• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.325-334
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• 2009

In this research, we propose a nonparametric test procedure for the right censored and grouped data under the additive hazards model. For deriving the test statistics, we use the likelihood principle. Then we illustrate proposed test with an example and compare the performance with other procedure by obtaining empirical powers. Finally we discuss some interesting features concerning the proposed test.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.291-296
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• 2010

In this paper, we consider a versatile test based on percentile tests. The versatile test may be useful when the underlying distributions are unknown or quite different types. We consider two kinds of combining functions for the percentile statistics, the quadratic and summing forms and obtain the limiting distributions under the null hypothesis. Then we illustrate our procedure with an example. Finally we discuss some interesting features of the test as concluding remarks.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.607-617
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• 2012

The goodness-of-fit test for multivariate normal distribution is important because most multivariate statistical methods are based on the assumption of multivariate normality. We propose goodness-of-fit test statistics for multivariate normality based on the modified squared distance. The empirical percentage points of the null distribution of the proposed statistics are presented via numerical simulations. We compare performance of several test statistics through a Monte Carlo simulation.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.233-238
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• 2003

Goodness of fit test statistics based on the information discrepancy have been shown to perform very well (Vasicek 1976, Dudewicz and van der Meulen 1981, Chandra et al 1982, Gohkale 1983, Arizona and Ohta 1989, Ebrahimi et al 1992, etc). Although the test is well defined for the non-censored case, censored case has not been discussed in the literature. Therefore we consider a goodness of fit test based on the partial Kullback-Leibler(KL) information with the type II censored data. We derive the partial KL information of the null distribution function and a nonparametric distribution function, and establish a goodness of fit test statistic. We consider the exponential and normal distributions and made Monte Calro simulations to compare the test statistics with some existing tests.

• International Journal of Reliability and Applications
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• v.7 no.2
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• pp.89-99
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• 2006

A new classes of life distribution, namely harmonic used better than aged in expectation in upper tail (HUBAEUT) is introduced. Testing exponentiality against this class is investigated using kernel method. The limiting null and nonnull distribution of the test statistics is normal and the null variance is calculated exactly. Selected critical values are tabulated for sample sizes of 5(1)40. Power of the test are estimated by simulation. the efficacies of the test statistics used for testing against HUBAEUT are calculated for som common alternatives and are compared to some other procedures. It is shown that proposed test is simple, has high relative efficiency and power for some commonly used alternatives. The set of real data are used as an examples to elucidate the use of the proposed test statistics for practical reliability.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.475-484
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• 2018

Gamma generalized linear models are useful for non-negative and skewed responses. However, these models have received less attention than Poisson and binomial generalized linear models. In particular, hypothesis testing for the significance of regression coefficients has not been thoroughly studied. In this paper we assess the performance of various test statistics for gamma generalized linear models based on numerical studies. Our results show that the likelihood ratio test and F-type test are generally recommended and that the partial deviance test should be avoided in practice.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.797-806
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• 2012

Fault detection and isolation (FDI) algorithms provide fault monitoring methods in GPS measurement to isolate abnormal signals from the GPS satellites or the acquired signal in receiver. In order to monitor the occurred faults, FDI generates test statistics and decides the case that is beyond a designed threshold as a fault. For such problem of fault detection and isolation, this paper presents and evaluates position domain integrity monitoring methods by formulating various pseudorange prediction methods and investigating the resulting test statistics. In particular, precise measurements like carrier phase and Doppler rate are employed under the assumption of fault free carrier signal. The presented position domain algorithm contains the following process; first a common pseudorange prediction formula is defined with the proposed variations in pseudorange differential update. Next, a threshold computation is proposed with the test statistics distribution considering the elevation angle. Then, by examining the test statistics, fault detection and isolation is done for each satellite channel. To verify the performance, simulations using the presented fault detection methods are done for an ideal and real fault case, respectively.

• Journal of Korean Society for Quality Management
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• v.14 no.2
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• pp.15-20
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• 1986

Using Fisher's method of combining two independent test statistics, we suggest a test for comparing two exponential populations with location and scale parameters and prove that it is asymptotically optimal in the sense of Bahadur efficiency.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.797-805
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• 2005

Multivariate unit root tests for the VAR(p) model have been commonly used in time series analysis. Several unit root tests were developed and recently Shin(2004) suggested a cointegration test based on weighted symmetric estimator. In this paper, we suggest a multivariate unit root test statistic based on the weighted symmetric estimator. Using a small simulation study, we compare the powers of the new test statistic with the statistics suggested in Shin(2004) and Fuller(1996).

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.489-501
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• 1999

In this paper, we consider the panel data regression model in which the disturbances have nested error component. We derive a Lagrange Multiplier(LM) test which is jointly testing for the presence of random individual effects and nested effects under the normality assumption of the disturbances. This test extends the earlier work of Breusch and Pagan(1980) and Baltagi and Li(1991). Further, it is shown that this LM test has the same asymptotic distribution without normality assumption of the disturbances.