Eunyoung Lee

An automatic recognition method of a car license plate using color image processing is presented. At first, background colors of a plate are extracted from an input car image. A neural network is used for more stable extraction. To find a plate region, a fixed ratio of horizontal and vertical length of a plate is used. To recognize characters in a plate, template matching and postprocessing techniques are used. Since the proposed method does not depend on line information of a plate it is very r

Bayesian information criterion (BIC) is known to identify the true model consistently as long as the predictor dimension is finite. Recently, its moderate modifications have been shown to be consistent in model selection even when the number of variables diverges. Those works have been done mostly in mean regression, but rarely in quantile regression. The best-known results about BIC for quantile regression are for linear models with a fixed number of variables. In this article, we investigate h

Summary Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models a

Purpose: The purpose of study was to identify how patients experienced chemotherapy-induced peripheral neuropathy (CIPN) and quality of life related to CIPN. Methods: This was a descriptive research. We collected data from 105 patients with chemotherapy-induced peripheral neuropathy. They completed a self-reported questionnaire including EORTC (Eastern Cooperative Oncology Group) CIPN20 and items related to their disease and peripheral neuropathy. The investigators filled in part of items about

PURPOSE: L-ascorbic acid (LAA) modifies the in vitro growth of leukemic cells from approximately 50% of patients with acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS). To test the hypothesis that depletion of LAA, alternating with supplementation to prevent scurvy, would provide therapeutic benefit, a single-arm pilot trial was conducted (ClinicalTrials.gov identifier: NCT00329498). Experimental results: During depletion phase, patients with refractory AML or MDS were placed on a

In this paper, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work is primarily motivated from many biomedical studies with correlated multiple responses such as the cancer cell-line encyclopedia project. We assume that the underlying regression coefficient matrix is simultaneously low-rank and row-wise sparse. We propose an intuitively appealing selection and estimation framewo

Varying coefficient models are useful generalizations of parametric linear models. They allow for parameters that depend on a covariate or that develop in time. They have a wide range of applications in time series analysis and regression. In time series analysis they have turned out to be a powerful approach to infer on behavioral and structural changes over time. In this paper, we are concerned with high dimensional varying coefficient models including the time varying coefficient model. Most

Summary We discuss Poisson reduced-rank models for low-dimensional summaries of high-dimensional Poisson vectors that allow inference on the location of individuals in a low-dimensional space. We show that under weak dependence conditions, which allow for certain correlations between the Poisson random variables, the locations can be consistently estimated using Poisson maximum likelihood estimation. Moreover, we develop consistent rules for determining the dimension of the location from the dis

In this article, we propose a new nonparametric estimator of the conditional quantile function. It is based on locally fitting a logistic model. We compare the new proposal with some existing methods. Those include the double-kernel technique of Yu and Jones (1998), the adjusted version of the Nadaraya–Watson estimator suggested by Hall et al. (1999) and the approach by Koenker and Bassett (1978) based on the ‘check function’ loss. The comparison is done by asymptotic mean squared error and a si