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The Hosmer Lemeshow goodness-of-fit test is not significant, with F = 4853 (8 df) and P = 0773 Table 135 summarizes the model The largest odds ratio in Table 135 is for those with membership less than six months relative to those with membership of 14 months It is hardly surprising that attrition is more likely to occur the longer a member is in the panel Other significant odds ratios are all consistent with the bivariate findings reported in Section 1331, with just one exception.

Single Differencing. The purpose of single differencing is to eliminate satellite clock bias. Consider the observation equations for two receivers, A and B observing the same satellite, j: LA = rA + ctA ct j + ZA IA + BA j j j j j LB = rB + ctB ct j + ZB IB + BB The single difference phase is de ned as the difference between these two: LAB LA LB j j j j j j j j = (rA + ctA ct j + ZA IA + BA) (rB + ctB ct j + ZB IB + BB) j j j j j j j j j j = (rA rB) + (ctA ctB) (ct ct ) + (ZA ZB) (IA IB) (BA BB) j j j j = rAB + c tAB + ZAB IAB + BAB where we use the double subscript to denote quantities identi ed with two receivers, and the triangular symbol as a mnemonic device, to emphasize that the difference is made between two points on the ground. The geometry of single differencing is illustrated in Figure 2.4. An assumption has been made that the satellite clock bias t j is effectively identical at the slightly different times that the signal was transmitted to A and to B.

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Consistent with the bivariate analysis, we see that: the odds of retention for males are only 78 % of those of females; the odds for Whites are 177 % of those for non-Whites; the odds for teens are four times those aged 55 and older; and the odds for 28-24-year-olds are only 34 % of those aged 55 and older However, while the bivariate analysis showed that persons living as married or never married attrite at higher rates than others, this is no longer the case once age is taken into account The effects for the indicators of the three experiments are as in the bivariate analysis, as one would expect (as allocation was at random): only the telephone interviewing protocol was significant..

already been subject to prepayment. Typically, newer mortgages prepay at a slower rate than more seasoned ones, and prepayment rates are expected to slow for pools that have already incurred heavy prepayments. These relationships can and do change over time, however. Exhibit 6.2 illustrates the effect of burnout, using two securities. The FHR 1899 SB is a security derived from an 8% pass-through with a weighted average coupon of 8.504% and a weighted average maturity (as of June 1999) of 317 months. The FNR 1992-137 S is a security derived from an 8% pass-through with a weighted average coupon of 8.579% and a weighted average maturity of 262 months. Without taking burnout into account, one would expect the FNR 1992-137 S to prepay faster, because the average coupon is 7.5 basis points higher, meaning that the mortgagees in the FNR 1992-137 S pool will have a greater incentive to re nance. However, the FNR 1992-137 S is more than ve years older than the FHR 1899 SB; it has already gone through one interest rate cycle and the remaining borrowers are somewhat less likely to re nance. As a result, the FNR 1992-137 S is somewhat burned out, and should show a consistently lower prepayment rate than the nonburned out FHR 1899 SB. However, this relationship may change over time and may even reverse itself, as burnout is a dynamic phenomenon. Burnout can have a signi cant effect on the value of a security. It represents another opportunity to exploit market inef ciency. If different market participants have different burnout rates factored into their models, their valuations could exhibit substantial differences. EXHIBIT 6.2

2.5 Condensation (desolvation)

Driven Middleware paradigm, which is an integration of MDA and QoS-enabled component middleware, resolves key challenges associated with DRE application integration; Section 7.4 provides a case study of applying Model Driven Middleware in the context of real-time avionics mission computing; Section 7.5 compares our work on Model Driven Middleware with related efforts; and Section 7.6 presents concluding remarks.

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