• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• 대한지리학회지
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• 제45권6호
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• pp.806-819
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• 2010

센서스 단위의 인구자료는 기초적인 인문사회 자료로 행정구역 단위로 요약되어 공간분석에 시용된다. 정밀한 인구 분포를 추정하기 위해 기존의 연구에서는 위성영상과 회귀분석 모형을 이용하였다. 하지만 회귀식에 의한 추정치는 공간자료의 공간적자기상관과 잔차 때문에 정확도에 있어 한계가 있었다. 본 연구는 회귀모형과 회귀모형에서 추출된 잔차에 대해 공간적자기상관을 고려하도록 크리깅 보간하는 RK모형(Regression Kriging Model)을 이용하여 인구분포의 추정 정확도를 향상하였다. RK모형을 적용하여 서울시의 4개구를 대상으로 사례분석을 하였으며, 모형의 효율성을 검증하기 위해 회귀분석만을 이용한 예측 결과와 RK모형을 이용한 예측 결과를 서로 비교하였다. 비교한 결과로 상관관계 계수 평균제곱근 오차, G 통계량 수치에서 RK모형의 추정 정확도가 기존의 회귀모형에 비해 높게 나온 것을 확인할수 있었다. 향후 정확한 인구추정을 위해 RK모형이 많이 활용될 수 있을 것이다.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.539-556
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• 2019

This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, "no matter what regression lines" and "no matter what the number of change points".

• Journal of the Korean Data and Information Science Society
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• 제7권1호
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• pp.87-92
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• 1996

A regression model with nested erroe structure is considered. The regression model includes two error terms that are independent and normally distributed with zero means and constant variances. This error structure of the model gives correlated response variables. The distributions of variance components in the regression model with nested error structure are dervied by using theorems for quadratic forms.

• Preventive Nutrition and Food Science
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• 제13권2호
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• pp.104-111
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• 2008

As a way to account for the variability of the primary model parameters in the secondary modeling of microbial growth, three different regression approaches were compared in determining the confidence interval of the temperature-dependent primary model parameters and the estimated microbial growth during storage: bootstrapped regression with all the individual primary model parameter values; bootstrapped regression with average values at each temperature; and simple regression with regression lines of 2.5% and 97.5% percentile values. Temperature dependences of converted parameters (log $q_o$, ${\mu}_{max}^{1/2}$, log $N_{max}$) of hypothetical initial physiological state, maximum specific growth rate, and maximum cell density in Baranyi's model were subjected to the regression by quadratic, linear, and linear function, respectively. With an advantage of extracting the primary model parameters instantaneously at any temperature by using mathematical functions, regression lines of 2.5% and 97.5% percentile values were capable of accounting for variation in experimental data of microbial growth under constant and fluctuating temperature conditions.

• 품질경영학회지
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• 제49권2호
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• 한국지능시스템학회논문지
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• 제16권4호
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• pp.499-505
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• 2006

Regularlization approach to regression can be easily found in Statistics and Information Science literature. The technique of regularlization was introduced as a way of controlling the smoothness properties of regression function. In this paper, we have presented a new method to evaluate linear and non-linear fuzzy regression model based on Tanaka's model using the idea of regularlization technique. Especially this method is a very attractive approach to model non -linear fuzzy data.

• 한국수자원학회논문집
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• 제41권1호
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• pp.17-25
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• 2008

수위-유량관계식의 유도와 실무적용에 있어 통상적으로 회귀분석의 특성을 간과하고 사용하는 경우가 종종 발생한다. 예를 들어 실무에서는 관측수위로부터 관측유량으로 회귀분석되어 만들어진 수위-유량관계식을 홍수모형으로부터 모의된 설계홍수유출량으로부터 설계홍수위를 환산하는데 사용되기도 한다. 그러나 독립과 종속변수가 서로 바뀌면, 관측치와 회귀식간 연직거리의 잔차들로부터 유도된 기존의 회귀분석에 의하여, 회귀식이 서로 달라지기 때문에 역으로 적용하여서는 안 된다. 본 연구에서는 이런 문제점을 해결하기위해 회귀식의 변수들을 상호 교환할 수 있는 최소자승 회귀분석의 새로운 알고리즘을 제안하였다. 새로운 방법을 낙동강유역의 본류 5개 수위표지점의 수위-유량관계식에 대하여 적용하였다. 3가지 회귀식이 유도되었는데, 이들은 각각 수위로부터 유량으로(model 1), 유량으로부터 수위로(model 2) 그리고 양방향(model 3)으로 유도된 수위-유량관계식을 비교하여 실무에서 잘못 적용되는 실수를 줄일 수 있는 새로운 방법을 제시하였다.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.277-289
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• 2003

We propose modified exact inferential methods in logistic regression model. Exact conditional distribution in logistic regression model is often highly discrete, and ordinary exact inference in logistic regression is conservative, because of the discreteness of the distribution. For the exact inference in logistic regression model we utilize the modified P-value. The modified P-value can not exceed the ordinary P-value, so the test of size $\alpha$ based on the modified P-value is less conservative. The modified exact confidence interval maintains at least a fixed confidence level but tends to be much narrower. The approach inverts results of a test with a modified P-value utilizing the test statistic and table probabilities in logistic regression model.

• 한국도로학회논문집
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• 제15권3호
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• pp.45-52
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• 2013

PURPOSES : This study is to construct the regression models of drainage asphalt concrete specimens and to provide the appropriate coefficients of hydraulic conductivity prediction models. METHODS: In terms of easy calculation of the hydraulic conductivity from porosity of asphalt concrete pavement, the estimation model of hydraulic conductivity was proposed using regression analysis. 10 specimens of drainage asphalt concrete pavement were made for measurement of the hydraulic conductivity. Hydraulic conductivity model proposed in this study was calculated by empirical model based on porosity and the grain size. In this study, it shows the compared results from permeability measured test and empirical equation, and the suitability of proposed model, using regression analysis. RESULTS: As the result of the regression analysis, the hydraulic conductivity calculated from the proposal model was similar to that resulted from permeability measured test. Also result of RMSE (Root Mean Square Error) analysis, a proposed regression model is resulted in more accurate model. CONCLUSIONS: The proposed model can be used in case of estimating the hydraulic conductivity at drainage asphalt concrete pavements in fields.