• Communications for Statistical Applications and Methods
• /
• v.13 no.2
• /
• pp.205-217
• /
• 2006

In this paper, we consider the problem of testing for a change of the parameter function ${\theta}(t)$ that may have a discontinuity at some unknown point ${\tau}$. We introduce a varying-h moving estimate to test the null hypothesis that ${\theta}(t)$ is continuous against the alternative that ${\theta}({\tau}-){\neq}{\theta}({\tau}+)$. Simulation results are provided for illustration.

• The Korean Journal of Applied Statistics
• /
• v.25 no.6
• /
• pp.977-985
• /
• 2012

Data quality and climate forecasting performance deteriorates because of long climate data contaminated by non-climatic factors such as the station relocation or new instrument replacement. For a trusted climate forecast, it is necessary to implement data quality control and test inhomogeneous data. Before the inhomogeneity test, a reference series was created by $d$ index to measure the temperature series relationship between the candidate and surrounding stations. In this study, a inhomogeneity test to each season and climatological station was performed on the daily mean temperatures, daily minimum temperatures and daily maximum temperatures. After comparing two inhomogeneity tests, the traditional and the adjusted SNHT method, we found the adjusted SNHT method was slightly superior to the traditional one.

• Communications for Statistical Applications and Methods
• /
• v.24 no.5
• /
• pp.519-531
• /
• 2017

We consider goodness-of-fit test statistics for Weibull distributions when data are randomly censored and the parameters are unknown. Koziol and Green (Biometrika, 63, 465-474, 1976) proposed the $Cram\acute{e}r$-von Mises statistic's randomly censored version for a simple hypothesis based on the Kaplan-Meier product limit of the distribution function. We apply their idea to the other statistics based on the empirical distribution function such as the Kolmogorov-Smirnov and Liao and Shimokawa (Journal of Statistical Computation and Simulation, 64, 23-48, 1999) statistics. The latter is a hybrid of the Kolmogorov-Smirnov, $Cram\acute{e}r$-von Mises, and Anderson-Darling statistics. These statistics as well as the Koziol-Green statistic are considered as test statistics for randomly censored Weibull distributions with estimated parameters. The null distributions depend on the estimation method since the test statistics are not distribution free when the parameters are estimated. Maximum likelihood estimation and the graphical plotting method with the least squares are considered for parameter estimation. A simulation study enables the Liao-Shimokawa statistic to show a relatively high power in many alternatives; however, the null distribution heavily depends on the parameter estimation. Meanwhile, the Koziol-Green statistic provides moderate power and the null distribution does not significantly change upon the parameter estimation.

• Journal of the Korean Statistical Society
• /
• v.15 no.1
• /
• pp.9-25
• /
• 1986

In this paper we propose a distribution-free rank test for ordered alternatives in a randomized block design and investigate the properties of the proposed test. The proposed test is an extension of the Page test to allow replications in each cell. Some asymptotic properties including ARE's are investigated. A small sample Monte Carlo study was performed to compare the powers of the test considered in this paper for small samples. The results show that our proposed test is robust and efficient in the case of equally-spaced treatment effects.

• Communications for Statistical Applications and Methods
• /
• v.22 no.5
• /
• pp.495-506
• /
• 2015

Volatility is a variation measure in finance for returns of a financial instrument over time. GARCH models have been a popular tool to analyze volatility of financial time series data since Bollerslev (1986) and it is said that volatility is highly persistent when the sum of the estimated coefficients of the squared lagged returns and the lagged conditional variance terms in GARCH models is close to 1. Regarding persistence, numerous methods have been proposed to test if such persistency is due to volatility shifts in the market or natural fluctuation explained by stationary long-range dependence (LRD). Recently, Lee et al. (2015) proposed a residual-based cumulative sum (CUSUM) test statistic to test volatility shifts in GARCH models against LRD. We propose a bootstrap-based approach for the residual-based test and compare the sizes and powers of our bootstrap-based CUSUM test with the one in Lee et al. (2015) through simulation studies.

• Communications for Statistical Applications and Methods
• /
• v.19 no.1
• /
• pp.129-134
• /
• 2012

In this paper we review a bi-aspect nonparametric test for the two-sample problem under the location translation model and propose a new one to accommodate a more broad class of underlying distributions. Then we compare the performance of our proposed test with other existing ones by obtaining empirical powers through a simulation study. Then we discuss some interesting features related to the bi-aspect test with a comment on a possible expansion for the proposed test as concluding remarks.

• Communications for Statistical Applications and Methods
• /
• v.5 no.3
• /
• pp.707-722
• /
• 1998

This paper evaluates a new clinical trial designs for low infection rate disease. This type of sparse disease reaction makes the traditional two sample t-test or Wilcoxon rank-sum test inefficient compared to a new test suggested. The new test, which is based solely on the larger changes, is shown to be more effective than existing method by simulation for small samples. However, this test can be shown to be connected to the locally most powerful rank test under certain practical conditions. This design is motivated in testing the treatment effects in periodontal disease research.

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

• Journal of the Korean Statistical Society
• /
• v.36 no.1
• /
• pp.149-156
• /
• 2007

This study suggests a new method for testing seasonal unit roots in a momentum threshold autoregressive (MTAR) process. This sign test is robust against heteroscedastic or heavy tailed errors and is invariant to monotone data transformation. The proposed test is a seasonal extension of the sign test of Park and Shin (2006). In the case of partial seasonal unit root in an MTAR model, a Monte-Carlo study shows that the proposed test has better power than the seasonal sign test developed for AR model.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.6
• /
• pp.1305-1310
• /
• 2010

In this paper, we consider a change point test for stochastic volatility models. By considering the relation between moments of the logarithms of squared returns and the parameters, we construct the cusum test to detect changes of the parameters. We also carry out a simulation study and verify that the proposed test is more powerful than the cusum test proposed by Kokoszka and Leipus (2000).