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Regression III: Robust regressions

- Outliers
- Tests for outlier detections
- Robust regressions
- Breakdown point
- Least trimmed squares
- M-estimators

Outliers

With outliers

Outliers are observations that deviate from all others significantly. They may occur by accident or they may be results of measurement errors. Presence of outliers may lead to misleading results. In the example shown on figure without outlier mean value is -0.41 and with outlier it is -0.01. If we want to test the hypothesis: H0:mean=0 then without outlier we conclude that H0 can be rejected (p-value is 0.009), however with outlier we cannot reject null-hypothesis (p-value is 0.98).

Analysis and dealing with outliers is an important ingredient of modern statistical analysis. Sometimes careful analysis of outliers, their removal or weighting down may change the conclusions considerably.

Outlier

Without outlier

Dealing with outliers

For simple cases such as mean, variance calculations one way of dealing with outliers is using trimmed data. For example in case above mean without outlier is -.41, with outlier -0.09 and trimmed mean with 10% removed is -0.33. Trimmed mean gives “better” than mean based on all data with outliers.

Often mean and other statistics are used for testing some hypothesis. In these cases using non-parameteric (wilcox.test) tests may be better alternatives to t.test (wilcox.test gives p-value 0.09 and t.test gives p-value 0.98).

For simple cases like mean, covariance calculations and test based on them usual approach is to use rank of observations instead of their values. Obviously when rank is used the power of tests will be reduced, however better conclusions could be more reliable.

Before carrying out analysis and tests it is always good idea to visualise data and explore to see if there are outliers.

Outlier detection: Grubbs’ test

It may be a good idea to test for outliers and remove them if possible before starting to analyse the data and doing hypothesis testing. One of the techniques for doing this is Grubbs’ test. It tests

H0: there is no outlier versus H1: there is an outlier

To do this Grubbs suggested using the statistic:

G = (max(y)-mean(y))/sd(y)

to test if the maximum value is outlier

G = (mean(y)-mean(y))/sd(y)

to test if the minimum value is outlier and

G = max(|yi-mean(y)|)/sd(y)

to test if maximum or minimum is outlier. There are versions for two outliers also.

Obviously test statistics will depend on the number of observations.

Grubbs’ test (grubbs.test) is available from the package outliers. It is not part of the standard R distributions.

Outlier detection: Grubbs’ test

Applying grubbs.test for the example we get:

Grubbs test for one outlier

data: del1

G = 2.9063, U = 0.0709, p-value = 9.858e-06

alternative hypothesis: highest value 4 is an outlier

Since p-value is very small we can reject null hypothesis that there is no outlier.

Once we are sure that there is an outlier then we remove it and carry out tests and/or analysis.

Outlier detection: Simulated distribution

If outliers package is not available then we can generate distribution for the statistics given above. Let us write for one of them (H0: maximum value is not outlier):

outdist = function(nsample,n){

ff = vector(length=n)

for (i in 1:n){rr=rnorm(nsample);ff[i]=max(rr-mean(rr))/sd(rr)}

ff

}

Now we can generate the distribution of the statistics for samples of different sizes. For example the distribution for sample of sizes 10,15 and 20 are shown on the figure. To generate the figure the following set of commands are used

oo10 = outdist(10,10000)

curve(ecdf(oo10)(x),from=0,to=7,lwd=3)

oo15 = outdist(15,10000)

curve(ecdf(oo15)(x),from=0,to=7,lwd=3)

oo20 = outdist(20,10000)

curve(ecdf(oo20)(x),from=0,to=7,lwd=3)

Obviously as the sample size increases probability that

large values will be genuinely observed will increase. That is why as sample size increases the distribution shifts to the right.

Outlier detection: Simulated distribution

Once we have the desired distributions we can calculate test and detect outliers. For example for the case we considered the sample size is 11. Let us generate empirical cumulative probability distribution and use it for outlier detection:

oo11=outdist(11,10000)

ec11 = ecdf(oo11)

st = max(del1-mean(del1))/sd(del1)

1-ec11(st)

This sequence of commands will produce p-values. If the maximum value is 4 then p-value is 0, if the maximum value is 2 then p-value is 0.001 and when maximum value is 1 then p-value is 0.04. We can reject null-hypothesis for first and the second case, however for the third case we should be careful.

Breakdown point

Breakdown point of an estimator is a fraction of a sample if changed arbitrarily that does not affect the estimation significantly. For example for mean value if we change one point arbitrarily we can change the mean value as much as we want. Let us take an example:

-0.8 -0.6 -0.3 0.1 -1.1 0.2 -0.3 -0.5 -0.5 -0.3 4.0

The sample size is 11, the mean value is -0.09. If we change the last value to 100 then the mean value becomes 8.72. So breakdown point of mean is 0.

Another limiting case is median. Median of the above sample is -0.3. If we change one value and make it extremely large then median will not change much. For example if we change the last value to -100 then median will become -0.5. Breakdown point for median is 0.5, i.e. more than 50% of the sample should be changed arbitrarily to change the median arbitrarily. Breakdown point 0.5 is the theoretical limit.

Efficiency of estimators with high breakdown point is usually worse than those with lower breakdown point. In other words variances of estimators with high breakdown point are larger.

Outliers and regression

Let us remind us the form of the least-squares equations for regressions. Again x is a vector of input (predictor) parameters, β is a vector of parameters, y is output, the number of sample points is n.

As we know in special case when g(x,β) =β, and β is a single value then least-squares estimation gives mean value of y. We can consider above estimation as an extension of mean value estimation. Breakdown point of this estimation is 0, so least-squares is very sensitive to outliers.

There are several approaches to deal with outliers in regression analysis. We will consider only two of them: 1) least-trimmed squares; 2) M-estimators

Regression: with an outlier

Outlier

Least trimmed squares

The result of default lqs

Least trimmed squares works iteratively.

- Set up initial values for the model parameters (for example using simple least squares method implemented in lm)
- Calculate squared residuals ri2=(yi-g(xi,β))2
- Sort squared residuals
- Remove fraction of observations for which squared residuals are large
- Minimise least squares using these observations only
- Repeat 2)-6) until convergence achieved.

The function lts in R does LTS and several others as a special case of LTS (least median and least quantile squares). The number of used obsevations for different methods are different. For least-median it is [(n+1)/2], for least quantile [(n+p+1)/2] and for LTS it is [n/2]+[(p+1)/2], where [] is the integer part of the argument

Robust M-estimators

An extension of least-squares to deal with outliers is written as:

Form the function ρ defines various forms of robust M-estimators. When ρ(z)=z2it becomes simple least-squares.

Let us first this function. To minimise this function let us use Gauss-Newton method. To use this method we need the first and second derivatives (more precisely an approximation for the second derivative)

Where ρ’, ρ’’ are the first and the second derivative of ρ. In Gauss-Newton methods the second term of the second derivative equation is usually ignored. Usually ρ’=ψ and ρ’’=w notations are used. If we look at the equations we can see that it looks like an extension of least-squares equations. The minimisation of the function is done iteratively using iteratively reweighted least squares (IRLS or IWLS).

ψ function is an influence function. Analysis of values of this function at the observations may help to understand outliers in the data and how are dealt with.

Forms of robust regression

Example of ρ and ψ (Geman and Mcclure function)

Robust M-estimators are usually chosen so that to make contribution of gradients for large residuals small, in other words to weight down large deviations. They can be chosen either using ρ or ψ.

Basic idea behind robust estimators is: For small differences behaviour of the function should be similar to that of least squares and for large deviations contributions should be weighted down. Different functions differ by degree of weighting.

Forms of robust regression

Most popular forms of robust estimators are:

- Huber
- Tukey’s bisquare
- Geman and Mcclure
- Welsch
- t-distribution (actually it is a little bit modified form of –log t distribution)

Robust estimators

The result of robust regression with Huber function is very good. In practice choice of the function will depend on the number and severity of outliers.

R commands for robust estimation

lqs – least trimmed, least median estimation

rlm – robust linear model estimation using M-estimator

outliers package may be helpful.

References

- P. J. Huber (1981) “Robust Statistics”.
- F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw and W. A. Stahel (1986) “Robust Statistics: The Approach based on Influence Functions”
- A. Marazzi (1993) “Algorithms, Routines and S Functions for Robust Statistics”,
- W. N. and Ripley, B. D. (2002) “Modern Applied Statistics with S”

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