• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.53-77
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• 1994

In a certain stochastic process, Cox's regression model is used to analyze multistate survival data. From this model, the regression parameter vectors, survival functions, and the probability of being in response function are estimated based on multistate Cox's partial likelihood and nonparametric likelihood methods. The asymptotic properties of these estimators are described informally through the counting process approach. An example is given to likelihood the results in this paper.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.1-11
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• 1993

The Cox's regression model is frequently used for covariate effects in survival data analysis, But, much of the statistical work has focused on asymptotic behavior so the small sample evaluation has been neglected. In this paper, we compare the small or moderate sample performances of the survival function estimators for the Cox's regression model using bootstrap method. The smoothed PL type estimator and the Link estimator are slightly better than corresponding the PL type estimator and the Nelson type estimator in the sense of the achieved error rates.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.327-340
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• 2008

This paper derives joint and conditional Lagrange multiplier tests based on information matrix for testing functional form and/or the presence of autocorrelation in a regression model. Small sample properties of these tests are assessed by Monte Carlo study and comparisons are made with LM tests based on Hessian matrix. The results show that the proposed $LM_E$ tests have the most appropriate finite sample performance.

• Advances in pediatric surgery
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• v.12 no.2
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• pp.202-212
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• 2006

The prognostic factors for extrahepatic biliary atresia (EHBA) after Kasai portoenterostomy include the patient's age at portoenterostomy (age), size of bile duct in theporta hepatis (size), clearance of jaundice after operation (clearance) and the surgeon's experience. The aim of this study is to examine the most significant prognostic factor of EHBA after Kasai portoenterostomy. This retrospective study was done in 51 cases of EHBA that received Kasai portoenterostomy by one pediatric surgeon. For the statistical analysis, Kaplan-Meier method, Logrank test and Cox regression test were used. A p value of less than 0.05 was considered to be significant. Fifteen patients were regarded as dead in this study, including nine cases of liver transplantation. There was no significant difference of survival to age. The age is also not a significant risk factor for survival in this study (Cox Regression test; p = 0.63). There was no significant difference in survival in relation to the size of bile duct. However, bile duct size was a significant risk factor for survival (Cox Regression test; p = 0.002). There was a significant difference in relation to survival and clearance (Kaplan-Meier method; p = 0.02). The clearing was also a significant risk factor for survival (Cox Regression test; p = 0.001). The clearance of jaundice is the most significant prognostic factor of EHBA after Kasai portoenterostomy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6773-6777
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• 2015

We used to LASSO-Cox method for determining prognostic factors of male breast cancer survival and showed the superiority of this method compared to Cox proportional hazard model in low sample size setting. In order to identify and estimate exactly the relative hazard of the most important factors effective for the survival duration of male breast cancer, the LASSO-Cox method has been used. Our data includes the information of male breast cancer patients in Fars province, south of Iran, from 1989 to 2008. Cox proportional hazard and LASSO-Cox models were fitted for 20 classified variables. To reduce the impact of missing data, the multiple imputation method was used 20 times through the Markov chain Mont Carlo method and the results were combined with Rubin's rules. In 50 patients, the age at diagnosis was 59.6 (SD=12.8) years with a minimum of 34 and maximum of 84 years and the mean of survival time was 62 months. Three, 5 and 10 year survival were 92%, 77% and 26%, respectively. Using the LASSO-Cox method led to eliminating 8 low effect variables and also decreased the standard error by 2.5 to 7 times. The relative efficiency of LASSO-Cox method compared with the Cox proportional hazard method was calculated as 22.39. The19 years follow of male breast cancer patients show that the age, having a history of alcohol use, nipple discharge, laterality, histological grade and duration of symptoms were the most important variables that have played an effective role in the patient's survival. In such situations, estimating the coefficients by LASSO-Cox method will be more efficient than the Cox's proportional hazard method.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.233-250
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• 2002

Cox's proportional hazard model is highly-used for the regression analysis of survival data in various fields. Regression diagnostics for the proportional hazards model, however, is not as well-known as the diagnostics for the classical linear models and so these diagnostic methods are not used widely in our practical data analyses. For this reason, we review the residuals proposed by several authors, and investigate how to use them in assessing the model. We also provide the results and interpretation with the analysis of PBC data using S-plus 2000 program.

• Journal of the Korean Home Economics Association
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• v.42 no.1
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• pp.69-79
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• 2004

This study has examined factors of young married women's new entry in labor market after the birth of their first child. For the dynamic analysis, the Cox Regression Hazard Model is applied. The following results are obtained： First, about 33％ of married women who did not have a job at the pre-birth enter in labor market at the post-birth. Second, compared to those out of the labor force, women who succeeded in finding their first jobs after the birth of their first child are more likely to be younger, have baby-sitters, have working experiences in the past, and have lower level of household income. Third, age, having baby-sitter and the experience of job transition are vital factors in entering the labor market after the first child birth.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.115-127
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• 2017

This paper proposes a Bayesian test for the equivalence of survival functions in multiple groups. Proposed Bayesian test use the model of Cox's regression with time-varying coefficients. B-spline expansions are used for the time-varying coefficients, and the proposed test use only the partial likelihood, which provides easier computations. Various simulations of the proposed test and typical tests such as log-rank and Fleming and Harrington tests were conducted. This result shows that the proposed test is consistent as data size increase. Specifically, the power of the proposed test is high despite the existence of crossing hazards. The proposed test is based on a Bayesian approach, which is more flexible when used in multiple tests. The proposed test can therefore perform various tests simultaneously. Real data analysis of Larynx Cancer Data was conducted to assess applicability.

• Journal of the Korean Wood Science and Technology
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• v.12 no.3
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• pp.3-8
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• 1984

It is shown that Paul and Jones' Rule of Mixtures modified by Smith and Cox's theory can be used for the fiber-reinforced, sulfur-based composites, when the constant for the linear regression equation is given. The computation results, programmed by Hewlett Packard 75C (HP 75C) using math rom pack for the linear regression form, expressed as $E_c=\frac{1}{3}aE_fV_f+bE_mV_m$, turn out to be a=3.27-3.54 b=-2.47~-2.80. This results indicate that the factors such as density of fiber mat and the amount of matrix used have nothing for affecting the numerical value of the constants a and b of the linear regression form. Conclusively this results also show that the Paul and Jones' Rule of Mixtures which has been used for the composites made by randomly-oriented long fiber can also be used for the composites made by short fiber with the same fiber orientation such as wood and lignocellulosic fibers.

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