimator of Powell, Stock, and Stoker (Econometrica 57, 1989). Asymptotic validity of the standard errors developed in this paper does not require the use of higher-order kernels, and the standard errors are robust in the sense that they accommodate (but do not require) bandwidths that are smaller than those for which conventional standard errors are valid. Moreover, the results of a Monte Carlo experiment suggest that the finite sample coverage rates of confidence intervals constructed using the standard errors developed in this papercoincide (approximately) with the nominal coverage rates across a nontrivial range of bandwidths. Copyright © Cambridge University Press 2013 A .
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generalized jackknife estimators of Weighted Average derivatives
Journal of the American Statistical Association, 2013Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:With the aim of improving the quality of asymptotic distributional approximations for nonlinear functionals of nonparametric estimators, this article revisits the large-sample properties of an important member of that class, namely a kernel-based Weighted Average derivative estimator. Asymptotic linearity of the estimator is established under weak conditions. Indeed, we show that the bandwidth conditions employed are necessary in some cases. A bias-corrected version of the estimator is proposed and shown to be asymptotically linear under yet weaker bandwidth conditions. Implementational details of the estimators are discussed, including bandwidth selection procedures. Consistency of an analog estimator of the asymptotic variance is also established. Numerical results from a simulation study and an empirical illustration are reported. To establish the results, a novel result on uniform convergence rates for kernel estimators is obtained. The online supplemental material to this article includes details on ...
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generalized jackknife estimators of Weighted Average derivatives
CREATES Research Papers, 2011Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:With the aim of improving the quality of asymptotic distributional approximations for nonlinear functionals of nonparametric estimators, this paper revisits the large-sample properties of an important member of that class, namely a kernel-based Weighted Average derivative estimator. Asymptotic linearity of the estimator is established under weak conditions. Indeed, we show that the bandwidth conditions employed are necessary in some cases. A bias-corrected version of the estimator is proposed and shown to be asymptotically linear under yet weaker bandwidth conditions. Consistency of an analog estimator of the asymptotic variance is also established. To establish the results, a novel result on uniform convergence rates for kernel estimators is obtained.
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robust data driven inference for density Weighted Average derivatives
Journal of the American Statistical Association, 2010Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:This paper presents a novel data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-Weighted Average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedures proposed by Cattaneo, Crump, and Jansson (2009) are coupled with this new data-driven bandwidth selector. The resulting robust data-driven confidence intervals compare favorably to the alternative procedures available in the literature. The online supplemental material to this paper contains further results from the simulation study.
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robust data driven inference for density Weighted Average derivatives
2009Co-Authors: Matias D Cattaneo, Richard K Crump, Michael JanssonAbstract:This paper presents a new data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-Weighted Average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedure proposed by Cattaneo, Crump, and Jansson (2009) is coupled with this new data-driven bandwidth selector. The resulting robust data-driven confidence intervals compare favorably to the alternative procedures available in the literature.
John Fetrow - One of the best experts on this subject based on the ideXlab platform.
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Herd composite somatic cell counts: Average linear score and Weighted Average somatic cell count score and milk production.
Journal of Dairy Science, 2010Co-Authors: John Fetrow, Kevin L. Anderson, Susan Sexton, K.r. ButcherAbstract:Abstract The relationship between herd production Weighted Average somatic cell count (Average of somatic cell count Weighted by individual cow milk production) and Average linear score (Average of individual cow linear score somatic cell counts) was investigated using three models. The model with the highest R 2 was log Weighted Average somatic cell count versus Average linear score. Use of the regression model results to convert an Average linear score to Weighted Average somatic cell count results in a bulk tank somatic cell count approximately twice that of conversion of a linear score somatic cell count when done on an individual cow basis. Rolling herd Average milk decreased 190kg as Average linear score increased by 1.
