Flexible Bayesian quantile curve fitting with shape restrictions under the Dirichlet process mixture of the generalized asymmetric Laplace distribution

2020 ◽  
Author(s):  
Genya Kobayashi ◽  
Taeyoung Roh ◽  
Jangwon Lee ◽  
Taeryon Choi
Keyword(s):  
Curve Fitting ◽  
Dirichlet Process ◽  
Laplace Distribution ◽  
Dirichlet Process Mixture ◽  
Asymmetric Laplace Distribution ◽  
Shape Restrictions

scholarly journals A Predictive Study of Dirichlet Process Mixture Models for Curve Fitting

2013 ◽  
Vol 41 (3) ◽  
pp. 580-605 ◽  
Author(s):  
Sara Wade ◽  
Stephen G. Walker ◽  
Sonia Petrone
Keyword(s):  
Mixture Models ◽  
Curve Fitting ◽  
Dirichlet Process ◽  
Dirichlet Process Mixture ◽  
Dirichlet Process Mixture Models ◽  
Predictive Study

Outlier detection in traffic data based on the Dirichlet process mixture model

2015 ◽  
Vol 9 (7) ◽  
pp. 773-781 ◽  
Author(s):  
Henry Y.T. Ngan ◽  
Anthony G.O. Yeh ◽  
Nelson H.C. Yung
Keyword(s):  
Outlier Detection ◽  
Mixture Model ◽  
Dirichlet Process ◽  
Traffic Data ◽  
Dirichlet Process Mixture ◽  
Dirichlet Process Mixture Model

A mean-CVaR-skewness portfolio optimization model based on asymmetric Laplace distribution

2014 ◽  
Vol 226 (1) ◽  
pp. 727-739 ◽  
Author(s):  
Shangmei Zhao ◽  
Qing Lu ◽  
Liyan Han ◽  
Yong Liu ◽  
Fei Hu
Keyword(s):  
Portfolio Optimization ◽  
Optimization Model ◽  
Laplace Distribution ◽  
Asymmetric Laplace Distribution ◽  
Model Based ◽  
Portfolio Optimization Model

scholarly journals Clustering disaggregated load profiles using a Dirichlet process mixture model

2015 ◽  
Vol 92 ◽  
pp. 507-516 ◽  
Author(s):  
Ramon Granell ◽  
Colin J. Axon ◽  
David C.H. Wallom
Keyword(s):  
Mixture Model ◽  
Dirichlet Process ◽  
Dirichlet Process Mixture ◽  
Dirichlet Process Mixture Model

Unsupervised Tracking With the Doubly Stochastic Dirichlet Process Mixture Model

2016 ◽  
Vol 17 (9) ◽  
pp. 2594-2599 ◽  
Author(s):  
Xing Sun ◽  
Nelson H. C. Yung ◽  
Edmund Y. Lam
Keyword(s):  
Mixture Model ◽  
Dirichlet Process ◽  
Dirichlet Process Mixture ◽  
Dirichlet Process Mixture Model ◽  
Doubly Stochastic

scholarly journals 0439 Nonlinear Dynamics Forecasting for Personalize Prognosis of Obstructive Sleep Apnea Onsets

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A168-A169
Author(s):  
T Le
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Real Time ◽  
Dirichlet Process ◽  
Clinical Symptoms ◽  
Risk Indicators ◽  
Dirichlet Process Mixture ◽  
Obstructive Sleep ◽  
Estimated Risk ◽  
Normal States

Abstract Introduction The emphasis on disease prevention, early detection, and preventive treatments will revolutionize the way sleep clinicians evaluate their patients. Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders with approximately 100 millions patients been diagnosed worldwide. The effectiveness of sleep disorder therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. Methods This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. The method includes (a) a representation of a transition state space network to characterize dynamic transition of apneic states (b) a Dirichlet-Process Mixture-Gaussian-Process prognostic method for estimating the distribution of the time estimate the remaining time until the onset of an impending OSA episode by considering the stochastic evolution of the normal states to an anomalous (apnea) Results The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of eight subjects from previous work. The average prediction accuracy (R2) is reported as 0.75%, with 87% of observations within the 95% confidence interval. Estimated risk indicators at 1 to 3 min till apnea onset are reported as 85.8 %, 80.2 %, and 75.5 %, respectively. Conclusion The present prognosis approach can be integrated with wearable devices to facilitate individualized treatments and timely prevention therapies. Support N/A

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