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where y ip is the prediction for case i. This estimator has properties of semiparametric efficiency and double-robustness (Robins et al., 1994; Robins and Rotnitsky, 2001; Scharfstein and Irizarry, 2003), in the sense that the estimate is consistent if just one of the models for prediction and weighting is correctly specified. However, since the calibration of the predictions is to correct effects of model misspecification, we believe that the calibration of the predictions (Equation 18.7) is unnecessary if the prediction model does not make strong parametric assumptions, as in Equation (18.4). This conjecture is supported by the simulation studies in Little and An (2004), where the PSPP method has similar performance to the calibration approach and dominates it for some choices of simulation parameters.

In the case of cross-sectional data (yi, xi; i 2 s), Graubard et al. (1997) obtained weighted decile groups G1,G2, . . . G10: n1 subjects with the smallest estimated probabil^ ^ ities pi pi under the null hypothesis H0 : pi = pi( ) are in the first group G1, the next n2 in the second group G2, and so forth, until the group G10 with n10 observations having the largest estimated probabilities is formed. The nl observations in Gl are chosen such that i2Glwi/ i2s wi % 1/10; l = 1,2, . . . 10. In the special case of equal weights, wi = w, this grouping method reduces to the Hosmer and Lemeshow (1980) method of grouping the subjects. We now extend the above weighted decile grouping method to the case of complex longitudinal survey data, following Horton et al. (1999). We make use of all nT estimated ^ ^ probabilities pit pit , under the null hypothesis H0 : pit = pit( ) with logit pit 0 0 x it and working independence, to form the weighted decile groups G1, G2, . . . G10. ^ The weights wi associated with pit are used in forming the groups. Note that a subject s ^ group membership can change for different time points t because pit for a subject i can change with time t. In the special case of equal weights wi = w, this grouping method reduces to the Horton et al. (1999) method of grouping for the longitudinal case. 20.4.2 Quasi-Score Test

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1.41 SSADH.=6274.307; SSPRA.=4.380; effect of a preliminary surface preparation is insignificant, while the effect of adhesion systems is significant. FADH.=150.422; FPRA.=0.315; FADH. PRA.=19.304; F3;40;0.95=2.84. F1;40;0.95=4.08; 1.42

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Case studies about the accuracy of polygraph testing are easy to nd in the polygraph literature However, they do not give us real insight into the accuracy of polygraph testing To obtain real insight, laboratory and eld studies need to be carried out Virtually all of these studies are related to two types of tests: the Guilty Knowledge Test ( 12) and the CQT In this section I will discuss the accuracy of the CQT To test the accuracy of the CQT both laboratory studies and eld studies have been conducted In previous chapters I have already discussed the differences between laboratory and eld studies In most CQT laboratory studies, liars committed a mock crime and were instructed to deny this wrongdoing when asked about it in the subsequent CQT.

In many ways, mean reversion systems are polar opposites of their trendfollowing counterparts. With trend-following systems we let our profits run and relied on so-called outliers to compensate for our low percentage of winning trades. Inexorably linked to this concept of letting profits run is a prohibition against exiting trades due to the length of a trade s duration. By contrast, with mean reversion systems, we can in some instances improve overall performance through the addition of a time-based exit criterion. Although in general I favor trading systems with the fewest parameters possible while still maintaining profitability, mean reversion traders should consider the introduction of time-based exits.1

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