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, 2018
We now focus on models for the joint effect of two continuous predictor variables. Additive models are convenient, but there is no reason to assume that they are always adequate. In the general bivariate models studied in this chapter, the joint effect of the two variables is a smooth, but otherwise unrestricted, function of these variables. Therefore,
J. Harezlak, D. Ruppert, M. Wand
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We now focus on models for the joint effect of two continuous predictor variables. Additive models are convenient, but there is no reason to assume that they are always adequate. In the general bivariate models studied in this chapter, the joint effect of the two variables is a smooth, but otherwise unrestricted, function of these variables. Therefore,
J. Harezlak, D. Ruppert, M. Wand
semanticscholar +2 more sources
Flow Cross K-function: a bivariate flow analytical method
International Journal of Geographical Information Science, 2019Spatial flow data represent meaningful interaction activities between pairs of corresponding locations, such as daily commuting, animal migration, and merchandise shipping.
Ran Tao, J. Thill
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Bivariate Beta distribution and multiplicative functions
European Journal of Mathematics, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gintautas Bareikis, Algirdas Mačiulis
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A new bivariate dimension reduction method for efficient structural reliability analysis
Mechanical Systems and Signal Processing, 2019Jun Xu, Chao Dang
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Estimating scale-invariant directed dependence of bivariate distributions
Computational Statistics & Data Analysis, 2021Asymmetry of dependence is an inherent property of bivariate probability distributions. Being symmetric, commonly used dependence measures such as Pearson’s r or Spearman’s ρ mask asymmetry and implicitly assume that a random variable Y is equally ...
R. Junker +2 more
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SVD—based approximations of bivariate functions
2005 IEEE International Symposium on Circuits and Systems, 2005A method to approximate functions of two variables is presented; it is suitable for hardware implementations based on digital or mixed signal architectures. Such a method is based on the properties of the singular value decomposition (SVD) of a matrix that stores the samples of the function to be approximated.
F. Bizzarri +2 more
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Decomposition of Bivariate Symmetric Density Function
Calcutta Statistical Association Bulletin, 1996For the analysis of square contingency tables, it is known that the symmetry model holds if and only if both the quasi-symmetry and the marginal homogeneity models hold (Caussinus, 1965; Bishop et al., 1975, p. 287). This paper shows that a similar decomposition for bivariate density function (instead of cell probabilities) holds.
Tomizawa, Sadao +2 more
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Bivariate p-boxes and maxitive functions
International Journal of General Systems, 2016We investigate the properties of the upper probability associated with a bivariate p-box, that may be used as a model for the imprecise knowledge of a bivariate distribution function. We give necessary and sufficient conditions for this upper probability to be maxitive, characterize its focal elements, and study which maxitive functions can be obtained
Ignacio Montes, Enrique Miranda
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Extrema detection of bivariate spline functions
Applied Mathematics and Computation, 2007The problem of finding the extrema of a two-variable spline function \(x(s,t)\) of degree \(k\geq 3\), in a rectangle \(\mathcal{D}\subset \mathbb{R}^2\), is examined. In this respect, a method is developed which allows to detect the extrema of \(x(s,t)\) as well as of their derivatives.
Kano, Hiroyuki +2 more
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