• Title/Summary/Keyword: nonparametric statistical method

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Estimation of long memory parameter in nonparametric regression

  • Cho, Yeoyoung;Baek, Changryong
    • Communications for Statistical Applications and Methods
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    • v.26 no.6
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    • pp.611-622
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    • 2019
  • This paper considers the estimation of the long memory parameter in nonparametric regression with strongly correlated errors. The key idea is to minimize a unified mean squared error of long memory parameter to select both kernel bandwidth and the number of frequencies used in exact local Whittle estimation. A unified mean squared error framework is more natural because it provides both goodness of fit and measure of strong dependence. The block bootstrap is applied to evaluate the mean squared error. Finite sample performance using Monte Carlo simulations shows the closest performance to the oracle. The proposed method outperforms existing methods especially when dependency and sample size increase. The proposed method is also illustreated to the volatility of exchange rate between Korean Won for US dollar.

Robust Nonparametric Regression Method using Rank Transformation

    • Communications for Statistical Applications and Methods
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    • v.7 no.2
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    • pp.574-574
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    • 2000
  • Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

Robust Nonparametric Regression Method using Rank Transformation

  • Park, Dongryeon
    • Communications for Statistical Applications and Methods
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    • v.7 no.2
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    • pp.575-583
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    • 2000
  • Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

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Nonparametric Method using Placement in an Analysis of a Covariance Model

  • Hwang, Dong-Min;Kim, Dong-Jae
    • Communications for Statistical Applications and Methods
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    • v.19 no.5
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    • pp.721-729
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    • 2012
  • Various methods control the influence of a covariate on a response variable. These methods are analysis of covariance(ANCOVA), RANK ANCOVA, ANOVA of (covariate-adjusted) residuals, and Kruskal-Wallis tests on residuals. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set that ignore the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. In this paper, we proposed the new nonparametric method on the ANCOVA model, as applying joint placement in a one-way layout on residuals as described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed procedure with those of the previous procedure.

Nonparametric analysis of income distributions among different regions based on energy distance with applications to China Health and Nutrition Survey data

  • Ma, Zhihua;Xue, Yishu;Hu, Guanyu
    • Communications for Statistical Applications and Methods
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    • v.26 no.1
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    • pp.57-67
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    • 2019
  • Income distribution is a major concern in economic theory. In regional economics, it is often of interest to compare income distributions in different regions. Traditional methods often compare the income inequality of different regions by assuming parametric forms of the income distributions, or using summary statistics like the Gini coefficient. In this paper, we propose a nonparametric procedure to test for heterogeneity in income distributions among different regions, and a K-means clustering procedure for clustering income distributions based on energy distance. In simulation studies, it is shown that the energy distance based method has competitive results with other common methods in hypothesis testing, and the energy distance based clustering method performs well in the clustering problem. The proposed approaches are applied in analyzing data from China Health and Nutrition Survey 2011. The results indicate that there are significant differences among income distributions of the 12 provinces in the dataset. After applying a 4-means clustering algorithm, we obtained the clustering results of the income distributions in the 12 provinces.

Nonparametric Tests in AB/BA/AA/BB Crossover Design

  • Nam, Jusun;Kim, Dongjae
    • Communications for Statistical Applications and Methods
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    • v.9 no.3
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    • pp.607-618
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    • 2002
  • Crossover design is often used in clinical trials about chronic diseases like hypertension, asthma and arthritis. In this paper, we suggest nonparametric approaches of Friedman-type rank test based on Bernard-van Elteren test and of aligned method keeping the information of blocks based on the AB/BA/AA/BB crossover design. The simulation results are presented to compare experimental error and power of several methods.

ON MARGINAL INTEGRATION METHOD IN NONPARAMETRIC REGRESSION

  • Lee, Young-Kyung
    • Journal of the Korean Statistical Society
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    • v.33 no.4
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    • pp.435-447
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    • 2004
  • In additive nonparametric regression, Linton and Nielsen (1995) showed that the marginal integration when applied to the local linear smoother produces a rate-optimal estimator of each univariate component function for the case where the dimension of the predictor is two. In this paper we give new formulas for the bias and variance of the marginal integration regression estimators which are valid for boundary areas as well as fixed interior points, and show the local linear marginal integration estimator is in fact rate-optimal when the dimension of the predictor is less than or equal to four. We extend the results to the case of the local polynomial smoother, too.

Bootstrap tack of Fit Test based on the Linear Smoothers

  • Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.9 no.2
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    • pp.357-363
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    • 1998
  • In this paper we propose a nonparametric lack of fit test based on the bootstrap method for testing the null parametric linear model by using linear smoothers. Most of existing nonparametric test statistics are based on the residuals. Our test is based on the centered bootstrap residuals. Power performance of proposed bootstrap lack of fit test is investigated via Monte carlo simulation.

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A nonparametric test for parallelism of regression lines against ordered alternatives (회귀직선 기울기의 순서성에 대한 비모수적 검정법)

  • 송문섭;이기훈;김순옥
    • The Korean Journal of Applied Statistics
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    • v.6 no.2
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    • pp.401-408
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    • 1993
  • This paper suggests a nonparametric test for the parallelism of several regression lines against ordered alternatives. The test statistic is an extension of the Potthoff statistic. The asymptotic variance of the proposed statistic is estimated by Bootstrap method. The proposed test are compared with the Adichie's parametric and nonparametric tests.

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An Improved Nonparametric Change Detection Algorithm Using Euler Number and Structure Tensor (오일러 수와 구조 텐서를 사용한 개선된 Nonparametric 변화 검출 알고리즘)

  • 이웅희;김태희;정동석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.10C
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    • pp.958-966
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    • 2003
  • Change detection algorithms based on frame difference are frequently used for finding moving objects in image sequences. These algorithms detect the change of frames using estimated statistical background model. But, if this estimated background model is different from the actual statistical distribution, false detections are generated. In this paper, we propose an improved change detection algorithm using euler number and structure tensor. The proposed mapping method which is based on the euler number can be used for reducing the false detections that generated by nonparametric change detection algorithm. In this paper, the change in the region of moving object also can be detected by the proposed method using structure tensor. Experimental result shows that the proposed method reduces the false detections effectively by 90% on "Weather", by 34% on "Mother & daughter" and by 43% on "Aisle" than an existing method does.