• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.613-618
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• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.965-971
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• 2004

In this paper we consider several nonparametric estimators for the mean residual life by using the partial moment approximation under the proportional hazard model. Also we compare the magnitude of mean square error of the proposed nonparametric estimators for mean residual life under the proportional hazard model.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.583-604
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• 2017

The most popular regression model for the analysis of time-to-event data is the Cox proportional hazards model. While the model specifies a parametric relationship between the hazard function and the predictor variables, there is no specification regarding the form of the baseline hazard function. A critical assumption of the Cox model, however, is the proportional hazards assumption: when the predictor variables do not vary over time, the hazard ratio comparing any two observations is constant with respect to time. Therefore, to perform credible estimation and inference, one must first assess whether the proportional hazards assumption is reasonable. As with other regression techniques, it is also essential to examine whether appropriate functional forms of the predictor variables have been used, and whether there are any outlying or influential observations. This article reviews diagnostic methods for assessing goodness-of-fit for the Cox proportional hazards model. We illustrate these methods with a case-study using available R functions, and provide complete R code for a simulated example as a supplement.

• International Journal of Reliability and Applications
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• v.2 no.1
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• pp.1-26
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• 2001

The purpose of this paper is to introduce a proportional reversed hazard rate model, in contrast to the celebrated proportional hazard model, and study some of its structural properties. Some criteria of ageing are presented and the inheritance of the ageing notions (of the base line distribution) by the proposed model are studied. Two important data sets are analyzed: one uncensored and the other having some censored observations. In both cases, the confidence bands for the failure rate and survival function are investigated. In one case the failure rate is bathtub shaped and in the other it is upside bath tub shaped and thus the failure rates are non-monotonic even though the baseline failure rate is monotonic. In addition, the estimates of the turning points of the failure rates are provided.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.605-616
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• 2004

In this paper we consider the proportional hazard models for survival analysis in the microarray data. For a given vector of response values and gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.675-688
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• 2020

In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment. In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.99-106
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• 2010

In this paper, we present two methods for obtaining prediction intervals for the times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. A numerical example and a Monte Carlo simulation study are presented to illustrate the prediction methods.

• Journal of Applied Reliability
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• v.1 no.2
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• pp.149-163
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• 2001

We study the normality of the maximum partial likelihood estimators for the proportional hazard model with informative censored data. The proposed models cover the cases in which the times to a primary event may be informatively or randomly censored and the times to a secondary event may be randomly censored. To estimate the parameters and to check the normality of the parameters in the model, we adopt the partial likelihood and counting process to use the martingale central limit theorem. Simulation studies are performed to examine the normality of the MPLE's for the five cases in which they depend upon the proportions of randomly censored and informative censored data.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.87-96
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• 2007

In this paper a methodology of identifying individual pipes according to the internal and external characteristics of pipe is developed, and the methodology is applied to a case study water distribution pipe break database. Using the newly defined individual pipes the hazard rates of the cast iron 6 inch pipes are modeled by implementing the proportional hazards modeling approach for consecutive pipe failures. The covariates to be considered in the modeling procedures are selected by considering the general availability of the data and the practical applicability of the modeling results. The individual cast iron 6 inch pipes are categorized into seven ordered survival time groups according to the total number of breaks recorded in a pipe to construct distinct proportional hazard model (PHM) for each survival time group (STG). The modeling results show that all of the PHMs have the hazard rate forms of the Weibull distribution. In addition, the estimated baseline survivor functions show that the survival probabilities of the STGs generally decrease as the number of break increases. It is found that STG I has an increasing hazard rate whereas the other STGs have decreasing hazard rates. Regarding the first failure the hazard ratio of spun-rigid and spun-flex cast iron pipes to pit cast iron pipes is estimated as 1.8 and 6.3, respectively. For the second or more failures the relative effects of pipe material/joint type on failure were not conclusive. The degree of land development affected pipe failure for STGs I, II, and V, and the average hazard ratio was estimated as 1.8. The effects of length on failure decreased as more breaks occur and the population in a GRID affected the hazard rate of the first pipe failure.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.615-618
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• 2004

The previous quantitative bankruptcy prediction models cannot include time dimension. To overcome this limit, various dynamic models using survival analysis are developed recently. This paper emphasizes that the proportionality assumption must be adapted with caution when the Cox's proportional hazard model is used to explain bankruptcy. It is shown that a non-proportional hazard model including a change point model is a proper alternative, when the proportionality assumption is violated by the change of macroeconomic environment, such as the financial crisis in 1997.