• Journal of the Korean Data and Information Science Society
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• 제26권2호
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• pp.505-516
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• 2015

We consider sparse high-dimensional linear regression models. Penalized regressions have been used as effective methods for variable selection and estimation in high-dimensional models. In penalized regressions, it is common practice to standardize variables before fitting a penalized model and then fit a penalized model with standardized variables. Finally, the estimated coefficients from a penalized model are recovered to the scale on original variables. However, these procedures produce a slightly different solution compared to the corresponding original penalized problem. In this paper, we investigate issues on the standardization of variables in penalized regressions and formulate the definition of the standardized penalized estimator. In addition, we compare the original penalized estimator with the standardized penalized estimator through simulation studies and real data analysis.

• Journal of the Korean Statistical Society
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• 제34권3호
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• pp.209-218
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• 2005

In response surfaces analysis, we often proceed by supposing that, over a limited region of factor space, a polynomial of only first or second degree might adequately approximate the true function. To find the best subset model, all possible quadratic regressions for response surfaces can be very valuable to get optimum solutions under some reasonable experimentations. However, there is a very hard computational burden to get all possible quadratic regressions. In practice, it is sufficient to consider only hierarchical models. In this paper, we propose an algorithm to get all possible hierarchical quadratic regressions for fitting response surfaces.

• 산업융합연구
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• 제2권2호
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• pp.127-147
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• 2004

This paper examines the time-series relations among expected return, risk, and book-to-market(B/M) at the portfolio level. The time-series analysis is a natural alternative to cross-sectional regressions. An alternative feature of the time-series regressions is that they focus on changes in expected returns, not on average returns. Using the time-series analysis, we can directly test whether the three-factor model explains time-varying expected returns better than the characteristic-based model. These results should help distinguish between the risk and mispricing stories. We find that B/M is strongly associated with changes in risk, as measured by the Fama and French(1993) three-factor model. After controlling for changes in risk, B/M contains little additional information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than the characteristic-based model.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 추계 학술발표회 논문집
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• pp.109-114
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• 2002

In this paper we propose a change-point estimator with left and right regressions using the sample Fourier coefficients on the orthonormal bases. The asymptotic properties of the proposed change-point estimator are established. The limiting distribution and the consistency of the estimator are derived.

• 한국조경학회지
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• 제31권3호
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• pp.74-82
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• 2003

Under the assumption that the temperature of one location is closely related to land use characteristics around that location, this study is carried out to assess the impact of urban land use patterns on air temperature. In order to investigate the relationship, GIS techniques and statistical analyses are utilized, after spatially connecting urban land use data in Seoul Metropolitan Area with atmospheric data observed at Automatic Weather Stations (AWS). The research method is as follows: (1) To find out important land use factors on temperature, simple linear regressions for a specific time period (pilot study) are conducted with urban land use characteristics, (2) To make a final model, multiple regressions are carried out with those factors and, (3) To verify that the final model could be appled to explain temperature variations beyond the period, the model is extensively used for 5 different time periods: 1999 as a whole; summer in 1999; 1998 as a whole; summer in 1998; August in 1998. The results of simple linear regression models in the pilot study show that transportation facilities and open space area are very influential on urban air temperature variations, which explain 66 and 61 percent of the variations, respectively. However, the other land use variables (residential, commercial, and mixed land use) are found to have weak or insignificant relationship to the air temperatures. Multiple linear regression with the two important variables in the pilot study is estimated, which shows that the model explains 75 percent of the variability in air temperatures with correct signs of regression coefficients. Thus, it is empirically shown that an increase in open space and a decrease in transportation facilities area can leads to the decrease in air temperature. After the final model is extensively applied to the 5 different time periods, the estimated models explain 68 ∼ 75 percent of the variations in the temperatures is significant regression coefficients for all explanatory variables. This result provides a possibility that one air temperature model for a specific time period could be a good model for other time periods near to the period. The important implications of this result to lessen high air temperature we: (1) to expand and to conserve open space and (2) to control transportation-related factors such as transportation facilities area, road pavement and traffic congestion.

• 응용통계연구
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• 제18권2호
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• pp.253-269
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• 2005

회귀모형의 기본가정은 추정된 계수들이 표본 내의 모든 관측값에 대해 일정하다는 것이다. 그러나 자료의 구조적 변화로 인해 모형의 추정계수 중 최소한 일부는 상이한 부분집합으로 전체 표본을 분할해야 하는 경우가 현실적으로는 흔히 존재한다. 본 연구에서는 두 회귀모형 계수들간의 동일성을 검정하는 방법을 확대${\cdot}$일반화하여 자료의 분할시점을 탐색하는 검정절차와 결합시킨 후 이를 최근 가장 큰 사회적 문제가 되고 있는 실업률의 구조변화 발생 여부와 시점을 판별하는 실증분석에 적용시켜 보았다.

• 한국지능시스템학회논문지
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• 제25권5호
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• pp.477-482
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• 2015

단기풍속 예측을 위한 진화적 선형 및 비선형 회귀분석 기반의 보정 기법을 비교한다. 모델의 체계적 오류를 교정하기 위한 효율적인 MOS(Model Output Statistics)의 개발이 필요하나, 기존의 선형회귀분석 기반의 보정기법은 다양한 기상요소의 복잡한 비선형 특성을 반영하기 힘들다. 이를 개선하기 위해서 유전 프로그래밍을 사용하여 풍속 예측에 대한 비선형 보정 수식을 생성하는 기법을 제안하고 기본 다중선형회귀분석법 및 Ridge, Lasso 회귀분석법과 비교한다. 더불어, 선형회귀분석법과 진화적 비선형회귀분석 기법의 인자 선택의 차이와 유사성을 비교하고 분석한다. 2007년~2013년의 KLAPS(Korea Local Analysis and Prediction System) 재분석자료를 사용하여 제주도와 부산지역의 격자점에 대한 실험을 수행한다.

• 한국수자원학회논문집
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• 제33권6호
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• pp.733-744
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• 2000

불확실한 저수지 유입량과 수요량 때문에 최적의 저수지 조작기준은 정량화하기 쉽지 않다. 그렇지만 저수지 조작에 일정 수요를 충족시키면서 모의 발생시킨 유입량 시계열을 반영함으로써 음해 추계적 최적화 접근법을 이용하면 조작기준은 작성 가능하다. 이에 본 연구에서는 한강수계 7개 저수지 계통에 적절하도록 수력발전 최대화를 선형추적으로 모형화하고 최적제어를 이용하여 최적조작을 수행하였다. 이 때 2001년 수도권 용수수요를 만족시키도록 한 최적 조작 모형에는 다지점 마코브 모형에 의해 모의 발생된 유입량 자료가 입력된다. 그리고 최적 결과에 대하여 회구 분석과 통계 분석을 수행하여 월별 조작기준을 작성하고 계절별 저수위별 확률을 제시하였다. 이때 상대적으로 저수용량이 커서 조절능력이 좋은 화천댐, 소양강댐, 충주댐은 저류와 방류의 회귀 관계가 잘 형성되었다. 작성된 조작기준으로 실제 운영 기간동안에 대하여 모의조작을 수행하여 그 효율성을 검증하였다.

• East Asian mathematical journal
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• 제38권5호
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• pp.617-641
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• 2022

In this study, we first present a brief overview of the Korean general insurance market. We then explore the characteristics of the loss ratios of the Korean general insurance industry and apply Markov regime-switching methodology to model the loss ratios of these insurance companies by line of business based on changes in economic regimes. This study applies a number of confirmatory tests such as Zivot-Andrews test (2002), the Chow (1960) test and the Bai and Perron (1998) to confirm the presence of structural breaks in the time series of the loss ratios by line of business. Then, we employ Markov regime-switching methodology to model these loss ratios. We find empirical evidence that the loss ratios reported by insurance companies in Korea is characterized by two distinct regimes; a regime with high volatility and a regime with low volatility, except for vehicle insurance. Our analyses suggest that macro-economic conditions have significant explanatory effect on loss ratios but the direction of effect differs based on the line of business and the regime. Unlike previous studies that have applied linear regressions or divided the samples into different periods and then apply linear regressions to model loss ratios, we argue for the application of Markov regime-switching methodology, which are able to automatically distinguish the different regimes that may be associated with the movements of loss ratios based on differing economic conditions and regulatory upheavals. This study provides a more in depth understanding of loss ratios in the general insurance industry and will be of value to insurance practitioners in modelling the loss ratios associated with their businesses to aid in their decision making. The results may also provide a basis for further studies in other markets apart from Korea as well as for shaping policy decisions related to loss ratios.