• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.35-47
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• 2022

The receiver operating characteristic (ROC) curve was developed to quantify the classification ability of marker values (covariates) on the response variable and has been extended to survival data with diverse missing data structure. When survival data is understood as binary data (status of being alive or dead) at each time point, the ROC curve expressed at every time point results in time-dependent ROC curve and time-dependent area under curve (AUC). In particular, a follow-up study brings the change of cohort and incomplete data structures such as censoring and competing risk. In this paper, we review time-dependent ROC estimators under several contexts and perform simulation to check the performance of each estimators. We analyzed a dementia dataset to compare the prognostic power of markers.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.88-94
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• 2011

This study was conducted to provide on a basic information for maintenance of roadside trees and identify the links between environmental pollution and tree damages (Ginkgo biloba, Platanus occidentalis) in Daejeon City by analyzing photosynthetic characteristics, water use efficiency, chlorophyll fluorescence and chlorophyll contents by the regions and plants. The investigations were conducted at Chungnam national university(CNU) considered as the least air-polluted area compared to other study sites, Daedeok science town(DS) which a new road with little traffic recently was built around, Daehwa industry complex(DIC) and Daejeon station(DS) which an old road with heavy is located around. The photosynthetic capacity of the two species were highest in CNU and lowest in the DS. However the water use efficiency was highest on the contrary to the photosynthetic capacity in DS. Chlorophyll fluorescence and chlorophyll contents were highest in CNU and lowest in DS as the photosynthetic capacity. On all of the test, Ginkgo biloba shows the more favorable physiological responses than Platanus occidentalis.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.71-80
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• 2001

Logistic regression model is one of the most popular linear models for a binary response variable and used for the estimation of probability function. In many practical situations, the probability function can be expressed by a bell shaped curve and such a function can be estimated by a second order logistic regression model. However, when the probability curve is asymmetric, the estimation results using a second order logistic regression model may not be precise because a second order logistic regression model is a symmetric function. In addition, even if a second order logistic regression model is used, the interpretation for the effect of second order term may not be easy. In this paper, in order to alleviate such problems, an estimation method for asymmetric probabiity curve based on a first order logistic regression model and iterative bi-section method is proposed and its performance is compared with that of a second order logistic regression model by a simulation study.