• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.1829-1831
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• 2012

Background: The generalized gamma distribution statistics constitute an extensive family that contains nearly all of the most commonly used distributions including the exponential, Weibull and log normal. A saturated version of the model allows covariates having effects through all the parameters of survival time distribution. Accelerated failure-time models assume that only one parameter of the distribution depends on the covariates. Methods: We fitted both the conventional GG model and the saturated form for each of its members including the Weibull and lognormal distribution; and compared them using likelihood ratios. To compare the selected parameter distribution with log logistic distribution which is a famous distribution in survival analysis that is not included in generalized gamma family, we used the Akaike information criterion (AIC; r=l(b)-2p). All models were fitted using data for 369 women age 50 years or more, diagnosed with stage IV breast cancer in BC during 1990-1999 and followed to 2010. Results: In both conventional and saturated parametric models, the lognormal was the best candidate among the GG family members; also, the lognormal fitted better than log-logistic distribution. By the conventional GG model, the variables "surgery", "radiotherapy", "hormone therapy", "erposneg" and interaction between "hormone therapy" and "erposneg" are significant. In the AFT model, we estimated the relative time for these variables. By the saturated GG model, similar significant variables are selected. Estimating the relative times in different percentiles of extended model illustrate the pattern in which the relative survival time change during the time. Conclusions: The advantage of using the generalized gamma distribution is that it facilitates estimating a model with improved fit over the standard Weibull or lognormal distributions. Alternatively, the generalized F family of distributions might be considered, of which the generalized gamma distribution is a member and also includes the commonly used log-logistic distribution.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.4
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• pp.110-124
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• 1996

This study was conducted to simulate long seres of synthetic monthly flows by multi-season first order Markov model with selection of best fitting frequency distribution, harmonic synthetic and harmonic regression models and to make a comparison of statistical parameters between observes and synthetic flows of five watersheds in Geum river system. The results obtained through this study can be summarized as follow. 1. Both gamma and two parameter lognormal distributions were found to be suitable ones for monthly flows in all watersheds by Kolmogorov-Smirnov test. 2. It was found that arithmetic mean values of synthetic monthly flows simulated by multi-season first order Markov model with gamma distribution are much closer to the results of the observed data in comparison with those of the other models in the applied watersheds. 3. The coefficients of variation, index of fluctuation for monthly flows simulated by multi-season first order Markov model with gamma distribution are appeared closer to those of the observed data in comparison with those of the other models in Geum river system. 4. Synthetic monthly flows were simulated over 100 years by multi-season first order Markov model with gamma distribution which is acknowledged as a suitable simulation modal in this study.

• Journal of Microbiology and Biotechnology
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• v.23 no.9
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• pp.1339-1346
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• 2013

Conventional chemotherapeutic regimens often accompany severe side effects and fail to induce complete regression of chemoresistant or relapsing metastatic cancers. The need for establishing more efficacious anticancer strategies led to the development of a combined modality treatment of chemotherapy in conjunction with immunotherapy or radiotherapy. It has been reported that poly-gamma-glutamate (${\gamma}$-PGA), a natural polymer composed of glutamic acids, increases antitumor activity by activating antigen-presenting cells and natural killer (NK) cells. Here, we investigated the antitumor effect of ${\gamma}$-PGA in combination with cyclophosphamide in a murine melanoma model. Whereas cyclophosphamide alone directly triggered apoptosis of tumor cells in vitro, ${\gamma}$-PGA did not show cytotoxicity in tumor cells. Instead, it activated macrophages, as reflected by the upregulation of surface activation markers and the secretion of proinflammatory factors, such as nitric oxide and tumor necrosis factor ${\alpha}$. When the antitumor effects were examined in a mouse model, combined treatment with cyclophosphamide and ${\gamma}$-PGA markedly suppressed tumor growth and metastasis. Notably, ${\gamma}$-PGA treatment dramatically increased the NK cell population in lung tissues, coinciding with decreased metastasis and increased survival. These data collectively suggest that ${\gamma}$-PGA can act as an immunotherapeutic agent that exhibits a synergistic antitumor effect in combination with conventional chemotherapy.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.95.2-95.2
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• 2012

We have compared the geoeffective parameters of halo coronal mass ejections (CMEs) to predict geomagnetic storms. For this we consider 50 front-side full halo CMEs whose asymmetric cone model parameters and earthward direction parameter were available. For each CME we use its projected velocity (Vp), radial velocity (Vr), angle between cone axis and sky plane (${\gamma}$) from the cone model, earthward direction parameter (D), source longitude (L), and magnetic field orientation (M) of the CME source region. We make a simple and multiple linear regression analysis to find out the relationship between CME parameters and Dst index. Major results are as follows. (1) $Vr{\times}{\gamma}$ has a higher correlation coefficient (cc = 0.70) with the Dst index than the others. When we make a multiple regression of Dst and two parameters ($Vr{\times}{\gamma}$, D), the correlation coefficient increases from 0.70 to 0.77. (2) Correlation coefficients between Dst index and $Vr{\times}{\gamma}$ have different values depending on M and L. (3) Super geomagnetic storms (Dst ${\leq}$ -200 nT) only appear in the western and southward events. Our results demonstrate that not only the cone model parameters together with the earthward direction parameter improve the relationship between CME parameters and Dst index but also the source longitude and its magnetic field orientation play a significant role in predicting geomagnetic storms.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.1-11
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• 2012

We present methods to study the log-density ratio of the conditional densities of the predictors given the response variable in the logistic regression model. This allows us to select which predictors are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. A simulation study shows that the linear and log terms are required in general. If the conditional distributions of xjy for the two groups overlap significantly, we need both the linear and log terms; however, only the linear or log term is needed in the model if they are well separated.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.310-317
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• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.407-419
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• 2006

In this paper, we analyze household overdue loans in Korea which has been causing serious social and economical problems. We consider customers of Bank A in Korea and focus on overdue cash services which have been snowballing in the past few years. From analysis of overdue loans, one can predict possible delays for current customers as well as build a credit evaluation and risk management system for future customers. As a statistical analytical tool, we propose a two-stage Generalized Linear regression Model (GLM) which assumes a logistic model for presence/non-presence of overdue and a gamma model for the amount of overdue in the case of overdue. We perform goodness of fit test for the two-stage model and select significant explanatory variables in each stage of the model. It turns out that age, the amount of credit loans from other financial companies, the amount of cash service from other companies, debit balance, the average amount of cash service, and net profit are important explanatory variables relevant to overdue credit card cash service in Korea.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.181-192
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• 2016

We consider several methods to approximate option prices with correction terms to the Black-Scholes option price. These methods are able to compute option prices from various risk-neutral distributions using relatively small data and simple computation. In this paper, we compare the performance of Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method of using Normal inverse gaussian distribution, and an asymptotic method of using nonlinear regression through simulation experiments and real KOSPI200 option data. We assume the variance gamma model in the simulation experiment, which has a closed-form solution for the option price among the pure jump $L{\acute{e}}vy$ processes. As a result, we found that methods to approximate an option price directly from the approximate price formula are better than methods to approximate option prices through the approximate risk-neutral density function. The method to approximate option prices by nonlinear regression showed relatively better performance among those compared.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9093-9099
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• 2014

A growing body of evidence suggests that the peroxisome proliferator-activated receptor-gamma ($PPAR{\gamma}$) gene may harbor targets for the chemoprevention of breast cancer. However, it is unclear whether polymorphisms in the $PPAR{\gamma}$ gene are associated with the susceptibility of breast cancer. We performed a candidate gene association study between $PPAR{\gamma}$ polymorphisms and breast cancer and a meta-analysis on the association of breast cancer with selected $PPAR{\gamma}$ variants. Six single nucleotide polymorphisms (SNPs) in the $PPAR{\gamma}$ gene were analyzed among 456 breast cancer patients and 461 controls from the National Cancer Center in Korea. Association between the polymorphisms and breast cancer risk were assessed using the Cochrane-Armitage test for trend and a multivariate logistic regression model. Two SNPs, rs3856806 and rs1801282, had been previously analyzed, thus enabling us to perform pooled analyses on their associations with breast cancer susceptibility. Our findings from the candidate gene association study showed no association between the $PPAR{\gamma}$ gene polymorphisms and breast cancer risk. A meta-analysis combining existing studies and our current study also refuted an association of the $PPAR{\gamma}$ gene with breast cancer. Our findings suggest that the $PPAR{\gamma}$ gene may not harbor variants that alter breast cancer susceptibility, although a moderate sample size might have precluded a decisive conclusion.