Statistical Learning Models

1963 ◽  
Vol 33 (5) ◽  
pp. 543
Author(s):  
Ronald A. Weitzman
Keyword(s):  
Statistical Learning ◽  
Learning Models

scholarly journals A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions

2021 ◽  
Vol 5 ◽  
pp. 100030
Author(s):  
Louis Ehwerhemuepha ◽  
Sidy Danioko ◽  
Shiva Verma ◽  
Rachel Marano ◽  
William Feaster ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Learning Models ◽  
Super Learner

Chapter VIII: Statistical Learning Models

1963 ◽  
Vol 33 (5) ◽  
pp. 543-555
Author(s):  
Ronald A. Weitzman
Keyword(s):  
Statistical Learning ◽  
Learning Models

Forecasting northern polar stratospheric variability with competing statistical learning models

2017 ◽  
Vol 143 (705) ◽  
pp. 1816-1827 ◽  
Author(s):  
Ivan Minokhin ◽  
Christopher G. Fletcher ◽  
Alexander Brenning
Keyword(s):  
Statistical Learning ◽  
Learning Models ◽  
Stratospheric Variability

scholarly journals Forecasting S&P 500 Stock Index Using Statistical Learning Models

2016 ◽  
Vol 06 (06) ◽  
pp. 1067-1075 ◽  
Author(s):  
Chongda Liu ◽  
Jihua Wang ◽  
Di Xiao ◽  
Qi Liang
Keyword(s):  
Statistical Learning ◽  
Stock Index ◽  
Learning Models

Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation

2016 ◽  
Vol 139 (5) ◽  
pp. 2640-2655 ◽  
Author(s):  
Carl R. Hart ◽  
Nathan J. Reznicek ◽  
D. Keith Wilson ◽  
Chris L. Pettit ◽  
Edward T. Nykaza
Keyword(s):  
Statistical Learning ◽  
Sound Propagation ◽  
Learning Models

scholarly journals An Analysis of Electrocardiograms for Instantaneous Sleep Potential Determination

2019 ◽  
Vol 13 (1) ◽  
pp. 1-10
Author(s):  
Oluwatosin Ogundare
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Statistical Learning ◽  
Deep Neural Network ◽  
Sleep Onset ◽  
Learning Models ◽  
Sleep Research ◽  
Ecg Signals ◽  
Predicted Values ◽  
Ecg Data

Background: The use of electrocardiograms to establish a relationship between the electrical activity of the heart and the intricacies of sleep is explored to propose a method to predict the time before sleep onset. Recorded Electrocardiograms (ECG) from the National Sleep Research Resource (NSRR) database are analyzed to extract the frequency domain characteristics and used to develop statistical learning models to predict the time before sleep onset. This is known as Time to Sleep (TTS) and is presented as a measure of wakefulness known as Sleep Potential (SP). Methods: Recorded ECG signals that encapsulate a progression from stage 0 (Awake) to stage 5 are sampled at 125 Hz. The Heart Rate Variability (HRV) information is derived by extracting a sequence of R peaks from the QRS complexes. A Fast Fourier Transform (FFT) of the RR tachogram ensues and features are extracted and used to train the multi-layer neural network. Results: A comparison of the measured vs. predicted values is presented to evaluate the performance of the Deep Neural Network (DNN) in predicting Sleep Potential (SP) values (time before sleep onset) from different points in the ECG derived power spectrum. Conclusion: The research demonstrates a way to generate information on sleep using ECG data which can be provided in real-time from various ambulatory ECG devices. Sleep Potential (SP) values can be very useful in documenting sleep history for better diagnosis and treatment of sleep disorders. It can also be used in the prevention of sleep-related accidents, especially car wrecks.

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