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Estimation in the Semiparametric Accelerated Failure Time Model With Missing Covariates: Improving Efficiency Through Augmentation

Jon Arni Steingrimsson, Robert L. Strawderman
2016 Figshare  
This article considers linear regression with missing covariates and a right censored outcome.  ...  With suitable modification, the proposed methodology can also be used to improve augmented estimators previously used for missing covariates in a Cox regression model.  ...  monotone missing covariate information and known missingness mechanisum; we do not assume that all failures are sampled.  ... 
doi:10.6084/m9.figshare.3487661 fatcat:g6jfudpoxjg7tpehhghjvaznl4

Semiparametric estimation of logistic regression model with missing covariates and outcome

Shen-Ming Lee, Chin-Shang Li, Shu-Hui Hsieh, Li-Hui Huang
2011 Metrika (Heidelberg)  
We consider a semiparametric method to estimate logistic regression models with missing both covariates and an outcome variable, and propose two new estimators.  ...  Both estimators are semiparametric as they do not require any model assumptions regarding the missing data mechanism nor the specification of the conditional distribution of the missing covariates given  ...  Conclusion We have proposed a semiparametric approach to estimate the logistic regression model with missing both covariates and an outcome variable.  ... 
doi:10.1007/s00184-011-0345-9 fatcat:y5iefjz7xbcy5an2cxs26gp22i

Semiparametric estimation of a zero-inflated Poisson regression model with missing covariates

T. Martin Lukusa, Shen-Ming Lee, Chin-Shang Li
2015 Metrika (Heidelberg)  
with incomplete Covariates May 2014 5 / 29 u = γ T X .  ...  Hall and shen (2010) [6], has proposed a robust ZIP estimator, Li (2010) [9], has studied the lack of fit for parametric test, and Li (2011) [8], proposed a semiparametric score test for ZIP.  ...  of θ. θ Wt which is the true weight IPW estimator of θ. θ Ws which is the semiparametric IPW estimator of θ.  ... 
doi:10.1007/s00184-015-0563-7 fatcat:pq5xuvuzlffh3aqos5apreaafy

Semiparametric analysis based on weighted estimating equations for transformation models with missing covariates

Bin Huang, Qihua Wang
2010 Journal of Multivariate Analysis  
case of missing covariates.  ...  Missing covariate data are very common in regression analysis.  ...  mean zero and covariance A −1 Σ 1 (A −1 ) .β may not be efficient since this method excludes all subjects with missing covariates when the true non-missingness probability π is used.  ... 
doi:10.1016/j.jmva.2010.02.008 fatcat:saroxmt2dzd53p5teja4j4hyou

Estimation of the Asymptotic Variance of Semiparametric Maximum Likelihood Estimators in the Cox Model with a Missing Time-Dependent Covariate

J.-F. Dupuy, M. Mesbah
2004 Communications in Statistics - Theory and Methods  
A recent approach for estimating the Cox model with a missing covariate jointly models the time-to-event and covariate. In the case of a time-dependent covariate, Dupuy and Mesbah [Dupuy, J.  ...  A frequently encountered problem however is occurrence of missing covariate values.  ...  have proposed a joint modeling approach for estimating the Cox model with a nonignorable missing time-dependent covariate.  ... 
doi:10.1081/sta-120030156 fatcat:v6ezzbyktrbf7misft5rzcxgpm

ML- and semiparametric estimation in logistic models with incomplete covariate data

Vanessa Didelez
2002 Statistica neerlandica (Print)  
Maximum likelihood estimation of regression parameters with incomplete covariate information usually requires a distributional assumption about the concerned covariates which implies a source of misspeci  ...  A simulation study is carried out to get an idea of the performance of the maximum likelihoodestimator under misspeci cation and to compare the semiparametric procedures with the maximum likelihood estimator  ...  The chosen missing mechanisms can beread o Table 1 . The rst mechanism means missing completely at random (MCAR) since the missingness is independent of Y and X 1 .  ... 
doi:10.1111/1467-9574.t01-1-00059 fatcat:nfcwpbsmjrfglfa4amj2kujj7u

Logistic regression with outcome and covariates missing separately or simultaneously

Shu-Hui Hsieh, Chin-Shang Li, Shen-Ming Lee
2013 Computational Statistics & Data Analysis  
Estimation methods are proposed for fitting logistic regression in which outcome and covariate variables are missing separately or simultaneously.  ...  The practical use of the proposed methods is illustrated with data from a cable TV survey study in Taiwan.  ...  Zhao et al. (2009) extended the semiparametric maximum likelihood method for missing covariate problems to deal with more general cases where covariates and/or responses are missing by design in which  ... 
doi:10.1016/j.csda.2013.03.007 fatcat:kcszz3f6cnbdzjthey4ofe3flu

Semiparametric Models: A Review of Progress since BKRW (1993) [chapter]

Jon A. Wellner, Chris A. J. Klaassen, Ya'acov Ritov
2006 Frontiers in Statistics  
This paper sketches a review of the developments in semiparametric statistics since the publication in 1993 of the monograph by Bickel, Klaassen, Ritov, and Wellner.  ...  Semiparametric efficient estimation of a conditional density with missing or mismeasured covariates. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 409 -424. 31. Robins, J. M. and Rotnitzky, A. (1992).  ...  A major development in this area was the systematic development of information bounds for semiparametric regression models with covariates missing at random by Robins, Rotnitzky, and Zhao (1994), Robins  ... 
doi:10.1142/9781860948886_0002 fatcat:6utx6nlukbhbhgkxd7zincz7x4

Bayesian semiparametric regression for longitudinal binary processes with missing data

Li Su, Joseph W. Hogan
2008 Statistics in Medicine  
In this article, we develop a Bayesian regression model for analyzing longitudinal binary process data, with emphasis on dealing with missingness.  ...  Serial dependence is allowed to depend on the time lag between adjacent outcomes as well as other relevant covariates. Inference is fully Bayesian.  ...  on dealing with missing data.  ... 
doi:10.1002/sim.3265 pmid:18351709 pmcid:PMC2581820 fatcat:ne75ch3e7zeevoohxmqnjea5su

Page 8589 of Mathematical Reviews Vol. , Issue 2003k [page]

2003 Mathematical Reviews  
62G05 62N01 62N02 62N05 Chen, Hua Yun (1-[LCC-PBE; Chicago, IL) Double-semiparametric method for missing covariates in Cox regression models.  ...  The proposed method can yield a more efficient estimator than the nonparametric imputation methods and does not require specification of the missingness mechanism when compared with the inverse probability  ... 

Efficiency Comparisons in Multivariate Multiple Regression with Missing Outcomes

Andrea Rotnitzky, Christina A Holcroft, James M Robins
1997 Journal of Multivariate Analysis  
generalized estimating equation (GEE) estimators when the complete vector of outcomes is not always observed, the missing data patterns are monotone and the data are missing completely at random (MCAR  ...  We show that when the covariance of the outcome given the covariates is constant, as opposed to the nonmissing data case: (a) the GLS estimator is more efficient than the OLS estimator, (b) the GLS estimator  ...  With monotone MCAR outcomes the estimators ; G , ; G , ; GEE and ; OLS are consistent for estimating ; but they may be less efficient than the semiparametric efficient estimator ; EFF of ; in the model  ... 
doi:10.1006/jmva.1997.1660 fatcat:qxdhc7j65rdctasjsaxtgfhtx4

Parametric and Semiparametric Model-Based Estimates of the Finite Population Mean for Two-Stage Cluster Samples with Item Nonresponse

Ying Yuan, Roderick J. A. Little
2007 Biometrics  
To obtain consistent estimates, we extend the standard random-effects model by modeling these two types of missing data mechanism.  ...  Specifically, we focus on two types of nonignorable nonresponse: nonresponse depending on covariates and underlying cluster characteristics, and depending on covariates and the missing outcome.  ...  In this case, the missingness of y ij only depends on covariates x ij , and the missing data mechanism is MAR.  ... 
doi:10.1111/j.1541-0420.2007.00816.x pmid:17489967 fatcat:bqq2x5pvbfbydpx76yvcnhdrcm

Empirical Likelihood Weighted Estimation of Average Treatment Effects [article]

Yuanyao Tan, Xialing Wen, Wei Liang, Ying Yan
2020 arXiv   pre-print
In theory, we show that the proposed ELW estimator is semiparametric efficient.  ...  We conduct simulations to make comparisons with other existing estimators, which confirm the efficiency and multiple robustness property of our proposed ELW estimator.  ...  To evaluate the efficiency of our estimator, we demonstrate the proposed estimator is semiparametric efficient given data without or with missingness.  ... 
arXiv:2008.12989v1 fatcat:7n4wal4drva4relsu2pzq7bcju

Mann-Whitney test with adjustments to pretreatment variables for missing values and observational study

Song Xi Chen, Jing Qin, Cheng Yong Tang
2012 Journal of The Royal Statistical Society Series B-statistical Methodology  
We also propose semiparametric extensions of the adjusted Mann-Whitney test which leads to dimension reduction for high dimensional covariate.  ...  This is because the missingness of the outcomes or the participation in the treatments may depend on certain pre-treatment variables.  ...  Appendix: Technical Details A.1 Proof of Lemma 1 We start with an expansion for the Mann-Whitney statistic W n which is used in proving Theorems 1 and 2.  ... 
doi:10.1111/j.1467-9868.2012.01036.x fatcat:p44r7ojthfc67p243vg4viklwu

Missing Data: A Non-ignorable Issue in Modern Biostatistics

Baojiang Chen, Kendra K Schmid
2017 Journal of Applied Bioinformatics & Computational Biology  
One major problem in dealing with missing response and missing covariates is that there can be an association between the missingness of the response and covariates.  ...  Chen and Ibrahim [7] develop semiparametric models for missing covariate and response data in regression models. Ignorable and non-ignorable missing mechanisms are considered.  ... 
doi:10.4172/2329-9533.1000e101 fatcat:csj63pjvvrcyfmyeq2oc3pewku
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