robust {logcondens} | R Documentation |
Performs robustification and Hermite interpolation in the iterative convex minorant algorithm as described in Rufibach (2006, 2007).
robust(x, w, eta, etanew, grad)
x |
Vector of independent and identically distributed numbers, with strictly increasing entries. |
w |
Optional vector of nonnegative weights corresponding to {x}, where w_1 > 0 and w_m > 0. These raw weights are normalized in order to sum to one. Default: w_i = 1/m. |
eta |
Current candidate vector. |
etanew |
New candidate vector. |
grad |
Gradient of L at current candidate vector eta. |
Returns a (possibly) new vector eta on the segment
(1 - t_0) eta + t_0 eta_{new}
such that the log-likelihood of this new eta is strictly greater than that of the initial eta and t_0 is chosen
according to the Hermite interpolation procedure described in Rufibach (2006, 2007).
Kaspar Rufibach, kaspar.rufibach@gmail.com
Lutz Duembgen, duembgen@stat.unibe.ch,
http://www.staff.unibe.ch/duembgen
Rufibach K. (2006) Log-concave Density Estimation and Bump Hunting for i.i.d. Observations.
PhD Thesis, University of Bern, Switzerland and Georg-August University of Goettingen, Germany, 2006.
Available at http://www.stub.unibe.ch/download/eldiss/06rufibach_k.pdf.
Rufibach, K. (2007) Computing maximum likelihood estimators of a log-concave density function. J. Stat. Comput. Simul. 77, 561–574.