• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.359-364
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• 2005

This paper considers a specification test of conditional Poisson regression model for time series count data. Although conditional models for count data have received attention and proposed in several ways, few studies focused on checking its adequacy. Motivated by the test of martingale difference assumption, a specification test via Ljung-Box statistic is proposed in the conditional model of the time series count data. In order to illustrate the performance of Ljung- Box test, simulation results will be provided.

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.433-444
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• 1996

In contrast to the multiplicative risk model, the additive risk model specifies that the hazard function with covariates is the sum of, rather than product of, the baseline hazard function and the regression function of covariates. We, in this paper, propose a method for checking the adequacy of the additive risk model based on partial-sum of matingale residuals. Under the assumed model, the asymptotic properties of the proposed test statistic and approximation method to find the critical values of the limiting distribution are studied. Several real examples are illustrated.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.27-41
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• 2008

A graphical method of checking the adequacy of a generalized linear model is proposed. The graph helps to assess the assumption that the link function of mean can be expressed as a linear combination of explanatory variables in the generalized linear model. For the graph the boosting technique is applied to estimate nonparametrically the relationship between the link function of the mean and the explanatory variables, though any other nonparametric regression methods can be applied. Through simulation studies with normal and binary data, the effectiveness of the graph is demonstrated. And we list some limitations and technical details of the graph.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.379-402
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• 1994

Graphical and numerical methods for checking the assumption of proportional hazards of Cox model for censored survival data are discussed. The strenths and weaknessess of several goodness of fit tests for the propotional hazards for the two-sample problem are evaluated with Monte Carlo simulations, and the tests of Schoenfeld (1980), Andersen (1982), Wei (1984), and Gill and Schumacher (1987) are considered. The goodness of fit methods are illustrated with the survival data of patients who had chronic liver disease and had been treated with the endoscopy injection sclerotheraphy. Two other examples of data known to have nonpropotional hazards are also used in the illustration.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.231-239
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• 2016

A graphical diagnostic method based on multiple case deletions in a regression context is introduced by using the sampling distribution of the difference between two least squares estimators with and without multiple cases. Principal components analysis plays a key role in deriving this diagnostic method. Multiple case deletions of test statistic are also considered when a new observation is fitted to a given regression model. The result is useful for detecting influential observations in econometric data analysis, for example in checking whether the consumption pattern at a later time is the same as the one found before or not, as well as for investigating the influence of cases in the usual regression model. An illustrative example is given.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.256-261
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• 2017

The notion of System of Systems (SoS), which is composed by many independent systems (i.e., Constituent Systems, CS), has emerged in various domains including social infrastructure. It is widely expected that complex requirements, which cannot be achieved in each CS-level, will be achieved in an SoS-level. While verification of SoS-level goal achievement is one of the most important problems, concrete case studies on SoS modeling and verification are still rare. In this paper, we focus on the fact that each CS performs an action for its own purpose by its own decision-making mechanism. We propose a novel Action-Benefit-Cost (ABC) SoS model which caters to the independent decision-making mechanisms of CSs. Using an abstract SoS example, this proposal provides a case study for the modeling and quantitative verification of the ABC SoS model.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.7
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• pp.551-560
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• 2004

A method how to determine the shock response spectrum from the FRF of the statistical energy analysis( SEA ) is presented here. The system of 3 different Plates connected by bolt joints is selected simulating missile structural sections Joined together. First, the SEA model was rendered by SEA parameters which were determined from experimental SEA method. Then, the mobility power was input to the SEA model and we can verify the validity of the model in the medium to high frequency range checking the reproduction of output average velocity. And, the shock induced shock response spectrum(SRS) was obtained using SEA FRF and arbitrarily chosen experimental FRF. We have compared the thus obtained SRS with actually measured SRS and they were relatively in good agreement. In this paper, we used the measured SEA FRF and therefore we have got the SRS well agreed with actually measured SHS even in the low frequency range. If the SEA FRF of well verified SEA model is used, the good result will come out in SEA effective frequency range which is more important at SRS.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.195-209
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• 1997

The joint modelling of mean and dispersion in quasi-likelihood models which greatly extend the scope of generalized linear models, is required in case that the dispersion parameter, the variance component of response variables, is not constant but changes by depending on any covariates. In this paper, by using statistical package GENSTAT(release 5.3.2, 1996) which makes a easily analyze real data through this joint modelling, we mention necessities that must consider this joint modelling rather than existing mean models through model checking based on graphic methods for esterase assay data introduced by Carrol and Ruppert(1987, pp.46-47), and then study methods finding reasonable joint model of mean and dispersion for this data.

• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.201-217
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• 2000

The method of generalized estimating equations (GEE) has become very popular for the analysis of longitudinal data. We extend this work to the use of M-estimators; the resultant regression estimates are robust to heavy tailed errors and to outliers. The proposed method does not require correct specification of the dependence structure between observation, and allows for heterogeneity of the error. However, an estimate of the dependence structure may be incorporated, and if it is correct this guarantees a higher efficiency for the regression estimators. A goodness-of-fit test for checking the adequacy of the assumed M-estimation regression model is also provided. Simulation studies are conducted to show the finite-sample performance of the new methods. The proposed methods are applied to a real-life data set.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.247-260
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• 1997

The quasi-likelihood models which greatly widened the scope of generalized linear models are widely used in data analysis where a likelihood is not available. Since a quasi-likelihood may not appear to be an ordinary likelihood for any known distribution in the natural exponential family, to fit the quasi-likelihood models the standard statistical packages such as GLIM, GENSTAT, S-PLUS and so on may not directly applied. SAS/IML is very useful for fitting of such models. In this paper, we present simple SAS/IML(version 6.11) program which helps to fit and analyze the quasi-likelihood models applied to the leaf-blotch data introduced by Wedderburn(1974), and the problem with deviance useful generally to model checking is pointed out, and then its solution method is mention through the data analysis based on this quasi-likelihood models checking.