• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.147-153
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• 2007

In this note, we obtained results related to multiparameter discrete exponential families on considering lattice or semi-lattice in place of N (Natural numbers) in view of Cacoullos-type inequalities via the same arguments in Papathanasiou (1990, 1993).

• Journal of the Korean Mathematical Society
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• v.39 no.4
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• pp.495-507
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• 2002

In this paper, the characterization results related to Chernoff-type inequalities are applied for exponential-type (continuous and discrete) families. Upper variance bound is obtained here with a slightly different technique used in Alharbi and Shanbhag [1] and Mohtashami Borzadaran and Shanbhag [8]. Some results are shown with assuming measures such as non-atomic measure, atomic measure, Lebesgue measure and counting measure as special cases of Lebesgue-Stieltjes measure. Characterization results on power series distributions via Chernoff-type inequalities are corollaries to our results.

• The Pure and Applied Mathematics
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• v.4 no.1
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• pp.97-104
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• 1997

The large deviations theorem of Cramer is extended to conditional probabilities in the following sense. Consider a random sample of pairs of random vectors and the sample means of each of the pairs. The probability that the first falls outside a certain convex set given that the second is fixed is shown to decrease with the sample size at an exponential rate which depends on the Kullback-Leibler distance between two distributions in an associated exponential familiy of distributions. Examples are given which include a method of computing the Bahadur exact slope for tests of certain composite hypotheses in exponential families.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.427-438
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• 2005

From a notion of d-pseudo-orthogonality for a sequence of poly-nomials ($d\;\in\;{2,3,\cdots}$), this paper introduces three different characterizations of natural exponential families (NEF's) with polynomial variance functions of exact degree 2d-1. These results provide extended versions of the Meixner (1934), Shanbhag (1972, 1979) and Feinsilver (1986) characterization results of quadratic NEF's based on classical orthogonal polynomials. Some news sets of polynomials with (2d-1)-term recurrence relation are then pointed out and we completely illustrate the cases associated to the families of positive stable distributions.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.133-149
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• 1995

Since probability density functions for the distributions belonging to the natural exponential families having power(>2) variance functions are expressed as infinite series, it is very difficult to deal with the distributins in spite of their usefulness. Therefore, tables for the percentiles of the distributions are obtained, and approximate percentiles are also obtained in this thesis. It is shown that the approximate percentiles can replace exact percentlies well for some distributions.

• Journal of the Korean Statistical Society
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• v.10
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• pp.128-139
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• 1981

The Bayesian sequential estimation problem for k parameters exponential families is considered using loss related to the Fisher information. Tractable expressions for the Bayes estimator and the posterior expected loss are found, and the myopic or one-step-ahead stopping rule is defined. Sufficient conditions are given for optimality of the myopic procedure, and the myopic procedure is shown to be asymptotically optimal in all cases considered.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.743-758
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• 2005

This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 1992.08a
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• pp.150-156
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• 1992

수치모델(e.g., Cartons et al., 1976)로 다방향 스펙트라를 예측하여 폭풍에 의한 스펙트라의 다방향성을 제시하여 왔다. apr시코 만에서 얻어진 현장 관측치에 근거하여 연구자들이(Niedzwecki and Whatley, 1991) 다방향 스펙트라를 cosine power, exponential and exponential series families 로 구성된 다방향 방향분산 함수를 제시함에 따라 삼차원적 해양 수치모델을 수립할 수 있으며 이 함수의 다양한 분산 parameter에 의한 다방향 불규칙 파랑의 물입자 흐름을 예측함에 따라 실제적인 계류 시스템의 반응을 검사하였다.(중략)

• Journal of the Korean Mathematical Society
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• v.48 no.2
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• pp.267-288
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• 2011

In this paper, we construct eight infinite families of ternary linear codes associated with double cosets with respect to certain maximal parabolic subgroup of the special orthogonal group $SO^-$(2n, q). Here q is a power of three. Then we obtain four infinite families of recursive formulas for power moments of Kloosterman sums with square arguments and four infinite families of recursive formulas for even power moments of those in terms of the frequencies of weights in the codes. This is done via Pless power moment identity and by utilizing the explicit expressions of exponential sums over those double cosets related to the evaluations of "Gauss sums" for the orthogonal groups $O^-$(2n, q).

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.275-283
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• 1999

In order to estimate a parameter $(\alpha,\beta^r), r\epsilonN$, in a distribution belonging to a location-scale family we usually use best invariant estimator (BIE) and best unbiased estimator (BUE). But in some conditions Ryu (1996) showed that BIE is better than BUE. In this paper we calculate risks of BIE and BUE in a normal and an exponential distribution respectively and calculate a percentage risk improvement exponential distribution respectively and calculate a percentage risk improvement (PRI). We find the sample size n which make no significant differences between BIE and BUE in a normal distribution. And we show that BIE is always significantly better than BUE in an exponential distribution. Also simulation in a normal distribution is given to convince us of our result.