Add to ORKGThis paper describes the LOESS procedure which is a new procedure in SAS/STAT® software for performing local regression. Features of this procedure are outlined and a brief description of the fitting method is given. Examples are given illustrating the use of this procedure in obtaining fitted surfaces as well as prediction confidence limits for both univariate and multivariate regressor data. An automatic method for selecting the smoothing parameter based on a bias corrected Akaike information criterion is described and used in these examples
If globally high dimensional data has locally only low dimensional distributions, it is advantageous...
This paper proposes a classical weighted least squares type of local polynomial smoothing for the an...
symmetric and asymmetric weights. Following Henderson (1916) who devel-oped a smoothing measure as a...
Linear least squares regression is among the most well known classical methods. This and other param...
Local regression methods model the relationship between an independent and dependent variable throug...
<p>Locally weighted scatterplot smoothing (LOESS) non parametric regression for the prediction of th...
<p>Locally weighted scatterplot smoothing (LOESS) non parametric regression for the prediction of th...
Cleveland (1979) is usually credited with the introduction of the locally weighted regression, Loess...
Local polynomial regression is a generalization of local mean smoothing as described by Nadaraya (19...
When estimating a regression function or its derivatives, local polynomials are an attractive choice...
<p>For a particular point (black cross) a local region is defined in articulator space by a Gaussian...
The local polynomial regression predictors developed by Cleveland (1979) have been applied to estima...
<p>Least squares linear regression is displayed with dashed lines; LOESS by solid lines for (A) the ...
Cleveland (1979) is usually credited with the introduction of the locally weighted regression, Loes...
在非參數判別分析中，我們利用區間Logistic迴歸模型 (Local logistic regression)估計貝氏準則的事後機率。在進行區間Logistic迴歸時，我們需要決定平滑參數值，我們取...
If globally high dimensional data has locally only low dimensional distributions, it is advantageous...
This paper proposes a classical weighted least squares type of local polynomial smoothing for the an...
symmetric and asymmetric weights. Following Henderson (1916) who devel-oped a smoothing measure as a...
Linear least squares regression is among the most well known classical methods. This and other param...
Local regression methods model the relationship between an independent and dependent variable throug...
<p>Locally weighted scatterplot smoothing (LOESS) non parametric regression for the prediction of th...
<p>Locally weighted scatterplot smoothing (LOESS) non parametric regression for the prediction of th...
Cleveland (1979) is usually credited with the introduction of the locally weighted regression, Loess...
Local polynomial regression is a generalization of local mean smoothing as described by Nadaraya (19...
When estimating a regression function or its derivatives, local polynomials are an attractive choice...
<p>For a particular point (black cross) a local region is defined in articulator space by a Gaussian...
The local polynomial regression predictors developed by Cleveland (1979) have been applied to estima...
<p>Least squares linear regression is displayed with dashed lines; LOESS by solid lines for (A) the ...
Cleveland (1979) is usually credited with the introduction of the locally weighted regression, Loes...
在非參數判別分析中，我們利用區間Logistic迴歸模型 (Local logistic regression)估計貝氏準則的事後機率。在進行區間Logistic迴歸時，我們需要決定平滑參數值，我們取...
If globally high dimensional data has locally only low dimensional distributions, it is advantageous...
This paper proposes a classical weighted least squares type of local polynomial smoothing for the an...
symmetric and asymmetric weights. Following Henderson (1916) who devel-oped a smoothing measure as a...