scholarly journals Characterization of a class of bivariate distribution functions

1975 ◽  
Vol 5 (2) ◽  
pp. 227-235 ◽  
Author(s):  
S. Tyan ◽  
J.B. Thomas
2019 ◽  
Vol 627 ◽  
pp. A157
Author(s):  
M. Cernetic ◽  
A. I. Shapiro ◽  
V. Witzke ◽  
N. A. Krivova ◽  
S. K. Solanki ◽  
...  

Context. Stellar spectra synthesis is essential for the characterization of potential planetary hosts. In addition, comprehensive stellar variability calculations with fast radiative transfer are needed to disentangle planetary transits from stellar magnetically driven variability. The planet-hunting space telescopes, such as CoRoT, Kepler, and TESS, bring vast quantities of data, rekindling the interest in fast calculations of the radiative transfer. Aims. We revisit the opacity distribution functions (ODF) approach routinely applied to speed up stellar spectral synthesis. To achieve a considerable speedup relative to the state of the art, we further optimize the approach and search for the best ODF configuration. Furthermore, we generalize the ODF approach for fast calculations of flux in various filters often used in stellar observations. Methods. In a parameter-sweep fashion, we generated ODF in the spectral range from UV to IR with different setups. The most accurate ODF configuration for each spectral interval was determined. We adapted the wavelength grid based on the transmission curve for calculations of the radiative fluxes through filters before performing the normal ODF procedure. Results. Our optimum ODF configuration allows for a three-fold speedup, compared to the previously used ODF configurations. The ODF generalization to calculate fluxes through filters results in a speedup of more than two orders of magnitude.


2004 ◽  
Vol 41 (A) ◽  
pp. 321-332 ◽  
Author(s):  
Paul Glasserman ◽  
David D. Yao

An optimal coupling is a bivariate distribution with specified marginals achieving maximal correlation. We show that optimal couplings are totally positive and, in fact, satisfy a strictly stronger condition we call the nonintersection property. For discrete distributions we illustrate the equivalence between optimal coupling and a certain transportation problem. Specifically, the optimal solutions of greedily-solvable transportation problems are totally positive, and even nonintersecting, through a rearrangement of matrix entries that results in a Monge sequence. In coupling continuous random variables or random vectors, we exploit a characterization of optimal couplings in terms of subgradients of a closed convex function to establish a generalization of the nonintersection property. We argue that nonintersection is not only stronger than total positivity, it is the more natural concept for the singular distributions that arise in coupling continuous random variables.


1999 ◽  
Vol 36 (2) ◽  
pp. 433-445 ◽  
Author(s):  
S. T. Rachev ◽  
I. Olkin

We exhibit solutions of Monge–Kantorovich mass transportation problems with constraints on the support of the feasible transportation plans and additional capacity restrictions. The Hoeffding–Fréchet inequalities are extended for bivariate distribution functions having fixed marginal distributions and satisfying additional constraints. Sharp bounds for different probabilistic functionals (e.g. Lp-distances, covariances, etc.) are given when the family of joint distribution functions has prescribed marginal distributions, satisfies restrictions on the support, and is bounded from above, or below, by other distributions.


2004 ◽  
Vol 41 (A) ◽  
pp. 321-332
Author(s):  
Paul Glasserman ◽  
David D. Yao

An optimal coupling is a bivariate distribution with specified marginals achieving maximal correlation. We show that optimal couplings are totally positive and, in fact, satisfy a strictly stronger condition we call the nonintersection property. For discrete distributions we illustrate the equivalence between optimal coupling and a certain transportation problem. Specifically, the optimal solutions of greedily-solvable transportation problems are totally positive, and even nonintersecting, through a rearrangement of matrix entries that results in a Monge sequence. In coupling continuous random variables or random vectors, we exploit a characterization of optimal couplings in terms of subgradients of a closed convex function to establish a generalization of the nonintersection property. We argue that nonintersection is not only stronger than total positivity, it is the more natural concept for the singular distributions that arise in coupling continuous random variables.


1992 ◽  
Vol 42 (3-4) ◽  
pp. 221-236 ◽  
Author(s):  
Uttam Bandyopadhyay ◽  
Gopaldeb Chattopadhyay

The object of the present investigation is to provide suitable nonparametric tests for testing the identity of two bivariate distribution functions F1 and F2 against a class of restricted alternatives when there is sample of fixed size m from F1 and the observations from F2 are drawn sequentially. Various large sample results of the proposed tests are formulated and examined. AMS Subject Classification: Primary 62010, Secondary 62020.


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