• Title/Summary/Keyword: nonparametric statistical method

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Nonparametric method using linear statistics in analysis of covariance model (공분산분석에서 선형위치통계량을 이용한 비모수 검정법)

  • Choi, Yoonjung;Kim, Dongjae
    • The Korean Journal of Applied Statistics
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    • v.30 no.3
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    • pp.427-439
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    • 2017
  • Quade (1967) proposed RANK ANCOVA, which is a nonparametric method to test differences between treatments when there are covariates. Hwang and Kim (2012) also proposed a joint placement test on covariate-adjusted residuals. In this paper, we proposed a new nonparametric method to control the effect of covariate on a response variable that uses linear statistics on covariate adjusted-residuals. The score function used in the linear statistics was proposed by Jeon and Kim (2016). Monte Carlo simulation is also conducted to compare the empirical powers of the proposed method with previous methods.

Nonparametric procedures using aligned method and joint placement in randomized block design with replications (반복이 있는 랜덤화 블록 계획법에서 정렬방법과 결합위치를 이용한 비모수 검정법)

  • Lee, Eunjee;Kim, Dongjae
    • 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.

A Comparative Study on the Performance of Bayesian Partially Linear Models

  • Woo, Yoonsung;Choi, Taeryon;Kim, Wooseok
    • Communications for Statistical Applications and Methods
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    • v.19 no.6
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    • pp.885-898
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    • 2012
  • In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.

THE STUDY OF PARAMETRIC AND NONPARAMETRIC MIXTURE DENSITY ESTIMATOR FOR FLOOD FREQUENCY ANALYSIS

  • Moon, Young-Il
    • Water Engineering Research
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    • v.1 no.1
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    • pp.49-61
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    • 2000
  • Magnitude-frequency relationships are used in the design of dams, highway bridges, culverts, water supply systems, and flood control structures. In this paper, possible techniques for analyzing flood frequency at a site are presented. A currently used approach to flood frequency analysis is based on the concept of parametric statistical inference. In this analysis, the assumption is make that the distribution function describing flood data in known. However, such an assumption is not always justified. Even though many people have shown that the nonparametric method provides a better fit to the data than the parometric method and gives more reliable flood estimates. the noparpmetric method implies a small probability in extrapolation beyond the highest observed data in the sample. Therefore, a remedy is presented in this paper by introducing an estimator which mixes parametric and nonparametric density estimate.

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Nonparametric method in one-way layout based on joint placement (일원배치법에서 결합위치를 이용한 비모수 검정법)

  • Jeon, Kyoung-Ah;Kim, Dongjae
    • 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.

A comparison study on regression with stationary nonparametric autoregressive errors (정상 비모수 자기상관 오차항을 갖는 회귀분석에 대한 비교 연구)

  • Yu, Kyusang
    • 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.

Nonparametric procedures based on aligned method and placement for ordered alternatives in randomized block design (랜덤화 블록 모형에서 정렬방법과 위치를 이용한 순서형 대립가설에 대한 비모수 검정법)

  • Kim, Hyosook;Kim, Dongjae
    • The Korean Journal of Applied Statistics
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    • v.29 no.4
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    • pp.707-717
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    • 2016
  • Nonparametric procedures in a randomized block design was proposed by Friedman (1937) as a general alternative as well as suggested as a test for ordered alternatives by Page (1963). These methods are used for the rank of treatments in each block. In this paper, we proposed nonparametric procedures using aligned method proposed by Hodges and Lehmann (1962) to reduce among block information and based on placement suggested by Kim (1999) in a randomized block design. We also perform a Monte Carlo study to compare the empirical powers of the proposed procedures and established method.

Comparison of estimation methods for expectile regression (평률 회귀분석을 위한 추정 방법의 비교)

  • Kim, Jong Min;Kang, Kee-Hoon
    • The Korean Journal of Applied Statistics
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    • v.31 no.3
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    • pp.343-352
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    • 2018
  • We can use quantile regression and expectile regression analysis to estimate trends in extreme regions as well as the average trends of response variables in given explanatory variables. In this paper, we compare the performance between the parametric and nonparametric methods for expectile regression. We introduce each estimation method and analyze through various simulations and the application to real data. The nonparametric model showed better results if the model is complex and difficult to deduce the relationship between variables. The use of nonparametric methods can be recommended in terms of the difficulty of assuming a parametric model in expectile regression.

A New Nonparametric Method for Prediction Based on Mean Squared Relative Errors (평균제곱상대오차에 기반한 비모수적 예측)

  • Jeong, Seok-Oh;Shin, Key-Il
    • Communications for Statistical Applications and Methods
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    • v.15 no.2
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    • pp.255-264
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    • 2008
  • It is common in practice to use mean squared error(MSE) for prediction. Recently, Park and Shin (2005) and Jones et al. (2007) studied prediction based on mean squared relative error(MSRE). We proposed a new nonparametric way of prediction based on MSRE substituting Jones et al. (2007) and provided a small simulation study which highly supports the proposed method.

Effective Computation for Odds Ratio Estimation in Nonparametric Logistic Regression

  • Kim, Young-Ju
    • Communications for Statistical Applications and Methods
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    • v.16 no.4
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    • pp.713-722
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    • 2009
  • The estimation of odds ratio and corresponding confidence intervals for case-control data have been done by traditional generalized linear models which assumed that the logarithm of odds ratio is linearly related to risk factors. We adapt a lower-dimensional approximation of Gu and Kim (2002) to provide a faster computation in nonparametric method for the estimation of odds ratio by allowing flexibility of the estimating function and its Bayesian confidence interval under the Bayes model for the lower-dimensional approximations. Simulation studies showed that taking larger samples with the lower-dimensional approximations help to improve the smoothing spline estimates of odds ratio in this settings. The proposed method can be used to analyze case-control data in medical studies.