scholarly journals Double/Debiased Machine Learning for Logistic Partially Linear Model

2021 ◽  
Author(s):  
Molei Liu ◽  
Yi Zhang ◽  
Doudou Zhou

Abstract We propose double/debiased machine learning approaches to infer parametric component of a logistic partially linear model. Our framework is based on a Neyman orthogonal score equation consisting two nuisance models for nonparametric component of the logistic model and conditional mean of the exposure with among the control group. To estimate the nuisance models, we separately consider the use of high dimensional (HD) sparse regression and (nonparametric) machine learning (ML) methods. In the HD case, we derive certain moment equations to calibrate the first order bias of the nuisance models, which preserves model double robustness property. In the ML case, we handle the non-linearity of the logit link through a novel and easy-to-implement “full model refitting” procedure. We evaluate our methods through simulation and apply them in assessing the effect of the emergency contraceptive (EC) pill on early gestation and new births based on a 2008 policy reform in Chile.

2004 ◽  
Vol 14 (06) ◽  
pp. 1975-1985
Author(s):  
RASTKO ŽIVANOVIĆ

The task of locating an arcing-fault on overhead line using sampled measurements obtained at a single line terminal could be classified as a practical nonlinear system identification problem. The practical reasons impose the requirement that the solution should be with maximum possible precision. Dynamic behavior of an arc in open air is influenced by the environmental conditions that are changing randomly, and therefore the useful practically application of parametric modeling is out of question. The requirement to identify only one parameter is yet another specific of this problem. The parameter we need is the one that linearly correlates the voltage samples with the current derivative samples (inductance). The correlation between the voltage samples and the current samples depends on the unpredictable arc dynamic behavior. Therefore this correlation is reconstructed using nonparametric regression. A partially linear model combines both, parametric and nonparametric parts in one model. The fit of this model is noniterative, and provides an efficient way to identify (pull out) a single linear correlation from the nonlinear time series.


2021 ◽  
pp. 096228022110028
Author(s):  
T Baghfalaki ◽  
M Ganjali

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.


2018 ◽  
Vol 52 (19) ◽  
pp. 11215-11222 ◽  
Author(s):  
Noel J. Aquilina ◽  
Juana Maria Delgado-Saborit ◽  
Stefano Bugelli ◽  
Jason Padovani Ginies ◽  
Roy M. Harrison

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jibo Wu ◽  
Yong Li

In this paper, we introduce a new restricted Liu estimator in a partially linear model when addition linear constraints are assumed to hold. We also consider the asymptotic normality of the new estimator. Finally, a numerical example and a simulation study are listed to illustrate the performance of the new estimator.


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