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Lectures on inference for stochastic processes

Published online by Cambridge University Press:  01 July 2016

C. C. Heyde*
Affiliation:
University of Melbourne

Abstract

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Type
Inference for Stochastic Processes
Copyright
Copyright © Applied Probability Trust 1985 

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References

Principal References

Basawa, I. V. and Prakasa Rao, B. L. S. (1980) Statistical Inference for Stochastic Processes. Academic Press, New York.Google Scholar
Basawa, I. V. and Scott, D. J. (1983) Asymptotic Optimal Inference for Non-ergodic Models. Lecture Notes in Statistics 17, Springer-Verlag, New York.CrossRefGoogle Scholar
Cox, D. R. (1975) Partial likelihood. Biometrika 62, 269276.Google Scholar
Cox, D. R. and Hinkley, D. V. (1974) Theoretical Statistics. Chapman and Hall, London.Google Scholar
Hall, P. and Heyde, C. C. (1980) Martingale Limit Theory and its Application. Academic Press, New York.Google Scholar
Heyde, C. C. and Cohen, J. E. (1985) Confidence intervals for demographic projections based on products of random matrices. Theoret. Popn Biol. Google Scholar
Nicholls, D. F. and Quinn, B. G. (1982) Random Coefficient Autoregressive Models: An Introduction. Lecture Notes in Statistics 11, Springer-Verlag, New York.Google Scholar
Rao, C. R. (1973) Linear Statistical Inference and its Application , 2nd edn. Wiley, New York.Google Scholar
Sweeting, T. J. (1980) Uniform asymptotic normality of the maximum likelihood estimator. Ann. Statist. 8, 13751381.Google Scholar