• Journal of the Korean Mathematical Society
• /
• v.56 no.6
• /
• pp.1503-1514
• /
• 2019

A rank 1 matrix has a factorization as $uv^t$ for vectors u and v of some orders. The arctic rank of a rank 1 matrix is the half number of nonzero entries in u and v. A matrix of rank k can be expressed as the sum of k rank 1 matrices, a rank 1 decomposition. The arctic rank of a matrix A of rank k is the minimum of the sums of arctic ranks of the rank 1 matrices over all rank 1 decomposition of A. In this paper we obtain characterizations of the linear operators that strongly preserve the symmetric arctic ranks of symmetric matrices over nonnegative reals.

• The Pure and Applied Mathematics
• /
• v.21 no.2
• /
• pp.121-128
• /
• 2014

The Boolean rank of a nonzero m $m{\times}n$ 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. In 1984, Beasley and Pullman showed that a linear operator preserves the Boolean rank of any Boolean matrix if and only if it preserves Boolean ranks 1 and 2. In this paper, we extend this characterization of linear operators that preserve the Boolean ranks of Boolean matrices. We show that a linear operator preserves all Boolean ranks if and only if it preserves two consecutive Boolean ranks if and only if it strongly preserves a Boolean rank k with $1{\leq}k{\leq}min\{m,n\}$.

• Journal of the Korean Mathematical Society
• /
• v.46 no.1
• /
• pp.113-123
• /
• 2009

A rank one matrix can be factored as $\mathbf{u}^t\mathbf{v}$ for vectors $\mathbf{u}$ and $\mathbf{v}$ of appropriate orders. The perimeter of this rank one matrix is the number of nonzero entries in $\mathbf{u}$ plus the number of nonzero entries in $\mathbf{v}$. A matrix of rank k is the sum of k rank one matrices. The perimeter of a matrix of rank k is the minimum of the sums of perimeters of the rank one matrices. In this article we characterize the linear operators that preserve perimeters of matrices over semirings.

• Kyungpook Mathematical Journal
• /
• v.54 no.4
• /
• pp.619-627
• /
• 2014

Let $\mathcal{M}(S)$ denote the set of all $m{\times}n$ matrices over a semiring S. For $A{\in}\mathcal{M}(S)$, zero-term rank of A is the minimal number of lines (rows or columns) needed to cover all zero entries in A. In [5], the authors obtained that a linear operator on $\mathcal{M}(S)$ preserves zero-term rank if and only if it preserves zero-term ranks 0 and 1. In this paper, we obtain new characterizations of linear operators on $\mathcal{M}(S)$ that preserve zero-term rank. Consequently we obtain that a linear operator on $\mathcal{M}(S)$ preserves zero-term rank if and only if it preserves two consecutive zero-term ranks k and k + 1, where $0{\leq}k{\leq}min\{m,n\}-1$ if and only if it strongly preserves zero-term rank h, where $1{\leq}h{\leq}min\{m,n\}$.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.241-250
• /
• 2015

An important scientific objective of longitudinal studies involves tracking the probability of a subject having certain health condition over the course of the study. Proper definitions and estimates of disease risk tracking have important implications in the design and analysis of long-term biomedical studies and in developing guidelines for disease prevention and intervention. We study in this paper a class of rank-tracking probabilities to describe a subject's conditional probabilities of having certain health outcomes at two different time points. Linear mixed effects models are considered to estimate the tracking probabilities and their ratios of interest. We apply our methods to an epidemiological study of childhood cardiovascular risk factors.

• International Journal of Reliability and Applications
• /
• v.6 no.1
• /
• pp.53-64
• /
• 2005

In this paper, we propose a nonparametric test procedure for the multivariate, grouped and right censored data for two sample problem. For the construction of the test statistic, we use the linear rank statistics for each component and apply the permutation principle for obtaining the null distribution. For the large sample case, the asymptotic distribution is derived under the null hypothesis with the additional assumption that two censoring distributions are also equal. Finally, we illustrate our procedure with an example and discuss some concluding remarks. In appendices, we derive the expression of the covariance matrix and prove the asymptotic distribution.

• Communications for Statistical Applications and Methods
• /
• v.23 no.3
• /
• pp.215-230
• /
• 2016

The powers of some tests for independence hypothesis against positive (negative) quadrant dependence in generalized Farlie-Gumbel-Morgenstern distribution are compared graphically by simulation. Some of these tests are usual linear rank tests of independence. Two other possible rank tests of independence are locally most powerful rank test and a powerful nonparametric test based on the $Cram{\acute{e}}r-von$ Mises statistic. We also evaluate the empirical power of the class of distribution-free tests proposed by Kochar and Gupta (1987) based on the asymptotic distribution of a U-statistic and the test statistic proposed by $G{\ddot{u}}ven$ and Kotz (2008) in generalized Farlie-Gumbel-Morgenstern distribution. Tests of independence are also compared for sample sizes n = 20, 30, 50, empirically. Finally, we apply two examples to illustrate the results.

• Proceedings of the Korean Statistical Society Conference
• /
• 2005.11a
• /
• pp.187-192
• /
• 2005

For the problem of variable selection in linear models, we consider the errors are correlated with V covariance matrix. Hocking's theorems on the effects of the overfitting and the undefitting in linear model are extended to the less than full rank and correlated error model, and to the ANCOVA model

• The Korean Journal of Applied Statistics
• /
• v.37 no.2
• /
• pp.239-253
• /
• 2024

This research applies both point-wise and pair-wise learning strategies within the learning-to-rank (LTR) framework to predict horse race rankings in Seoul. Specifically, for point-wise learning, we employ a linear model and random forest. In contrast, for pair-wise learning, we utilize tools such as RankNet, and LambdaMART (XGBoost Ranker, LightGBM Ranker, and CatBoost Ranker). Furthermore, to enhance predictions, race records are standardized based on race distance, and we integrate various datasets, including race information, jockey information, horse training records, and trainer information. Our results empirically demonstrate that pair-wise learning approaches that can reflect the order information between items generally outperform point-wise learning approaches. Notably, CatBoost Ranker is the top performer. Through Shapley value analysis, we identified that the important variables for CatBoost Ranker include the performance of a horse, its previous race records, the count of its starting trainings, the total number of starting trainings, and the instances of disease diagnoses for the horse.

• Journal of Korea Multimedia Society
• /
• v.12 no.1
• /
• pp.26-30
• /
• 2009

We propose an adaptive Recursive Least Rank(RLR) L-filter which uses an L-estimator in order statistics and is based on rank estimate in robust statistics. The proposed RLR L-filter is a non-linear adaptive filter using non-linear adaptive algorithm and adapts itself to optimal filter in the sense of least dispersion measure of errors with non-homogeneous step size. Therefore the filter may be suitable for applications when the transmission channel is nonlinear channels such as Gaussian noise or impulsive noise, or when the signal is non-stationary such as image signal.