A unified minorization-maximization approach for fast and accurate estimation in high-dimensional parametric and semiparametric models

2018 ◽  
Author(s):  
Xifen Huang
Keyword(s):  
Semiparametric Models ◽  
High Dimensional ◽  
Accurate Estimation ◽  
Parametric And Semiparametric Models

Bounds for the hazard rate and the reversed hazard rate of the convolution of dependent random lifetimes

2019 ◽  
Vol 56 (4) ◽  
pp. 1033-1043 ◽  
Author(s):  
Félix Belzunce ◽  
Carolina Martínez-Riquelme
Keyword(s):  
Recent Result ◽  
Upper Bound ◽  
Hazard Rate ◽  
Rate Function ◽  
Semiparametric Models ◽  
Hazard Rate Function ◽  
Reversed Hazard Rate ◽  
Parametric And Semiparametric Models ◽  
Reversed Hazard Rate Function

AbstractAn upper bound for the hazard rate function of a convolution of not necessarily independent random lifetimes is provided, which generalizes a recent result established for independent random lifetimes. Similar results are considered for the reversed hazard rate function. Applications to parametric and semiparametric models are also given.

scholarly journals High-dimensional semi-supervised learning: in search of optimal inference of the mean

Biometrika ◽  
2021 ◽  
Author(s):  
Yuqian Zhang ◽  
Jelena Bradic
Keyword(s):  
Supervised Learning ◽  
Semiparametric Models ◽  
High Dimensional ◽  
Robust Estimator ◽  
Consistent Estimation ◽  
Heterogeneous Treatment Effects ◽  
Double Robust ◽  
Fundamental Challenge ◽  
The Mean ◽  
Linear And Nonlinear

Abstract A fundamental challenge in semi-supervised learning lies in the observed data’s disproportional size when compared with the size of the data collected with missing outcomes. An implicit understanding is that the dataset with missing outcomes, being significantly larger, ought to improve estimation and inference. However, it is unclear to what extent this is correct. We illustrate one clear benefit: root-n inference of the outcome’s mean is possible while only requiring a consistent estimation of the outcome, possibly at a rate slower than root-n. This is achieved by a novel k-fold cross-fitted, double robust estimator. We discuss both linear and nonlinear outcomes. Such an estimator is particularly suited for models that naturally do not admit root-n consistency, such as high-dimensional, nonparametric, or semiparametric models. We apply our methods to the heterogeneous treatment effects.

A fast resample method for parametric and semiparametric models

2014 ◽  
Vol 179 (2) ◽  
pp. 128-133 ◽  
Author(s):  
Timothy B. Armstrong ◽  
Marinho Bertanha ◽  
Han Hong
Keyword(s):  
Semiparametric Models ◽  
Parametric And Semiparametric Models
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