By P. Sint (auth.), T. Havránek, Z. à idák, M. Novák (eds.)
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Extra info for Compstat 1984: Proceedings in Computational Statistics
2 gives a precise interpretation to their informal definition of a likelihood. arbitrary, Given The result is general in that the choice of J l and J 2 is and the partition of J need not be by consecutive rows. the above likelihood can efficiency. results, the choice of method for constructing be purely made on the grounds of the computational We contend that the first method, based on the limit of the renormalized posterior density of y, is most efficient because it can be calculated recursively via the modified Kalman filter.
M2) with fixed 8. 's 1. [we used 80 - 1. 01 in our study]. For each of 10 obtained situations we computed all upper mentioned estimates. The proportion of trimming (resp. 05 • Results are summarized in tables 1-5. and figures 1-6. 05T(x) and line 5 to the r· 05W (x),the signs "-" represent r (x). n n 1 To ESTIMATION OF THE REGRESSION ~(xl 1 + + FOR VARIOUS ESTIMATES AND/OR LAWS OF E:=V-~(xl F':9. g. f. le 5 2 I ~ fT. 4 Fig. t. (£1'" N (£1'" N 3 2 4 3 4 2 5 ci -.. . J. g. 9N • o. 5 53 SOME NUMERICAL RESULTS FOR PARAMETRIC AND NONPARAMETRIC ESTIMATORS Real value 1.
References Akaike H. (1974), Markovian representation of a stochastic process and its application to the analysis of autoregressive moving average processes, Ann. Inst. Stat. Math. 26, 363-387. Akaike H. (1978), Covariance matrix computation of the state variable of a stationary Gaussian process, Ann. Inst. Stat. Math. 30, 499-504. O. B. (1979), optimal Filtering, Englewood Cliffs, New Jersey, prentice Hall. F. (1979), An algorithm for the exact likelihood of a mixed autoregressive moving average process, Biometrika 66, 59-65.
Compstat 1984: Proceedings in Computational Statistics by P. Sint (auth.), T. Havránek, Z. à idák, M. Novák (eds.)