• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.887-896
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• 2016

The Neyman-Scott Rectangular Pulse (NSRP) model is used to model the hourly rainfall series. This model uses a modest number of parameters to represent the rainfall processes and underlying physical phenomena such as the arrival of a storm or rain cells. In this paper, we proposed approximated likelihood function for the NSRP model and applied the proposed method to precipitation data in Seoul.

• Annual Conference on Human and Language Technology
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• 2006.10e
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• pp.262-267
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• 2006

대화 인터페이스 시스템(dialogue interface system)은 인간과 컴퓨터의 상호작용을 위한 도구로서 자연언어(natural language)를 사용하여 정보를 공유하거나 특정 업무를 수행하는 프로그램이다. 대화 인터페이스 시스템에 대한 기존의 연구들은 영역 의존적인 스크립트나 계획 추론을 위한 계획 지식을 이용해 왔다. 스크립트 모델(script model)은 제한적인 실용시스템 개발을 위해 주로 연구되었고 계획에 기반한 모델은 대화의 원리를 이해하는 분야에서 주로 연구되어 왔다. 그러나 기존의 모델들은 시스템 확장이 매우 어려우며 예측하지 못한 사용자 발화에 대하여 대응이 어렵기 때문에 매우 제한적인 영역이나 정해진 형태의 대화만을 처리할 수 있다. 본 논문에서는 이런 단점들을 보완하기 위하여 통계에 기반한 다 영역(multi-domain) 대화 모델을 제안한다. 제안된 시스템은 각 작업들에 대하여 해당 작업에 적합한 영역 모델(domain model)을 잘 알려진 프레임 구조를 따르면서 사용자 의도 파악과 시스템 의도 생성에 통계적 방법을 사용한다. 이러한 하이브리드 형태의 구조 덕분에 제안된 시스템은 영역 확장성과 이식성이 뛰어나다는 장점을 가진다.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.377-387
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• 1998

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced latent variables that indicates with component of the Superposition model. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Metropolis algorithms along with Gibbs steps are proposed to preform the Bayesian inference of such models. for model determination, we explored the Pre-quential conditional predictive Ordinate(PCPO) criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions, we consider in this paper Superposition of Musa-Okumoto and Erlang(2) models. A numerical example with simulated dataset is given.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.291-299
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• 2016

This paper discusses how to establish estimable functions when there are fixed and random effects in design models. It proves that estimable functions of mixed models are not related to random effects. A fitting constants method is used to obtain sums of squares due to random effects and Hartley's synthesis is used to calculate coefficients of variance components. To test about the fixed effects the degrees of freedom associated with divisor are determined by means of the Satterthwaite approximation.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.279-289
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• 2016

Bioequivalence trials based on higher order crossover designs have recently been conducted for highly variable drugs since the Ministry of Korea Food and Drug Safety (MFDS) added new regulations in 2013 to widen bioequivalence limits for highly variable drugs. However, a statistical discussion of higher order crossover designs have not been discussed yet. This research proposes the statistical inference of bioequivalence based on $3{\times}3$ crossover design and discusses it with the MFDS regulations. An illustrated example is also given.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.345-354
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• 2016

Inhomogeneous Poisson process models are widely applied to landslide data to understand how environmental variables systematically influence the risk of landslides. However, those models cannot successfully explain the clustering phenomenon of landslide locations. In order to overcome this limitation, we propose to use a Cauchy cluster process model and show how it improves the goodness of fit to the landslide data in terms of K-function. In addition, a numerical study is performed to select the optimal estimation method for the Cauchy cluster process.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.445-455
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• 2022

In this paper, we introduce existing Bayesian methods for high-dimensional sparse regression models and compare their performance in various simulation scenarios. Especially, we focus on the variational Bayes approach proposed by Ray and Szabó (2021), which enables scalable and accurate Bayesian inference. Based on simulated data sets from sparse high-dimensional linear regression models, we compare the variational Bayes approach with other Bayesian and frequentist methods. To check the practical performance of the variational Bayes in logistic regression models, a real data analysis is conducted using leukemia data set.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.75-85
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• 1994

In this paper, I introduce the goodness-of-fit test statistics for exponential distribution using accelerated life test data. The ALT lifetime data were obtained by assuming step-stress ALT model, specially TRV model introduced by DeGroot and Goel(1979). The critical values are obtained for proposed test statistics, Kolmogorov-Smirnov, Kuiper, Watson, Cramer-von Mises, Anderson-Darling type, under various sample sizes and significance levels. The powers of the five test statistic are compared through Monte-Cairo simulation technique.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.05a
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• pp.5-7
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• 2003

의류용 섬유제품의 쾌적성을 객관적으로 측정하려는 시도가 계속되고 있지만, 아직 소비자의 미묘한 차이를 검출하는 능력을 재현하기에는 거리가 있어 보인다. 객관적 측정법들은 재현성과 신뢰성 면에서 제각기 자신들의 우수한 성능을 자랑하지만, 검출능력의 민감성은 주관적 평가에 훨씬 못 미치고 있기 때문에 비현실적인 경우가 많다. 본 연구의 목적은 주관적 평가의 신뢰성을 높이고 통계적인 추론이 가능한 보다 진보된 형식의 주관적 평가법을 개발하고 의류용 직물의 촉감평가에 응용하는 것이다. 국내에서 생산되는 여름용 캐쥬얼 웨어를 대상으로 여러 형태의 주관적 평가법을 적용하여 그 촉감을 평가하였다. 평정척도법, 표준 비교법, 순위법, Thurstone일대 비교법, Quad 분석법 등을 순차적으로 적용하여 평가자 판단의 일관성 여부, 평가자간의 일치성 여부, 재현성 및 논리성 여부를 통계적으로 평가한 후 이를 통과한 평가자들을 대상으로 실험을 반복한 후, 직물 촉감과 관련된 물리적 속성들과의 관련성을 고찰하였다.