• Journal of the Korean Statistical Society
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• 제30권2호
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• pp.231-246
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• 2001

Local linear smoothing enjoys several excellent theoretical and numerical properties, an in a range of applications is the method most frequently chosen for fitting curves to noisy data. Nevertheless, it suffers numerical problems in places where the distribution of design points(often called predictors, or explanatory variables) is spares. In the case of univariate design, several remedies have been proposed for overcoming this problem, of which one involves adding additional ″pseudo″ design points in places where the orignal design points were too widely separated. This approach is particularly well suited to treating sparse bivariate design problem, and in fact attractive, elegant geometric analogues of unvariate imputation and interpolation rules are appropriate for that case. In the present paper we introduce and develop pseudo dta rules for bivariate design, and apply them to real data.

• 응용통계연구
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• 제25권4호
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• pp.661-668
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• 2012

For displaying multivariate numerical data on a 2D plane by the projection, principal components biplot and the GGobi are two main tools of data visualization. The biplot is very useful for capturing the global shape of the dataset, by representing $n$ observations and $p$ variables simultaneously on a single graph. The GGobi shows a dynamic movie of the images of $n$ observations projected onto a sequence of unit vectors floating on the $p$-dimensional sphere. Even though these two methods are certainly very valuable, there are drawbacks. The biplot is too condensed to describe the detailed parts of the data, and the GGobi is too burdensome for ordinary data analyses. In this paper, "the local projective display(LPD)" is proposed for visualizing multivariate numerical data. Main steps of the LDP are 1) $k$-means clustering of the data into $k$ subsets, 2) drawing $k$ principal components biplots of individual subsets, and 3) sequencing $k$ plots by Hurley's (2004) endlink algorithm for cognitive continuity.

• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.367-379
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• 2004

This article deals with the nonparametric analysis of longitudinal data when there exist possible correlations among repeated measurements for a given subject. We consider a quasi-likelihood regression model where a transformation of the regression function through a link function is linear in time-varying coefficients. We investigate the local polynomial approach to estimate the time-varying coefficients, and derive the asymptotic distribution of the estimators in this quasi-likelihood context. A real data set is analyzed as an illustrative example.

• 대한원격탐사학회지
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• 제31권1호
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• pp.21-28
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• 2015

The essential idea of de-noising is referring to neighboring pixels of a center pixel to be updated. Conventional adaptive de-noising filters use local statistics, i.e., mean and variance, of neighboring pixels including the center pixel. The drawback of adaptive de-noising filters is that their performance becomes low when edges are contained in neighboring pixels, while anisotropic diffusion de-noising filters remove adaptively noises and preserve edges considering intensity difference between neighboring pixel and the center pixel. The anisotropic diffusion de-noising filters, however, use only intensity difference between neighboring pixels and the center pixel, i.e., local statistics of neighboring pixels and the center pixel are not considered. We propose a new connectivity function of two adjacent pixels using statistics of neighboring pixels and apply connectivity function to diffusion coefficient. Experimental results using an aerial image corrupted by uniform and Gaussian noises showed that the proposed algorithm removed more efficiently noises than conventional diffusion filter and median filter.

• 응용통계연구
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• 제24권6호
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• pp.1129-1138
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• 2011

지방자치제가 정착되면서 시군구와 같은 소지역 단위의 실업률에 대한 통계는 실업대책이나 고용지원 사업을 위한 예산 편성과 같은 지역 노동시장정책의 수립은 물론 정책 집행 후 그에 대한 사후평가를 하는데 있어서도 중요한 기준을 제공해주기 때문에 지자체 기관장들의 관심의 대상이 되고 있는 실정이다. 그러나 경제적, 시간적으로 많은 제약이 따르는 기초자치단체에서 지속적으로 실업률 통계를 생산하는 데에는 자료의 신뢰성, 연속성및 시의성 면에서 많은 문제를 노정하고 있다. 본 연구에서는 소지역 실업률 추정치에 대한 신뢰성(변동계수 25% 이하)을 확보하면서 통계생산에 소요되는 조사비용을 최소화할 수 있는 효율적인 추정법 및 최소 표본조사구 수를 모의실험을 통하여 제시하고자 한다.

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.365-376
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• 2020

Interval-valued data, a type of symbolic data, is given as an interval in which the observation object is not a single value. It can also occur frequently in the process of aggregating large databases into a form that is easy to manage. Various regression methods for interval-valued data have been proposed relatively recently. In this paper, we introduce a nonparametric regression model using the kernel function and a nonlinear regression model for the interval-valued data. We also propose applying the local linear regression model, one of the nonparametric methods, to the interval-valued data. Simulations based on several distributions of the center point and the range are conducted using each of the methods presented in this paper. Various conditions confirm that the performance of the proposed local linear estimator is better than the others.

• 응용통계연구
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• 제7권2호
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• pp.299-311
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• 1994

시계열 자료에서 우리는 이상 관측자료들을 흔히 발견하게 된다. 한 점의 이상 관측자료를 탐지하는 방법은 여러가지가 소개되었지만 연속적인 시점에서 이상자료가 존재하는 경우에 기존의 기법은 적절하지 못한 면이 있다. 이 논문에서는 그러한 자료들을 국소구조변화의 결과로 해석하고 그 변화의 크기를 모형화하는 방법을 제시하였다. 이 모형을 이용하여 그러한 국소구조변화를 탐지할 수 있는 통계량과 탐지과정을 제안하였다. 모의실험과 실제 자료의 분석을 수행하여 제안된 기법의 유용성을 평가하였다.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.471-483
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• 1998

The influence of observations on the misclassification probability in multiple discriminant analysis under the equal covariance assumption is investigated by the local influence method. Under an appropriate perturbation we can get information about influential observations and outliers by studying the curvatures and the associated direction vectors of the perturbation-formed surface of the misclassification probability. We show that the influence function method gives essentially the same information as the direction vector of the maximum slope. An illustrative example is given for the effectiveness of the local influence method.

• Communications for Statistical Applications and Methods
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• 제16권6호
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• pp.1047-1054
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• 2009

Local smoothing jump detection procedure is a popular method for detecting jump locations and the performance of the jump detector heavily depends on the choice of the bandwidth. However, little work has been done on this issue. In this paper, we propose the bootstrap bandwidth selection method which can be used for any kernel-based or local polynomial-based jump detector. The proposed bandwidth selection method is fully data-adaptive and its performance is evaluated through a simulation study and a real data example.

• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.435-447
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• 2004

In additive nonparametric regression, Linton and Nielsen (1995) showed that the marginal integration when applied to the local linear smoother produces a rate-optimal estimator of each univariate component function for the case where the dimension of the predictor is two. In this paper we give new formulas for the bias and variance of the marginal integration regression estimators which are valid for boundary areas as well as fixed interior points, and show the local linear marginal integration estimator is in fact rate-optimal when the dimension of the predictor is less than or equal to four. We extend the results to the case of the local polynomial smoother, too.