• Journal of the Korean Statistical Society
• /
• 제36권1호
• /
• pp.57-76
• /
• 2007

Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.

• Communications for Statistical Applications and Methods
• /
• 제26권1호
• /
• pp.69-78
• /
• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• Proceedings of the Korean Information Science Society Conference
• /
• 한국정보과학회 2002년도 봄 학술발표논문집 Vol.29 No.1 (B)
• /
• pp.499-501
• /
• 2002

최근의 합성음성단위 연결을 통한 음성합성 방법의 잘 알려진 문제점은 연결 부분에서 불연속이 발생한다는 것이다. 본 논문에서는 음성을 합성할 때 나타나는 스펙트럼의 불연속을 제거하기 위하여 개선된 스펙트럼 스무딩 방법을 제안한다. 그리고 보다 좋은 스무딩의 결과를 얻기 위하여 음성합성의 단위로는 문맥에 민감한 클러스터링된 다이폰을 사용한다. 스무딩 방법에서는 연결 구간에서의 다이폰 바운더리에서의 양쪽 스펙트럼의 분포를 고려하여 시간에 따라 가중치를 다르게 주어 스무딩을 수행한다. 또한 가중치를 결정할 때 비선형 함수인 B-Spline함수를 사용하여 스무딩을 수행하여 보다 자연스러운 스펙트럼을 생성 할 수 있었다.

• Journal of Korean Society for Quality Management
• /
• 제32권4호
• /
• pp.265-273
• /
• 2004

We got the growth distance curve by spline smoothing method with observed marketing data and the growth velocity curve by the derivation of the growth distance curve. Using this growth velocity curve, we defined the several characteristic points which describe the variation of marketing data. In this paper, to specify several patterns of marketing data, we suggested characteristic function by using these characteristic points. In addition, we applied characteristic function to the seventeen brands of electric home products data.

• Journal of the Korean Statistical Society
• /
• 제24권1호
• /
• pp.233-242
• /
• 1995

In this paper we will prove the existence and uniqueness of the smoothest density with prescribed moments. The space of functions considered is the Sobolev space $W^2_m[0,1]$ and the target functional to be minimized is the seminorm $$\mid$$\mid$f^{(m)}$\mid$$\mid$_{L^2}$, which measures the roughness of the function f.

• Journal of Astronomy and Space Sciences
• /
• 제24권4호
• /
• pp.269-274
• /
• 2007

We established a regional ionospheric model for investigating ionospheric TEC (Total Electron Contents) variations over the Korean Peninsula during major geomagnetic storms. In order to monitor the ionospheric TEC variations, we used nine permanent GPS reference stations uniformly distributed in South Korea operated by the Korea Astronomy and Space Science Institute (KASI). The cubic spline smoothing (CSS) interpolation method was used to analyze the characteristics of the ionospheric TEC variations. It has been found that variations of TEC over the Korean Peninsula increase when a major geomagnetic storm occurred on November 20, 2003. The TEC has increased about one and a half of those averaged quite days at the specific time during a geomagnetic storm. It has been indicated that the KASI GPS-derived TEC has a correlation with the geomagnetic storm indices (eq. Kp and Dst indices).

• The Korean Journal of Applied Statistics
• /
• 제24권3호
• /
• pp.465-475
• /
• 2011

In this paper, we have considered the problems of the estimation of the gross areas of a factory site relating to the areas of industrial complex lands based on a statistical forecasting model and the results of a sampling survey. In respect to the data of a gross areas of a factory site, we have only the sizes from 1981-2003. In 2009, the Korea Industrial Complex Corp. conducted a sampling survey to estimate its bulk size, and investigate the demands of its sizes for the next five years. In this study, we have adopted the sampling survey results, and have created a statistical growth model for the gross areas of a factory site to improve the prediction for the areas of a factory site. The three-different parts of data: the results of areas of a factory site by Korea National Statistical Office, imputation results by the statistical forecasting model, and sampling survey results have used as the basis for analysis. The combination of the three-different parts of data has created a new forecasting value of the areas of a factory site through the spline smoothing method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• 제19권11호
• /
• pp.2106-2115
• /
• 1994

As a pre processing for an edge detection process. edge preserving smoothing algorithm is proposed. For this purpose we used the interpolation method using B-spline basis function and scaling of digital images. By approximation of continuous function from descrete data using B-spline basis function. undetermined data between two sample can be computed. so that they smooth the surfaces of objects. Some edges having mainly low frequency components are detected using down scaling of the images. Edge maps from proposed pre processed images are hardly affected by the varying space constants($\sigma$) and threshold values used in detecting zero-crossing.

• Journal of the Korean Institute of Intelligent Systems
• /
• 제17권1호
• /
• pp.72-79
• /
• 2007

In this paper, It propose a new restoration algorithm of high resolution, which is reconstructed to high resolution image using low resolution image informations. The proposed algorithm is constructed based on super resolution theory, it is consisted of progressive steps of the integration and construction. It reduced a lot of data-processing capacity and noise with integration through sub-pixel movement and wavelet basis through a higher resolution. As a result, it is shown that the main information is maintained and the error rate is improved. Using expansion fuzzy wavelet B-spline interpolation in stage of construction, it is confirmed that we can achieve smoothing image and good resolution without blur and block.

• Journal of the Korean Data and Information Science Society
• /
• 제28권3호
• /
• pp.709-720
• /
• 2017

In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.