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Thread4 Thread1 s1 r1 {s2,s3,s8,s10} s5 {s3,s7,s8,s10} r2 { } r9 r5 {s9} r4 { } s8 s9 s10 s3 (a) Thread2 Thread3 Thread4 s6

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In this chapter we study, within a modelling framework, the joint treatment of nonignorable dropout and informative sampling for longitudinal survey data, by specifying the probability distribution of the observed measurements when the sampling design is informative. This might be a useful approach for longitudinal surveys with initial nonresponse (which can be thought of as a form of informative sampling) and subsequent nonignorable attrition. The sample distribution of the observed measurements model is extracted from the population distribution model, assumed to be multivariate normal. The sample distribution is derived first by identifying and estimating the conditional expectations of first-order sample inclusion probabilities (assuming complete response at the first time period), given the study variable, based on a variety of models such as linear, exponential, logit and probit. Next, we consider a logistic model for the informative dropout process. The proposed method combines two methodologies used in the analysis of sample surveys: for the treatment of informative sampling and of informative dropout. One incorporates the dependence of the first-order inclusion probabilities at the initial time period on the study variable (see Eideh and Nathan, 2006), while the other incorporates the dependence of the probability of nonresponse on unobserved or missing observations (see Diggle and Kenward, 1994). An empirical example based on data from the British Labour Force Survey illustrates the methods proposed.





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Free software was firmly established in 1998 when the term open source was coined. A group of people actively involved in working with Linux and other free software projects determined that a new name was needed that could be used to identify all software projects that provided access to source code without restrictions on changes or redistribution. They wanted to include the broader spectrum of free software, not just those programs covered under the GPL. They also wanted to make these software projects more palatable for businesses in the software industry. The FSF with its philosophy and rhetoric about free software had gone a long way toward endearing some programmers, but had alienated many, including businesses. The open source movement grew out of a desire to promote the concepts of free software but to avoid its confrontational past. They wanted to help businesses understand how this free software could be used to their benefit. This new name, open source software, quickly spread and was adopted by many. The Open Source Initiative (OSI) was formed to promote open source software and the concepts behind it. The OSI Web site can be found at the following URL:

4.2. Let {X(t) : t R} be a centered stationary Gaussian process having covariance function (t s) = E(X(s)X(t)), normalized by (0) = 1. Assume that satis es hypothesis (A2) in Proposition 4.2 and denote, as usual, 2 = (0), the second spectral moment. Let = inf{t > 0 : (t) = 1}. Exclude the trivial case in which 2 = 0. Let MT = maxt [0,T ] X(t). (a) Prove that > 0. (b) Show that P (X(t) = X(t + ) for all t R) = 1

This formula uses the COUNTA function to count the number of nonempty cells in column A. This value (minus 1) is used as the second argument for the OFFSET function. For example, if the last value is in row 100, COUNTA returns 100. The OFFSET function returns the value in the cell 99 rows down from cell A1, in the same column. If column A has one or more empty cells interspersed, which is frequently the case, the preceding formula won t work because the COUNTA function doesn t count the empty cells. The following array formula returns the contents of the last nonempty cell in the first 500 rows of column A:

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