Summary of the chapter Sample Clauses

Summary of the chapter. ‌ In this chapter, we have presented 3 novel voxel-wise heritability estimation meth- ods trying to improve the estimation accuracy and statistical sensitivity with a controlled false positive rate (specificity), and reduce the computational complexity. The simple LR-SD method based on linear regression modeling with squared differ- ences of paired observations has been introduced and established. This method is found to have comparable or even better estimation accuracy and statistical power relative to the existing methods and the other two newly proposed methods. Simu- lation studies also show that LR-SD is the most computationally efficient approach overall and will never encounter any convergence problems compared with the other iterative methods. These time-efficient, accurate and non-iterative properties of LR-SD make it more flexible and feasible to be applied for permutation inference to correct for the family-wise error rate and bootstrapping inference to construct the confidence intervals. Table 3.3: For all n = 111 subjects, the estimates, 95% bootstrapping confidence intervals from 1000 bootstrap replicates (“95% CI”) and permutation-based p-values derived using 1000 permutations for the unweighted mean summaries of h2 for her- itability and c2 for shared environmental factor, and the maximum statistics of Tmax, Kmax and Mmax from the voxel- and cluster-wise inferences are obtained for p p p 3 brain ROI’s including both amygdalas, left amygdala and right amygdala. Both Amygdalas Estimate 95% CI P-value h2 0.4329 (0.2145, 0.6012) 0.003 c2 0.0037 (0.0000, 0.1682) / Tmax p 6.3357 / 0.125 Kmax p 97 / 0.017 Mmax p 360.264 / 0.026 Left Amygdala Estimate 95% CI P-value h2 0.4108 (0.1993, 0.6093) 0.005 c2 0.0019 (0.0000, 0.1421) / Tmax p 6.3357 / 0.037 Kmax p 70 / 0.006 Mmax p 261.017 / 0.008 Right Amygdala Estimate 95% CI P-value h2 0.4564 (0.2218, 0.6367) 0.004 c2 0.0056 (0.0000, 0.2095) / Tmax p 5.7304 / 0.151 Kmax p 97 / 0.023 Mmax p 360.264 / 0.032 − Table 3.4: For both groups of CPCU+ (denoted as “+” and with n+ = 50 subjects) and Negative (denoted as “–” and with n = 61 subjects), the estimates (“Est”) and permutation-based p-values derived using 1000 permutations (“P”) for the un- weighted mean summaries of h2 for heritability and c2 for shared environmental factor, and the maximum statistics of Tmax, Kmax and Mmax from the voxel- and p p p cluster-wise inferences are obtained for 3 brain ROI’s including both amygdalas, left amygdala and right...
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Summary of the chapter. The shown requirements in this chapter are merely estimations and by no means in a final state, since this project is still under development. Any upcoming changes on one of the three areas (software, hardware, network) will be incorporated in future releases of this deliverable.
Summary of the chapter. The described installation and configuration procedures for the software packages are used for the first prototype and may be adapted in future prototypes. Furthermore, some software packages may be removed or added according to further requirements and developments of the system. These changes will be included in upcoming releases of this deliverable.
Summary of the chapter. In this chapter, I show how a simple process of directional harmony can be analyzed using φ-Correspondence. The analysis follows from basic assumptions of the theory, such 33 Notice that unlike CORR in ABC, RELATE-[+sib] is violated when there is a single element not in correspondence, since the constraint does not evaluate two segments at the same time. Since you need at least two elements to form a correspondence relation in φ-Correspondence, there is no candidate that can satisfy the constraint without banning that specific segment. as that correspondence is headed, that heads like to align to edges, and that heads xxxx privileged positions. There is also a clear division of labor between different types of constraints: ALIGN constraints require alignment to an edge; φ-Correspondence constraints determine the set of elements that participate in the harmony; I/O-Correspondence constraints target input/output faithfulness. Not only do the constraints do only “one thing,” but the general nature of the constraint definitions allows us to relate them to general classes of constraints (Generalized Alignment, Positional Faithfulness, and Correspondence).

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