conditional expectation


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conditional expectation

[kən′dish·ən·əl ‚ek‚spek′tā·shən]
(mathematics)
If X is a random variable on a probability space (Ω, F,P), the conditional expectation of X with respect to a given sub σ-field F′ of F is an F′-measurable random variable whose expected value over any set in F′ is equal to the expected value of X over this set.
(statistics)
The expected value of a conditional distribution.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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I can then employ the conditional expectation of the error term via the truncated conditional expectation function.
He covers the basic financial instruments; fundamental principles of financial modeling and arbitrage valuation of derivatives; the concept of conditional expectation, the discrete time binomial model and its application to stochastic finance; the most important results from the theory of martingales in the theory and application of stochastic finance; more advanced concepts such as the Randon-Nikodym derivative, equivalent martingale measure, non-arbitrage, and complete general markets; American derivative securities using the binomial model and general markets; fixed-income markets and the interest rate theory in discrete time; arbitrage pricing; credit risk; and the Heath-Jarrow-Morton model for the evolution of forward rate process.
By substituting an estimated value for the conditional expectation of [Mu], a new source of error is introduced.
To simplify notation, denote next period's (in state j) conditional expectation of [Mathematical Expression Omitted].
Expectational rationality implies that DPe should be generated as the prediction of the macromodel, i.e., DPe = E(DP) and taking the conditional expectation of (B.3) yields: DPe= ' d1 { 1E(DM)+ 2[E(DR*)+DPREM]-Q}
He covers money and markets (including interest rates) fair games (including hedging and arbitrage), set theory, measurable functions (including the Borel field), probability spaces (including random variables and stochastic processes) expected values, continuity and integrability, conditional expectation, martingales (discrete, continuous, and in convergence), the Black- Scholes formula itself, and stochastic integration.
In Section 2 we give a general statistical interpretation of Simpson's paradox using conditional expectation. In the next two sections, we show through examples how the Simpson's paradox can occur in categorical data and in time-to-event data.
In the random design, the conditional expectation (23) can be rewritten as follows:
For the sake of comparison, the conditional expectation method is developed for the considered random differential equations as well as the Monte-Carlo method.
Here, [F.sub.-1] = {0, [OMEGA]}, [F.sub.n] = [sigma]([Z.sub.k]; 0 [less than or equal to] k [less than or equal to] n) for n [member of] N and E[* | [F.sub.n]] means the conditional expectation given [F.sub.n].
In this paper, we propose a new extraction algorithm using the conditional expectation for the skewed source signal with the maximal absolute value of skewness.
The conditional expectation of the above equation at period N-1 is

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