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The interval length k(t1) may be thought of as the duration of an effect and would come from subject-matter considerations. We have allowed it to depend in general on t1, anticipating that the effect of T1 on the hazard of T2 might not have constant duration. Although we have given the definition of the close precursor in terms of survivor functions, it can be expressed approximately in terms of hazard functions, as follows: T1 is a close precursor of T2 if, for suitably chosen k(s) and d > 0, h2 ujT1 s > h2 u d; for u 2 (s, s+k(s)). Then d is seen to correspond to an additive lower bound to a shortterm change in the hazard function. In either formulation, the motivation is that the more closely T2 tends to follow T1, the greater the plausibility for a causal connection might be. Since Equation (19.1) reflects approximately a short-term raising of the hazard function for T2, it is not difficult to formulate an analogue for point process intensities, giving us an alternative way of modelling events less tied to a time origin. Blossfeld and Mills (2003) use interdependent point processes to model interrelated family events, namely entry into marriage (for individuals in a consensual union) and first pregnancy/ childbirth; see also Lawless (2003) for some discussion of intensity models. 19.2.2 Nonparametric Test for Close Precursor

2 Add the egg and vanilla to the bowl and mix well. 3 In another bowl, combine the flour, baking soda, and salt. Add this mixture to the first

3.70 3.64 3.59 3.54 3.50

This AntiPattern is mainly the result of an insufficient understanding of the idea that services really need to be self-contained, standalone functional components. Shortcuts or alternate approaches are taken to improve performance, or reduce the amount of development time to create the services, but in the long run, this means that the service is really incomplete. The following defines some specific causes.

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Bethlehem J G and Keller, W J (1987) Linear weighting of sample survey data Journal of Official Statistics, 3, 141 153 Collins, L M, Schafer, J L and Kam, C M (2001) A comparison of inclusive and restrictive strategies in modern missing data procedures Psychological Methods, 6, 330 351 Couper, M P (2000) Web surveys: A review of issues and approaches Public Opinion Quarterly, 64, 464 494 De Leeuw, E D (2005) Dropout in longitudinal data In B S Everitt and D C Howell (Eds), Encyclopedia of Statistics in Behavioral Science (Vol 1, pp 515 518) Chichester: John Wiley & Sons, Inc De Leeuw, E D and De Heer, W (2002) Trends in household survey nonresponse: a longitudinal and international comparison In R M Groves, D A Dillman, J L Eltinge and R J A Little (Eds), Survey Nonresponse (pp 41 54) New York: John Wiley & Sons, Inc.

De Leeuw, E D, Hox, J and Huisman, M (2003) Prevention and treatment of item nonresponse Journal of Official Statistics, 19, 153 176 Dempster, A P, Laird, N M and Rubin, D B (1977) Maximum likelihood from incomplete data via the EM algorithm Journal of the Royal Statistical Society, B, 39, 1 38 Deville, J C and Sarndal, C E (1992) Calibration estimators in survey sampling Journal of the American Statistical Association, 87, 376 382 Efron, B (1994) Missing data, imputation, and the bootstrap Journal of the American Statistical Association, 89, 463 475 Fitzmaurice, G M, Laird, N M and Ware, J H (2004) Applied Longitudinal Analysis New York: John Wiley & Sons, Inc Hajek, J (1981) Sampling from a Finite Population New York: Marcel Dekker Kalton, G and Flores Cervantes, I (2003) Weighting methods Journal of Official Statistics, 19, 81 97 Kish, L (1992).

Weighting for unequal Pi Journal of Official Statistics, 8, 183 200 Lee, S (2006) Propensity score adjustment as a weighting scheme for volunteer panel web surveys Journal of Official Statistics, 22, 329 349 Lehtonen, R and Pahkinen, E (2004) Practical Methods for Design and Analysis of Complex Surveys New York: John Wiley & Sons, Inc Little, R D and Vartivarian, S (2005) Does weighting for nonresponse increase the variance of survey means Survey Methodology, 31, 161 168 Little, R J A and Rubin, D B (2002) Statistical Analysis with Missing Data (2nd edn) New York: John Wiley & Sons, Inc Potter, F J (1988) Survey of procedures to control extreme sampling weights In Proceedings of the American Statistical Association, Section on Survey Research Methods (pp 453 458) Washington, DC: American Statistical Association Potter, F J (1990) A study of procedures to identify and trim extreme sampling weights.

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