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In this likelihood, f 13 ( ) is constructed from the MGF M13 (s), given in (83), for each of the nine people, and f 24 ( ) is constructed from the MGF M24 (s), given in (728), for each of the two patients The computation is performed using (86) for the Bayes predictive density and also by maximizing the combined likelihoods of the complete and incomplete data Figure 84 gives resulting density estimates Figures 85 and 86 give the resulting predictive survivor and hazard functions Figure 87 compares the complete data density with the density incorporating the incomplete data for both the MLE and Bayesian estimation techniques Since there are only 11 incomplete observations, it seems surprising that the effect on the density is so dramatic This is especially obvious when examining the quantile values for the incomplete data case, Table 8.





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From m = 0 t o m = M - 1, repeat:

Human beings may be complicated creatures with sometimes mystifying behaviour, but at root, we can understand their choices in a fairly universal way As a result, a lot can be gained by studying choice behaviour because if we can understand the how and why of the choices people made in the past, we stand a very good chance of understanding the choices they re going to make in the future Understanding (and even, sometimes, predicting) future choice behaviour is very important because major shifts in the economic environment are typically the result of millions of small individual decisions that add up to a major trend For example, the circumstances that cause millions of individuals to choose between pursuing work or further education accumulate over the years, producing major effects on the unemployment rate.

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And the choices these individuals make about how much of their pay cheques to save or spend determine whether interest rates rise or fall, and also whether gross domestic product (GDP) and overall economic output increase or decrease (We discuss GDP in 4) In order to predict how self-interested individuals make their choices, economists have created a model of human behaviour that assumes rationality and the ability to calculate subtle trade-offs between possible choices This model is a three-stage process: 1 Evaluate how happy each possible option can make you 2 Look at the constraints and trade-offs limiting your options 3 Choose the option that maximises your overall happiness Although not a fully complete description of human choice behaviour, this model generally makes accurate predictions However, many people question this explanation of human behaviour.

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1, and the complete data case, Table 73 The mean of the Bayes predictive distribution of the complete data in Table 73 is 63946, the mean of the data with just the uncensored observations is 50142 The mean of the predictive density for the complete data with the incomplete data is 47591, lower than either of the other cases To investigate this further, we compute estimates of the branch waiting time means for the combined complete and incomplete data with censoring (see Table 82) These branch waiting time means are in line with those of the complete data given in Table 74 The overall mean of the predictive distribution for the data of Table 82 is 38301 Recall the discussion of the data in Table 74.

Here are three common objections: Are people really so self-interested Aren t people often motivated by what s best for others Are people really aware at all times of all their options How are they supposed to choose rationally among new things that they have never tried before Are people really free to make decisions Aren t they constrained by legal, moral and social standards.

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