• 응용통계연구
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• 제25권2호
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• pp.321-330
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• 2012

In this paper we study some basic statistics in quantile regression. In particular, we investigate the residual, goodness-of-fit statistic and the effect of one or few observations on estimates of regression coefficients. In addition, we compare the proposed goodness-of-fit statistic with the statistic considered by Koenker and Machado (1999). An illustrative example based on real data sets is given to see the numerical performance of the proposed basic statistics.

• 대한수학회지
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• 제47권6호
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• pp.1137-1146
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• 2010

In this paper, we study the divergence based goodness of fit test for partially observed sample from diffusion processes. In order to derive the limiting distribution of the test, we study the asymptotic behavior of the residual empirical process based on the observed sample. It is shown that the residual empirical process converges weakly to a Brownian bridge and the associated phi-divergence test has a chi-square limiting null distribution.

• Communications for Statistical Applications and Methods
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• 제8권1호
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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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• 제7권1호
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• pp.277-284
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• 2000

The transformed sample Lorenz curve provides a powerful and easily computed goodness-of-fit test for exponentiality which does not depend on the unknown scale parameter. We compare the power of the transformed sample Lorenz curve statistic with the other goodness-of-fit tests for exponentiality against various alternatives through Monte Carlo methods and discuss the results.

• International Journal of Reliability and Applications
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• 제9권1호
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• pp.71-78
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• 2008

Based on the goodness of fit approach, a new test is presented for testing exponentiality versus exponential better (worse) than used (EBU (EWU)) class of life distributions. The new test is much simpler to compute, asymptotically normal, enjoys good power and performs better than previous tests in terms of power and Pitman asymptotic efficiencies for several alternatives.

• International Journal of Reliability and Applications
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• 제8권1호
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• pp.41-51
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• 2007

In this paper, we use the moments of order statistics derived by Lieblein (1955) to develop the correlation goodness-of-fit test for the Rayleigh distribution. In such we simulate the percentage points of the test statistics for the one-parameter and two-parameter cases. In addition, we calculate the power of the proposed tests based on some alterative distributions. Finally, we apply the procedures developed in the paper to some real data.

• Journal of the Korean Data and Information Science Society
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• 제6권1호
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• pp.31-37
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• 1995

We consider density estimates of the usual type generated by a kernel function. By using the limit theorems for the maximum of normalized deviation of the estimate from its expected value, we propose to use data dependent bandwidth in the tests of goodness of fit based on these statistics. Also a small sample Monte Carlo simulation is conducted and proposed method is compared with Kolmogorov-Smirnov test.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.967-976
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• 1999

In this paper we study the Hellinger distance based goodness-of-fit tests that are analogs of likelihood ratio tests. The minimum Hellinger distance estimator (MHDE) in normal models provides an excellent robust alternative to the usual maximum likelihood estimator. Our simulation results show that the Hellinger deviance test (Simpson 1989) based goodness-of-fit test is robust when data contain outliers. The proposed hellinger deviance test(Simpson 1989) is a more direcct method for obtaining robust inferences than an automated outlier screen method used before the likelihood ratio test data analysis.

• 대한수학회보
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• 제53권2호
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• pp.351-363
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• 2016

In this paper, we consider the entropy-based goodness of fit test (Vasicek's test) for a composite hypothesis. The test measures the discrepancy between the nonparametric entropy estimate and the parametric entropy estimate obtained from an assumed parametric family of distributions. It is shown that the proposed test is asymptotically normal under regularity conditions, but is affected by parameter estimates. As a remedy, a bootstrap version of Vasicek's test is proposed. Simulation results are provided for illustration.

• Communications for Statistical Applications and Methods
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• 제19권4호
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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.