• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.229-235
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• 2002

A generalized Stein-rule estimator of the vector of regression coefficients in linear regression model is considered and its properties are analyzed according to the criterion of Pitman nearness. A comparative study shows that the generalized Stein-rule estimator representing a class of estimators contains particular members which are better than the usual Stein-rule estimator according to the Pitman closeness.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.169-176
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• 2008

We explore the structure and usefulness of three dimensional CERES plot as a basic tool for dealing with curvature as a function of the new predictors in generalized linear models. If predictors have nonlinear effects and there are nonlinear relationships among the predictors, the partial residual plot is not able to display the correct functional form of the predictors. Unlike this plots, the CERES plot can show the correct form. This is illustrated by simulated data.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.423-433
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• 2014

Test statistics using cumulative sums of residuals have been widely used in various regression models including generalized linear models(GLM). Recently, Pan and Lin (2005) extended this testing procedure to the generalized linear mixed models(GLMM) having random effects, in which we encounter difficulties in computing the marginal likelihood that is expressed as an integral of random effects distribution. The Gaussian quadrature algorithm is commonly used to approximate the marginal likelihood. Many commercial statistical packages provide an option to apply this type of goodness-of-fit test in GLMs but available programs are very rare for GLMMs. We suggest a computational algorithm to implement the testing procedure in GLMMs by a freely accessible R package, and also illustrate through practical examples.

• Journal of the Korean Statistical Society
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• v.21 no.1
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• pp.70-79
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• 1992

The generalized linear models with random regrssors case are studied for bootstrapping. Only the natural link functions are considered. It is shown that the bootstrap approximation to the distribution of the maximum likelihood estimators is valid for almost all sample sequences. A slight extension of this model is also considered.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.6
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• pp.813-821
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• 1997

Three-dimensional turbulent incompressible flow through the tip clearance of a linear turbine rotor cascade with high turning angle has been analyzed numerically. As a preliminary study to predict the tip clearance loss realistically, a generalized k-.epsilon. model derived by RNG (renormalized group) method is used for the modeling of Reynolds stresses to account for the strain rate of turbulent flow. The effects of the tip clearance flow on the passage vortex, the total pressure loss are considered qualitatively. The existences of vena contract and tip clearance vortex have been confirmed and it has been shown that as the size of the tip clearance increases, the accumulated flow through the tip clearance and the total pressure loss downstream of the cascade increase.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.809-817
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• 2012

We propose a semiparametric inference for a generalized varying coefficient partially linear model(VCPLM) for negative binomial data. The VCPLM is useful to model real data in that varying coefficients are a special type of interaction between explanatory variables and partially linear models fit both parametric and nonparametric terms. The negative binomial distribution often arise in modelling count data which usually are overdispersed. The varying coefficient function estimators and regression parameters in generalized VCPLM are obtained by formulating a penalized likelihood through smoothing splines for negative binomial data when the shape parameter is known. The performance of the proposed method is then evaluated by simulations.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.147-160
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• 2020

In this paper, we study an algorithm that automatically determines the orders of past observations and conditional mean values that play an important role in count time series models. Based on the orders of the ARIMA model, the algorithm constitutes the order candidates group for time series generalized linear models and selects the final model based on information criterion among the combinations of the order candidates group. To evaluate the proposed algorithm, we perform small simulations and empirical analysis according to underlying models and time series as well as compare forecasting performances with the ARIMA model. The results of the comparison confirm that the time series generalized linear model offers better performance than the ARIMA model for the count time series analysis. In addition, the empirical analysis shows better performance in mid and long term forecasting than the ARIMA model.

• Journal of the Korean Mathematical Society
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• v.47 no.2
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• pp.385-407
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• 2010

To study the relationship between the levels of chemical pollutants and the number of daily total hospital admissions for respiratory diseases and to find the effect of temperature/relative humidity on the admission number, Wong et al. [17] introduced the varying-coefficient single-index model (VCSIM). As pointed out, it is a popular multivariate nonparametric fitting technique. However, the tests of the model have not been very well developed. In this paper, based on the estimators obtained by the local linear technique, the average method and the one-step back-fitting technique in the VCSIM, the generalized likelihood ratio (GLR) tests for varying-coefficient parts on the VCSIM are established. Under the null hypotheses the new proposed GLR tests follow the $\chi^2$-distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters, known as Wilks phenomenon. Simulations are conducted to evaluate the test procedure empirically. A real example is used to illustrate the performance of the testing approach.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.765-775
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• 2010

For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1083-1090
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• 2016

In clinical studies, different types of outcomes (e.g. repeated measures data and time-to-event data) for the same subject tend to be observed, and these data can be correlated. For example, a response variable of interest can be measured repeatedly over time on the same subject and at the same time, an event time representing a terminating event is also obtained. Joint modelling using a shared random effect is useful for analyzing these data. Inferences based on marginal likelihood may involve the evaluation of analytically intractable integrations over the random-effect distributions. In this paper we propose a joint HGLM approach for analyzing such outcomes using the HGLM (hierarchical generalized linear model) method based on h-likelihood (i.e. hierarchical likelihood), which avoids these integration itself. The proposed method has been demonstrated using various numerical studies.