An efficient monotone data augmentation algorithm for Bayesian analysis of incomplete longitudinal data

2015 ◽  
Vol 104 ◽  
pp. 146-152 ◽  
Author(s):  
Yongqiang Tang
Keyword(s):  
Longitudinal Data ◽  
Bayesian Analysis ◽  
Data Augmentation ◽  
Augmentation Algorithm ◽  
Monotone Data

Component-mixing Strategy: A Decomposition-based Data Augmentation Algorithm for Motor Imagery Signals

2021 ◽  
Author(s):  
Binghua Li ◽  
Zhiwen Zhang ◽  
Feng Duan ◽  
Zhenglu Yang ◽  
Qibin Zhao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Data Augmentation ◽  
Augmentation Algorithm

scholarly journals Data Augmentation Algorithm Based on Generative Antagonism Networks (GAN) Model for Optical Transmission Networks (OTN)

2021 ◽  
Author(s):  
Liang Chen ◽  
Kunpeng Zheng ◽  
Yang Li ◽  
Xuelian Yang ◽  
Han Zhang ◽  
...  
Keyword(s):  
Optical Transmission ◽  
Fault Location ◽  
Data Augmentation ◽  
High Reliability ◽  
Data Traffic ◽  
Transmission Networks ◽  
Large Bandwidth ◽  
Augmentation Algorithm ◽  
Length Data ◽  
High Level

OTN (Optical Transmission Networks) is one of the mainstream infrastructures over the ground-transmission networks, with the characteristics of large bandwidth, low delay, and high reliability. To ensure a stable working of OTN, it is necessary to preform high-level accurate functions of data traffic analysis, alarm prediction, and fault location. However, there is a serious problem for the implementation of these functions, caused by the shortage of available data but a rather-large amount of dirty data existed in OTN. In the view of current pretreatment, the extracted amount of effective data is very less, not enough to support machine learning. To solve this problem, this paper proposes a data augmentation algorithm based on deep learning. Specifically, Data Augmentation for Optical Transmission Networks under Multi-condition constraint (MVOTNDA) is designed based on GAN Mode with the demonstration of variable-length data augmentation method. Experimental results show that MVOTNDA has better performances than the traditional data augmentation algorithms.

LDA-based data augmentation algorithm for acoustic scene classification

2020 ◽  
Vol 195 ◽  
pp. 105600
Author(s):  
Yan Leng ◽  
Weiwei Zhao ◽  
Chan Lin ◽  
Chengli Sun ◽  
Rongyan Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Scene Classification ◽  
Augmentation Algorithm

scholarly journals A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal or t distributions

2019 ◽  
Vol 29 (6) ◽  
pp. 1542-1562 ◽  
Author(s):  
Yongqiang Tang
Keyword(s):  
Repeated Measures ◽  
Data Augmentation ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Monte Carlo Algorithm ◽  
Sensitivity Analyses ◽  
Special Cases ◽  
Monotone Data ◽  
T Distribution ◽  
Skew Normal

The mixed effects model for repeated measures has been widely used for the analysis of longitudinal clinical data collected at a number of fixed time points. We propose a robust extension of the mixed effects model for repeated measures for skewed and heavy-tailed data on basis of the multivariate skew-t distribution, and it includes the multivariate normal, t, and skew-normal distributions as special cases. An efficient Markov chain Monte Carlo algorithm is developed using the monotone data augmentation and parameter expansion techniques. We employ the algorithm to perform controlled pattern imputations for sensitivity analyses of longitudinal clinical trials with nonignorable dropouts. The proposed methods are illustrated by real data analyses. Sample SAS programs for the analyses are provided in the online supplementary material.

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