Applied Statistical Decision TheoryDivision of Research, Graduate School of Business Adminitration, Harvard University, 1961 - Business & Economics - 356 pages "In the field of statistical decision theory, Raiffa and Schlaifer have sought to develop new analytic techniques by which the modern theory of utility and subjective probability can actually be applied to the economic analysis of typical sampling problems." --From the foreword to their classic work "Applied Statistical Decision Theory," First published in the 1960s through Harvard University and MIT Press, the book is now offered in a new paperback edition from Wiley |
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Page xv
... quantity q of some commodity should be stocked when the demand d is unknown and an opportunity loss will be incurred if d is not equal to q . We then define the problem of point estimation as the problem which arises when the decision ...
... quantity q of some commodity should be stocked when the demand d is unknown and an opportunity loss will be incurred if d is not equal to q . We then define the problem of point estimation as the problem which arises when the decision ...
Page 63
... quantity of information in the sample , then it seems natural to interpret the component m as summarizing the import of this information ; or since the ex- pected value of ñ given μ is equal to μ , we naturally tend to think of m as an ...
... quantity of information in the sample , then it seems natural to interpret the component m as summarizing the import of this information ; or since the ex- pected value of ñ given μ is equal to μ , we naturally tend to think of m as an ...
Page 178
... quantity in question is a certainty equivalent in the sense that treatment of this summary measure as if it were the true value of the quantity will lead to exactly the same course of action that would be chosen if an analysis were ...
... quantity in question is a certainty equivalent in the sense that treatment of this summary measure as if it were the true value of the quantity will lead to exactly the same course of action that would be chosen if an analysis were ...
Contents
The Problem and the Two Basic Modes of Analysis | 3 |
Univariate Normalized Mass and Density Functions | 7 |
Combination of Formal and Informal Analysis | 17 |
Copyright | |
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Common terms and phrases
a₁ a₂ approximation assign assumption Bernoulli process beta function binomial choose compute conditional measure conjugate Conjugate prior cost cumulative function data-generating process decision maker decision problem decision tree defined definition denote estimate evaluated EVPI EVSI example expected terminal opportunity expected utility expected value experiment experimental outcome extensive form Ezle Figure follows gamma gamma-2 given h is known h is unknown k₁ k₂ kernel li(a li(e li(eo likelihood linear marginal measure mass function n₁ normalized density function observations obtain optimal act optimal sample parameter perfect information Poisson possible posterior density posterior distribution preposterior analysis prior density prior distribution prior expected probability quantity random variable sample information Section stopping process Substituting sufficient statistic Table terminal act terminal analysis terminal opportunity loss terminal utility theorem tion u₁ utility characteristic value of perfect vector vi(e