Hostname: page-component-7479d7b7d-t6hkb Total loading time: 0 Render date: 2024-07-11T21:46:34.124Z Has data issue: false hasContentIssue false

Statistical inference for stochastic processes

Published online by Cambridge University Press:  01 July 2016

Mark Brown*
Affiliation:
Cornell University

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Second conference on stochastic processes and applications
Copyright
Copyright © Applied Probability Trust 1973 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Barlow, R. E. and Proschan, F. (1965) Mathematical Theory of Reliability. John Wiley, New York.Google Scholar
[2] Bickel, P. J. and Dorsum, K. S. (1969) Tests for monotone failure rate based on normalized spacings. Ann. Math. Statist. 40, 12161235.CrossRefGoogle Scholar
[3] Billingsley, P. (1961) Statistical Inference for Markov Processes. University of Chicago Press.Google Scholar
[4] Cox, D. R. and Lewis, P. A. W. (1966) The Statistical Analysis of Series of Events. Methuen, London.CrossRefGoogle Scholar
[5] Cox, D. R. and Lewis, P. A. W. (1972) Multivariate point processes, Proc. 6th Berkeley Symposium Math. Statist. Prob. CrossRefGoogle Scholar
[6] Grandell, J. (1972) Statistical inference for doubly stochastic Poisson processes. [9] (below) 6790.Google Scholar
[7] Hajék, J. (1962) Asymptotically most powerful rank order tests. Ann. Math. Statist. 33, 11241147.CrossRefGoogle Scholar
[8] Lecam, L. (1960) Locally Asymptotically Normal Families of Distribution. University of California Press.Google Scholar
[9] Lewis, P. A. W. (1972) Stochastic Point Processes. John Wiley, New York.Google Scholar
[10] Parzen, E. (1967) Time Series Analysis Papers. Holden Day, San Francisco.Google Scholar
[11] Roussas, G. G. and Johnson, R. A. (1969) Asymptotically most powerful tests in Markov processes. Ann. Math. Statist. 40, 12071215.Google Scholar
[12] Weiss, L. and Wolfowitz, J. Asymptotic Methods in Statistics. In preparation.Google Scholar