GYfilt

GYfilt computes the Gervini-Yohai univariate outlier identifier

expand all in page

Syntax

Description

example

weights =GYfilt(x) GYfilt with all the default options.

example

weights =GYfilt(x, Name, Value) GYfilt with option alpha.

Examples

expand all

  • GYfilt with all the default options.

    • weights=GYfilt(randn(100,1));

  • GYfilt with option alpha.

    • alpha=0.999;
weights=GYfilt(randn(100,1),'alpha',alpha);

    Input Arguments

    expand all

    x — Input vector. Vector.

    A vector with n elements that contains the univariate data.

    Data Types: single| double

    Name-Value Pair Arguments

    Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

    Example: 'alpha',0.99 , 'centering',false , 'iterating',false , 'niter',20

    alpha —coverage probability.scalar.

    Scalar in the interval [0.5 1). The default coverage probability is 0.95.

    Example: 'alpha',0.99

    Data Types: double

    centering —centering the data.boolean.

    If centering is true, input data are preliminary centered.

    The default value of centering is true.

    Example: 'centering',false

    Data Types: logical

    iterating —iterative procedure.boolean.

    If Boolean is true, then an iterative adaptive procedure is applied. The default value of iterating is true.

    Example: 'iterating',false

    Data Types: logical

    niter —maximum number of iterations in the iterative adaptive procedure.positive integer.

    This option is used just if previous iterating is true. The default value of niter is 10.

    Example: 'niter',20

    Data Types: double

    Output Arguments

    expand all

    weights —Boolean vector of weights. Logical

    A boolean vector with n elements that contains false in correspondence of the units declared as outliers.

    References

    Gervini, D. and Yohai, V.J. (2002), A class of robust and fully efficient regression estimators, "Annals of Statistics", Vol. 30, pp. 583-616.

    See Also

    This page has been automatically generated by our routine publishFS