• Title, Summary, Keyword: nonparametric regression

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Nonparametric Estimation in Regression Model

  • Han, Sang Moon
    • Communications for Statistical Applications and Methods
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    • v.8 no.1
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    • pp.15-27
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    • 2001
  • One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

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Nonparametric Regression with Genetic Algorithm (유전자 알고리즘을 이용한 비모수 회귀분석)

  • Kim, Byung-Do;Rho, Sang-Kyu
    • Asia pacific journal of information systems
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    • v.11 no.1
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    • pp.61-73
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    • 2001
  • Predicting a variable using other variables in a large data set is a very difficult task. It involves selecting variables to include in a model and determining the shape of the relationship between variables. Nonparametric regression such as smoothing splines and neural networks are widely-used methods for such a task. We propose an alternative method based on a genetic algorithm(GA) to solve this problem. We applied GA to regression splines, a nonparametric regression method, to estimate functional forms between variables. Using several simulated and real data, our technique is shown to outperform traditional nonparametric methods such as smoothing splines and neural networks.

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First Order Difference-Based Error Variance Estimator in Nonparametric Regression with a Single Outlier

  • Park, Chun-Gun
    • Communications for Statistical Applications and Methods
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    • v.19 no.3
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    • pp.333-344
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    • 2012
  • We consider some statistical properties of the first order difference-based error variance estimator in nonparametric regression models with a single outlier. So far under an outlier(s) such difference-based estimators has been rarely discussed. We propose the first order difference-based estimator using the leave-one-out method to detect a single outlier and simulate the outlier detection in a nonparametric regression model with the single outlier. Moreover, the outlier detection works well. The results are promising even in nonparametric regression models with many outliers using some difference based estimators.

An estimator of the mean of the squared functions for a nonparametric regression

  • Park, Chun-Gun
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.3
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    • pp.577-585
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    • 2009
  • So far in a nonparametric regression model one of the interesting problems is estimating the error variance. In this paper we propose an estimator of the mean of the squared functions which is the numerator of SNR (Signal to Noise Ratio). To estimate SNR, the mean of the squared function should be firstly estimated. Our focus is on estimating the amplitude, that is the mean of the squared functions, in a nonparametric regression using a simple linear regression model with the quadratic form of observations as the dependent variable and the function of a lag as the regressor. Our method can be extended to nonparametric regression models with multivariate functions on unequally spaced design points or clustered designed points.

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통계학의 비모수 추정에 관한 역사적 고찰

  • 이승우
    • Journal for History of Mathematics
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    • v.16 no.3
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    • pp.95-100
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    • 2003
  • The recent surge of interest in the more technical aspects of nonparametric density estimation and nonparametric regression estimation has brought the subject into public view. In this paper, we investigate the general concept of the nonparametric density estimation, the nonparametric regression estimation and its performance criteria.

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Optimal Designs for Multivariate Nonparametric Kernel Regression with Binary Data

  • Park, Dong-Ryeon
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.243-248
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    • 1995
  • The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

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Improvement of Boundary Bias in Nonparametric Regression via Twicing Technique

  • Jo, Jae-Keun
    • Communications for Statistical Applications and Methods
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    • v.4 no.2
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    • pp.445-452
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    • 1997
  • In this paper, twicing technique for the improvement of asymptotic boundary bias in nonparametric regression is considered. Asymptotic mean squared errors of the nonparametric regression estimators are derived at the boundary region by twicing the Nadaraya-Waston and local linear smoothing. Asymptotic biases of the resulting estimators are of order$h^2$and$h^4$ respectively.

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Nonparametric Estimation using Regression Quantiles in a Regression Model

  • Han, Sang-Moon;Jung, Byoung-Cheol
    • The Korean Journal of Applied Statistics
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    • v.25 no.5
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    • pp.793-802
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    • 2012
  • One proposal is made to construct a nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of the idea of minimizing approximate variance of a proposed estimator using regression quantiles. This nonparametric estimator and some other L-estimators are studied and compared with well known M-estimators through a simulation study.

PRELIMINARY DETECTION FOR ARCH-TYPE HETEROSCEDASTICITY IN A NONPARAMETRIC TIME SERIES REGRESSION MODEL

  • HWANG S. Y.;PARK CHEOLYONG;KIM TAE YOON;PARK BYEONG U.;LEE Y. K.
    • Journal of the Korean Statistical Society
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    • v.34 no.2
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    • pp.161-172
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    • 2005
  • In this paper a nonparametric method is proposed for detecting conditionally heteroscedastic errors in a nonparametric time series regression model where the observation points are equally spaced on [0,1]. It turns out that the first-order sample autocorrelation of the squared residuals from the kernel regression estimates provides essential information. Illustrative simulation study is presented for diverse errors such as ARCH(1), GARCH(1,1) and threshold-ARCH(1) models.

Nonparametric Regression with Left-Truncated and Right-Censored Data

  • Park, Jinho
    • Communications for Statistical Applications and Methods
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    • v.6 no.3
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    • pp.791-800
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    • 1999
  • Gross and Lai(1996) proposed a new approach for ordinary regression with left-truncated and right-censored (I.t.r.c) data. This paper shows how to apply nonparametric algorithms such as multivariate adaptive regression splines to 1.t.r.c data.

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