• Journal of the Korean Data and Information Science Society
• /
• v.28 no.5
• /
• pp.1191-1204
• /
• 2017

Weighted least squares (WLS) estimation is often easily used for the data with heteroscedastic errors because it is intuitive and computationally inexpensive. However, WLS estimator is less robust to a few outliers and sometimes it may be inefficient. In order to overcome robustness problems, Box-Cox transformation, Huber's M estimation, bisquare estimation, and Yohai's MM estimation have been proposed. Also, more efficient estimations than WLS have been suggested such as Bayesian methods (Cepeda and Achcar, 2009) and semiparametric methods (Kim and Ma, 2012) in heteroscedastic error models. Recently, Çelik (2015) proposed the weight methods applicable to the heteroscedasticity patterns including butterfly-distributed residuals and megaphone-shaped residuals. In this paper, we review heteroscedastic regression estimators related to robust or efficient estimation and describe their properties. Also, we analyze cost data of U.S. Electricity Producers in 1955 using the methods discussed in the paper.

• The Korean Journal of Applied Statistics
• /
• v.16 no.2
• /
• pp.351-364
• /
• 2003

This paper derives Lagrange multiplier tests based on Double-Length Artificial Regression and Outer-Product Gradient for testing linear and log-linear panel regressions against Box-Cox alternatives. The proposed DLR based LM tests are easy to implement in an error component model. From the Monte Carlo study, the DLR based LM tests are recommended for testing functiona forms.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.1
• /
• pp.251-261
• /
• 2004

The paper focuses primarily on the standard linear multiple regression model where the parameter of interest is a ratio of two regression coefficients. The general model includes the calibration model, the Fieller-Creasy problem, slope-ratio assays, parallel-line assays, and bioequivalence. We provide an orthogonal transformation (cf. Cox and Reid (1987)) of the original parameter vector. Also, we give some remarks on the difficulties associated with likelihood based confidence interval.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1311-1327
• /
• 2016

We propose a multi-state model for analyzing semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the 'illness-death model', which composes three states, such as 'healthy', 'diseased', and 'dead'. The state of 'diseased' can be considered as an intermediate event. Two more states are added into the illness-death model to describe missing events caused by a loss of follow-up before the end of the study. One of them is a state of 'LTF', representing a lost-to-follow-up, and the other is an unobservable state that represents the intermediate event experienced after LTF occurred. Given covariates, we employ the Cox proportional hazards model with a normal frailty and construct a full likelihood to estimate transition intensities between states in the multi-state model. Marginalization of the full likelihood is completed using the adaptive Gaussian quadrature, and the optimal solution of the regression parameters is achieved through the iterative Newton-Raphson algorithm. Simulation studies are carried out to investigate the finite-sample performance of the proposed estimation procedure in terms of the empirical coverage probability of the true regression parameter. Our proposed method is also illustrated with the dataset adapted from Helmer et al. (2001).

• Industry Promotion Research
• /
• v.7 no.1
• /
• pp.1-8
• /
• 2022

We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (t_i, δ_i) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.

• Journal of Korean Biological Nursing Science
• /
• v.21 no.4
• /
• pp.292-299
• /
• 2019

Purpose: The purpose of this study was to determine whether poor preoperative nutritional status in elderly patients exhibited a negative influence on postoperative clinical outcomes. Methods: The medical records of 645 elderly patients were examined retrospectively. The patients had undergone major surgery between January 2017 and January 2018. Their nutritional status was measured using the Nutritional Risk Screening 2002. The data were analyzed using the chi-squared test, the Mann-Whitney U test, logistic regression, linear regression, Cox proportional hazards regression, and the Kaplan-Meier analysis. Results: Preoperative malnutrition was found in 73 patients (11.3%). Poor preoperative nutritional status was significantly associated with pressure ulcers, length of hospitalization, discharge to patient care facilities rather than home, and mortality rate at three months. Conclusion: Preoperative malnutrition in elderly patients was associated with negative postoperative clinical outcomes. These results indicate that an effective nutritional program before surgery can lead to a more rapid postoperative recovery.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.2
• /
• pp.257-269
• /
• 2012

Global crisis expedites the change in the environment of industry and puts small size enterprises in danger of mass bankruptcy. Because of this, domestic small size enterprises is an urgent need of restructuring. Based on the small business data registered in the Credit Guarantee Fund, we estimated the survival probability in the context of the survival analysis. We also analyzed the survival time which are distinguished depending on the types of business in the small business. Financial variables were also conducted using COX regression analysis of small businesses by types of business. In terms of types of business wholesale and retail trade industry and services were relatively high in the survival probability than light, heavy, and the construction industries. Especially the construction industry showed the lowest survival probability. In addition, we found that construction industry, the bigger BIS (bank of international settlements capital ratio) and current ratio are, the smaller default-rate is. But the bigger borrowing bond is, the bigger default-rate is. In the light industry, the bigger BIS and ROA (return on assets) are, the smaller a default-rate is. In the wholesale and retail trade industry, the bigger bis and current ratio are, the smaller a default-rate is. In the heavy industry, the bigger BIS, ROA, current ratio are, the smaller default-rate is. Finally, in the services industry, the bigger current ratio is, the smaller a default-rate is.

• Communications for Statistical Applications and Methods
• /
• v.13 no.1
• /
• pp.101-111
• /
• 2006

One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

• Journal of the Korean Statistical Society
• /
• v.20 no.1
• /
• pp.85-92
• /
• 1991

A set of conditions ensuring local asymptotic normality for independent but not necessarily identically distributed observations in semiparametric models is presented here. The conditions are turned out to be more direct and easier to verify than those of Oosterhoff and van Zwet(1979) in semiparametric models. Examples considered include the simple linear regression model and Cox's proportional hazards model without censoring where the covariates are not random.

• The Korean Journal of Applied Statistics
• /
• v.19 no.3
• /
• pp.461-479
• /
• 2006

This paper derives joint and conditional Lagrange multiplier tests based on Double-Length Artificial Regression(DLR) for testing functional form and/or the presence of individual(time) effect in a panel regression model. Small sample properties of these tests are assessed by Monte Carlo study, and comparisons are made with LM tests based on Outer Product Gradient(OPG). The results show that the proposed DLR based LM tests have the most appropriate finite sample performance.