A robust imputation method for missing responses and covariates in sample selection models

A robust imputation method for missing responses and covariates in sample selection models

Sample selection arises when the outcome of interest is partially observed in a study. Although sophisticated statistical methods in the parametric and non-parametric framework have been proposed to solve this problem, it is yet unclear how to deal with selectively missing covariate data using simpl...

Bibliografiset tiedot
Päätekijät:	Ogundimu, E, Collins, G
Aineistotyyppi:	Journal article
Kieli:	English
Julkaistu:	SAGE Publications 2017

Samankaltaisia teoksia

Imputation of Missing Clinical Covariates for Downstream Classification Problems
Tekijä: Benjamin Agbo, et al.
Julkaistu: (2023-01-01)

On the Relation between Prediction and Imputation Accuracy under Missing Covariates
Tekijä: Burim Ramosaj, et al.
Julkaistu: (2022-03-01)

Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods
Tekijä: Seaman Shaun R, et al.
Julkaistu: (2012-04-01)

Robust regression imputation for analyzing missing data
Tekijä: Rana, Md. Sohel, et al.
Julkaistu: (2012)

Robust Random Regression Imputation method for missing data in the presence of outliers
Tekijä: John, Ahamefule Happy
Julkaistu: (2013)

Iterative Robust Semi-Supervised Missing Data Imputation
Tekijä: Nikos Fazakis, et al.
Julkaistu: (2020-01-01)

Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates
Tekijä: Yunquan Song, et al.
Julkaistu: (2022-06-01)

Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes
Tekijä: Nesrin Alkan, et al.
Julkaistu: (2019-08-01)

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.
Tekijä: Marshall, A, et al.
Julkaistu: (2010)

The Effects of Data Imputation on Covariance and Inverse Covariance Matrix Estimation
Tekijä: Tuan L. Vo, et al.
Julkaistu: (2024-01-01)

Imputation of clinical covariates in time series
Tekijä: Bertsimas, Dimitris, et al.
Julkaistu: (2021)

Imputation of missing genotypes: an empirical evaluation of IMPUTE
Tekijä: Steinberg Martin H, et al.
Julkaistu: (2008-12-01)

ExtraImpute: a novel machine learning method for missing data imputation
Tekijä: Alabadla, Mustafa, et al.
Julkaistu: (2022)

Imputing missing genotypes: effects of methods and patterns of missing data
Tekijä: Ogut Funda, et al.
Julkaistu: (2011-09-01)

Variational Autoencoding with Conditional Iterative Sampling for Missing Data Imputation
Tekijä: Shenfen Kuang, et al.
Julkaistu: (2024-10-01)

Evaluation of machine learning methods for covariate data imputation in pharmacometrics
Tekijä: Dominic Stefan Bräm, et al.
Julkaistu: (2022-12-01)

Imputing Covariance for Meta-Analysis in the Presence of Interaction
Tekijä: Enwu Liu, et al.
Julkaistu: (2024-12-01)

Multiple Imputation of Composite Covariates in Survival Studies
Tekijä: Lily Clements, et al.
Julkaistu: (2022-03-01)

Trust score measurement method for web donor selection and imputation of missing values
Tekijä: Jaya, M. Izham, et al.
Julkaistu: (2021)

Dealing with missing data in covariates: The missing indicator method
Tekijä: Jolani, Shahab, et al.
Julkaistu: (2022-09-01)

Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network
Tekijä: Hongsen Ou, et al.
Julkaistu: (2024-02-01)

Flexible Imputation of Missing Data
Tekijä: Hakan Demirtas
Julkaistu: (2018-07-01)

Comparison of regression imputation methods of baseline covariates that predict survival outcomes
Tekijä: Nicole Solomon, et al.
Julkaistu: (2021-01-01)

Performance of selected imputation techniques for missing variances in meta-analysis
Tekijä: Nik Idris, Nik Ruzni, et al.
Julkaistu: (2013)

The Feature Selection Effect on Missing Value Imputation of Medical Datasets
Tekijä: Chia-Hui Liu, et al.
Julkaistu: (2020-03-01)

Multiple imputation of missing data under missing at random: including a collider as an auxiliary variable in the imputation model can induce bias
Tekijä: Elinor Curnow, et al.
Julkaistu: (2023-09-01)

Missing Value Imputation Methods for Electronic Health Records
Tekijä: Konstantinos Psychogyios, et al.
Julkaistu: (2023-01-01)

Multiple imputation methods for missing multilevel ordinal outcomes
Tekijä: Mei Dong, et al.
Julkaistu: (2023-05-01)

Robust and efficient estimation for nonlinear model based on composite quantile regression with missing covariates
Tekijä: Qiang Zhao, et al.
Julkaistu: (2022-02-01)

The effect of missing values for covariates.
Tekijä: Whitlock, G, et al.
Julkaistu: (2002)

ProJect: a powerful mixed-model missing value imputation method
Tekijä: Kong, Weijia, et al.
Julkaistu: (2023)

Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse
Tekijä: Mingxuan FAN, et al.
Julkaistu: (2023-11-01)

Missing value imputation for diabetes prediction
Tekijä: Luo, Fei, et al.
Julkaistu: (2023)

Quantum Circuit for Imputation of Missing Data
Tekijä: Claudio Sanavio, et al.
Julkaistu: (2024-01-01)

Imputing missing distances in molecular phylogenetics
Tekijä: Xuhua Xia
Julkaistu: (2018-07-01)

Missing Data Imputation for Supervised Learning
Tekijä: Jason Poulos, et al.
Julkaistu: (2018-04-01)

Missing Data Imputation for Categorical Variables
Tekijä: Jaroslav Horníček, et al.
Julkaistu: (2022-09-01)

Multiple Imputation of Multilevel Missing Data
Tekijä: Simon Grund, et al.
Julkaistu: (2016-10-01)

Imputing missing values in modelling the PM10 concentrations
Tekijä: Nuradhiathy Abd Razak,, et al.
Julkaistu: (2014)

Low-dimensional models for missing data imputation in road networks
Tekijä: Asif, Muhammad Tayyab, et al.
Julkaistu: (2014)