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If(MarketPosition = -1) then shortLiqPoint = MaxList(EntryPrice + 25% of averageRange,EntryPrice + 3.00 points) {normal reversal use 25%} if(MarketPosition(1) = 1 and BarsSinceEntry(0) = 1 and (Low[1] <= longLiqPoint and longLiqPoint > longFBOPoint) then curTrdType = +2 if(curTrdType = +2) then shortLiqPoint = MinList(EntryPrice 15% of averageRange,EntryPrice - 3.00 points) {long liq reversal use 15%} if(Low <= EntryPrice 50% of averageRange) shortLiqPoint = EntryPrice if(Time < 1200 and buysToday = 0 and shortLiqPoint <> EntryPrice) then Buy ("ShortLiqRev") next bar at shortLiqPoint stop else BuyToCover ("ShortLiq") next bar at shortLiqPoint stop If(time >= 1430) then shortLiqPoint = MinList(shortLiqPoint, Highest(High,3))



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Lotka and Volterra originally assumed that prey density (N) increased exponentially, quanti ed by r1, in the absence of predators (see also equation 2.10): dN = r1N dt (6.1)





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It is the smallest family if it is minimum with respect to the number of items among the generative families. As the coef cients are in Q, we are looking for a basis of the vectorial subspace of left cancellers of C. Thus we can compute this family by some variant of the Gauss elimination. GAUSS ELIMINATION. The algorithm proceeds transition by transition: it starts from a generative family of ows for the matrix reduced to the k rst transitions and builds a generative family for the matrix reduced to the k + 1 rst transitions. Initially (k = 0); there is no condition and the generative family is de ned by { }p P . p Let t be the next transition to be examined and {v1 , . . . , vn } the current family.

Now we will express mathematically what we have just described, based on Eq. (4.8). The second-harmonic output power pout,H 2 is related to the fundamental output power pout,F and the second-order harmonic output intercept point OIP2H by 2 pout,F pout,H 2 = . (4.9) pOIP2,H

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Maas, 394 Mabbott, 49, 212, 213, 214, 215, 216 MacAllister, 237, 238, 239 Maccini, 54, 520 MacDonald, 499, 498 Machemer, 337 Maciag, 365

fail When a Hessian is non-invertible purely because of an interaction between the model and the data (and not because of rounding and other numerical errors), this means that the desired variance matrix does not exist; the likelihood function may still contain considerable information about the questions of interest As such, discarding data and analyses with this valuable information, even if the information cannot be summarized as usual, is an inef cient and potentially biased procedure In 6 Gill and King provide a new method for applying generalized inverses to Hessian problems that can provide results even in circumstances where it is not usually possible to invert the Hessian and obtain coef cient standard errors Ecological inference, the problem of inferring individual behavior from aggregate data, was (and perhaps still is) arguably once the longest-standing unsolved problem in modern quantitative social science.

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When in 1997 King provided a new method that incorporated both the statistical information in Goodman s regression and the deterministic information in Duncan and Davis s bounds, he garnered tremendous acclaim as well as persistent criticism In this book we report the rst comparison of the numerical properties of competing approaches to the ecological inference problem The results illuminate the trade-offs among correctness, complexity, and numerical sensitivity More important than this list of new ideas, which we hope the reader will explore, this is the rst general theoretical book on statistical computing that is focused purely on the social sciences As social scientists ourselves, we recognize that our data analysis and estimation processes can differ substantially from those described in a number of (even excellent) texts All too often new ideas in statistics are presented with examples from biology.

Our goal will be to limit the maximum spur level that is produced for a given range of possible input signal levels This range will include the maximum levels of undesired signals and possibly of the desired signal, if its spurs can be a problem The maximum spur level in a synthesizer is set by spectral purity requirements In a receiver, it may be set below the minimum desired signal by some required signal-to-interference ratio or, if we are concerned about misidentifying received signals, it might be related to a detection threshold or the noise level As noted above, it is helpful to deal with relative spur levels, how far the spurs are below the desired 1 1 product Relative spur levels can be improved by reducing signal strength as long as n exceeds 1 The greater the value of n, the faster the spur level changes with signal strength.

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