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intellectual abilities call be applied to all persons. Although these analyses illustrate only a limited set of substantive hypotheses about dynamic growth processes, these are key questions in aging research. An example of the segmented models tted to the BradwayMcArdle data has been published by Cudeck & DuToit (2001). Using data from persons who had data on at least one of the last three occasions (N = 74), these authors t a nonlinear mixed model based on Equation 18.32 and found an estimated transition age ( 3 = 18.6, 3 = 0.60) where the polynomial of the rst phase changes to the linear decline component ( 1 = .141, 3 = 0.05; 2 = .571) of the second phase. The estimated mean response shows a growth curve with rapid increases and gradual decline (after Age[t] = 18.6). The variability of these estimated parameters allows for a variety of different curves, and some of these are drawn in Figure 18.13. Although the trend is decreasing overall, a few individuals actually exhibit increases, while for others the response is essentially constant into old age. . . . The two individuals in Figure (A) had large differences in intercepts, i0 (70.8 versus 91.9); those in Figure (B) had large differences in slopes, i1 ( .32 versus 0.04); those in Figure (C) had large differences in transition age, i3 (14.1 versus 23.6) (Cudeck & duToit, 2001; p. 13). The addition of individual differences in transition points contributes to our understanding of these growth curves. Four alternative latent difference score models (Figure 18.12) were tted to the nonverbal scores (Figure 18.6). To facilitate computer programming (e.g., Mx) the original data were rescaled into 5-year age segments (i.e., 30 to 35, 35 to 40, etc.). A baseline no-change model (NCS) was tted with only three parameters and the results using this approach were comparable to those of the baseline growth model (M0). This was also true for a constant change score (CCS; only) model, and the result was identical to that of the linear basis model (M1). The proportional change model (PCS; only), not t earlier, shows a minor improvement in t ( 2 = 5 on df = 1). To t the dual change model (Equation 18.34), the additive slope coef cient was xed for identi cation purposes ( = 1), but the mean of the slopes was allowed to be free ( s ). This allowed estimation of the effects for nonverbal with (a) inertial effects ( = 1.38), (b) initial-level means ( 0 = 32) at Age = 5, and (c) a linear slope mean ( s = 81) for each 5-year period after Age = 5. The goodness-of- t of the DCS model can be compared to that of every other nested alternative, and these comparisons show the best t was achieved using this model ( 2 = 785 on df = 2; 2 = 485 on df = 1; 2 = 385 on df = 1). From these results we calculate the expected group trajectories and the 5-year latent change accumulation as the combination of Equations 18.32
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Capnet Category Income $50,000 > $50,000 Total 574 582 Loss 49.7% 50.3% 1156 None 17,635 4,133 81.0% 19.0% 21,768 Gain < $5000 685 140 83.0% 17.0% 825 Gain $5000 122 1129 9.8% 90.2% 1251
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Part of our strategy was to report two types of best models, one (containing no principal components) for use solely in target prediction, and the other (containing principal components) for all other purposes, including customer pro ling. The subset of variables that were highly correlated with each other were shunted to a principal components analysis, which extracted two components from these seven correlated variables. Principal component 1 represented purchasing habits and was expected to be highly indicative of promotion response. Next, the BIRCH clustering algorithm was applied. Three clusters were uncovered: (1) moderate-spending career shoppers, (2) low-spending casual shoppers, and (3) frequent, high-spending, responsive shoppers. Cluster 3, as expected, had the highest promotion response rate. Thus, the classi cation models contained the following inputs: r Model collection A (included principal components analysis: models appropriate for customer pro ling, variable analysis, or prediction
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Here ri 2 1; 2; . . . ; N is an integer number for the ranking order of the ith neuron (i 1, 2, . . ., N) to the sth data point (s 1, 2, . . ., M). This ranking order is de ned by the Euclidean distance between the ith neuron and the sth data point. The closest neuron (in the sense of minimal Euclidean distance) is called the winning neuron and gets ranking order r 1. The second closest neuron gets r 2, and so on. The initial learning rate R0 and the steepness parameters TC and TW have to be preset before the learning. In the further simulation we use the following settings: T 500; R0 0:15 and TC TW T=5: One typical example for information compression of a raw data set containing M 800 data by using N 60 neurons is given in Figure 11.3. The unsupervised learning algorithm described above is referred to as algorithm Fixed when compared to the next two learning algorithms presented in Section 11.4.
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