• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.193-205
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• 1999

An approximate Bayes criterion for Behrens-Fisher problem (testing equality of means of two normal populations with unequal variances) is proposed and examined. Development of the criterion involves derivation of approximate Bayes factor using the imaginary training sample approachintroduced by Spiegelhalter and Smith (1982). The proposed criterion is designed to develop a Bayesian test criterion having a closed form, so that it provides an alternative test to those based upon asymptotic sampling theory (such as Welch's t test). For the suggested Bayes criterion, numerical study gives comparisons with a couple of asymptotic classical test criteria.

• Journal of applied mathematics & informatics
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• v.30 no.5_6
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• pp.993-1003
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• 2012

The family of graphs $\hat{W}_{4n}$ is defined by taking the simple whee graph $W_{n+1}$ with $n$ rim vertices and then adding three extra vertices on every rim edge of the wheel. In this paper, the critical group of this whole family of graphs is investigated.

• Journal of applied mathematics & informatics
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• v.4 no.1
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• pp.63-82
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• 1997

In experimental situations where n two or three level fac-tors are involoved and n observations are taken then the D-optimal first order saturated design is an $n{\times}n$ matrix with elements $\pm$1 or 0, $\pm$1, with the maximum determinant. Cononical forms are useful for the specification of the non-isomorphic D-optimal designs. In this paper we study canonical forms such as the Smith normal form the first sec-ond and the jordan canonical form of D-optimal designs. Numerical algorithms for the computation of these forms are described and some numerical examples are also given.

• Journal of the Society of Naval Architects of Korea
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• v.29 no.4
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• pp.58-65
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• 1992

In this paper, wave resistance for a submerged body is calculated by a higher order panel method. The Neumann-Kelvin problem is solved by the source or normal dipole distribution method. The body surface is represented by a bicubic B-spline and the singularity strengths are approximated by a bilinear form. The results calculated by the higher order panel method are compared with those by the lowest order panel method developed by Hess & Smith. The convergence rate of the higher order panel method is much better than the lowest order panel method. But the wave resistance calculated by the higher order panel method still shows discrepancy with an analytic solution at low Froude number like that by the lowest order panel method.

• Ocean Systems Engineering
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• v.10 no.2
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• pp.227-241
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• 2020

This paper deals with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems. Problem and objective: Based on the relaxed method, the chaotic system can be stabilized by regulating appropriately the parameters of dither. Scope and method: If the frequency of dither is high enough, the trajectory of the closed-loop dithered chaotic system and that of its corresponding model-the closed-loop fuzzy relaxed system can be made as close as desired. Results and conclusion: The behavior of the closed-loop dithered chaotic system can be rigorously predicted by establishing that of the closed-loop fuzzy relaxed system.

• Kyungpook Mathematical Journal
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• v.64 no.2
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• pp.197-204
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• 2024

Let R = 𝕂[x] be a univariate polynomial ring over an algebraically closed field 𝕂 of characteristic zero. Let A ∈ M_m,m(R) be an m×m matrix over R with non-zero determinate det(A) ∈ R. In this paper, utilizing linear-algebraic techniques, we investigate the relationship between a basis for the syzygy module of f₁, . . . , f_m and a basis for the syzygy module of g₁, . . . , g_m, where [g₁, . . . , g_m] = [f₁, . . . , f_m]A.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.73-84
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• 2002

Linear regression models with inequality constraints on the coefficients are frequently used in economic models due to sign or order constraints on the coefficients. In this paper, we propose a Bayesian approach to selecting significant explanatory variables in linear regression models with inequality constraints on the coefficients. Bayesian variable selection requires computation of posterior probability of each candidate model. We propose a method which computes all the necessary posterior model probabilities simultaneously. In specific, we obtain posterior samples form the most general model via Gibbs sampling algorithm (Gelfand and Smith, 1990) and compute the posterior probabilities by using the samples. A real example is given to illustrate the method.