No CrossRef data available.
Article contents
A new form of Jensen's inequality and its application to statistical experiments
Published online by Cambridge University Press: 17 February 2009
Abstract
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Jensen's inequality for the expectation of a convex function of a random variable is proved for a wide class of convex functions defined on a space of probability measures. The result is applied to statistical experiments using the concept of Blackwell-sufficiency. In particular, we show a monotonicity result for the expected information of Poisson-experiments. As an application to economics we consider the introduction of new production technologies.
- Type
- Research Article
- Information
- Copyright
- Copyright © Australian Mathematical Society 1995
References
[1]Araujo, A. and Giné, E., The central limit theorem for real and Banach valued random variables (John Wiley & Sons, New York, 1980).Google Scholar
[2]Bauer, H., Probability theory and elements of measure theory (Academic Press, London, 1981).Google Scholar
[3]Billingsley, P., Convergence and probability measures (John Wiley & Sons, New York, 1968).Google Scholar
[5]Blackwell, D., “Equivalent comparisons of experiments”, Ann. Math. Stat. 24 (1953) 265–272.CrossRefGoogle Scholar
[7]DeGroot, M. H., “Changes in utility as information”, Theory and Decision 17 (1984) 287–303.CrossRefGoogle Scholar
[8]Giesy, D. P., “Strong law of large numbers for independent sequences of Banach space-valued random variables”, Lecture Notes in Mathematics, No. 526, (Springer, New York, 1976).CrossRefGoogle Scholar
[10]Hille, E. and Phillips, R. S., “Functional analysis and semi-groups”, AMS Colloquium Publications Vol. 31, (AMS, Providence, Rhode Island, 1957).Google Scholar
[11]Hinderer, K., Foundations of non-stationary dynamic programming with discrete time parameter (Springer, Berlin, 1970).CrossRefGoogle Scholar
[12]Hinderer, K., Grundbegriffe der wahrscheinlichkeitstheorie (Springer, Berlin, 1985).Google Scholar
[13]Cam, L. Le, Asymptotic methods in statistical decision theory (Springer, New York, 1986).CrossRefGoogle Scholar
[14]Marschak, J., “Economics of information systems”, J. Amer. Statist. Assoc. 66 (1971) 192–219.CrossRefGoogle Scholar
[15]Marschak, J. and Miyasawa, K., “Economic comparability of information systems”, Internat. Eco-nom. Rev. (1968) 137–174.CrossRefGoogle Scholar
[16]Perlman, M. D., “Jensen's inequality for a convex vector-valued function on an infinite-dimensional space”, J. Multivariate Anal. 4 (1974) 52–65.CrossRefGoogle Scholar
[17]Rieder, U. and Wagner, H., “Structured policies in the sequential design of experiments”, Ann. Oper. Res. 32 (1991) 165–188.CrossRefGoogle Scholar
[18]Rieder, U. and Zagst, R., “Monotonicity and bounds for convex stochastic control models”, ZOR - Methods and Models of Operations Research 39 (1994).Google Scholar
[19]Strasser, H., Mathematical theory of statistics (De Gruyter, Berlin, 1985).CrossRefGoogle Scholar
[20]Tonks, I., “Bayesian learning and the optimal investment decision of the firm”, The Economic Journal 93 (1983) 87–98.CrossRefGoogle Scholar
[21]Zagst, R., “Blackwell-Informativität in stochastischen Kontrollmodellen”, Ph. D. Thesis, University of Ulm, 1991.Google Scholar
You have
Access