Results 261 to 270 of about 6,859,440 (291)
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Communications in Statistics - Theory and Methods, 2003
Simple expressions are presented that relate cumulants to central moments without involving moments about the origin. These expressions are used to obtain recursive formulae for the central moments of the gamma distribution, with exponential and chi-square distributions as special cases.
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Simple expressions are presented that relate cumulants to central moments without involving moments about the origin. These expressions are used to obtain recursive formulae for the central moments of the gamma distribution, with exponential and chi-square distributions as special cases.
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Weighted central moments in pattern recognition
Pattern Recognition Letters, 2000Abstract We consider Cartesian moments for generating translation and rotation invariant descriptors in two-dimensional pattern recognition. In a number of situations the noise tolerance can be improved by introducing a spatial weight function. In rotation independent classification this function compensates for the fact that simple central moments ...
Balslev, I., Døring, K., Eriksen, R.D.
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Central Moments, Stochastic Dominance, and the Moment Rules
SSRN Electronic Journal, 2017In this paper, we first develop some properties to state the relationships between the central moments and stochastic dominance for both the general utility functions and the polynomial utility functions. This leads to draw preferences of both risk averters and risk seekers on their choices of assets with different moments.
Raymond Honfu Chan +3 more
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On the 10th Central Moment of the Bernstein Polynomials
Results in Mathematics, 2019A sharp upper bound for the 10th central moment of the classical Bernstein polynomials is proved.
José A. Adell, Daniel Cárdenas-Morales
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Moment Generating Functions and Central Moments
2018This section deals with the moment generating functions (m.g.f.) up to sixth order of some discretely defined operators. We mention the m.g.f. and express them in expanded form to obtain moments, which are important in the theory of approximation relevant to problems of convergence.
Vijay Gupta +3 more
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Central Moments, Stochastic Dominance, Moment Rule, and Diversification
SSRN Electronic Journal, 2019In this paper, we first develop some properties to state the relationships among central moments, stochastic dominance (SD), risk-seeking stochastic dominance (RSD), and integrals for the general utility functions and the polynomial utility functions of both risk averters and risk seekers.
Raymond Honfu Chan +3 more
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Central moments in quantum chemistry
International Journal of Quantum Chemistry, 2008AbstractWe define central moments of operators on finite‐dimensional vector spaces and study some of their basic aspects. Central moments may be viewed as generalizations of the dispersion of a Hermitian operator. We show how eigenvalues may be represented by central moments, and how central moments may be used to obtain Krylov subspace approximations ...
David W. Small, Martin Head‐Gordon
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On the Three-Dimensional Central Moment Lattice Boltzmann Method [PDF]
Journal of Statistical Physics, 2011 A three-dimensional (3D) lattice Boltzmann method based on central moments is derived. Two main elements are the local attractors in the collision term and the source terms representing the effect of external and/or self-consistent internal forces. For suitable choices of the orthogonal moment basis for the three-dimensional, twenty seven velocity ...
Kannan N Premnath, Sanjoy Banerjee
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On the computation of sample central moments
International Journal of Mathematical Education in Science and Technology, 1988The central moments of a random sample are often needed in many different applications. Although they can be conveniently computed by first calculating the moments about the origin and then applying the basic relationships between them and these moments, the results may be erroneous because of the truncation and rounding effects.
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Tables of Dimensionless Central Moments
Journal of the Engineering Mechanics Division, 1980Central moments normalized by the corresponding standard deviations raised to a power equal to the order considered are tabulated for distributions commonly encountered in structural reliability such as: 1) Normal; 2) gamma; 3) exponential; 4) chi-square; 5) extreme type I; 6) extreme type II; and 7) Weibull distributions.
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