Recovery of missing data via wavelets followed by high-dimensional modeling

2017 ◽  
Author(s):  
Ercan Gürvіt ◽  
N. A. Baykara
Keyword(s):  
Missing Data ◽  
High Dimensional ◽  
Dimensional Modeling

A subspace ensemble framework for classification with high dimensional missing data

2016 ◽  
Vol 28 (4) ◽  
pp. 1309-1324 ◽  
Author(s):  
Hang Gao ◽  
Songlei Jian ◽  
Yuxing Peng ◽  
Xinwang Liu
Keyword(s):  
Missing Data ◽  
High Dimensional

scholarly journals Performance-optimized neural networks as an explanatory framework for decision confidence

2021 ◽  
Author(s):  
Taylor W Webb ◽  
Kiyofumi Miyoshi ◽  
Tsz Yan So ◽  
Sivananda Rajananda ◽  
Hakwan Lau
Keyword(s):  
Neural Network ◽  
Signal Detection Theory ◽  
Deep Neural Network ◽  
High Dimensional ◽  
Decision Variable ◽  
Sensory Inputs ◽  
Decision Confidence ◽  
Dimensional Modeling ◽  
Principled Explanation ◽  
Low Dimensional

Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional modeling frameworks, such as signal detection theory or Bayesian inference, leaving open the question of how decision confidence operates in the domain of high-dimensional, naturalistic stimuli. To address this, we developed a deep neural network model optimized to assess decision confidence directly given high-dimensional inputs such as images. The model naturally accounts for a number of puzzling dissociations between decisions and confidence, suggests a principled explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.

Variable selection for ultra-high dimensional quantile regression with missing data and measurement error

2020 ◽  
pp. 096228022094153
Author(s):  
Yongxin Bai ◽  
Maozai Tian ◽  
Man-Lai Tang ◽  
Wing-Yan Lee
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Variable Selection ◽  
Quantile Regression ◽  
Measurement Errors ◽  
Estimation Method ◽  
High Dimensional ◽  
Data Set ◽  
Cancer Data ◽  
Selection For

In this paper, we consider variable selection for ultra-high dimensional quantile regression model with missing data and measurement errors in covariates. Specifically, we correct the bias in the loss function caused by measurement error by applying the orthogonal quantile regression approach and remove the bias caused by missing data using the inverse probability weighting. A nonconvex Atan penalized estimation method is proposed for simultaneous variable selection and estimation. With the proper choice of the regularization parameter and under some relaxed conditions, we show that the proposed estimate enjoys the oracle properties. The choice of smoothing parameters is also discussed. The performance of the proposed variable selection procedure is assessed by Monte Carlo simulation studies. We further demonstrate the proposed procedure with a breast cancer data set.

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