• Journal of the Korean Data and Information Science Society
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• 제8권1호
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• pp.59-70
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• 1997

We introduce several estimators of the location and the scale parameters of the two-parameter exponential distribution, and then compare these estimators by the mean square error (MSE). Using the parametric bootstrap estimators and the delete-d jackknife, we obtain the bootstrap and the delete-d jackknife confidence intervals for the location and the scale parameters and compare the bootstrap confidence intervals with the delete-d jackknife confidence intervals by length and coverage probability through Monte Carlo method.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.241-246
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• 2013

In this study, we propose a nonparametric simultaneous test procedure for the location translation and scale parameters. We consider the Wilcoxon rank sum test for the location translation parameter and the Mood test for the scale parameter with the quadratic and maximal types of combining functions. Then we derive the limiting null distributions of the combining functions. We illustrate our procedure with an example and compare efficiency by obtaining the empirical powers through a simulation study. Finally, we discuss some interesting features related to the nonparametric simultaneous tests.

• 응용통계연구
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• 제4권2호
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• pp.129-135
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• 1991

위치와 척도 모수에 대하여 간단히 $L_1$ 불편 추정량을 구하는 방법을 요약 소개하고, $L_1$ 불편 추정량에 대한 자연적인 퍼짐의 측도로 제안된 diffusivity를 이용하는 예를 든다.

• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.329-344
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• 2007

In this paper, we shall propose the AMLEs of the scale parameter and the location parameter in the two-parameter Rayleigh distribution based on progressive Type-II censored samples when one parameter is known. We also propose the AMLEs of the two parameters in the Rayleigh distribution based on progressive Type-II censored samples when two parameters are unknown. We simulate the mean squared errors of the proposed estimators through Monte Carlo simulation for various censoring schemes.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.221-227
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• 2002

The concept of pivot has been widely used in various classical inferences. In this paper, it is proved by use of pivotal quantities that the Bayesian inferences can be arrived at the same results of classical inferences for the location-scale parameters models under the assumption of non-informative prior distributions. Some theorems are proposed in which the posterior distribution and the sampling distribution of a pivotal quantity coincide. The theorems are applied illustratively to some statistical models.

• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.603-613
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• 2005

We propose some estimators of the location parameter and derive the approximate maximum likelihood estimators (AMLEs) of the scale parameter in the exponential distribution based on multiply Type-II censored samples. We calculate the moments for the proposed estimators of the location parameter, and the AMLEs which are the linear functions of the order statistics. We compare the proposed estimators in the sense of the mean squared error (MSE) for various censored samples.

• Journal of the Korean Data and Information Science Society
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• 제15권1호
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• pp.227-237
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• 2004

In this paper we presented a Bayesian inference approach for estimating the location and scale parameters of the lognormal distribution using iterative Gibbs sampling algorithm. We also presented estimation of location parameter by two non iterative methods, importance sampling and weighted bootstrap assuming scale parameter as known. The estimates by non iterative techniques do not depend on the specification of hyper parameters which is optimal from the Bayesian point of view. The estimates obtained by more sophisticated Gibbs sampler vary slightly with the choices of hyper parameters. The objective of this paper is to illustrate these tools in a simpler setup which may be essential in more complicated situations.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.629-638
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• 2005

We derive the approximate maximum likelihood estimators of the scale parameter and location parameter of the extreme value distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.91-102
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• 2019

In this paper, we propose a new estimation method based on a weighted linear regression framework to obtain some estimators for unknown parameters in a two-parameter Rayleigh distribution under a progressive Type-II censoring scheme. We also provide unbiased estimators of the location parameter and scale parameter which have a nuisance parameter, and an estimator based on a pivotal quantity which does not depend on the other parameter. The proposed weighted least square estimator (WLSE) of the location parameter is not dependent on the scale parameter. In addition, the WLSE of the scale parameter is not dependent on the location parameter. The results are compared with the maximum likelihood method and pivot-based estimation method. The assessments and comparisons are done using Monte Carlo simulations and real data analysis. The simulation results show that the estimators ${\hat{\mu}}_u({\hat{\theta}}_p)$ and ${\hat{\theta}}_p({\hat{\mu}}_u)$ are superior to the other estimators in terms of the mean squared error (MSE) and bias.

• 한국콘텐츠학회논문지
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• 제21권3호
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• pp.616-625
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• 2021

심층신경망은 임의의 함수를 근사화하는 방법으로 선형모델로 근사화한 후에 비선형 활성함수를 이용하여 추가적 근사화를 반복하는 근사화 방법이다. 이 과정에서 근사화의 성능 평가 방법은 손실함수를 이용한다. 기존 심층학습방법에서는 선형근사화 과정에서 손실함수를 고려한 근사화를 실행하고 있지만 활성함수를 사용하는 비선형 근사화 단계에서는 손실함수의 감소와 관계가 없는 비선형변환을 사용하고 있다. 본 연구에서는 기존의 활성함수에 활성함수의 크기를 변화시킬 수 있는 크기 파라메터와 활성함수의 위치를 변화시킬 수 있는 위치 파라미터를 도입한 파라메트릭 활성함수를 제안한다. 파라메트릭 활성함수를 도입함으로써 활성함수를 이용한 비선형 근사화의 성능을 개선시킬 수 있다. 각 은닉층에서 크기와 위치 파라미터들은 역전파 과정에서 파라미터들에 대한 손실함수의 1차 미분계수를 이용한 학습과정을 통해 손실함수 값을 최소화시키는 파라미터를 결정함으로써 심층신경망의 성능을 향상시킬 수 있다. MNIST 분류 문제와 XOR 문제를 통하여 파라메트릭 활성함수가 기존의 활성함수에 비해 우월한 성능을 가짐을 확인하였다.