Mass transportation problems with capacity constraints

1999 ◽  
Vol 36 (2) ◽  
pp. 433-445 ◽  
Author(s):  
S. T. Rachev ◽  
I. Olkin
Keyword(s):  
Joint Distribution ◽  
Bivariate Distribution ◽  
Capacity Constraints ◽  
Distribution Functions ◽  
Mass Transportation ◽  
Marginal Distributions ◽  
Transportation Problems ◽  
The Family ◽  
Additional Capacity ◽  
Additional Constraints

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.

Mass transportation problems with capacity constraints

1999 ◽  
Vol 36 (02) ◽  
pp. 433-445 ◽  
Author(s):  
S. T. Rachev ◽  
I. Olkin
Keyword(s):  
Joint Distribution ◽  
Bivariate Distribution ◽  
Capacity Constraints ◽  
Distribution Functions ◽  
Mass Transportation ◽  
Marginal Distributions ◽  
Transportation Problems ◽  
The Family ◽  
Additional Capacity ◽  
Additional Constraints

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.

scholarly journals Comparing Johnson’s SBB, Weibull and Logit-Logistic bivariate distributions for modeling tree diameters and heights using copulas

2016 ◽  
Vol 25 (1) ◽  
pp. 07 ◽  
Author(s):  
Jose Javier Gorgoso-Varela ◽  
Juan Daniel García-Villabrille ◽  
Alberto Rojo-Alboreca ◽  
Klaus Von Gadow ◽  
Juan Gabriel Álvarez-González
Keyword(s):  
Joint Distribution ◽  
Likelihood Function ◽  
Bivariate Distribution ◽  
Rank Test ◽  
Bivariate Distributions ◽  
Multivariate Distributions ◽  
Marginal Distributions ◽  
North West ◽  
The North ◽  
Signed Rank Test

Aim of study: In this study we compare the accuracy of three bivariate distributions: Johnson’s SBB, Weibull-2P and LL-2P functions for characterizing the joint distribution of tree diameters and heights.Area of study: North-West of Spain.Material and methods: Diameter and height measurements of 128 plots of pure and even-aged Tasmanian blue gum (Eucalyptus globulus Labill.) stands located in the North-west of Spain were considered in the present study. The SBB bivariate distribution was obtained from SB marginal distributions using a Normal Copula based on a four-parameter logistic transformation. The Plackett Copula was used to obtain the bivariate models from the Weibull and Logit-logistic univariate marginal distributions. The negative logarithm of the maximum likelihood function was used to compare the results and the Wilcoxon signed-rank test was used to compare the related samples of these logarithms calculated for each sample plot and each distribution.Main results: The best results were obtained by using the Plackett copula and the best marginal distribution was the Logit-logistic.Research highlights: The copulas used in this study have shown a good performance for modeling the joint distribution of tree diameters and heights. They could be easily extended for modelling multivariate distributions involving other tree variables, such as tree volume or biomass.

scholarly journals A general stochastic model for bivariate episodes driven by a gamma sequence

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Charles K. Amponsah ◽  
Tomasz J. Kozubowski ◽  
Anna K. Panorska
Keyword(s):  
Stochastic Model ◽  
Joint Distribution ◽  
Pareto Distribution ◽  
Integral Transforms ◽  
Bivariate Distribution ◽  
Conditional Distributions ◽  
General Stochastic Model ◽  
Heavy Tailed ◽  
Discrete Pareto Distribution ◽  
Special Case

AbstractWe propose a new stochastic model describing the joint distribution of (X,N), where N is a counting variable while X is the sum of N independent gamma random variables. We present the main properties of this general model, which include marginal and conditional distributions, integral transforms, moments and parameter estimation. We also discuss in more detail a special case where N has a heavy tailed discrete Pareto distribution. An example from finance illustrates the modeling potential of this new mixed bivariate distribution.

scholarly journals Error Uncertainty Analysis in Planar Closed-Loop Structure with Joint Clearances

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1872
Author(s):  
Yushu Yu ◽  
Jinglin Li ◽  
Xin Li ◽  
Yi Yang
Keyword(s):  
Joint Distribution ◽  
Error Probability ◽  
Closed Loop ◽  
Open Loop ◽  
Distribution Functions ◽  
Positional Error ◽  
Loop Structure ◽  
Virtual Link ◽  
Joint Clearances ◽  
Probability Density Function Pdf

For planar closed-loop structures with clearances, the angular and positional error uncertainties are studied. By using the vector translation method and geometric method, the boundaries of the errors are analyzed. The joint clearance is considered as being distributed uniformly in a circle area. A virtual link projection method is proposed to deal with the clearance affected length error probability density function (PDF) for open-loop links. The error relationship between open loop and closed loop is established. The open-loop length PDF and the closed-loop angular error PDF both approach being Gaussian distribution if there are many clearances. The angular propagation error of multi-loop structures is also investigated by using convolution. The positional errors of single and multiple loops are both discussed as joint distribution functions. Monte Carlo simulations are conducted to verify the proposed methods.

A bivariate distribution in regeneration

1975 ◽  
Vol 12 (4) ◽  
pp. 837-839 ◽  
Author(s):  
Kai Lai Chung
Keyword(s):  
Boundary Point ◽  
Joint Distribution ◽  
Bivariate Distribution ◽  
Regenerative Phenomenon

The joint distribution of the time since last exit, and the time until next entrance, into a unique boundary point is given in Formula (1) below. The boundary point may be replaced by a regenerative phenomenon.

Dependent Random Variables with Independent Subsets - II

1990 ◽  
Vol 33 (1) ◽  
pp. 24-28 ◽  
Author(s):  
Y. H. Wang
Keyword(s):  
Joint Distribution ◽  
Random Variables ◽  
Marginal Distributions ◽  
Dependent Random Variables ◽  
One Dimensional ◽  
The One

AbstractIn this paper, we consolidate into one two separate problems - dependent random variables with independent subsets and construction of a joint distribution with given marginals. Let N = {1,2,3,...} and X = {Xn; n ∊ N} be a sequence of random variables with nondegenerate one-dimensional marginal distributions {Fn; n ∊ N}. An example is constructed to show that there exists a sequence of random variables Y = {Yn; n ∊ N} such that the components of a subset of Y are independent if and only if its size is ≦ k, where k ≧ 2 is a prefixed integer. Furthermore, the one-dimensional marginal distributions of Y are those of X.

scholarly journals Radial Velocity Profiles for Anisotropic Spherically Symmetric Clusters: An Example

1988 ◽  
Vol 126 ◽  
pp. 691-692
Author(s):  
Herwig Dejonghe
Keyword(s):  
Distribution Function ◽  
Radial Velocity ◽  
Velocity Dispersion ◽  
Mass Density ◽  
Distribution Functions ◽  
Spherically Symmetric ◽  
Line Profiles ◽  
Anisotropic Models ◽  
The Family

A 1-parameter family of anisotropic models is presented. They all satisfy the Plummer law in the mass density, but have different velocity dispersions. Moreover, the stars are not confined to a particular subset of the total accessible phase space. This family is mathematically simple enough to be explored analytically in detail. The family is rich enough though to allow for a 3-parameter generalization which illustrates that even when both the mass density and the velocity dispersion profiles are required to be the same, a degeneracy in the possible distribution functions persists. The observational consequences of the degeneracy can be studied by calculating the observable radial velocity line profiles obtained with different distribution functions. It turns out that line profiles are relatively sensitive to changes in the distribution function. They therefore can be considered to be more natural observables when a determination of the distribution function is desired.

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