• Journal of Korean Academy of Nursing
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• v.54 no.3
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• pp.418-431
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• 2024

Purpose: This study aimed to identify factors influencing oncofertility and to explore the oncofertility experiences of patients with gynecological cancer using quantitative and qualitative methods, respectively. Methods: An explanatory sequential mixed-methods study was conducted. The quantitative study involved 222 patients with gynecological cancer recruited from online cafes and hospitals. Data were analyzed using IBM SPSS Statistics 28. For qualitative research, eight patients with gynecological cancer were interviewed. Data were analyzed using theme analysis method. Results: Oncofertility performance was quantitatively assessed in 40 patients (18.0%). Factors that significantly affected oncofertility were fertility preservation awareness (odds ratio [OR] = 14.97, 95% confidence interval [CI]: 4.22~53.08), number of children planned before cancer diagnosis (OR = 6.08, 95% CI: 1.89~19.62; OR = 5.04, 95% CI: 1.56~16.29), monthly income (OR = 3.29, 95% CI: 1.23~8.86), social support (OR = 1.08, 95% CI: 1.01~1.17), and anxiety (OR = 0.79, 95% CI: 0.66~0.95). Qualitative results showed three theme clusters and eight themes: (1) themes for determinant factors affecting oncofertility selection: 'desire to have children' and 'special meaning of the uterus and ovaries;' (2) themes for obstructive factors affecting oncofertility selection: 'fertility preservation fall behind priorities,' 'confusion caused by inaccurate information,' and 'my choice was not supported;' (3) themes for support factors affecting oncofertility selection: 'provide accurate and reasonable information about oncofertility,' 'addressing the healthcare gap,' and 'need financial support for oncofertility.' Conclusion: Financial support, sufficient information, social support, and anxiety-relief interventions are required for oncofertility in patients with gynecological cancer.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.17-21
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• 2016

This paper investigates the importance of the computational overhead when machine learning methods, such as SVM, LASSO, AdaBoosting and AdaBagging, are used for automatic security classification.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.1-5
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• 2002

We consider the problem of optimal bandwidth choice for nonparametric classification, based on kernel density estimators, where the problem of interest is distinguishing between two univariate distributions. When the densities intersect at a single point, optimal bandwidth choice depends on curvatures of the densities at that point. The problem of empirical bandwidth selection and classifying data in the tails of a distribution are also addressed.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.743-758
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• 2005

This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.125-134
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• 2006

This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.45-61
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• 2002

We consider the problem of estimating binomial proportions in the presence of nonignorable nonresponse using the Bayesian selection approach. Inference is sampling based and Markov chain Monte Carlo (MCMC) methods are used to perform the computations. We apply our method to study doctor visits data from the Korean National Family Income and Expenditure Survey (NFIES). The ignorable and nonignorable models are compared to Stasny's method (1991) by measuring the variability from the Metropolis-Hastings (MH) sampler. The results show that both models work very well.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.883-885
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• 1997

High impedance fault(HIF) is random in its behavior even in a similar environment. The detection of Ire HIF has focused on the development of algorithms based on harmonic, parameters of the arc currents. However, a fact that proper selection of the harmonic parameters, rather than algorithm selection, is more important is shown in this paper by applying three different performance evaluation methods on two HIF detection algorithms using eight harmonic parameters.

• Korean Journal of Plant Resources
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• v.3 no.1
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• pp.1-30
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• 1990

The traditional plant imprownent methods consisted of pure line selection, cross breeding, heterosis breeding, polyploid breeding, mutati-onbreeding, ect.Biotechmoiogy is divided into gene spliclng , monocle-nal antibodies , protein engineering , agricultural research, and microbiological engineering. Of these , high plants deal with agricultural research, and the importent part of which is tissue culture and celLculture , Tissue .culture and cell culture are again divided into embryoculture, test tube fertilization, anther and pollen culture, somatichybridization , transformation, recombination, recombinant DNA moleculehybrid plasmid, ect For these haploid production, protoplast culture,protoplast fusion, selection and propagation, ect. , the technical sett-lement is needed.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.25-33
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• 1998

We review a multivariate version of Kendall's tau based on direction vectors of observations. And with this statistic we propose an analog of the forward variable selection method which selects a set of independent variables for further studies to build the eventual predicting model. This method does not assume the distributions of observations and the linear model and it is strong to the outliers with high asymptotic efficiencies relative to the parametric Pearson's correlation coefficient.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.813-820
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• 1999

Supprot vector machine(SVM) is a new and very promising regression and classification technique developed by Vapnik and his group at AT&T Bell Laboratories. in this paper we provide a brief overview of SVM for regression. Furthermore we describe Bayesian model selection based on macKay's evidence framework for SVM regression.