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Because in general E log T log E , this condition is not exactly satis ed, however We therefore introduce small corrective deterministic factors g j , discussed below, and rede ne the yj as yj log mj g j , so that Eyj aj a by de nition Any kind of linear regression of yj on j constitutes an unbiased estimator of a, as the lack of bias does not require decorrelation between the yj , nor knowledge of their variances or distributions A weighted regression where the weights are related to the variances s2 of the yj is preferable, however, as this is the minimum variance j unbiased estimator (MVUE) for the regression problem [48] Intuitively this re nement is signi cant as we know that the s2 are far from equal.



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To exploit the j optimality, however, the correlation factors g j and the variances s2 must be j calculated a dif cult task They can nonetheless be well approximated, provided simplifying idealized properties are adopted The particular idealizations chosen here are the following: ID1a: For each xed j the dX j; are stationary sequences of uncorrelated variables IDIb: The processes dX j; and dX jH ; , j T jH , are uncorrelated ID2: The process X and, hence, the processes dX j; are Gaussian The above conditions may appear unduly restrictive at rst; however, the underlying effectiveness of the method is based on P1 and P2, ID1 ID2 being added.

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It s an old truism that anything important enough and valuable enough on a computer eventually gets pushed down onto the operating system. Windows Explorer acts like Internet Explorer if you type a URL into the address eld at the top of the folder. Frequently used tools and protocols end up in the Accessories folder. Mac users laugh at all the different programs Windows users use when they simply have the features at their ngertips. Social is one of those. There will be a Collaborate button on every application. There will be a Where Are They button on every map. You will have the ability to see search results sorted by which ones your friends clicked on, and product reviews will be sorted by who you know rather than geography or recency. Knowing that your friend Jay really liked that movie but your friend Brian didn t contains

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mainly to extend the quantitative analysis. Robustness with respect to departures from these idealizations is discussed in detail below. Note that ID1a and ID1b together make ID1, the idealization of complete decorrelation. It is split here to highlight the fact that ID1b, independence between scales, is not needed for the key results. Under ID1a and ID2 it can be shown [75] that g j is a negative, increasing function of nj only, given by g j c nj =2 = ln 2 log2 nj =2 ; 2:20

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where c z GH z =G z is the so-called Psi function and G z the Gamma function. This function can easily be calculated for all values of nj . Under ID1a and ID2, with g j as above, the variables yj log mj g j are scaled and shifted logarithms of Chi-squared variables, satisfying Eyj ja log2 cf C; Var yj s2 z 2; nj =2 = ln2 2; j 2:21 2:22

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where z z; n is a generalized Riemann Zeta function (see Gradshteyn and Ryzhik [38, p.1072]). There may be other sets of idealizations under which the g j and s2, if they j cannot be expressed in closed form, may nonetheless be accurately calculated. In such cases the estimator would have the same properties including a variance that is known, although no longer expressible in terms of standard functions. ^ The estimator a of a is the slope of a weighted linear regression of yj on j given by ^ a where S 1=s2 , Sj j yj Sj Sj =s2 j wj yj ; SSjj Sj2 j2 =s2 . j 2:23

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