• Journal of the Korean Statistical Society
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• 제2권1호
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• pp.25-35
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• 1973

No Abstract, See Full Text

• 대한기계학회논문집A
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• 제33권12호
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• pp.1449-1454
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• 2009

The performance variation of a multibody system is affected by a variation of various design variables of the system. And the effects of design variable variations on the performance variation must be considered in design of a multibody system. Accordingly, a variation analysis of a multibody system needs to be conducted in design of a multibody system. For a variation analysis of a performance, population mean and variance which are called statistical parameters of design variables are needed. However, an evaluation of statistical parameters of design variables is impossible in many practical cases. Therefore, an estimation of statistical parameters of the performance based on sample mean and variance which are called statistic of design variables is needed. In this paper, the variation analysis method for a multibody system based on design variable samples was proposed. And, using the proposed method, a variation analysis of the vehicle ride comfort based on sample statistic of design variables was conducted.

• Journal of the Korean Data and Information Science Society
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• 제24권5호
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.115-134
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• 1994

Polynomial errors-in-variables model with one predictor variable and one response variable is defined and an estimator of model is derived following the Booth's linear model estimation procedure. Since polynomial model is nonlinear function of the unknown regression coefficients and error-free predictors, it is nonlinear model in errors-in-variables model. As a result of applying linear model estimation method to nonlinear model, some additional assumptions are necessary. Hence, an estimator is derived under the assumption that the error variances are decrasing as sample size increases. Asymptotic propoerties of the derived estimator are provided. A simulation study is presented to compare the small sample properties of the derived estimator with those of OLS estimator.

• Journal of the Korean Statistical Society
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• 제28권2호
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• pp.265-277
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• 1999

Classical and inverse estimation methods are two well known methods in statistical calibration problems. When there are outliers, both methods have large MSE's and could not estimate the input value correctly. We suggest median calibration estimation based on the LD-statistics. To investigate the robust performances, the influence function of the median calibration estimator is calculated and compared with other methods. When there are outliers in the response variables, the influence function is found to be bounded. In simulation studies, the MSE's for each calibration methods are compared. The estimated inputs as well as the performance of the influence functions are calculated.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.61-72
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• 2006

When using the well-known variance estimator of Sen (1953) and Yates and Grundy (1953) in inclusion probability proportional to size sampling, we often encounter the problems due to the calculation of the joint probabilities. Sarndal (1996) and Knottnerus (2003) proposed alternative strategies for variance estimation to avoid those problems in the traditional method. We discuss some of practical issues that arise when they are used. Also, we describe the traditional strategy using a sampling procedure available in a statistical software. It would be one of the attractive choices for design-based variance estimation.

• Communications for Statistical Applications and Methods
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• 제26권4호
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• pp.385-410
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• 2019

This paper proposes a new class of distribution using the concept of exponentiated of distribution function that provides a more flexible model to the baseline model. It also proposes a new lifetime distribution with different types of hazard rates such as decreasing, increasing and bathtub. After studying some basic statistical properties and parameter estimation procedure in case of complete sample observation, we have studied point and interval estimation procedures in presence of type-II censored samples under a classical as well as Bayesian paradigm. In the Bayesian paradigm, we considered a Gibbs sampler under Metropolis-Hasting for estimation under two different loss functions. After simulation studies, three different real datasets having various nature are considered for showing the suitability of the proposed model.

• Journal of the Korean Statistical Society
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• 제30권2호
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• pp.179-192
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• 2001

When a statistical model has a hierarchical structure such as multilayer perceptrons in neural networks or Gaussian mixture density representation, the model includes distribution with unidentifiable parameters when the structure becomes redundant. Since the exact structure is unknown, we need to carry out statistical estimation or learning of parameters in such a model. From the geometrical point of view, distributions specified by unidentifiable parameters become a singular point in the parameter space. The problem has been remarked in many statistical models, and strange behaviors of the likelihood ratio statistics, when the null hypothesis is at a singular point, have been analyzed so far. The present paper studies asymptotic behaviors of the maximum likelihood estimator and the Bayesian predictive estimator, by using a simple cone model, and show that they are completely different from regular statistical models where the Cramer-Rao paradigm holds. At singularities, the Fisher information metric degenerates, implying that the cramer-Rao paradigm does no more hold, and that he classical model selection theory such as AIC and MDL cannot be applied. This paper is a first step to establish a new theory for analyzing the accuracy of estimation or learning at around singularities.

• 한국수학사학회지
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• 제17권1호
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• pp.97-108
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• 2004

There are so many methods in population estimation such as logistic estimation and compound interest estimation. If we have some pieces of information about population of one tribe, we can estimate progenitor chronology of that tribe used by inverse estimation. In this paper, we describe several theory of population estimation, and develop mathematical method for estimation progenitor chronology from prior general data and statistical estimation theory. Several examples are illustrated.

• 한국ITS학회 논문지
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• 제10권1호
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• pp.67-75
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• 2011

본 논문은 기존의 단일값(점추정)으로 제시하던 검지기 성능평가 결과를 통계적 신뢰구간(구간추정)으로 제시하기 위한 검지기 성능평가 방안을 제시했다. 일반적으로 구간추정은 점추정에 비해 표본 통계의 더 많은 정보를 제공하기 때문에 기존 단일값으로 제시해 오던 검지기 성능평가 결과의 신뢰성을 향상시킬 수 있다. 방법론은 크게 표본 추출, 평가척도 분석, 평가결과 제시의 세 부분으로 나누어진다. 표본추출 방법에는 다양한 통계적 표본 추출 방법이 있지만 교통, 조도, 기상조건에 따라 변화하는 차량검지기 성능의 특성상 층화추출법이 통계적 신뢰구간 제시를 위한 가장 적합한 방법론으로 간주되었다. 또한 기존에 널리 사용된 검지기 성능평가 척도들의 특징을 면밀히 분석하여 평가자로 하여금 해당 검지자료에 적합한 평가척도를 선택할 수 있는 프로세스를 정립하였다. 마지막으로 평가기간 전체(예. 30분)와 개별분석 단위(예. 1분) 평가결과의 통계적 신뢰구간을 반영하기 위한 방법론을 제시했다. 본 연구는 기존 검지기 성능평가 결과의 단일값 제시로 인해 불가능 했던 신뢰구간 제시를 가능하게 함에 따라 검지기 성능평가 결과의 신뢰성을 향상시킬 수 있을 것으로 판단된다.