scholarly journals Geometric Multi-model Fitting with a Convex Relaxation Algorithm

Author(s):  
Paul Amayo ◽  
Pedro Pinies ◽  
Lina M. Paz ◽  
Paul Newman
Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 156-164 ◽  
Author(s):  
Keith A. Markus

Abstract. Bollen and colleagues have advocated the use of formative scales despite the fact that formative scales lack an adequate underlying theory to guide development or validation such as that which underlies reflective scales. Three conceptual impediments impede the development of such theory: the redefinition of measurement restricted to the context of model fitting, the inscrutable notion of conceptual unity, and a systematic conflation of item scores with attributes. Setting aside these impediments opens the door to progress in developing the needed theory to support formative scale use. A broader perspective facilitates consideration of standard scale development concerns as applied to formative scales including scale development, item analysis, reliability, and item bias. While formative scales require a different pattern of emphasis, all five of the traditional sources of validity evidence apply to formative scales. Responsible use of formative scales requires greater attention to developing the requisite underlying theory.


1978 ◽  
Vol 23 (11) ◽  
pp. 937-938
Author(s):  
JAMES R. KLUEGEL

2001 ◽  
Vol 40 (05) ◽  
pp. 164-171 ◽  
Author(s):  
B. Nowak ◽  
H.-J. Kaiser ◽  
S. Block ◽  
K.-C. Koch ◽  
J. vom Dahl ◽  
...  

Summary Aim: In the present study a new approach has been developed for comparative quantification of absolute myocardial blood flow (MBF), myocardial perfusion, and myocardial metabolism in short-axis slices. Methods: 42 patients with severe CAD, referred for myocardial viability diagnostics, were studied consecutively with 0-15-H2O PET (H2O-PET) (twice), Tc-99m-Tetrofosmin 5PECT (TT-SPECT) and F-18-FDG PET (FDG-PET). All dato sets were reconstructed using attenuation correction and reoriented into short axis slices. Each heart was divided into three representative slices (base, rnidventricular, apex) and 18 ROIs were defined on the FDG PET images and transferred to the corresponding H2O-PET and TT-SPECT slices. TT-SPECT and FDG-PET data were normalized to the ROI showing maximum perfusion. MBF was calculated for all left-ventricular ROIs using a single-compartment-model fitting the dynamic H2O-PET studies. Microsphere equivalent MBF (MBF_micr) was calculated by multiplying MBF and tissue-fraction, a parameter which was obtained by fitting the dynamic H2O-PET studies. To reduce influence of viability only well perfused areas (>70% TT-SPECT) were used for comparative quantification. Results: First and second mean global MBF values were 0.85 ml × min-1 × g-1 and 0.84 ml × min-1 × g1, respectively, with a repeatability coefficient of 0.30 ml ÷ min-1 × gl. After sectorization mean MBF_micr was between 0.58 ml × min1 ÷ ml"1 and 0.68 ml × min-1 × ml"1 in well perfused areas. Corresponding TT-SPECT values ranged from 83 % to 91 %, and FDG-PET values from 91 % to 103%. All procedures yielded higher values for the lateral than the septal regions. Conclusion: Comparative quantification of MBF, MBF_micr, TT-SPECT perfusion and FDG-PET metabolism can be done with the introduced method in short axis slices. The obtained values agree well with experimentally validated values of MBF and MBF_micr.


2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.


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