• Journal of Korean Institute of Industrial Engineers
• /
• v.38 no.4
• /
• pp.249-253
• /
• 2012

This paper suggests a numerical method for valuation of American options under the Kou model (double exponential jump diffusion model). The method is based on approximation of underlying asset price using a finite-state, time-homogeneous Markov chain. We examine the effectiveness of the proposed method with simulation results, which are compared with those from the conventional numerical method, the finite difference method for PIDE (partial integro-differential equation).

• Environmental and Resource Economics Review
• /
• v.22 no.1
• /
• pp.179-214
• /
• 2013

This paper estimated the value of waterside landscape and ecosystem of Han River basin with a double-bound dichotomous choice type of CVM. We used the exponential willingness to pay model to represent the nonnegative willingness to pay. This model is found to be especially important in analyzing a double-bound dichotomous choice model. The total willingness to pay was estimated as 705.5 billion won per annum. This suggests that current budget size for water quality and ecosystem conservation for Han River needs to be expanded.

• Korean Journal of Organic Agriculture
• /
• v.30 no.2
• /
• pp.231-239
• /
• 2022

The purpose of this study is to provide meaningful information for various stakeholders' decision-making process through forecasting of domestic beef demand. Three different exponential smoothing models were evaluated, and a double exponential smoothing model was used to forecast domestic beef demand based on time-series data, As a result of the forecast, domestic beef consumption is expected to increase by 37,000 to 40,000 tons per year from 2020 to 2025.

• The Korean Journal of Applied Statistics
• /
• v.10 no.1
• /
• pp.105-119
• /
• 1997

Barnwal and Paul(1988) derived the likelihood ratio statistic and $C(\alpha)$ statistic for testing the equality of the means of several groups of count data in the presence of a common dispersion parameter. These tests are generalized to be applicable without the restriction of a common dispersion parameter. And the assumed model of data is also extended from negative binomial to double exponential Poisson model. Monte Carlo simulations show the superiority of $C(\alpha)$ statistic based on the double exponential Poisson family which has a very simple form and requires estimates of the parameters only under the null hypothesis.

• Nutritional Sciences
• /
• v.9 no.3
• /
• pp.201-211
• /
• 2006

The purpose of this study was to provide dietitians with the guidance in forecasting meal counts for a university/college foodservice facility. The forecasting methods to be analyzed were the following: naive model 1, 2, and 3; moving average, double moving average, simple exponential smoothing, double exponential smoothing, Holt's, and Winters' methods, and simple linear regression. The accuracy of the forecasting methods was measured using mean squared error and Theil's U-statistic. This study showed how to project meal counts using 10 forecasting methods for dietitians. The results of this study showed that WES was the most accurate forecasting method, followed by $na\ddot{i}ve$ 2 and naive 3 models. However, naive model 2 and 3 were recommended for using by dietitians in university/college dining facilities because of the accuracy and ease of use. In addition, the 2000 spring semester data were better than the 2000 fall semester data to forecast 2001spring semester data.

• Communications for Statistical Applications and Methods
• /
• v.4 no.1
• /
• pp.259-269
• /
• 1997

Bayesian inference for a record value statistics(RVS) model of nonhomogeneous Poisson process is considered. We seal with Bayesian inference for double exponential, Gamma, Rayleigh, Gumble RVS models using Gibbs sampling and Metropolis algorithm and also explore Bayesian computation and model selection.

• The Korean Journal of Applied Statistics
• /
• v.16 no.2
• /
• pp.335-349
• /
• 2003

In this paper, we use Bayesian method for model selection of poisson vs. negative binomial distribution, and normal, double exponential vs. cauchy distribution. The fractional Bayes factor of O'Hagan (1995) was applied to Bayesian model selection under the assumption of noninformative improper priors for all parameters in the models. Through the analyses of real data and simulation data, we examine the usefulness of the fractional Bayes factor in comparison with intrinsic Bayes factors of Berger and Pericchi (1996, 1998).

• Communications for Statistical Applications and Methods
• /
• v.9 no.1
• /
• pp.101-113
• /
• 2002

In this paper we introduce a new score generating (unction for the rank regression in the linear regression model. The score function compares the $\gamma$'th and s\`th power of the tail probabilities of the underlying probability distribution. We show that the rank estimate asymptotically converges to a multivariate normal. further we derive the asymptotic Pitman relative efficiencies and the most efficient values of $\gamma$ and s under the symmetric distribution such as uniform, normal, cauchy and double exponential distributions and the asymmetric distribution such as exponential and lognormal distributions respectively.

• Bulletin of the Korean Chemical Society
• /
• v.18 no.9
• /
• pp.1006-1010
• /
• 1997

The nature and analysis methods of complicated decay profiles found in forced Rayleigh scattering (FRS) have been investigated for the probe diffusion of methyl yellow in 2-propanol. The complementary shifted and ground state grating effect, which is known to be the origin of non-single exponential decays, was analyzed by non-linear regression fitting to a double exponential model function. We confirmed that the parameters were highly correlated so that it was difficult to extract a unique set of parameters in the presence of experimental noise. Nevertheless, a reasonable range of decay time constants could be estimated from the grating spacing dependence.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.4887-4907
• /
• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.