• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.29 no.1
• /
• pp.143-150
• /
• 2006

Box-Cox model and T-factor method have been widely used to measure economic depreciations for industrial property. The Box-Cox model which combines economic efficiency with depreciation pattern is here extended to the reliability function. To do so a Rayleigh distribution which has been used to estimate the reliability of current assets was chosen as an efficiency curve of marginal productivity. Such an approach provides the possibility to classify the efficiency curves into four categories. It is also possible to analyze the types of depreciation curves. Therefore, the power family of a non-linear Box-Cox model could be set at certain constant values, then the model can be transformed into a linear model to estimate the economic depreciation rates by utilizing the reliability function. Estimating the resultant linear regression equation requires minimal number of observations, while at the same time facilitating the test of hypothesis on depreciation rates.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.4
• /
• pp.723-736
• /
• 2013

The importance of lapse rate is highly increasing due to the introduction of Cash Flow Pricing system, non-refund-of-reserve insurance policy, and IFRS (International Financial Reporting System) to the Korean insurance market. Researches on lapse rate have mainly focused on simple data analysis and regression analysis, etc. However, lapse rate can be analyzed by survival analysis and can be well explained in terms of several covariates with Cox proportional hazard model. Guaranteed minimum benefits embedded in variable annuities require more elegant statistical analysis of lapse rate. Hence, this paper analyzes data of policyholders with variable annuities by using Cox proportional hazard model. The key variables of policy holder that influences the lapse rate are payment method, premium, lapse insured to term insured, reserve-GMXB ratio, and age.

• The Korean Journal of Applied Statistics
• /
• v.34 no.6
• /
• pp.957-968
• /
• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• Journal of the Korean Statistical Society
• /
• v.34 no.3
• /
• pp.173-183
• /
• 2005

The Box-Cox power transformation is generally used for variance stabilization. Recently, Shin and Kang (2001) showed, under the Box-Cox transformation, invariant properties to the original model under the large mean and relatively small variance assumptions in time series analysis. In this paper we obtain some invariant properties in spatial statistics. Spatial statistics, Invariant Property, Variogram, Box-Cox power Transformation.

• Proceedings of the Korean Society of Applied Pharmacology
• /
• 2003.04a
• /
• pp.113-130
• /
• 2003

1. We have established a model system to study sodium chloride, an environmental factor, induced gene regulations. 2. ${\alpha}$ENaC, cox-2, and c-fos genes are regulated by sodium chloride at mRNA level as well as at protein level. 3. Regulation of ${\alpha}$ENaC requires syntheses of new protein(s). 4. COX-2 may have a important role for homeostasis in hypertonic situation.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.4
• /
• pp.795-801
• /
• 2011

In machine learning and statistics it is often the case that some variables are not important, while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables in the kernel Cox regression. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of relevant variables in the kernel Cox regression. Experimental results are then presented which indicate the performance of the proposed method.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.11
• /
• pp.6751-6755
• /
• 2013

Background: Research on cancers with a high rate of mortality such as those occurring in the stomach requires using models which can provide a closer examination of disease processes and provide researchers with more accurate data. Various models have been designed based on this issue and the present study aimed at evaluating such models. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. Cox-Snell Residuals and Akaike Information Criterion were used to compare parametric and semi-parametric Cox models in modeling transition rates among different states of a multi-state model. R 2.15.1 software was used for all data analyses. Results: Analysis of Cox-Snell Residuals and Akaike Information Criterion for all probable transitions among different states revealed that parametric models represented a better fitness. Log-logistic, Gompertz and Log-normal models were good choices for modeling transition rate for relapse hazard (state $1{\rightarrow}state$ 2), death hazard without a relapse (state $1{\rightarrow}state$ 3) and death hazard with a relapse (state $2{\rightarrow}state$ 3), respectively. Conclusions: Although the semi-parametric Cox model is often used by most cancer researchers in modeling transition rates of multistate models, parametric models in similar situations- as they do not need proportional hazards assumption and consider a specific statistical distribution for time to occurrence of next state in case this assumption is not made - are more credible alternatives.

• Knowledge Management Research
• /
• v.11 no.2
• /
• pp.95-109
• /
• 2010

The objectives of this study are to identify the survival function (hazard function) of small and medium enterprises by using technology rating data for the companies guaranteed by Korea Technology Finance Corporation (KOTEC), and to figure out the factors that affects their survival. To serve the purposes, this study uses Kaplan-Meier Analysis as a non-parametric method and Cox proportional hazards model as a semi-parametric one. The 17,396 guaranteed companies that assessed from July 1st in 2005 to December 31st in 2009 are selected as samples (16,504 censored data and 829 accident data). The survival time is computed with random censoring (Type III) from July in 2005 as a starting point. The results of the analysis show that Kaplan-Meier Analysis and Cox proportional hazards model are able to readily estimate survival and hazard function and to perform comparative study among group variables such as industry and technology rating level. In particular, Cox proportional hazards model is recognized that it is useful to understand which technology rating items are meaningful to company's survival and how much they affect it. It is considered that these results will provide valuable knowledge for practitioners to find and manage the significant items for survival of the guaranteed companies through future technology rating.

• Genomics & Informatics
• /
• v.5 no.3
• /
• pp.95-101
• /
• 2007

In this paper, we consider the variable selection methods in the Cox model when a large number of gene expression levels are involved with survival time. Deciding which genes are associated with survival time has been a challenging problem because of the large number of genes and relatively small sample size (n<

• The Korean Journal of Physiology and Pharmacology
• /
• v.10 no.1
• /
• pp.45-50
• /
• 2006

Melittin-induced pain model has been known to be very useful for the study of pain mechanism. Melittin-induced nociceptive responses are reported to be modulated by the changes in the activity of excitatory amino acid receptor, calcium channel, spinal serotonin receptor and extracellular signaling-regulated kinase. The present study was undertaken to investigate the role of cyclooxygenase (COX) in the melittin-induced nociception. Changes in mechanical threshold, flinchings and paw thickness were measured before and after intraplantar injection of melittin in the rat hind paw. Also studied were the effects of intraperitonealy administered diclofenac (25 mg & 50 mg/kg), piroxicam (10 mg & 20 mg/kg) and meloxicam (10 mg & 20 mg/kg) on the melittin-induced nociceptions. Intraplantar injection of melittin caused marked reduction of mechanical threshold that was dose-dependently attenuated by non-selective COX inhibitor (diclofenac) and selective COX-1 inhibitor (piroxicam), but not by COX-2 inhibitor (meloxicam). Melittin-induced flinchings were strongly suppressed by non-selective COX and COX-1 inhibitor, but not by COX-2 inhibitor. None of the COX inhibitors had inhibitory effects on melittin-induced increase of paw thickness (edema). These experimental findings suggest that COX-1 plays an important role in the melittin-induced nociceptive responses.