mveeda monitors Minimum volume ellipsoid for a series of values of bdp

n=200; v=3; randn('state', 123456); Y=randn(n,v); % Contaminated data Ycont=Y; Ycont(1:5,:)=Ycont(1:5,:)+5; RAW=mveeda(Ycont);

n=200; v=3; randn('state', 123456); Y=randn(n,v); % Contaminated data Ycont=Y; Ycont(1:5,:)=Ycont(1:5,:)+5; RAW=mveeda(Ycont,'plots',1,'msg',0);

n=200; v=3; randn('state', 123456); Y=randn(n,v); % Contaminated data Ycont=Y; Ycont(1:5,:)=Ycont(1:5,:)+3; [RAW,REW]=mveeda(Ycont);

`Y`

— Input data.
Matrix.n x v data matrix; n observations and v variables. Rows of Y represent observations, and columns represent variables.

Missing values (NaN's) and infinite values (Inf's) are allowed, since observations (rows) with missing or infinite values will automatically be excluded from the computations.

**
Data Types: **`single|double`

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
```

.

```
'bdp',[0.5 0.4 0.3 0.2 0.1]
```

,```
'nsamp',10000
```

,```
'refsteps',0
```

,```
'reftol',1e-8
```

,```
'conflev',0.99
```

,```
'nocheck',1
```

,```
'plots',1
```

,```
'msg',false
```

,```
'ysaveRAW',1
```

,```
'ysaveREW',1
```

`bdp`

—breakdown point.scalar | vector.It measures the fraction of outliers the algorithm should resist. In this case any value greater than 0 but smaller or equal than 0.5 will do fine.

The default value of bdp is a sequence from 0.5 to 0.01 with step 0.01

**Example: **```
'bdp',[0.5 0.4 0.3 0.2 0.1]
```

**Data Types: **`double`

`nsamp`

—Number of subsamples.scalar.Number of subsamples of size v which have to be extracted (if not given, default = 500).

**Example: **```
'nsamp',10000
```

**Data Types: **`double`

`refsteps`

—Number of refining iterations.scalar.Number of refining iterationsin each subsample (default = 3).

refsteps = 0 means "raw-subsampling" without iterations.

**Example: **```
'refsteps',0
```

**Data Types: **`single | double`

`reftol`

—scalar.default value of tolerance for the refining steps.The default value is 1e-6;

**Example: **```
'reftol',1e-8
```

**Data Types: **`single | double`

`conflev`

—Confidence level.scalar.Number between 0 and 1 containing confidence level which is used to declare units as outliers.

Usually conflev=0.95, 0.975 0.99 (individual alpha) or 1-0.05/n, 1-0.025/n, 1-0.01/n (simultaneous alpha).

Default value is 0.975

**Example: **```
'conflev',0.99
```

**Data Types: **`double`

`nocheck`

—Scalar.if nocheck is equal to 1 no check is performed on matrix Y.As default nocheck=0.

**Example: **```
'nocheck',1
```

**Data Types: **`double`

`plots`

—Plot on the screen.scalar.If plots is equal to 1, it generates a plot which monitors raw mve Mahalanobis distances against values of bdp.

**Example: **```
'plots',1
```

**Data Types: **`double or structure`

`msg`

—boolean.display | not messages on the screen.If msg==true (default) messages are displayed on the screen about estimated time to compute the final estimator else no message is displayed on the screen.

**Example: **```
'msg',false
```

**Data Types: **`logical`

`ysaveRAW`

—scalar that is set to 1 to request that the data matrix Y
is saved into the output structure RAW.this feature is meant at simplifying the use of function malindexplot.Default is 0, i.e. no saving is done.

**Example: **```
'ysaveRAW',1
```

**Data Types: **`double`

`ysaveREW`

—scalar that is set to 1 to request that the data matrix Y
is saved into the output structure REW.this feature is meant at simplifying the use of function malindexplot.Default is 0, i.e. no saving is done.

**Example: **```
'ysaveREW',1
```

**Data Types: **`double`

`RAW`

— description
StructureStructure which contains the following fields

Value | Description |
---|---|

`Loc` |
length(bdp)-by-v matrix containing estimate of location for each value of bdp |

`Cov` |
v-by-v-by-length(bdp) 3D array containing robust estimate of covariance matrix for each value of bdp |

`BS` |
(v+1)-by-length(bdp) matrix containing the units forming best subset for each value of bdp |

`MAL` |
n x length(bdp) matrix containing the estimates of the robust Mahalanobis distances (in squared units) for each value of bdp |

`Outliers` |
n x length(bdp) matrix. Boolean matrix containing the list of the units declared as outliers for each value of bdp using confidence level specified in input scalar conflev |

`Singsub` |
Number of subsets without full rank. Notice that out.singsub > 0.1*(number of subsamples) produces a warning. |

`Weights` |
n x 1 vector containing the estimates of the weights. These weights determine which are the h observations which have been used to compute the final MVE estimates. |

`bdp` |
vector which contains the values of bdp which have been used |

`h` |
vector. Number of observations which have determined MVE for each value of bdp. |

`Y` |
Data matrix Y. |

`class` |
'mveeda'. This is the string which identifies the class of the estimator |

`REW`

— description
StructureStructure which contains the following fields:

Value | Description |
---|---|

`Loc` |
The robust location of the data, obtained after reweighting, if the RAW.cov is not singular. Otherwise the raw MVE center is given here. |

`Cov` |
The sequence of robust covariance matrices, obtained after reweighting and multiplying with a finite sample correction factor and an asymptotic consistency factor, if the raw MVE is not singular. |

`MAL` |
n x length(bdp) matrix containing the estimates of the robust Mahalanobis distances (in squared units) for each value of bdp |

`Weights` |
n x length(bdp) matrix containing the estimates of the weights. These weights determine which are the h observations which have been used to compute the final MVE estimates. |

`Outliers` |
A vector containing the list of the units declared as outliers after reweighting. |

`Y` |
Data matrix Y. |

`class` |
'mveeda' |

`varargout`

—Indices
of the subsamples extracted for
computing the estimate.
`C : `

matrix of size nsamp-by-vFor each subset $J$ of $v+1$ observations $\mu_J$ and $C_J$ are the centroid and the covariance matrix based on subset $J$.

Rousseeuw and Leroy (RL) (eq. 1.25 chapter 7, p. 259) write the objective function for subset $J$ as

\[ RL_J=\left( med_{i=1, ..., n} \sqrt{ (y_i -\mu_J)' C_J^{-1} (y_i -\mu_J) } \right)^v \sqrt|C_J| \] Maronna Martin and Yohai (MMY), eq. (6.57), define $\Sigma_J = C_j / |C_j|^{1/v}$ and write the objective function for subset $J$ as \[ MMY_J = \hat \sigma \left( (y_i -\mu_J)' \Sigma_J^{-1} (y_i -\mu_J) \right) |C_J|^{1/v} = \hat \sigma \left( (y_i -\mu_J)' C_J^{-1} (y_i -\mu_J) \right) |C_J|^{1/v} \]where $\hat \sigma \left( (y_i -\mu_J)' C_J^{-1} (y_i -\mu_J) \right) = med_{i=1, ..., n}(y_i -\mu_J)' C_J^{-1} (y_i -\mu_J)$.

Note that $MMY_J= (RL)^{2/v}$.

To RAW.cov a consistency factor has been applied which is based on chi2inv(1-bdp,v). On the other hand to REW.cov the usual asymptotic consistency factor is applied. In this case we have used the empirical percentage of trimming that is the ratio hemp/n where hemp is the number of units which had a MD smaller than the cutoff level determined by thresh=chi2inv(conflev,v); MD are computed using RAW.loc and RAW.cov.

The mve method is intended for continuous variables, and assumes that the number of observations n is at least 5 times the number of variables v.

Rousseeuw, P.J. (1984), Least Median of Squares Regression, "Journal of the American Statistical Association", Vol. 79, pp. 871-881.

Rousseeuw, P.J. and Leroy A.M. (1987), Robust regression and outlier detection, Wiley New York.

This function follows the lines of MATLAB/R code developed during the years by many authors.

For more details see http://www.econ.kuleuven.be/public/NDBAE06/programs/ and the R library robustbase http://robustbase.r-forge.r-project.org/ The core of these routines, e.g. the resampling approach, however, has been completely redesigned, with considerable increase of the computational performance.