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I need to implement a backward stepwise regression. I read the chapter from "The Elements of Statistical Learning" however the explanation is poor here:

Backward-stepwise selection starts with the full model, and sequentially deletes the predictor that has the least impact on the fit. The candidate for dropping is the variable with the smallest Z-score This is taken from chapter 3.3.2 pg:59

I need to know what is the "full model" mentioned here? What will I delete at each level acording to the Z-score? I need to know how this algorithm works step by step.

EDIT: I try to implement the code on matlab. I don't know I'm totally wrong or not :/ Comment on this function please

function [] = BackwardStepWise(X,y,N,p)  % X is Nxp matrice y is Nx1 matrice

X = [ones(N,1) X];

% vector for holding column numbers 
v= 1:p;

for k=p-1:-1:1

        %create matrix with the selected columns
        T= [ones(N,1)];
        for j=1:k
            %add column to the matrix
            T = [T X(:,v(1,j)+1)];
        endfor
        %evaluate beta
        beta = inv(T' * T) * T' * y;

    %calculate Z-scores for each column
    sigmahat_sq = (y-T*beta)'*(y-T*beta)/(N-p-1);
    TT = inv(T'*T);
        Zmin = 100000;
        Zminindex = -1;
    for i=1:size(beta,2)
        z(i) = beta(i)/sqrt(sigmahat_sq*TT(i,i));
                if(z(i) < Zmin)
            Zmin = z(i);
            Zminindex = i;
        endif
    endfor

    %drop column which has the smallest Z score (edit v vector)
    v2 = [];
    for i=1:size(v,2)
        if(i == Zminindex)
            continue;
        else
        v2 = [v2 v(i)]; 
        endif       
    endfor  
    v = v2;

endfor

endfunction
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    $\begingroup$ The full model holds all possible independent variables (predictors) candidates. $\endgroup$ – Andre Silva Dec 16 '13 at 20:57
  • $\begingroup$ And at each step you'll delete a variable. $\endgroup$ – Jeremy Miles Dec 17 '13 at 4:13
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    $\begingroup$ There is no context or page number for this quotation but it seems a model of concise explanation to me! $\endgroup$ – Nick Cox Dec 17 '13 at 14:52
  • $\begingroup$ @NickCox I edited the post adding chapter and page number $\endgroup$ – Berke Cagkan Toptas Dec 17 '13 at 19:02
  • $\begingroup$ Thanks; this is widely known and used book; nevertheless naming authors, publisher and date would do no harm. $\endgroup$ – Nick Cox Dec 17 '13 at 19:18
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  1. Set up an exit criterion for the p-value. Any independent variable with a p-value higher than this criterion will be removed. There isn't any golden rule on what to set, for exploratory purpose you may see p > 0.2 being removed. Someone may used 0.05, etc.
  2. Set up your full model. Generally, it's the model that contains all independent variables from which you wish to select the predictive bunch.
  3. Fit the full model.
  4. Check the p-values (or t-statistics). If all p-values are less than the exit criterion, then it's the final model. If any of them exceeds the exit criterion, then the one with the highest p-value (aka lowest t-statistics) will be removed. This is pertinent to your "dropping the variable with the smallest z-score."
  5. Fit the model with the remaining independent variables again, repeat steps 4 and 5 until either no independent variable is left or no independent variable has a p-value larger than the exit criterion.

Here is an example using Stata:

. sysuse auto
. stepwise, pr(.2): reg mpg weight turn headroom foreign price
                      begin with full model
p = 0.9238 >= 0.2000  removing price
p = 0.7047 >= 0.2000  removing headroom
p = 0.2045 >= 0.2000  removing turn

      Source |       SS       df       MS              Number of obs =      74
-------------+------------------------------           F(  2,    71) =   69.75
       Model |   1619.2877     2  809.643849           Prob > F      =  0.0000
    Residual |  824.171761    71   11.608053           R-squared     =  0.6627
-------------+------------------------------           Adj R-squared =  0.6532
       Total |  2443.45946    73  33.4720474           Root MSE      =  3.4071

------------------------------------------------------------------------------
         mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |  -.0065879   .0006371   -10.34   0.000    -.0078583   -.0053175
     foreign |  -1.650029   1.075994    -1.53   0.130      -3.7955    .4954422
       _cons |    41.6797   2.165547    19.25   0.000     37.36172    45.99768
------------------------------------------------------------------------------

The independent variables are weight, turn, headroom, foreign, and price. The dependent variable is mpg. The exit criterion is p > 0.2. The first got removed was price (p = 0.9238), followed by headroom (p = 0.7047) and turn (p = 0.2045). The remaining ones have p < 0.2, so they stay.

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  • $\begingroup$ I tried to implement that function. But I didn't understand your first step. :/ Could you check my code?(I edited the question) $\endgroup$ – Berke Cagkan Toptas Dec 17 '13 at 19:09
  • $\begingroup$ @CagkanToptas, I don't use MATLAB so probably can't give you useful feedback in a timely manner. MATLAB does have a stepwise regression module, perhaps you can find out if they provide the source code? $\endgroup$ – Penguin_Knight Dec 17 '13 at 19:43
  • $\begingroup$ @CagkanToptas: you can find the MATLAB code for stepwisefit.m under toolbox/stats/stats/stepwisefit.m. It is "standard" MATLAB code so it shouldn't be hard to read. $\endgroup$ – usεr11852 Dec 17 '13 at 23:21

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