• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.713-727
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• 2005

Among different regression approaches, nonparametric procedures perform well under different conditions. In practice it is very hard to identify which is the best procedure for the data at hand, thus model combination is of practical importance. In this paper, we focus on one dimensional regression with fixed design. Polynomial regression, local regression, and smoothing spline are considered. The data are split into two parts, one part is used for estimation and the other part is used for prediction. Prediction performances are used to assign weights to different regression procedures. Simulation results show that the combined estimator performs better or similarly compared with the estimator chosen by cross validation. The combined estimator generates a similar risk to the best candidate procedure for the data.

• 한국자동차공학회논문집
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• 제22권3호
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• pp.114-122
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• 2014

Sensor fusion is the one of the main research topics. It offers the highly reliable estimation of vehicle movement by processing and mixing several sensor outputs. But unfortunately, every sensor has drift which degrades the performance of sensor. It means a single degraded sensor output may affect whole sensor fusion system. Drift in most research is ideally assumed to be zero because it's usually a nonlinear model and has sample variation. Plus, it's very difficult for the acceleration to separate drift from the output signal since it contains many contributors such as vehicle acceleration, slope angle, pitch angle, surface condition and so on. In this paper, modified spline interpolation is introduced as a dynamic temperature compensation method covering sample variation. Using the last known output and the first initial output is suggested to build and update compensation factor. When the system has more compensation data, the system will have better performance of compensated output because of the regression compensation model. The performance of the dynamic temperature compensation system is evaluated by measuring offset drift between with and without the compensation.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.765-777
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• 2009

본 연구에서는 대기오염물질 중에서 주목을 받고 있는 분진 직경이 10마이크로미터 (micrometer) 이내의 것을 대상으로 실제로 증가하는지 감소하는지를 알아보기 위하여 교락 요인을 제외한 분진을 대상으로의 순수한 장기 추세를 연구하였다. 자료는 1996년에서 2000년까지 서울시의 기상 변수들 (최대기온, 평균습도, 최대풍속, 일사량)과 27지점에서 얻은 분진 직경이 10마이크로미터 이내의 것을 이용한다. 이 자료를 이용하여 분진과 비선형 관계를 보이는 기상 변수들의 회귀 스플라인을 이용하여 계절성을 통제한 일반화 부가모형을 세웠다. 그 결과 증가가 아닌 감소하는 순수 장기 추세를 얻을 수 있었다.

• 토지주택연구
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• 제12권4호
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• pp.31-40
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• 2021

본 연구는 연령에 따른 주택수요의 비단조성에 착안하여 주택 소유 및 거주 형태를 추정하였다. 이를 위해 스플라인 로짓 모형을 채택하였고 생애주기에 따라 주택소유수요가 비단조적임을 밝혀내었다. 우리나라 가구의 무주택 확률은 생애주기에 따라 가변적이다. 청년층일 때 소형 무주택으로 시작하여 중장년 때 중대형 주택을 소유하게 되고 노년층에는 주택을 처분하고 소형주택을 선호하게 되는 것으로 나타났다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 춘계 학술발표회 논문집
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• pp.167-170
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• 2003

We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: an approximate crossvalidation scheme to estimate the smoothing parameter inherent in regression splines, as well as Monte Carlo Expectation Maximization (MCEM) and Bayesian methods to fit the regression spline model. We compare the approximate cross-validation approach, MCEM and Bayesian approaches using simulation, showing that they appear approximately equally efficient, with the approximate cross-validation method being computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.

• International Journal of Aeronautical and Space Sciences
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• 제17권1호
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• pp.64-72
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• 2016

The coverage area of a GNSS regional ionospheric delay model is mainly determined by the distribution of GNSS ground monitoring stations. Extrapolation of the ionospheric model data can extend the coverage area. An extrapolation algorithm, which combines observed ionospheric delay with the environmental parameters, is proposed. Neural network and least square regression algorithms are developed to utilize the combined input data. The bi-harmonic spline method is also tested for comparison. The IGS ionosphere map data is used to simulate the delays and to compute the extrapolation error statistics. The neural network method outperforms the other methods and demonstrates a high extrapolation accuracy. In order to determine the directional characteristics, the estimation error is classified into four direction components. The South extrapolation area yields the largest estimation error followed by North area, which yields the second-largest error.

• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.257-262
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• 1996

본 논문은 스플라인 희귀모형에서 평활모수를 추정할 때 사전 작업으로 영향력 진단을 하는 문제를 다룬다. 평활모수의 추정방법으로 일반화최대우도함수법을 사용할 때, 얻어지는 추정 치에 영향을 주는 관측치를 진단하는 측도를 제안하고, 찾아낸 영향력 관측치를 수정하여 올바른 평활모수 추정치를 찾는 방법을 소개한다.

• Journal of the Korean Data and Information Science Society
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• 제9권1호
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• pp.29-36
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• 1998

본 논문은 Eubank (1994, 1997)에 의해 이론적으로 제안된 선형 평활스플라인 추정량에 대한 알고리즘을 개발함으로 선형 스플라인의 추정을 보다 쉽고 효율적으로 사용할 수 있도록 하는데 목적이 있다. 이 알고리즘을 이용하여 여러가지 모형의 예들에 대하여 추정량의 적합성을 조사하였고, 제시된 선형 평활스플라인 추정량이 비모수 함수 추정의 도구로서 잘 적합됨을 알 수 있었다.

• 품질경영학회지
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• 제23권4호
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• pp.58-63
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• 1995

To access the quality of a fit to a set of data it is always useful to conduct a posteriori analysis involving the examination of residuals, detection of influential data values, etc. Smoothing splines are a type of nonparametric regression estimators for the diagnostic problem. And leverage value, Cook's distance, and DFFITS are used for detecting influential data. Since high leverage points will always have small residuals, the new diagnostic measures including of properties of leverage and residuals are needed. In this paper, we propose FVARATIO version as diagnostic measure in nonparametric regression. Also we consider the rough bound as analogy with linear regression case.

• Nuclear Engineering and Technology
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• 제48권6호
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• pp.1315-1320
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• 2016

The present study aims to predict the heat transfer characteristics around a square cylinder with different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further, the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.