Description Usage Arguments Details Value References See Also Examples

This function provides `ng`

optimal quantization
grids for `X`

, with `N`

fixed.

1 | ```
choice.grid(X, N, ng = 1, p = 2)
``` |

`X` |
vector or matrix that we want to quantize. |

`N` |
size of the quantization grids. |

`ng` |
number of quantization grids needed. |

`p` |
L_p norm optimal quantization. |

This function works for any dimension of `X`

. If the covariate
is univariate, `X`

is a vector while `X`

is a matrix with `d`

rows when the covariate is `d`

-dimensional.

An array of dimension `d`

*`N`

*`ng`

that
corresponds to `ng`

quantization grids.

Charlier, I. and Paindaveine, D. and Saracco, J.,
*Conditional quantile estimator based on optimal
quantization: from theory to practice*, Submitted.

Pages, G. (1998) *A space quantization method for numerical
integration*, Journal of Computational and Applied Mathematics, 89(1), 1-38

`QuantifQuantile`

, `QuantifQuantile.d2`

and
`QuantifQuantile.d`

1 2 3 4 | ```
X <- runif(300,-2,2)
N <- 10
ng <- 20
choice.grid(X,N,ng)
``` |

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