Description Usage Arguments Value Note Author(s)

Given a vector of observations *x = (x_1, …, x_m)* with pairwise distinct entries and
a vector of weights *w =(w_1, …, w_m)* s.t. *∑_{i=1}^m w_i = 1*, this function computes a function *\hat φ_{MLE}* (represented by the vector *(\hat φ_{MLE}(x_i))_{i=1}^m*) supported by *[x_1, x_m]* such that

*L(φ) = ∑_{i=1}^m w_i φ(x_i) - ∑_{j=1}^{m-1} (x_{j+1} - x_j) J(φ_j, φ_{j+1})*

is maximal over all continuous, piecewise linear functions with knots in *{x_1, …, x_m}.*

1 |

`x` |
Vector of independent and identically distributed numbers, with strictly increasing entries. |

`w` |
Optional vector of nonnegative weights corresponding to |

`phi_o` |
Optional starting vector. |

`prec` |
Threshold for the directional derivative during the Newton-Raphson procedure. |

`print` |
print = TRUE outputs log-likelihood in every loop, print = FALSE does not. Make sure to tell R to output (press CTRL+W). |

`phi` |
Resulting column vector |

`L` |
Value |

`Fhat` |
Vector of the same length as
for |

This function is not intended to be invoked by the end user.

Kaspar Rufibach, kaspar.rufibach@gmail.com,

http://www.kasparrufibach.ch

Lutz Duembgen, duembgen@stat.unibe.ch,

http://www.imsv.unibe.ch/about_us/staff/prof_dr_duembgen_lutz/index_eng.html

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