• The Korean Journal of Applied Statistics
• /
• v.17 no.2
• /
• pp.219-228
• /
• 2004

In this paper we construct prediction intervals for nonlinear time series models using the bootstrap. We compare these prediction intervals to traditional asymptotic prediction intervals using quasi-score estimation function and M-quasi-score estimating function comprising bounded functions. Simulation results show that the bootstrap method leads to improved accuracy. The accuracy of the bootstrap is empirically demonstrated with the consumer price index.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.3
• /
• pp.493-502
• /
• 2010

For establishing forecasting confidence interval for exchange rate, it is critical to estimate distribution of the exchange rate properly. In this thesis, we use block bootstrap method to estimate the distribution of the exchange rate via sum of its daily ratios. As a result, an easier and more accurate forecasting method is provided.

• Communications for Statistical Applications and Methods
• /
• v.16 no.2
• /
• pp.287-297
• /
• 2009

In terms of generalized autoregressive conditional heteroscedastic(GARCH) model, estimation of prediction interval based on likelihood is quite sensitive to distribution of error. Moveover, it is not an easy job to construct prediction interval for conditional variance. Recent studies show that the bootstrap method can be one of the alternatives for solving the problems. In this paper, we introduced the bootstrap approach proposed by Pascual et al. (2006). We employed it to Korean stock price data set.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.895-902
• /
• 2011

This paper is mainly concerned about providing confidence prediction interval for exchange rate during economic crisis. Our proposed method is to use block bootstrap method for prediction interval for next day. It is shown that block bootstrap method is particularly effective for interval prediction of exchange rate during economic crisis.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.5
• /
• pp.909-916
• /
• 2010

This study aims to establish storage stability conditions for cook-chilled korean ethenic foods. In order to achieve this aims, we establish a probability model of microbial counts of cook-chilled korean side dishes product-seasoned soybean sprouts. And seasoned soybean sprouts were stored during 1 to 5 days under constant temperature conditions at 0, 5, 10 and $15^{\circ}C$. Next we find confidence intervals for variation in the microbiological quality of seasoned soybean sprouts.

• The Korean Journal of Applied Statistics
• /
• v.27 no.4
• /
• pp.645-653
• /
• 2014

P-value is the probability of observing a current sample and possibly other samples departing equally or more extremely from the null hypothesis toward postulated alternative hypothesis. When p-value is less than a certain level called ${\alpha}$(= 0:05), researchers claim that the alternative hypothesis is supported empirically. Unfortunately, some findings discovered in that way are not reproducible, partly because the p-value itself is a statistic vulnerable to random variation. Boos and Stefanski (2011) suggests calculating the upper limit of p-value in hypothesis testing, using a bootstrap predictive distribution. To determine the sample size of a replication study, this study proposes thought experiments by simulating boosted bootstrap samples of different sizes from given observations. The method is illustrated for the cases of two-group comparison and multiple linear regression. This study also addresses the reproducibility of the points in the given 95% confidence interval. Numerical examples show that the center point is covered by 95% confidence intervals generated from bootstrap resamples. However, end points are covered with a 50% chance. Hence this study draws the graph of the reproducibility rate for each parameter in the confidence interval.

• The Korean Journal of Applied Statistics
• /
• v.35 no.3
• /
• pp.347-356
• /
• 2022

This article is concerned with asymmetric volatility models for financial time series. A generalization of standard single-threshold volatility model is discussed via multiple-threshold in which we specialize to twothreshold case for ease of presentation. An empirical illustration is made by analyzing S&P500 data from NYSE (New York Stock Exchange). For comparison measures between competing models, parametric bootstrap method is used to generate forecast distributions from which summary statistics of CP (Coverage Probability) and PE (Prediction Error) are obtained. It is demonstrated that our suggestion is useful in the field of asymmetric volatility analysis.

• The Korean Journal of Applied Statistics
• /
• v.17 no.3
• /
• pp.489-498
• /
• 2004

In this paper, a new form of linear models referred to as generalized weighted linear models is proposed. The proposed models assume that the relationship between the response variable and explanatory variables can be modelled by a distribution function of the response mean and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models in which the parameter space may not be consistent with the space derived from linear predictors. The maximum likelihood estimation with Lagrange's undetermined multipliers is used to estimate the parameters and resampling method is applied to compute confidence intervals and to test hypotheses.