Earticle

The purpose of this research is to measure risk in common stocks in Korean financial firms by industrial clusters applying a nonparametric methodology, which is Monte Carlo simulation, but also to identify the most critical factor explaining the volatility of stocks in financial firms and in each sector of financial firms (banks, insurance companies, and investment and security trading companies). The study suggests that the stock returns of Korean firms are covariated because of this parallel shift factor. The result shows similar VaRs and ESs for each industry when using a factor analytic approach.

Various methods control the influence of a covariate on a response variable. These methods are analysis of covariance(ANCOVA), RANK ANCOVA, ANOVA of (covariate-adjusted) residuals, and Kruskal-Wallis tests on residuals. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set that ignore the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. In this paper, we proposed the new nonparametric method on the ANCOVA model, as applying joint placement in a one-way layout on residuals as described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed procedure with those of the previous procedure.

Kruskal and Wallis (1952) proposed typical nonparametric method in one-way layout problem. A special feature of this procedure is use of rank in mixed samples. In this paper, the new procedure based on placement as extension of the two sample placement tests described in Orban and Wolfe (1982) was proposed. Some critical values in small sample cases and comparative results of a Monte Carlo power study are presented.

This paper is concerned with the development of a nonparametric procedure for the statistical inference about the nonlinear regression parameters. A confidence region and a hypothesis testing procedure based on a class of signed linear rank statistics are proposed and the asymptotic distributions of the test statistic both under the null hypothesis and under a sequence of local alternatives are investigated. Some desirable asymptotic properties including the asymptotic relative efficiency are discussed for various score functions.

비교유전체학의 주요 주제 중 유전자서열을 분류하고 단백질기능을 예측하는 연구가 있으며, 이를 위해 단백질 구조, 공통서열 및 바인딩 위치 예측등의 방법과 함께, 전유전체 서열에서 구해지는 유사도 그래프를 분석해 상동유전자를 검색하는 계산학적인 접근방법이 있다. 유사도그래프를 사용한 방법은 서열에 대한 기존 지식에 의존하지 않는 장점이 있지만 유사도 하한값과 같은 주관적인 임계값이 필요한 단점이 있다. 본 논문에서는 반복적으로 그래프를 분해하는 이전의 방법을 일반화시켜, 유사도 그래프에 기반한 유전자 서열군집분석 방법론과 객관적이고 안정적인 파라미터 임계값 계산 방법을 제안한다. 제시된 방법으로 알려진 미생물 유전체 서 열을 분석하여 이전의 방법인 BAG 알고리즘 결과와 비교했다.

One large topic in comparative genomics is to predict functional annotation by classifying protein sequences. Computational approaches for function prediction include protein structure prediction, sequence alignment and domain prediction or binding site prediction. This paper is on another computational approach searching for sets of homologous sequences from sequence similarity graph. Methods based on similarity graph do not need previous knowledges about sequences, but largely depend on the researcher's subjective threshold settings. In this paper, we propose a genome sequence clustering method of iterative testing and graph decomposition, and a simple method to calculate a strict threshold having biochemical meaning. Proposed method was applied to known bacterial genome sequences and the result was shown with the BAG algorithm's. Result clusters are lacking some completeness, but the confidence level is very high and the method does not need user-defined thresholds.

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

In additive nonparametric regression, Linton and Nielsen (1995) showed that the marginal integration when applied to the local linear smoother produces a rate-optimal estimator of each univariate component function for the case where the dimension of the predictor is two. In this paper we give new formulas for the bias and variance of the marginal integration regression estimators which are valid for boundary areas as well as fixed interior points, and show the local linear marginal integration estimator is in fact rate-optimal when the dimension of the predictor is less than or equal to four. We extend the results to the case of the local polynomial smoother, too.

In this article the fuzzy number rank and the fuzzy rank transformation method are introduced in order to analyse the non-parametric fuzzy regression model which cannot be described as a specific functional form such as the crisp data and fuzzy data as a independent and dependent variables respectively. The effectiveness of fuzzy rank transformation methods is compared with other methods through the numerical examples.

A nonparametric bootstrap procedure with an LSTAR modeling of the valuation ratio is applied to the continuously compounded real stock return and the log of the price-dividend process. The empirical distribution of the test statistics shows that the evidence for a stock return predictability weakens when we take care of nonlinearity dynamics in the regressor. We split the sample into two regimes and implement the long-horizon predictability tests. Results show that the stock return is predictable in the stationary regime, while the test statistic under the null of unpredictability is insignificant in the non-stationary regime.

In this paper we consider nonparametric tests using an aligned method for umbrella alternatives in a randomized block design with replications. We also assume that there are no interaction effects in the model. The test statistic that we propose is based on Mack, Wolfe (1981)’s statistic, where they have originally suggested testing for umbrella alternatives. Throughout simulation study we compare empirical powers of our proposed statistic with Hettmansperger, Norton (1987)’s statistic and Mack, Skilling (1980)’s statistic. From the simulation results we can show that Hettmansperger, Norton’s statistic has good powers for Cauchy distribution only, but our statistic is wholly efficient for underlying distributions.

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

In recent years, kernel type estimates are abundant. In this paper, we propose a bandwidth selection method for kernel regression of fixed design based on bootstrap procedure. Mathematical properties of proposed bootstrap-based bandwidth selection method are discussed. Performance of the proposed method for small sample case is compared with that of cross-validation method via a simulation study.

반복이 있는 랜덤화블록 모형에서의 비모수적 검정 방법에는 Mack과 Skillings (1980)가 제안한 방법이 있다. 이 방법은 각각의 관측값을 사용하는 대신 각 블록에서의 반복된 관측값들의 평균을 사용하여 검정하는 방법이다. 따라서 관측치들의 정보를 손실할 수 있다는 단점이 있다. 본 논문에서는 이러한 단점을 보완하기 위해 정렬방법과 선형 위치통계량을 이용한 비모수 검정법을 제안하였다. 또한 몬테카를로 모의실험(Monte-Carlo Study)을 통하여 기존의 방법과 제안한 방법의 검정력을 비교하였다.

Mack and Skillings (1980) proposed a nonparametric method in a randomized block design with replications. This method employs the mean of observations instead of each observation. However, it has the inherent disadvantage that there may be a loss of information. In this paper, we proposed a nonparametric method that employees an aligned method and linear placement statistics to supplement its weakness. A Monte-Carlo study is performed to compare the power of the proposed method with previous methods.

랜덤화 블록 계획법(randomized block design)에서 대립가설형태에 따라 많은 비모수적인 방법들이 제안되었다. 일반대립가설에서 대표적으로 Fridman (1937)의 검정법이 있고, 순서형 대립가설에서는 Page (1963)의 검정법이 있다. 우산형 대립가설에 대한 비모수적 방법으로는 일원 배치 모형에서 k개의 표본 문제에 대하여 Mack과 Wolfe (1981)의 검정법이 있다. 본 논문에서는 랜덤화 블록 계획법(randomized block design)에서 우산형대립가설에 대하여 블록 간의 정보를 이용한 Hodges와 Lehmann (1962)의 정렬방법과 위치를 이용한 Kim (1999)의 검정법을 이용하여 검정법을 제안하였다. 또한, Monte carlo 모의실험을 통하여 제안된 검정법과 기존의 검정법을 비교하였다.

Nonparametric methods in randomized block design were suggested by Friedman (1937) for general alternatives and were also proposed by Page (1963) for ordered alternatives in one-way layout; in addition, K-sample rank tests for umbrella alternatives were suggested by Mack and Wolfe (1981). In this paper, we proposed a nonparametric method of umbrella alternatives for randomized block design using the aligned method proposed by Hodges and Lehmann (1962) to use block information and using placement suggested by Kim (1999). Monte Carlo simulation was also adapted to compare the power of the proposed procedure with previous methods.

반복이 있는 랜덤화 블록 계획법(randomized block design with replications)에서의 대표적인 검정법은 Mack이 제안한 방법과 Mack과 Skillings이 제안한 방법이 있다. 이 방법은 각 블록의 처리에서 반복된 각 관측값 대신에 반복된 관측값들의 평균을 이용하여 순위를 매기기 때문에 정보의 손실이 발생할 가능성이 있다. 이를 보완하기 위해 본 논문에서는 Chung과 Kim (2007)이 제안한 결합위치(joint placement) 방법에 점수함수(score function)를 적용한 선형위치통계량(linear placement statistics)을 이용한 검정방법을 제안하였다. 또한 Monte Carlo simulation study를 통해 기존의 방법들과 검정력을 비교하였다.

Typical Nonparametric methods for randomized block design with replications are two methods proposed by Mack (1981) and Mack and Skillings (1980). This method is likely to cause information loss because it uses the average of repeated observations instead of each repeated observation in the processing of each block. In order to compensate for this, we proposed a test method using linear placement statistics, which is a score function applied to the joint placement method proposed by Chung and Kim (2007). Monte Carlo simulation study is adapted to compare the power with previous methods.

공변량(covariate)이 존재하는 경우, 각 처리군 간 효과의 차이를 검정하기 위한 대표적인 비모수적 방법에는 Quade (1967)가 제안한 검정법이 있다. 또한 반응변수에 대해 공변량으로 단순선형회귀분석을 실시하여 얻은 잔차에 대해 일원배치분산분석과 Kruskal Wallis가 제안한 방법을 적용하는 방법, 그리고 Hwang과 Kim (2012)이 제안한 비모수적 도구인 위치(placement)를 이용한 방법이 있다. 본 논문에서는 공분산분석 모형에서 Hwang과 Kim (2012)이 제안한 방법을 확장하여 공분산분석에서의 새로운 방법을 제안하였다. 또한 모의실험(Monte Carlo simulation study)을 통하여 기존의 검정법들과 제안한 방법의 검정력을 비교하였다.

Quade (1967) proposed RANK ANCOVA, which is a nonparametric method to test differences between treatments when there are covariates. Hwang and Kim (2012) also proposed a joint placement test on covariate-adjusted residuals. In this paper, we proposed a new nonparametric method to control the effect of covariate on a response variable that uses linear statistics on covariate adjusted-residuals. The score function used in the linear statistics was proposed by Jeon and Kim (2016). Monte Carlo simulation is also conducted to compare the empirical powers of the proposed method with previous methods.

독립된 세 개 이상의 처리 간에 차이 유무를 검정하는 비모수적 방법에는 Kruskal과 Wallis (1952)가 제안한 검정법이 있다. 세 개 이상의 다른 모집단으로부터 결합된 표본관측 값들의 순위를 이용한 검정기법이다. 본 논문에서는 Chung과 Kim (2007)이 제안한 결합위치 방법을 확장하여 일원배치모형에서 새로운 방법을 제안하였다. 또한 모의실험(Monte Calro simulation study)를 통하여 기존의 검정법과 제안한 방법의 검정력을 비교하였다.

Kruskal and Wallis (1952) proposed a nonparametric method to test the differences between more than three independent treatments. This procedure uses rank in mixed sample combined with more than three unlike populations. This paper proposes a the new procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed method with previous methods.

임상시험에서 약의 복용량에 따라 처리 효과가 증가하다가, 부작용으로 인해 일정 용량 수준에서 감소하는 추세를 보일 수 있다. 이러한 경향을 우산형 패턴이라 하며, 우산형 패턴의 대립가설에 대한 검정은 이러한 경향이 사전에 예측 가능할 때 유용하다. 이 논문에서는 Orban과 Wolfe (1982)가 제안한 선형 위치(linear placement)를 이용하여 일원배치법에서 우산형 대립가설의 비모수적 검정법을 제안하였다. 또한 Monte Carlo 모의실험을 통하여 기존의 방법들과 검정력(power)을 비교하였다.

The treatment effect in clinical tests depending on dose of the drug; however, it can show a decreasing trend in fixed dose level due to side effects. The trend is known as an umbrella pattern; in addition, the method for the umbrella alternative is quite useful when the tendency is predicted in advance. In this paper, we propose a nonparametric method of umbrella alternatives for a one-way layout by using linear placement described in Orban and Wolfe (1982). The Monte Carlo simulation is adapted to compare the power of proposed procedure with previous methods.