• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• Environmental and Resource Economics Review
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• v.2 no.2
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• pp.41-53
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• 1992

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.95-103
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• 2013

Nonparametric procedure in randomized block design (RBD) was proposed by Friedman (1937) for general alternatives. Also Page (1963) suggested the test for ordered alternatives in RBD. In this paper, we proposed the new nonparametric method in randomized block design using aligned method suggested by Hodges and Lehmann (1962) and the joint placement described in Chung and Kim (2007). Also, Monte Carlo simulation study was adapted to compare the power of the proposed procedure with those of previous procedure.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1105-1112
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• 2011

Mack (1981), Skilling and Wolfe (1977, 1978) proposed typical nonparametric method in randomized block design with replications. In this paper, we proposed the procedures based on placement as extension of the two sample placement tests described in Orban and Wolfe (1982) and treatment versus control tests described in Kim (1999). Also Monte Carlo simulation study is adapted to compare power of the proposed procedure with those of previous procedures.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.729-739
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• 2016

Kruskal and Wallis (1952) proposed a nonparametric method to test the differences between more than three independent treatments. This procedure uses rank in mixed sample combined with more than three unlike populations. This paper proposes a the new procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed method with previous methods.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.157-169
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• 2016

We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.291-299
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• 2017

Mack and Skillings (1980) proposed nonparametric procedures in a randomized block design with replications as general alternatives. This method is used to find the difference in the treatment effect; however, it can cause a loss of inter block information using the ranking in each block. In this paper, we proposed new nonparametric procedures in a randomized block design with replications using an aligned method proposed by Hodges and Lehmann (1962) that used information of blocks and based on the joint placement suggest by Chung and Kim (2008). We also compared the power of the test of the proposed procedures and established a method through Monte Carlo simulation.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.701-717
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• 2017

The interest of Korean society and government on future demographic structures is increasing due to rapid aging. Korea's mortality rate is decreasing, but the declined gap is variable. In this study, we compare the Lee-Carter, Lee-Miller, Booth-Maindonald-Smith model and functional data model (FDM) as well as Coherent FDM using non-parametric smoothing technique. We are then examine a reasonable model for projecting on mortality declined rate trend in terms of accuracy of mortality rate by ages and life expectancy. The possibility of using non-parametric techniques for the prediction of mortality in Korea was also examined. Based on the analysis results, FDM and Coherent FDM, which uses the non-parametric technique and reflects the trend of recent data, are excellent. As a result, FDM and Coherent FDM are good fit, and predictability is also excellent assuming no significant future changes.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.677-692
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• 2018

The total fertility rate of Korea was 1.05 in 2017, showing a return to the 1.08 level in the year 2005. 1.05 is a very low fertility level that is far from replacement level fertility or safety zone 1.5. The number may indicate a low fertility trap. It is therefore important to predict fertility than at any other time. In the meantime, we have predicted the age-specific fertility rate and total fertility rate by various statistical methods. When the data trend is disconnected or fluctuating, it applied a nonparametric method applying the smoothness and weight. In addition, the Bayesian method of using the pre-distribution of fertility rates in advanced countries with reference to the three-stage transition phenomenon have been applied. This paper examines which method is reasonable in terms of precision and feasibility by applying estimation, forecasting, and comparing the results of the recent variability of the Korean fertility rate with parametric, non-parametric and Bayesian methods. The results of the analysis showed that the total fertility rate was in the order of KOSTAT's total fertility rate, Bayesian, parametric and non-parametric method outcomes. Given the level of TFR 1.05 in 2017, the predicted total fertility rate derived from the parametric and nonparametric models is most reasonable. In addition, if a fertility rate data is highly complete and a quality is good, the parametric model approach is superior to other methods in terms of parameter estimation, calculation efficiency and goodness-of-fit.