scholarly journals Efficiencies from Spatially-Correlated Uncertainty and Sampling in Continuous-Variable Ordinal Optimization

2008 ◽  
Vol 1 (1) ◽  
pp. 323-338
Author(s):  
Vicente J. Romero
Keyword(s):  
Continuous Variable ◽  
Ordinal Optimization ◽  
Spatially Correlated

Quadratic Discriminant Analysis of Spatially Correlated Data

2001 ◽  
Vol 6 (2) ◽  
pp. 15-28 ◽  
Author(s):  
K. Dučinskas ◽  
J. Šaltytė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Gaussian Random Field ◽  
Correlated Data ◽  
Covariance Matrices ◽  
Classification Rule ◽  
Spatial Correlations ◽  
Linear Quadratic ◽  
Spatial Correlation Function ◽  
Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

AUTOMATIC ESTIMATION OF SPATIALLY CORRELATED NOISE VARIANCE IN SPECTRAL DOMAIN FOR IMAGES

2014 ◽  
Vol 73 (6) ◽  
pp. 511-527 ◽  
Author(s):  
V.V. Abramova ◽  
S. K. Abramov ◽  
V. V. Lukin ◽  
A. A. Roenko ◽  
Benoit Vozel
Keyword(s):  
Spectral Domain ◽  
Correlated Noise ◽  
Noise Variance ◽  
Spatially Correlated ◽  
Automatic Estimation ◽  
Spatially Correlated Noise

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

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.

Prediction of Haemoglobin Level by Some Probability Distribution

2020 ◽  
Vol 9 (1) ◽  
pp. 84-88
Author(s):  
Govinda Prasad Dhungana ◽  
Laxmi Prasad Sapkota
Keyword(s):  
Probability Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Continuous Variable ◽  
Hemoglobin Level ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Two Parameters ◽  
Best Fit

 Hemoglobin level is a continuous variable. So, it follows some theoretical probability distribution Normal, Log-normal, Gamma and Weibull distribution having two parameters. There is low variation in observed and expected frequency of Normal distribution in bar diagram. Similarly, calculated value of chi-square test (goodness of fit) is observed which is lower in Normal distribution. Furthermore, plot of PDFof Normal distribution covers larger area of histogram than all of other distribution. Hence Normal distribution is the best fit to predict the hemoglobin level in future.

scholarly journals Aortic Valve Calcification is Associated with Future Cognitive Impairment

2021 ◽  
pp. 1-7
Author(s):  
Hojune E. Chung ◽  
Jessica Chen ◽  
Dhairyasheel Ghosalkar ◽  
Jared L. Christensen ◽  
Alice J. Chu ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Aortic Valve ◽  
Lung Cancer Screening ◽  
Continuous Variable ◽  
Smoking History ◽  
Event Analysis ◽  
Aortic Valve Calcification ◽  
Valve Calcification ◽  
Ckd Stage ◽  
New Diagnosis

Background: While an association between atherosclerosis and dementia has been identified, few studies have assessed the longitudinal relationship between aortic valve calcification (AVC) and cognitive impairment (CI). Objective: We sought to determine whether AVC derived from lung cancer screening CT (LCSCT) was associated with CI in a moderate-to-high atherosclerotic risk cohort. Methods: This was a single site, retrospective analysis of 1401 U.S. veterans (65 years [IQI: 61, 68] years; 97%male) who underwent quantification of AVC from LCSCT indicated for smoking history. The primary outcome was new diagnosis of CI identified by objective testing (Mini-Mental Status Exam or Montreal Cognitive Assessment) or by ICD coding. Time-to-event analysis was carried out using AVC as a continuous variable. Results: Over 5 years, 110 patients (8%) were diagnosed with CI. AVC was associated with new diagnosis of CI using 3 Models for adjustment: 1) age (HR: 1.104; CI: 1.023–1.191; p = 0.011); 2) Model 1 plus hypertension, hyperlipidemia, diabetes, CKD stage 3 or higher (glomerular filtration rate <  60 mL/min) and CAD (HR: 1.097; CI: 1.014–1.186; p = 0.020); and 3) Model 2 plus CVA (HR: 1.094; CI: 1.011–1.182; p = 0.024). Sensitivity analysis demonstrated that the association between AVC and new diagnosis of CI remained significant upon exclusion of severe AVC (HR: 1.100 [1.013–1.194]; p = 0.023). Subgroup analysis demonstrated that this association remained significant when including education in the multivariate analysis (HR: 1.127 [1.030–1.233]; p = 0.009). Conclusion: This is the first study demonstrating that among mostly male individuals who underwent LCSCT, quantified aortic valve calcification is associated with new diagnosis of CI.

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