Topic
Weight function
About: Weight function is a research topic. Over the lifetime, 3342 publications have been published within this topic receiving 64732 citations.
Papers published on a yearly basis
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Papers
TL;DR: In this article, an element-free Galerkin method which is applicable to arbitrary shapes but requires only nodal data is applied to elasticity and heat conduction problems, where moving least-squares interpolants are used to construct the trial and test functions for the variational principle.
Abstract: An element-free Galerkin method which is applicable to arbitrary shapes but requires only nodal data is applied to elasticity and heat conduction problems. In this method, moving least-squares interpolants are used to construct the trial and test functions for the variational principle (weak form); the dependent variable and its gradient are continuous in the entire domain. In contrast to an earlier formulation by Nayroles and coworkers, certain key differences are introduced in the implementation to increase its accuracy. The numerical examples in this paper show that with these modifications, the method does not exhibit any volumetric locking, the rate of convergence can exceed that of finite elements significantly and a high resolution of localized steep gradients can be achieved. The moving least-squares interpolants and the choices of the weight function are also discussed in this paper.
5,807 citations
TL;DR: A new numerical method based on a combination of the classical shape derivative and of the level-set method for front propagation, which can easily handle topology changes and is strongly dependent on the initial guess.
2,609 citations
TL;DR: A class of predictive densities is derived by weighting the observed samples in maximizing the log-likelihood function, effective in cases such as sample surveys or design of experiments, where the observed covariate follows a different distribution than that in the whole population.
2,312 citations
TL;DR: In this article, the authors proposed two preference conditions that are necessary and sufficient for concavity and convexity of the weighting function, and tested these conditions using preference ladder data with weighting functions proposed by Tversky and Kahneman.
Abstract: When individuals choose among risky alternatives, the psychological weight attached to an outcome may not correspond to the probability of that outcome. In rank-dependent utility theories, including prospect theory, the probability weighting function permits probabilities to be weighted nonlinearly. Previous empirical studies of the weighting function have suggested an inverse S-shaped function, first concave and then convex. However, these studies suffer from a methodological shortcoming: estimation procedures have required assumptions about the functional form of the value and/or weighting functions. We propose two preference conditions that are necessary and sufficient for concavity and convexity of the weighting function. Empirical tests of these conditions are independent of the form of the value function. We test these conditions using preference "ladders" a series of questions that differ only by a common consequence. The concavity-convexity ladders validate previous findings of an S-shaped weighting function, concave up to p < 0.40, and convex beyond that probability. The tests also show significant nonlinearity away from the boundaries, 0 and 1. Finally, we fit the ladder data with weighting functions proposed by Tversky and Kahneman Tversky, Amos, Daniel Kahneman. 1992. Advances in prospect theory: Cumulative representation of uncertainty. J. Risk and Uncertainty5 297-323. and Prelec Prelec, Dražen. 1995. The probability weighting function. Unpublished paper..
1,120 citations
TL;DR: In this paper, it was shown that the single-particle distribution for an expanding relativistic gas described by a distribution function obeying the Boltzmann transport equation is not of the form of an integral over collective motions of a velocity weight function times a "Lorentz-transformed" rest-frame distribution function.
Abstract: We find that the single-particle distribution $\frac{\mathrm{EdN}}{{d}^{3}p}$ for an expanding relativistic gas described by a distribution function obeying the Boltzmann transport equation is not of the form of an integral over collective motions of a velocity weight function times a "Lorentz-transformed" rest-frame distribution function. This casts doubt on the algorithms of Milekhin and Hagedorn for determining the single-particle distribution function in their models of particle production. For the hydrodynamic model, the correct algorithm is presented.
1,090 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 18 |
| 2024 | 29 |
| 2023 | 34 |
| 2022 | 115 |
| 2021 | 120 |
| 2020 | 181 |