 Usage:  b = lts(x, y{, h, all, mult})   

 

 Input:



  x                      n x p design matrix of explanatory variables. 

                         

  y                      n x 1 vector, dependent variable. 

                         

  h                      optional, trimming constant; default value is 

                         [n/2]+[(p+1)/2], where n is the number of observations 

                         and p is the number of parameters. h should belong to 

                         {[(n+1)/2],...,n} and must be bigger than p. 

                         

  all                    optional, logical flag for the exact computation (nonzero = TRUE), 

                         default value is 0 (FALSE). If ci = 0, an approximation 

                         of the estimator is computed. If ci != 0, the estimate 

                         is computed precisely - this can be rather time demanding 

                         and applicable only for small sample sizes. The number of 

                         iterations corresponds in this case to n over h. 

                         

  mult                   optional, affects the maximal number of iterations, 

                         after which the algorithm is stoped (if convergence 

                         was not achieved earlier), default value equals to 1. 

                         The maximal number of iterations is currently 

                         '(600 * h) * times', so this variable allow 

                         to reduce or extend this number. 

                         

                         

 Output:



  b                      p x 1 vector of estimated coefficients. 

                         

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(C) MD*TECH Method and Data Technologies, 21.9.2000

