# Huber norm approximation to the tilted absolute value cost function

### Description

Huber norm approximation to the tilted absolute value cost function used to fit a QRNN model. Optional left censoring is supported.

### Usage

1 2 |

### Arguments

`weights` |
weight vector of length returned by |

`x` |
covariate matrix with number of rows equal to the number of samples and number of columns equal to the number of variables. |

`y` |
predictand column matrix with number of rows equal to the number of samples. |

`n.hidden` |
number of hidden nodes in the QRNN model. |

`tau` |
desired tau-quantile. |

`lower` |
left censoring point. |

`eps` |
epsilon value used in |

`Th` |
hidden layer transfer function; use |

`Th.prime` |
derivative of the hidden layer transfer function |

`penalty` |
weight penalty for weight decay regularization. |

### Value

numeric value indicating tilted absolute value cost function, along with attribute containing vector with gradient information.

### See Also

`qrnn.nlm`

, `qrnn.fit`