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For example, ChangeFinder approximates the input data items' distribution through a mixture of normal distributions.
The algorithms of this category use feedback (a sample of input data items) to e.g., learn the current input data items' distribution (β-CUSUM, ChangeFinder [13]) or fill one or more baseline windows4 (ADWIN2, Meta-algorithm).
Table 6 in Appendix 1 describes the final items distribution for each culture type.
These effects are due to a gap between the means of the latent variable distribution and the items distribution.
Person-item response thresholds locations is shown in Figure 1; this represents the frequency distribution of the patients with different disabilities and the number of response categories of items distribution, to measure the different disabilities (in logit units).
The final version of the C-PPAC (see the online supplement), described in figure 3, also showed good performance and items distribution, as per the PSI (0.75 in factor 1 with Dynaport, 0.74 in factor 1 with Actigraph, and 0.93 in factor 2) and person-item map (fig. 4).
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Evacuation and relief item distribution may be considered as subtopics of transportation.
Anaya-Arenas et al. [8] and de la Torre et al. [9] specifically surveyed relief item distribution.
Rasch analysis also revealed that the 10 items defined a common, single ability continuum with widespread measurement range and quite regular item distribution, and showed a satisfactory reliability.
This stage of the analysis was used to examine the quality of completion (patient compliance with the DSQ), item distribution (floor and ceiling effects) and item discrimination.
Table 3 Stage 1: Item analysis of the DSQ (version 4.0) to assess quality of completion by patients, item score patterns, item distribution and item discrimination at baseline (visit 2) in patients with EoE.
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