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Super-Efficient Prediction Based on High-Quality Marker Information

Published online by Cambridge University Press:  29 August 2014

Jens Perch Nielsen
Jens Perch Nielsen*
Affiliation:
Codan, Gammel Kongevej 60, 1799 Copenhagen, Denmark
*
Codan, Gammel Kongevej 60, 1799 Copenhagen, Denmark
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Abstract

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Nielsen (1999) showed the surprising fact that a nonparametric one-dimensional hazard as a function of time can be estimated -consistently if a high quality marker is observed. In this paper we show that the hazard relevant for predicting remaining duration time, given the current status of a high quality marker, can be estimated -consistently if a Markov type property holds for the high quality marker.

Keywords

Counting ProcessHazardKernelMarkerNonparametric estimationPredictionSurvival analysisAsset-Liability managementDataminingHigh frequency data
Type
Articles
Information
ASTIN Bulletin: The Journal of the IAA , Volume 30 , Issue 2 , November 2000 , pp. 295 - 303
Copyright
Copyright © International Actuarial Association 2000

References

Bickel, P.J., Klaassen, C.A.J., Ritov, Y. and Wellner, J.A. (1993) Efficient and adaptive estimation for semiparametric models. The John Hopkins University Press, Baltimore and London.Google Scholar
Choi, S., Laoakos, S.W., Schooley, R.T. and Volberding, P.A. (1993) CD4+ lymphocytes are an incomplete surrogate marker for clinical progression in persons with asymptomatic HIV infection taking zidovudine. Ann. Internal Med. 118, 674–680.CrossRefGoogle ScholarPubMed
Engle, R.F. and Russell, J.R. (1998) Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econometrika 5, 1127–1162.CrossRefGoogle Scholar
Fusaro, R., Nielsen, J.P. and Scheike, T. (1993) Marker dependent hazard estimation. An application to Aids. Statistics in Medicine 12, 843–865.CrossRefGoogle ScholarPubMed
Linton, O.B. and Nielsen, J.P. (1995) A kernel method of estimating structured nonparametric regression based on marginal integration. Biometrika 82, 93–101.CrossRefGoogle Scholar
Macauley, F.R. (1938) Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the U.S. since 1856. New York: NBER.Google Scholar
Nielsen, J.P. (1998) Multiplicative bias correction in kernel hazard estimation. Scand. J. Statist 25, 541–553.CrossRefGoogle Scholar
Nielsen, J.P. (1999) Super efficient hazard estimation based on high quality marker information. Biometrika 86, 227–232.CrossRefGoogle Scholar
Nielsen, J.P. and Linton, O.B. (1995) Kernel estimation in a marker dependent hazard model. Ann. Statist. 23, 1735–1748.CrossRefGoogle Scholar
Nielsen, J.P., Linton, O.B. and Bickel, P. (1998) On a semiparametric survival model with flexible covariate effect. Ann. Statist. 26, 215–241.CrossRefGoogle Scholar
Redington, F.M. (1952) Review of the principle of life office valuations. Journal of the institute of actuaries 18, 286–340.CrossRefGoogle Scholar
Shen, P. and Starr, R.M. (1998) Liquidity of the treasury bill market and the term structure of interest rates. Journal of Economics and Business 50, 401–417.CrossRefGoogle Scholar