Calibration of Local Correlation Models to Market Smiles

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

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Faculty Opinions recommendation of Particle finding in electron micrographs using a fast local correlation algorithm.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1013367.190253 ◽

2003 ◽

Author(s):

Henning Stahlberg

Keyword(s):

Electron Micrographs ◽

Local Correlation ◽

Correlation Algorithm

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Performance Analysis of Naïve Bayes Correlation Models in Machine Learning

International Journal of Psychosocial Rehabilitation ◽

10.37200/ijpr/v24i4/pr201088 ◽

2020 ◽

Vol 24 (04) ◽

pp. 1153-1157

Author(s):

Uma Pavan Kumar Dr.K ◽

Kalimuthu Dr.M

Keyword(s):

Machine Learning ◽

Performance Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Correlation Models

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The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

Scientific Reports ◽

10.1038/s41598-021-93836-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Halit Cinarka ◽

Mehmet Atilla Uysal ◽

Atilla Cifter ◽

Elif Yelda Niksarlioglu ◽

Aslı Çarkoğlu

Keyword(s):

Case Reports ◽

Time Varying ◽

Conditional Correlation ◽

Web Based ◽

Specific Symptom ◽

Sliding Windows ◽

Dynamic Conditional Correlation ◽

The United Kingdom ◽

Correlation Models ◽

Google Search

AbstractThis study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

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