• Communications for Statistical Applications and Methods
• /
• 제7권2호
• /
• pp.574-574
• /
• 2000

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.

• Communications for Statistical Applications and Methods
• /
• 제7권2호
• /
• pp.575-583
• /
• 2000

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.

• Journal of the Korean Data and Information Science Society
• /
• 제15권3호
• /
• pp.617-624
• /
• 2004

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.

• 대한수학회지
• /
• 제50권1호
• /
• pp.127-136
• /
• 2013

The term rank of a matrix A is the least number of lines (rows or columns) needed to include all the nonzero entries in A. In this paper, we obtain a characterization of linear transformations that preserve term ranks of matrices over antinegative semirings. That is, we show that a linear transformation T from a matrix space into another matrix space over antinegative semirings preserves term rank if and only if T preserves any two term ranks $k$ and $l$.

• 대한수학회지
• /
• 제52권3호
• /
• pp.625-636
• /
• 2015

The Boolean rank of a nonzero $m{\times}n$ Boolean matrix A is the least integer k such that there are an $m{\times}k$ Boolean matrix B and a $k{\times}n$ Boolean matrix C with A = BC. We investigate the structure of linear transformations T : $\mathbb{M}_{m,n}{\rightarrow}\mathbb{M}_{p,q}$ which preserve Boolean rank. We also show that if a linear transformation preserves the set of Boolean rank 1 matrices and the set of Boolean rank k matrices for any k, $2{\leq}k{\leq}$ min{m, n} (or if T strongly preserves the set of Boolean rank 1 matrices), then T preserves all Boolean ranks.

• 응용통계연구
• /
• 제19권1호
• /
• pp.111-120
• /
• 2006

이단계 이수준 균형지분모형에서 주효과 및 지분효과를 검정하기 위한 모수적 검정과 순위변환을 이용한 검정은 전반적으로 제1종 오류율이 상당히 유사하며, 주효과 및 지분효과를 검정하기 위한 순위변환통계량의 검정력은 모수적 통계량의 검정력보다 상대적으로 뛰어난 수준임을 보여준다. 한편 효과의 크기와 표본의 크기를 증가시킬수록 모수적 통계량과 순위변환 통계량의 검정력 증가량의 크기는 현저하게 향상되며, 특히 지수분포와 같은 비대칭분포하에서 모든 인자가 고정일때 순위변환 통계량의 검정력이 모수적 통계량의 검정력보다 월등히 높은 수준임을 나타낸다.

• Communications for Statistical Applications and Methods
• /
• 제16권5호
• /
• pp.781-790
• /
• 2009

본 논문에서는 퍼지수를 포함한 모수적 회귀모형을 추정하기 위하여 분포무관추정량으로 알려진 순위 변환방법과 Theil 방법을 소개한다. 순위 변환방법은 퍼지수의 ${\alpha}$-수준집합의 중심과 폭에 대한 순위를 이용하고 Theil 방법은 ${\alpha}$-수준집합의 중심과 폭에 대한 추정한 값들의 중위수를 이용한다. 예제를 이용하여 분포무관추정량으로 추정된 퍼지회귀모형의 효율성을 최소자승법과 여러 가지 방법으로 추정된 퍼지회귀모형과 비교한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권12호
• /
• pp.5785-5804
• /
• 2019

The information of localization is a fundamental requirement in wireless sensor network (WSN). The method of distance vector-hop (DV-Hop), a range-free localization algorithm, can locate the ordinary nodes by utilizing the connectivity and multi-hop transmission. However, the error of the estimated distance between the beacon nodes and ordinary nodes is too large. In order to enhance the positioning precision of DV-Hop, fast triangle flip bat algorithm, which is based on curve strategy and rank transformation (FTBA-TCR) is proposed. The rank is introduced to directly select individuals in the population of each generation, which arranges all individuals according to their merits and a threshold is set to get the better solution. To test the algorithm performance, the CEC2013 test suite is used to check out the algorithm's performance. Meanwhile, there are four other algorithms are compared with the proposed algorithm. The results show that our algorithm is greater than other algorithms. And this algorithm is used to enhance the performance of DV-Hop algorithm. The results show that the proposed algorithm receives the lower average localization error and the best performance by comparing with the other algorithms.

• 대한수학회보
• /
• 제51권6호
• /
• pp.1841-1850
• /
• 2014

It is known that the ranks of the semigroups $\mathcal{SOP}_n$, $\mathcal{SPOP}_n$ and $\mathcal{SSPOP}_n$ (the semigroups of orientation preserving singular self-maps, partial and strictly partial transformations on $X_n={1,2,{\ldots},n}$, respectively) are n, 2n and n + 1, respectively. The idempotent rank, defined as the smallest number of idempotent generating set, of $\mathcal{SOP}_n$ and $\mathcal{SSPOP}_n$ are the same value as the rank, respectively. Idempotent can be seen as a special case (with m = 1) of m-potent. In this paper, we investigate the m-potent ranks, defined as the smallest number of m-potent generating set, of the semigroups $\mathcal{SOP}_n$, $\mathcal{SPOP}_n$ and $\mathcal{SSPOP}_n$. Firstly, we characterize the structure of the minimal generating sets of $\mathcal{SOP}_n$. As applications, we obtain that the number of distinct minimal generating sets is $(n-1)^nn!$. Secondly, we show that, for $1{\leq}m{\leq}n-1$, the m-potent ranks of the semigroups $\mathcal{SOP}_n$ and $\mathcal{SPOP}_n$ are also n and 2n, respectively. Finally, we find that the 2-potent rank of $\mathcal{SSPOP}_n$ is n + 1.

• 한국음향학회:학술대회논문집
• /
• 한국음향학회 1992년도 학술논문발표회 논문집 제11권 1호
• /
• pp.123-126
• /
• 1992

A new class of data transformation matri is introduced for estimation of angles of arrivals by the rank reduction of multiple wideband sources. The proposed unitary focusing matri minimizes the average of the squared norm of focusing error over the angles of interest without a priori knowledge of source locations. The merit that result as a consequence is a lower resolution threshold. These matrices can be applied to the case of the multigroup sources. Simulations and the comparison of statistical performance are compared with the algorithms (especially, spatial resampling method) which does not require the pre-estimation.