• Journal of Korean Institute of Industrial Engineers
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• v.38 no.4
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• pp.244-248
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• 2012

We present a simple numerical method for pricing American put options under a constant elasticity of variance (CEV) model. Our analysis is done in a general framework where only the risk-neutral transition density of the underlying asset price is given. We obtain an integral equation of early exercise premium. By exploiting a modification of the integral equation, we propose a novel and simple numerical iterative valuation method for American put options.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.195-207
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• 1991

We consider the development of simple regression-like diagnostics for assessing the sensitivity of an optimal design to deviations from the assumptions of constant error variance. This contains a review of Cook's local influence approach and an application of local influence aproach to D-optimal experimental design. The method is applied in a number of simple examples in Section 3. Conclusions and directions for further research follow in Section 4.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.140-155
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• 1993

The characteristics of $I_{\lambda}$-optimality, one of the linear criteria suggested by Fedorov (1972) are investigated with respect to the D-and heteroscedastic G-optimality in case of non-constant variance function. Though having limited results obtained from simple models, we may conclude that $I_{\lambda}$-optimality is sometimes preferred to the heteroscedastic G-optimality suggested newly bv Wong and Cook (1992) in the sense that the experimenter's belief in weighting function exists in $I_{\lambda}$-optimality criterion, not to mention its computational simplicity.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.141-150
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• 1998

In this paper, we developed one-stage cluster randomized response model for obtaining quantitative data by using the Greenberg et al. model(1971) when the population was made up of sensitive quantitative clusters. We obtained the minimum variance by calculating the cluster's size and the optimum number of sample clusters under the some given constant cost. We compared the efficiency of our model with the Greenberg et al. model by simple random sampling.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.37-46
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• 2020

Recurrent event data frequently occur in clinical studies, demography, engineering reliability and so on (Cook and Lawless, The Statistical Analysis of Recurrent Events, Springer, 2007). Sometimes, two or more different but related type of recurrent events may occur simultaneously. In this study, our interest is to estimate the covariate effect on bivariate recurrent event times with zero inflations. Such zero inflation can be related with susceptibility. In the context of bivariate recurrent event data, furthermore, such susceptibilities may be different according to the type of event. We propose a joint model including both two intensity functions and two cure rate functions. Bivariate frailty effects are adopted to model the correlation between recurrent events. Parameter estimates are obtained by maximizing the likelihood derived under a piecewise constant hazard assumption. According to simulation results, the proposed method brings unbiased estimates while the model ignoring cure rate models gives underestimated covariate effects and overestimated variance estimates. We apply the proposed method to a set of bivariate recurrent infection data in a study of child patients with leukemia.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.447-456
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• 2012

For a sensitive survey in which the population is comprised of several clusters with a quantitative attribute, we present an additive quantitative randomized response model by cluster sampling that adapts a two-stage cluster sampling instead of a simple random sample based on Himmelfarb-Edgell's additive quantitative attribute model and Gjestvang-Singh's one. We also derive optimum values for the number of 1st stage clusters and the optimum values of observation units in a 2nd stage cluster under the condition of minimizing the variance given constant cost. We can see that Himmelfarb-Edgell's model is more efficient than Gjestvang-Singh's model under the condition of cluster sampling.

• Journal of the Optical Society of Korea
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• v.15 no.3
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• pp.232-236
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• 2011

We analyze the linear equalizers used in optically amplified on-off-keyed (OOK) systems to combat chromatic dispersion (CD) and polarization mode dispersion (PMD), and we derive the mathematical minimum mean squared error (MMSE) performance of these equalizers. Currently, the MMSE linear equalizer for optical OOK systems is obtained by simulations using adaptive approaches such as least mean squared (LMS) or constant modulus algorithm (CMA), but no theoretical studies on the optimal solutions for these equalizers have been performed. We model the optical OOK systems as square-law nonlinear channels and compute the MMSE equalizer coefficients directly from the estimated optical channel, signal power, and optical noise variance. The accuracy of the calculated MMSE equalizer coefficients and MMSE performance has been verified by simulations using adaptive algorithms.

• International Journal of Reliability and Applications
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• v.17 no.1
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• pp.85-105
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• 2016

In this paper accelerated life testing is incorporated in quality control technique of acceptance sampling plan to induce early failures in high reliability products.Stress under accelerated condition can be applied in constant-stress, step-stress and progressive-stress or combination of such loadings. A ramp-stress results when stress is increased linearly (from zero) with time. In this paper optimum failure-censored ramp-stress accelerated life test sampling plan for log-logistic distribution has been formulated with cost considerations. The log-logistic distribution has been found appropriate for insulating materials. The optimal plans consist in finding optimum sample size, sample proportion allocated to each stress, and stress rate factor such that producer's and consumer's interests are safeguarded. Variance optimality criterion is used when expected cost per lot is not taken into consideration, and bilevel programming approach is used in cost optimization problems. The methods developed have been illustrated using some numerical examples, and sensitivity analyses carried out in the context of ramp-stress ALTSP based on variable SSP for proportion nonconforming.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.141-150
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• 2015

Graphical paradigms for assessing the adequacy of models in logistic regression are discussed. The residual plot has been widely used as a graphical tool for evaluating the adequacy of the model. However, this approach works well only for linear models with constant variance, and the alternative approach, the marginal model plot, has its defects as well. We suggest a Chi-residual plot that overcomes the potential shortcomings of the marginal model plot.

• The Journal of Information Technology
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• v.3 no.1
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• pp.37-44
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• 2000

The purchasing estimate price of diamond is affected by the factors of carat, color, clarity, certificate, cut and price with the unit of $/carat. The object of this study is to obtain the linear regression model for such purchasing estimate price and to test statistically. The optimum model is the simple regression model of $^y{\;}:{\;}10^2{\;}/{\;}(-1.5575{\;}+{\;}0.3099{\;}logx){\;}+{\;}{\varepsilon}$ statistically satisfied by the lack of fit test and has the characteristics of normality, constant variance and symmetry.