• 한국전자파학회논문지
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• 제15권7호
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• pp.669-676
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• 2004

등 간격 선형 어레이로 입사하는 광대역 인코히어런트 신호의 도래각을 효율적으로 추정하는 새로운 알고리즘을 제안한다. 변환행렬을 구성하기 위하여 CSM 방법이 초기 추정각을 요구하는 반면에 제안된 방법은 전혀 초기 추정각을 필요로 하지 않는다. 이 방법의 연산과정은 먼저 신호부각 방법에 의하여 중심주파수에서의 신호 부공간을 추정 한 다음 신호 부공간 회전 방법을 통한 직교변환행렬을 구성하는 것이다. 시뮬레이션 결과 제안된 방법이 CSM 방법보다 표본바이어스 면에서 우수한 성능을 제공함을 알 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.247-255
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• 2003

Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.265-276
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• 1996

The nonparametric deconvolution problems are studied to recover an unknown density when the data are contaminated with Gaussian error. We propose the estimator which is a linear combination of kernel type estimates of derivertives of the observed density function. We show that this estimator is consistent and also consider the properties of estimator at small sample by simulation.

• Journal of the Korean Statistical Society
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• 제19권2호
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• pp.145-150
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• 1990

In this article we develop the marginal posterior density of the model parameters in the multiresponse regression models when missing values exist only in one response. The resulting density resolves a couple of problems in the estimation approach proposed by Box, Draper, and Hunter (1970) and provides a general interpretation for relationship between the estimates of the missing values and the parameters.

• Journal of the Korean Data and Information Science Society
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• 제23권3호
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• pp.587-593
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• 2012

We study Bayesian estimates for finite population proportions in multinomial problems. To do this, we consider a three-stage hierarchical Bayesian model. For prior, we use Dirichlet density to model each cell probability in each cluster. Our method does not require complicated computation such as Metropolis-Hastings algorithm to draw samples from each density of parameters. We draw samples using Gibbs sampler with grid method. We apply this algorithm to a couple of simulation data under three scenarios and we estimate the finite population proportions using two kinds of approaches We compare results with the point estimates of finite population proportions and their standard deviations. Finally, we check the consistency of computation using differen samples drawn from distinct iterates.

• 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.

• 응용통계연구
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• 제24권4호
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• pp.721-733
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• 2011

We consider the state price densities that are implicit in financial asset prices. In the pricing of an option, the state price density is proportional to the second derivative of the option pricing function and this relationship together with no arbitrage principle imposes restrictions on the pricing function such as monotonicity and convexity. Since the state price density is a proper density function and most of the shape constraints are caused by this, we propose to estimate the state price density directly by specifying candidate densities in a flexible nonparametric way and applying methods of regularization under extra constraints. The problem is easy to solve and the resulting state price density estimates satisfy all the restrictions required by economic theory.

• 경영과학
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• 제32권3호
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• pp.29-36
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• 2015

In this research, we analyze the sensitivity of the network density to the estimates for the Bass model parameters with both theoretical model and a simulation. Bass model describes the process that the non-adopters in the market potential adopt a new product or an innovation by the innovation effect and imitation effect. The imitation effect shows the word of mouth effect from the previous adopters to non-adopters. But it does not divide the underlying network structure from the strength of the influence over the network. With a network based Bass model, we found that the estimate for the imitation coefficient is highly sensitive to the network density and it is decreasing while the network density is decreasing. This finding implies that the interpersonal influence can be under-looked when the network density is low. It also implies that both of the network density and the interpersonal influence are important to facilitate the diffusion of an innovation.

• 수산해양기술연구
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• 제45권3호
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• pp.157-164
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• 2009

Due to change of various marine environments according to seawater temperature rising, Japanese common squid(Todarodes pacificus), which was distributed in East Sea, was recently caught in Yellow Sea during a summer season from 2006. The fishery resources density research was carried out in Korea-China Provisional Water Zone using trawl fishing gear and acoustics in National Fisheries Research & Development Institute in Korea. This paper showed the analysis on the acoustical backscattering strength by two frequencies(38kHz, 120kHz) for Japanese common squid by acoustical scattering theoretical model based on size distribution for survey period, and estimate the density distribution for squid s integrated layer which was extracted from any scatterers distributed in water column using two frequency difference method which has been used to distinguish fish shoals or specific target scatterers from sound scattering layer which is composed of various zooplankton. Furthermore, the entire range of their density estimation was suggested using by Monte Carlo simulation under considering each uncertainty such as size distributions or swimming angle and so on in survey area.

• 한국대기환경학회지
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• 제25권4호
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• pp.339-346
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• 2009

The objectives of this study were to introduce spatial interpolation methods which have been applied in recent papers, to apply three methods (nearest monitor, inverse distance weighting, kriging) to domestic data (Ulsan cohort) as an example of estimating the personal exposure levels. We predicted the personal exposure estimates of 2,102 participants in Ulsan cohort using spatial interpolation methods based on information of their residential address. We found that there was a similar tendency among the estimates of each method. The correlation coefficients between predictions from pairs of interpolation methods (except for the correlation coefficient between nearest montitor and kriging of CO and $SO_2$) were generally high (r=0.84 to 0.96). Even if there are some limitations such as location and density of monitoring station, spatial interpolation methods can reflect spatial aspects of air pollutant and spatial heterogeneity in individual level so that they provide more accurate estimates than monitor data alone. But they may still result in misclassification of exposure. To minimize misclassification for better estimates, we need to consider individual characteristics such as daily activity pattern.