With the NLIN procedure you can perform weighted nonlinear least squares regression in situations where the weights are functions of the parameters. To minimize a weighted sum of squares, you assign an expression to the _WEIGHT_ variable in your PROC NLIN statements. When the _WEIGHT_ variable depends on the model parameters, the estimation technique is known as iteratively reweighted least squares (IRLS). In this situation you should employ the NOHALVE option in the PROC NLIN statement. Because the weights change from iteration to iteration, it is not reasonable to expect the weighted residual sum of squares to decrease between iterations. The NOHALVE option removes that restriction.
Examples where IRLS estimation is used include robust regression via Mestimation (Huber 1964, 1973), generalized linear models (McCullagh and Nelder 1989), and semivariogram fitting in spatial statistics (Schabenberger and Pierce 2002, Sect. 9.2). There are dedicated SAS/STAT procedures for robust regression (the ROBUSTREG procedure) and generalized linear models (the GENMOD and GLIMMIX procedures). Examples of weighted least squares fitting of a semivariogram function can be found in Chapter 98, The VARIOGRAM Procedure.
In this example we show an application of PROC NLIN for Mestimation only to illustrate the connection between robust regression and weighted least squares. The ROBUSTREG procedure is the appropriate tool to fit these models with SAS/STAT software.
Mestimation was introduced by Huber (1964, 1973) to estimate location parameters robustly. Beaton and Tukey (1974) applied the idea of Mestimation in regression models and introduced the biweight (or bisquare) weight function. See Holland and Welsch (1977) for this and other robust methods. Consider a linear regression model of the form
In weighted least squares estimation you seek the parameters that minimize
where is the weight associated with the th observation. The normal equations of this minimization problem can be written as
In Mestimation the corresponding equations take on the form
where is a weighing function. The BeatonTukey biweight, for example, can be written as
Substitution into the estimating equation for Mestimation yields weighted least squares equations
The biweight function involves two constants, and . The scale can be fixed or estimated from the fit in the previous iteration. If is estimated, a robust estimator of scale is typically used. In this example is fixed at . A common value for the constant is .
The following DATA step creates a SAS data set of the population of the United States (in millions), recorded at 10year intervals starting in 1790 and ending in 1990. The aim is to fit a quadratic linear model to the population over time.
title 'U.S. Population Growth'; data uspop; input pop :6.3 @@; retain year 1780; year = year+10; yearsq = year*year; datalines; 3929 5308 7239 9638 12866 17069 23191 31443 39818 50155 62947 75994 91972 105710 122775 131669 151325 179323 203211 226542 248710 ;
The PROC NLIN code that follows fits this linear model by Mestimation and IRLS. The weight function is set to a zero or nonzero value depending on the value of the scaled residual. The NOHALVE option removes the requirement that the (weighted) residual sum of squares must decrease between iterations.
title 'Beaton/Tukey Biweight Robust Regression using IRLS'; proc nlin data=uspop nohalve; parms b0=20450.43 b1=22.7806 b2=.0063456; model pop=b0+b1*year+b2*year*year; resid = popmodel.pop; sigma = 2; k = 4.685; if abs(resid/sigma)<=k then _weight_=(1(resid / (sigma*k))**2)**2; else _weight_=0; output out=c r=rbi; run;
Parameter estimates from this fit are shown in Output 62.2.1, and the computed weights at the final iteration are displayed in Output 62.2.2. The observations for 1940 and 1950 are highly discounted because of their large residuals.
Beaton/Tukey Biweight Robust Regression using IRLS 
Source  DF  Sum of Squares  Mean Square  F Value  Approx Pr > F 

Model  2  113564  56782.0  49454.5  <.0001 
Error  18  20.6670  1.1482  
Corrected Total  20  113585 
Parameter  Estimate  Approx Std Error 
Approximate 95% Confidence Limits 


b0  20828.7  259.4  20283.8  21373.6 
b1  23.2004  0.2746  23.7773  22.6235 
b2  0.00646  0.000073  0.00631  0.00661 
Obs  pop  year  yearsq  rbi  sigma  k  _weight_ 

1  3.929  1790  3204100  0.93711  2  4.685  0.98010 
2  5.308  1800  3240000  0.46091  2  4.685  0.99517 
3  7.239  1810  3276100  1.11853  2  4.685  0.97170 
4  9.638  1820  3312400  0.95176  2  4.685  0.97947 
5  12.866  1830  3348900  0.32159  2  4.685  0.99765 
6  17.069  1840  3385600  0.62597  2  4.685  0.99109 
7  23.191  1850  3422500  0.94692  2  4.685  0.97968 
8  31.443  1860  3459600  0.43027  2  4.685  0.99579 
9  39.818  1870  3496900  1.08302  2  4.685  0.97346 
10  50.155  1880  3534400  1.06615  2  4.685  0.97427 
11  62.947  1890  3572100  0.11332  2  4.685  0.99971 
12  75.994  1900  3610000  0.25539  2  4.685  0.99851 
13  91.972  1910  3648100  2.03607  2  4.685  0.90779 
14  105.710  1920  3686400  0.28436  2  4.685  0.99816 
15  122.775  1930  3724900  0.56725  2  4.685  0.99268 
16  131.669  1940  3763600  8.61325  2  4.685  0.02403 
17  151.325  1950  3802500  8.32415  2  4.685  0.04443 
18  179.323  1960  3841600  0.98543  2  4.685  0.97800 
19  203.211  1970  3880900  0.95088  2  4.685  0.97951 
20  226.542  1980  3920400  1.03780  2  4.685  0.97562 
21  248.710  1990  3960100  1.33067  2  4.685  0.96007 
You can obtain this analysis more conveniently with PROC ROBUSTREG. The procedure reestimates the scale parameter robustly between iterations. To obtain an analysis with a fixed scale parameter as in this example, use the following PROC ROBUSTREG statements:
proc robustreg data=uspop method=m(scale=2); model pop = year year*year; output out=weights weight=w; run; proc print data=weights; run;
Note that the computation of standard errors in the ROBUSTREG procedure is different from the calculations in the NLIN procedure.