• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.551-562
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• 1995

We consider the iterated bootstrap method in regression model with heterogeneous error variances. The iterated wild bootstrap confidence intervla of the mean response is considered. It is shown that the iterated wild bootstrap confidence interval has coverage error of order $n^{-1}$ wheresa percentile method interval has an error of order $n^{-1/2}$. The simulation results reveal that the iterated bootstrap method calibrates the coverage error of percentile method interval successfully even for the small sample size.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.61-73
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• 2016

We consider the wild bootstrap Ljung-Box (LB) test for autocorrelation in residuals of fitted multivariate time series models. The asymptotic chi-square distribution under the IID assumption is traditionally used for the LB test; however, size distortion tends to occur in the usage of the LB test, due to the conditional heteroskedasticity of financial time series. In order to overcome such defects, we propose the wild bootstrap LB test for autocorrelation in residuals of fitted vector autoregressive and error correction models. The simulation study and real data analysis are conducted for finite sample performance.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.467-474
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• 2006

In this paper we present a weighted support vector machine(SVM) and a weighted least squares support vector machine(LS-SVM) for the prediction in the heteroscedastic regression model. By adding weights to standard SVM and LS-SVM the better fitting ability can be achieved when errors are heteroscedastic. In the numerical studies, we illustrate the prediction performance of the proposed procedure by comparing with the procedure which combines standard SVM and LS-SVM and wild bootstrap for the prediction.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.645-660
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• 2001

In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.1-14
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• 2001

This paper discusses choosing the weight function of the Hardle and Mammen statistic in nonparametric goodness-of-fit test for regression curve. For this purpose, we modify the Hardle and Mammen statistic and derive its asymptotic distribution. Some results on the test statistic from the wild bootstrapped sample are also obtained. Through Monte Carlo experiment, we check the validity of these results. Finally, we study the powers of the test and compare with those of the Hardle and Mammen test through the simulation.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.397-409
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• 2018

The world financial markets are inter-linked in ways that varies according to market and time. We examine the causality of change focusing on the Korean market as related to the U.S. (S&P 500), Japan (Nikkei 225), Hong-Kong (HSI), and European (DAX) markets. In order to capture time-varying causality running from and to the Korea stock market, we apply the Granger causality test under a VAR model with a wild bootstrap rolling-window approach. We also propose a new concept of a significant causality ratio to measure the intensity of the Granger causality in each time unit. There are many asymmetric strengths in mutual Granger causal relationships. Moreover, there are cases with significant Granger causal relations only in one direction. The period with the most severe Granger causality both running from and to the KOSPI market is the GFC. The market that formed the two-way Granger causal relationship with the KOSPI market for the longest period is the S&P 500. The HSI and DAX markets have the strongest two-way Granger causal relationship with the KOSPI shortly after 2000, and the Nikkei market had the strongest two-way Granger causal relationship with the KOSPI market before the Asian financial crisis.

• Journal of Species Research
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• v.8 no.3
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• pp.313-317
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• 2019

Bats influence overall ecosystem health by regulating species diversity and being a major source of zoonotic viruses. Hence, there is a need to elucidate their migration, population structure, and phylogenetic relationship. The complete mitochondrial genome is widely used for studying the genome-level characteristics and phylogenetic relationship of various animals due to its high mutation rate, simple structure, and maternal inheritance. In this study, we determined the complete mitogenome sequence of the bird-like noctule (Nyctalus aviator) by Illumina next-generation sequencing. The sequences obtained were used to reconstruct a phylogenic tree of Vespertilionidae to elucidate the phylogenetic relationship among its members. The mitogenome of N. aviator is 16,863-bp long with a typical vertebrate gene arrangement, consisting of 13 protein-coding genes (PCGs), 22 transfer RNA genes, 2 ribosomal RNA genes, and 1 putative control region. Overall, the nucleotide composition is as follows: 32.3% A, 24.2% C, 14.3% G, and 29.2% T, with a slight AT bias (61.5%). The base composition of the 13 PCGs is as follows: 30.3% A, 13.4% G, 31.0% T, and 25.2% C. The phylogenetic analysis, based on 13 concatenated PCG sequences, infers that N. aviator is closely related to N. noctula with a high bootstrap value (100%).

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.63-75
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• 2022

This paper proposes the so-called MOSUM-based sparse projection method for change points detection in high-dimensional time series. Our method is inspired by Wang and Samworth (2018), however, our method improves their method in two ways. One is to find change points all at once, so it minimizes sequential error. The other is localized so that more robust to the mean changes offsetting each other. We also propose data-driven threshold selection using block wild bootstrap. A comprehensive simulation study shows that our method performs reasonably well in finite samples. We also illustrate our method to stock prices consisting of S&P 500 index, and found four change points in recent 6 years.