• Journal of the Korean Statistical Society
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• v.32 no.2
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• pp.103-119
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• 2003

Properties of stochastic regression imputation are discussed under the uniform within-cell response model. Variance estimator is proposed and its asymptotic properties are discussed. A limited simulation is also presented.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.205-213
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• 2012

Transforming response variable is a general tool to adapt data to a linear regression model. However, it is well known that response transformations in linear regression are very sensitive to one or a few outliers. Many methods have been suggested to develop transformations that will not be influenced by potential outliers. Recently Cheng (2005) suggested to using a trimmed likelihood estimator based on the idea of the least trimmed squares estimator(LTS). However, the method requires presetting the number of outliers and needs many computations. A new method is proposed, that can solve the problems addressed and improve the robustness of the estimates. The method uses a stepwise procedure, suggested by Hadi and Simonoff (1993), to detect outliers that determine response transformations.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.423-434
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• 2007

Imputation using a regression model is a method to preserve the correlation among variables and to provide imputed point estimators. We discuss the implementation of regression imputation using fractional imputation. By a suitable choice of fractional weights, the fractional regression imputation can take the form of hot deck fractional imputation, thus no artificial values are constructed after the imputation. A variance estimator, which extends the method of Kim and Fuller (2004), is also proposed. Results from a limited simulation study are presented.

• Journal of Korean Society for Quality Management
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• v.19 no.1
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• pp.1-15
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• 1991

In this paper, we discuss and review various measures which have been presented for studying outliers, high-leverage points, and influential observations when ridge regression estimation is adopted. We derive the influence function for ${\underline{\hat{\beta}}}\small{R}$, the ridge regression estimator, and discuss its various finite sample approximations when ridge regression is postulated. We also study several diagnostic measures such as Welsh-Kuh's distance, Cook's distance etc.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.335-345
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• 2000

Most context use the classical norms on function spaces as the error criteria. Since these norms are all based on the vertical distances between the curves, these can be quite inappropriate from a visual notion of distance. Visual errors in Marron and Tsybakov(1995) correspond more closely to "what the eye sees". Simulation is performed to compare the performance of the regression smoothers in view of MISE and the visual error. It shows that the visual error can be used as a possible candidate of error criteria in the kernel regression estimation.

• Journal of the Korean Statistical Society
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• v.11 no.1
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• pp.59-68
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• 1982

We propose that, in order to control the inflation and general instability associated with the least squares estimates, we can use the ridge estimator $$ \hat{B}^* = (X'X+kI)^{-1}X'Y : k \leq 0$$ for the regression coefficients B in multivariate regression. Our hope is that by accepting some bias, we can achieve a larger reduction in variance. We show that such a k always exists and we derive the formula obtaining k in multivariate ridge regression.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.605-617
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• 2014

Small area estimation is a statistical inference method to overcome large variance due to a small sample size allocated in a small area. A shrinkage estimator obtained by minimizing relative error(RE) instead of MSE has been suggested. The estimator takes advantage of good interpretation when the data range is large. A semiparametric estimator is also studied for small area estimation. In this study, we suggest a semiparametric shrinkage small area estimator and compare small area estimators using labor statistics.

• Journal of the Korean Statistical Society
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• v.26 no.1
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• pp.117-130
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• 1997

We study the asymptotic behavior of the maximum partial likelihood estimator in the Cox proportional hazards model in the presence of nuisance parameters when the entry of patients is staggered. When entry of patients is simultaneous and there is only one regression parameter in the Cox model, the efficient score process of the partial likelihood is martingale and converges weakly to a time-chnaged Brownian motion. Our problem is to get a similar result in the presence of nuisance parameters when entry of patient is staggered.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.75-82
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• 1994

It is known that collinearity among the explanatory variables in generalized linear models inflates the variance of maximum likelihood estimators. A ridge-type estimator is presented using penalized likelihood. A method for choosing a shrinkage parameter is discussed and this method is based on a prediction-oriented criterion, which is Mallow's $C_L$ statistic in a linear regression setting.