Clustered Encouragement Designs with Individual by Frangakis C.E.

By Frangakis C.E.

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4) θ∈Nδ1 (θ) ¯ N (θn ) = 0 does not affect the form of the Then the choice made when X n,i ODE at θ. 21), and that F (·) does as follows. Recall the definition of U not depend explicitly on θ in this example. Let G(θ) denote the set of all possible values of the vector ¯ N (θ))U ¯ N (θ), i ≤ r} {−EFxi (X i i ¯ N (θ) = 0, and let over the choices made for the derivatives Fxi (·) when X i co denote the convex hull. Define the convex upper semicontinuous set    co  ¯ G(θ) = δ>0  G(θ) . 5) θ∈Nδ (θ) If there is only one choice at θ and the mean values are continuous at θ, ¯ then G(θ) contains only one point, namely, g¯(θ).

Comments on the algorithms. 14)) since the Φ−1 n is replaced by some rather arbitrary real number n . This affects the direction of the step as well as −1 the norm of the step size. For large n, Φ−1 /n by the law of large n ≈ Q numbers. 15)) is determined by the “eigenvalue spread” of Q. 18) work well. 1. Note on time-varying systems and tracking. 5, where a “discounted” or ‘forgetting factor” form, which weight recent errors more heavily, is used to track time-varying systems. A second adaptive loop is added to optimize the discount factor, and this second loop has the stochastic approximation form.

Two classes of patterns are of interest, either pattern A or pattern A¯ (pattern “not A”). A sequence of patterns is drawn at random from a ¯ Let yn = 1 if the pattern drawn on trial n given distribution on (A, A). (n = 0, 1, . ) is A, and let yn = −1 otherwise. The patterns might be samples of a letter or a number. The patterns themselves are not observed but are known only through noise-corrupted observations of particular characteristics. ” Typically, the scanned sample will be processed to extract “features,” such as the number of separate segments, loops, corners, etc.

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