View source: R/HodgesLehmann.R
| HodgesLehmann | R Documentation |
G find a modified prior f that bounds minimax risk.There are two variants both minimize Fisher information for location via conic optimization:
\min \sum \frac{(f_{i+1}-f_i )^2}{( f_{i+1}+f_i )/2} = \sum \frac{u_i^2}{v_i/2} \approx I(F)
\Leftrightarrow \quad \min \sum w_i \; \mbox{s.t.} \; u_i^2 \leq 2 v_i w_i
Huber Variant as proposed in Efron and Morris (1971) imposing constraint
f(x) = \alpha \Phi * G + (1-\alpha) h(x)
Mallows Variant as proposed in Bickel (1983) imposing constraints
f(x) = \alpha \Phi * G + (1-\alpha) h(x), \; h(x) = \Phi * H
N.B. When the grid is not equispaced, one would have to include grid spacings.
HodgesLehmann(grid, G, alpha, type = "Huber", ...)
grid |
grid on which to interpolate |
G |
initial prior |
alpha |
contamination proportion |
type |
either "Huber" or "Mallows" |
... |
other arguments to be passed to Mosek. |
An object of class density with solution
R. Koenker and J. Gu
Bickel, P. (1983), Minimax estimation of the mean of a normal distribution subject to doing well at a point, in M. H. Rizvi, J. S. Rustagi & D. Siegmund, eds, ‘Recent Advances in Statistics: Papers in Honor of Herman Chernoff on his Sixtieth Birthday’, Academic Press, pp. 511–528
Efron, B. & Morris, C. (1971), ‘Limiting the risk of Bayes and empirical Bayes estimators part I: the Bayes case’, Journal of the American Statistical Association 66, 807–815.
Hodges, J. L. & Lehmann, E. L. (1952), ‘The use of previous experience in reaching statistical decisions’, The Annals of Mathematical Statistics pp. 396–407.
Huber, P. (1964), ‘Robust estimation of a location parameter’, The Annals of Mathematical Statistics pp. 73–101.
Huber, P. (1974) "Fisher Information and Spline Interpolation." Ann. Statist. 2 (5) 1029 - 1033,
Mallows, C. (1978), ‘Problem 78-4, minimizing an integral’, SIAM Review 20, 183– 183.
HuberSpline
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