• Communications for Statistical Applications and Methods
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• 제29권4호
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• pp.471-486
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• 2022

Autoregressive moving average (ARMA) models involve nonlinearity in the model coefficients because of unobserved lagged errors, which complicates the likelihood function and makes the posterior density analytically intractable. In order to overcome this problem of posterior analysis, some approximation methods have been proposed in literature. In this paper we first review the main analytic approximations proposed to approximate the posterior density of ARMA models to be analytically tractable, which include Newbold, Zellner-Reynolds, and Broemeling-Shaarawy approximations. We then use the Kullback-Leibler divergence to study the relation between these three analytic approximations and to measure the distance between their derived approximate posteriors for ARMA models. In addition, we evaluate the impact of the approximate posteriors distance in Bayesian estimates of mean and precision of the model coefficients by generating a large number of Monte Carlo simulations from the approximate posteriors. Simulation study results show that the approximate posteriors of Newbold and Zellner-Reynolds are very close to each other, and their estimates have higher precision compared to those of Broemeling-Shaarawy approximation. Same results are obtained from the application to real-world time series datasets.

• Journal of Applied and Pure Mathematics
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• 제4권3_4호
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• pp.185-209
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• 2022

Here we exhibit multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝ^N, N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the generalized symmetrical sigmoid function. The approximations are point-wise and uniform. The related feed-forward neural network is with one hidden layer.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.551-562
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• 2007

This paper concerns a relationship between rough sets, intuitionistic fuzzy sets and ring theory. We consider a ring as a universal set and we assume that the knowledge about objects is restricted by an intuitionistic fuzzy ideal. We apply the notion of intutionistic fuzzy ideal of a ring for definitions of the lower and upper approximations in a ring. Some properties of the lower and upper approximations are investigated.

• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.219-234
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• 1993

A measuring instrument must be calibrated for accurate inferences of an unknown quantity. Bayesian calibration designs with respect to squared error loss based on a linear model are discussed in Kim and Barlow (1992). In this paper, we consider approximations of the optimal calibration designs using the idea of Gaussian inflence diagrams. The approximation is evaluated by means of numerical calculations, where it is compared with the exact values from the numerical integration.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1993년도 봄 학술발표회 논문집
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• pp.113-118
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• 1993

A method for approximating the failure surfaces for columns in compression and biaxial bending was proposed by using the moments along the line of a diagonal of the section. This method showed the better approximations for the failure surfaces of columns than the method of ACI. To calculate the moments along the line of a diagonal of the section, an approximate method which is not influenced by the number of steel s and the location of inner steels was proposed This method gave satisfactory approximations for practical sections of columns.

• 대한수학회보
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• 제52권5호
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• pp.1445-1465
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• 2015

In this paper, the long time behavior of the convective Cahn-Hilliard equation in two dimensions is considered, semidiscrete and completely discrete spectral approximations are constructed, error estimates of optimal order that hold uniformly on the unbounded time interval $0{\leq}t<{\infty}$ are obtained.

• 대한수학회보
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• 제38권3호
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• pp.505-516
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• 2001

We show the existence and uniqueness of solutions for the Cauchy problem for nonlinear evolution equations with the strong damping: ${\upsilon}"(t)-M(｜{\nablauu}(t)｜^2){\triangle}u(t)-{\delta}{\triangle}u'(t)=f(t)$. As an application, a Kirchhoff model with viscosity is given.

• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.189-201
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• 1996

In this paper, we study the saddlepoint approximations for the ratio of independent random variables. In Section 2, we derive the saddlepoint approximation to the density. And in Section 3, we derive two approximation formulae for the tail probability, one by following Daniels'(1987) method and the other by following Lugannani and Rice's (1980). In Section 4, we represent some numerical examples which show that the errors are small even for small sample size.

• East Asian mathematical journal
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• 제31권5호
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• pp.685-693
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• 2015

In this paper, we introduce fully discrete mixed discontinuous Galerkin approximations for parabolic problems. And we analyze the error estimates in $l^{\infty}(L^2)$ norm for the primary variable and the error estimates in the energy norm for the primary variable and the flux variable.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
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• pp.109-112
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• 2000

Many applications in computer graphics require highly detailed complex models. However, the level of detail may vary considerably according to applications. It is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplification algorithm which uses iterative contractions of edges to simplify models and maintains surface error approximations using a quadric metric. In this paper, we present an improved quadric error metric for simplifying meshes. The new metric, based on subdivided edge classification, results in more accurate simplified meshes. We show that a subdivided edge classification captures discontinuities efficiently. The new scheme is demonstrated on a variety of meshes.