• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.371-378
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• 2003

The compression index of clay distributed in the west and south coast of the Korean Peninsula had been studied. Compression index was obtained from the conventional consolidation test, and was conducted accordingly to obtain the field virgin compression curve by means of Schmertmann's graphical correction. To examine a correlation closely between physical properties of soils($e_o$, LL, w) and compression index(Cc), linen. and non-linear regression analysis were employed based on the data collected from tests. The conclusions are as follows. The compression index obtained by means of Schmereann's graphical correction is about 1.16 times for the value of original oedometer test curve for U/D samples. Non-liner regression curve was preferable to establish a correlation equation rather than linear regression curve. All derived equations so far achieved have been summarized and given. However, linear equation is better for practical use so that part by part simplified linear equations were also suggested alternatively together with their own non-linear regression curve.

• The Korean Journal of Financial Studies
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• v.13 no.1
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• pp.77-92
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• 2007

한국증권시장을 포함한 대부분의 지역증권시장이 미국 뉴욕증권시장의 움직임에 반응하거나 동조현상을 보인다는 사실은 이미 경험적으로 혹은 통계적으로 널리 수용되고 있다. 본 연구는 그러한 반응에 비선형성이 존재하는가를 일별 주가수익률을 데이터로 활용하여 우선적으로 검정한다. 그러한 검정결과에 입각하여 비선형성을 내재화시킨 계량분석모형이 주가수익률을 설명하고 예측하는데 도움을 줄 수 있는가를 확인한다. 본 연구에서는 이러한 비선형성에 관련된 정보를 유도하기 위하여 평활전이(자기)회귀분석모형(STR)을 이용한다. STR모형은 국면전환을 야기하는 전이변수를 명시적으로 확인할 수 있고 다양한 국면전환형태를 모형에 수용할 수 있는 장점을 가지고 있다. KOSPI수익률의 비선형성에 대한 검정결과는 귀무가설인 선형성이 기각되는 것으로 나타났으며, 그러한 비선형성의 형태는 미국증권시장이 하강기에 처한 경우에 상승기에 처한 상태보다 민감한 동조현상을 보이는 것으로 나타났다. 하지만 추정된 STR모형이 주가의 변동을 설명하거나 예측하는데 여타의 모형보다 나은 능력을 가지는가에 대해서는 긍정적인 결과를 얻지 못하였다.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.463-473
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• 2018

Variable selection algorithms for linear regression models of large data are considered. Many algorithms are proposed focusing on the speed and the robustness of algorithms. Among them variance inflation factor (VIF) regression is fast and accurate due to the use of a streamwise regression approach. But a VIF regression is susceptible to outliers because it estimates a model by a least-square method. A robust criterion using a weighted estimator has been proposed for the robustness of algorithm; in addition, a robust VIF regression has also been proposed for the same purpose. In this article a fast and robust variable selection method is suggested via a VIF regression with detecting and removing potential outliers. A simulation study and an analysis of a dataset are conducted to compare the suggested method with other methods.

• The Korean Journal of Financial Management
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• v.17 no.1
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• pp.161-180
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• 2000

본 연구는 주가를 예측하는데 있어서 선형 회귀모형을 이용하는 방법과 비선형 인공신경망 모형을 이용하는 방법을 비교 분석하여, 어떤 모형이 더 우수한 예측성과를 내는지를 검증한다. 자본시장에서 투자자들은 접근하는 정보가 다르고 각기 상이한 예측 변수들을 토대로 나름대로의 예측치를 만들어 낸다. 이렇게 볼 때 개별 투자자들이 이용하는 다양한 정보집합을 결합하여 단일의 뛰어난 정보집합을 만들어내는 것은 매우 어려운 과제이다. 따라서 본 연구에서는 이용 가능한 소수의 예측 변수들을 어떤 방식으로 결합하는 것이 예측오차의 분산을 최소화할 수 있는지에 대한 현실적인 접근방법을 모색하고자 한다. 거시경제변수나 시장자료를 입력변수로 사용한 기존 연구와는 달리 본 연구에서는 재무제표 정보를 입력변수로 사용하였다 즉, 대차대조표의 최종요약치인 주당 지분의 장부가치와 손익계산서의 최종요약치인 주당 순이익을 입력변수로 사용했으며 1991년부터 1995년까지의 추정(학습)결과를 토대로 모형을 선택하여 1996년의 제무제표 정보로 1997년의 주가를 예측하는 것이 본 연구의 과제이다. 연구결과, 대체로 선형회귀모형에 비해 비선형 신경망 모형이 예측오차의 분산을 감소시키는 것으로 나타났다.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1357-1363
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• 2018

In addition to advances in information technology, machine learning approach is applied to a variety of applications, and is expanding to a variety of areas. In this paper, we propose a software analysis method that applies linear regression to analyse software similarity from the code distribution of the software. The characteristics of software can be expressed by instructions contained within the program, so the distribution information of instructions is used as learning data. In addition, a learning procedure with the learning data generates a linear regression model for software similarity analysis. The proposed method is evaluated with real world Java applications. The proposed method is expected to be used as a basic technique to determine similarity of software. It is also expected to be applied to various software analysis techniques through machine learning approaches.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.147-147
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• 1994

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.770-773
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• 2017

Linear Regression 문제를 토대로 변형하여 선형회귀분석, 2차함수모형 회귀분석, '단조 증가(감소)' 3차 함수 모형 회귀분석과 그에 따라 변형되는 gradient descent 알고리즘을 기술한다.

• Journal of the Korean Chemical Society
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• v.45 no.5
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• pp.422-428
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• 2001

Retention relative times(RRTs) of PAH molecules and their derivatives in gas chromatography are trained and predicted in testing sets using a multiple linear regression(MLR) and an artificial neural network(ANN). The main descriptors of PAHs and their derivatives in QSRR are the square root of molecular weight(sqmw), molecular connectivity($^1{\chi}_v$), molecular dipole moment(D) and length-to-breadth ratios(L/B). The results of MLR shows that a heavy molecule has a propensity for long retention time. L/B closely related with slot model is a good descriptor in MLR. On the other hand, ANN which is not effected by the linear dependencies among the descriptors were exclusively based on molecular weight and molecular dipole moment. The variances which shows the accuracy of prediction for retention times in testing sets are 1.860, 0.206 for MLR and ANN, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.

• 대한방사선방어학회:학술대회논문집
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• 2011.11a
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• pp.326-327
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• 2011

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.21-32
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• 2010

Graphical regression is a paradigm for obtaining regression information using plots without model assumptions. The general goal of this approach is to find lowdimensional sufficient summary plots without loss of important information. Model assessments using residual plots are less likely to be successful in models that are not linear. As an alternative approach, marginal model plots provide a general graphical method for assessing the model. We apply the methods of graphical regression and model assessment using marginal model plots to the logistic regression model.