• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.117-126
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• 2010

Clinical researchers often analyze survival data as binary outcomes using the logistic regression method. This paper examines the information loss resulting from analyzing survival time as binary outcomes. We first demonstrate that, under the proportional hazard assumption, this binary discretization does result in a significant information loss. Second, when fitting a logistic model to survival time data, researchers inadvertently use the maximal statistic. We implement a numerical study to examine the properties of the reference distribution for this statistic, finally, we show that the logistic regression method can still be a useful tool for analyzing survival data in particular when the proportional hazard assumption is questionable.

• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.425-445
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• 2021

A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.49-60
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• 2006

There are six approaches to constructing standardized coefficient for logistic regression. The standardized coefficient based on Kruskal's information theory is known to be the best from a conceptual standpoint. In order to calculate this standardized coefficient, the coefficient of determination based on entropy loss is used among many kinds of coefficients of determination for logistic regression. In this paper, this standardized coefficient is obtained by using four kinds of coefficients of determination which have the most intuitively reasonable interpretation as a proportional reduction in error measure for logistic regression. These four kinds of the sixth standardized coefficient are compared with other kinds of standardized coefficients.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.47-64
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• 2020

In this paper, we derive some estimators of the scale parameter of the exponentiated half-logistic distribution based on the multiply Type-I hybrid censoring scheme. We assume that the shape parameter λ is known. We obtain the maximum likelihood estimator of the scale parameter σ. The scale parameter is estimated by approximating the given likelihood function using two different Taylor series expansions since the likelihood equation is not explicitly solved. We also obtain Bayes estimators using prior distribution. To obtain the Bayes estimators, we use the squared error loss function and general entropy loss function (shape parameter q = -0.5, 1.0). We also derive interval estimation such as the asymptotic confidence interval, the credible interval, and the highest posterior density interval. Finally, we compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. The average length of 95% intervals and the corresponding coverage probability are also obtained.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.247-263
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• 2001

This paper concerns a problem of classification based on training dta. A framework of information geometry is given to elucidate the characteristics of discriminant functions including logistic discrimination and AdaBoost. We discuss a class of loss functions from a unified viewpoint.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.1943-1948
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• 2014

Purpose: The prevalence of weight loss in esophageal carcinoma patients is high and associated with impairment of physical function, increased psychological distress and low quality of life. It is not known which factors may contribute to weight loss in patients with esophageal carcinoma during radiotherapy in China. The objective of this study was to identify the associated demographic and clinical factors influencing weight loss. Methods: We evaluated 159 esophageal carcinoma patients between August 2010 and August 2013 in a crosssectional, descriptive study. Patient characteristics, tumor and treatment details, psychological status, adverse effects, and dietary intake were evaluated at baseline and during radiotherapy. A multivariate logistic regression analyss was performed to identify the potential factors leading to weight loss. Results: 64 (40.3%) patients had weight loss ${\geq}5%$ during radiotherapy. According to logistic regression analysis, depression, esophagitis, and loss of appetite were adverse factors linked to weight loss. Dietary counseling, early stage disease and total energy intake ${\geq}1441.3$ (kcal/d) were protective factors. Conclusions It was found that dietary counseling, TNM stage, total energy intake, depression, esophagitis, and loss of appetite were the most important factors for weight loss. The results underline the importance of maintaining energy intake and providing dietary advice in EC patients during RT. At the same time, by identifying associated factors, medical staff can provide appropriate medical care to reduce weight loss. Further studies should determine the effect of these factors on weight loss and propose a predictive model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.7-10
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• 2001

The development of information communication technology leads the growth of logistic industry including delivery service as well as electronic commerce. The researchers predict that it will be still improving for the next several years. The logistic information system of railroad courier has been growing for a long time with small-package delivery transportation which is similar to the land-road delivery system. Despite of the long-time growth, it is recently in pain of the great loss since the 1990's, due to the failure to satisfy the customer's need for door-to-door delivery service. But the logistic information system of railroad still has the great potential. There are so many benefits such as timeliness, Punctuality, speed, multi-node storage base, transportation efficiency, energy frugality, environmental sociability, and so on. If the railroad logistic system plays a role of a portion of the nation-wide logistic with other logistic system, the synergy through the balancing logistic will also get much of international competitive advantages. So the objective of this research is to design the model and prototype of the web-based logistic system from which railroad service provider(Korean National Railroad), delivery service providers, and the customers can share the best effective delivery information.

• Tunnel and Underground Space
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• v.30 no.2
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• pp.149-163
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• 2020

This paper presents a logistic regression and GIS based urban ground sink susceptibility assessment using underground facility information considering soil particle loss. In the underground environment, the particle loss due to water flow or groundwater level change leads to the occurrence and expansion of cavities, which directly affect the ground sink. Four different contributory factors were selected according to the two underground facility domains (water pipeline area, sewer pipeline area) and subway line area. The logistic regression method was used to analyze the correlation and to derive the regression equation between the ground sink inventory and the contributory factors. Based on these results, three ground sink susceptibility maps were generated. The results obtained from this study are expected to provide basic data on the area susceptible to ground sink and needed to safety monitoring.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.977-989
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• 2011

The half logistic distribution has been used intensively in reliability and survival analysis especially when the data is censored. In this paper, we provide Bayesian estimation of the shape parameter and reliability function in the generalized half logistic distribution based on progressively Type-II censored data under various loss functions. We here consider conjugate prior and noninformative prior and corresponding posterior distributions are obtained. As an illustration, we examine the validity of our estimation using real data and simulated data.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.164-169
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• 2008

The increasing late-payment rate of credit card customers caused by a recent economic downturn are incurring not only reduced profit of department stores but also significant loss. Under this pressure, the objective of credit forecasting is extended from presumption of good or bad customers to contribution to revenue growth. As a method of managing defaults of department store credit card, this study classifies credit delinquents into some clusters, analyzes repaying patterns of customers in each cluster, and develops credit forecasting system to manage delinquents of department store credit card using data of Korean D department store's delinquents. The model presented by this study uses Kohonen network, a kind of artificial neural network of data mining techniques to cluster credit delinquents into groups. Logistic regression model is also used to predict repayment rate of customers of each cluster per period. The accuracy of presented system for the whole clusters is 92.3%.