Peisong Han

发表

DOUBLY ROBUST AND LOCALLY EFFICIENT ESTIMATION WITH MISSING OUTCOMES

Lu Wang, Peisong Han, X. Peter, 2016 .

Longitudinal data analysis using the conditional empirical likelihood method

Lu Wang, P. X. Song, Peisong Han, 2014 .

Conditional Empirical Likelihood Approach to Statistical Analysis with Missing Data.

Peisong Han, 2013 .

A note on improving quadratic inference functions using a linear shrinkage approach

P. X. Song, Peisong Han, 2011 .

Empirical and conditional likelihoods for two‐phase studies

J. Lawless, Peisong Han, Menglu Che, 2020, Canadian Journal of Statistics.

Empirical Likelihood Estimation Using Auxiliary Summary Information with Different Covariate Distributions

J. Lawless, Peisong Han, 2019, Statistica Sinica.

Comment

J. Lawless, Peisong Han, 2016 .

A multiply robust Mann-Whitney test for non-randomised pretest-posttest studies with missing data

Changbao Wu, Peisong Han, Shixiao Zhang, 2020 .

Understanding the Tradeoffs Between Travel Burden and Quality of Care for In-center Hemodialysis Patients

Garrett W. Gremel, Peisong Han, R. Hirth, 2020, Medical care.

Empirical likelihood inference for non-randomized pretest-posttest studies with missing data

Changbao Wu, Peisong Han, Shixiao Zhang, 2019, Electronic Journal of Statistics.

Improving estimation efficiency for regression with MNAR covariates

J. Lawless, Peisong Han, Menglu Che, 2019, Biometrics.

Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference

Peisong Han, Shixiao Zhang, Changbao Wu, 2022, International Statistical Review.

Robust causal inference of drug‐drug interactions

S. Hennessy, Peisong Han, T. Miano, 2022, Statistics in medicine.

COVID-19 Risk Factors and Mortality Outcomes Among Medicare Patients Receiving Long-term Dialysis

Garrett W. Gremel, Peisong Han, R. Hirth, 2021, JAMA network open.

Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

M. Sjoding, Peisong Han, Xinwei He, 2021, medRxiv.

Multiply Robust Estimation in Regression Analysis With Missing Data

Peisong Han, Peisong Han, 2014 .

Estimation with missing data: beyond double robustness

Lu Wang, Peisong Han, Lu Wang, 2013 .

Integrating information from existing risk prediction models with no model details

J. M. Taylor, B. Mukherjee, Peisong Han, 2022, The Canadian journal of statistics = Revue canadienne de statistique.

Data Integration with Oracle Use of External Information from Heterogeneous Populations

Peisong Han, Yuqi Zhai, 2022, J. Comput. Graph. Stat..

Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods

Lauren J. Beesley, Stephen A. Goutman, S. Batterman, 2020, J. Comput. Graph. Stat..

Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out

Peisong Han, Peisong Han, 2016 .

Covid-19 pandemic and lockdown impacts: A description in a longitudinal study of bipolar disorder

M. McInnis, A. Yocum, Peisong Han, 2021, Journal of Affective Disorders.

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality

S. Jolly, M. Sjoding, Peisong Han, 2023, Scientific reports.

A general framework for quantile estimation with incomplete data

Linglong Kong, Peisong Han, Xingcai Zhou, 2019, Journal of the Royal Statistical Society. Series B, Statistical methodology.

Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation

Peisong Han, Peisong Han, 2016 .

Stability of personality traits in bipolar disorder: Findings from a longitudinal cohort: Personality and bipolar disorder.

M. McInnis, S. Zöllner, A. Yocum, 2021, Journal of affective disorders.

Late chronotype predicts more depressive symptoms in bipolar disorder over a 5 year follow-up period

M. McInnis, A. Yocum, Peisong Han, 2021, International Journal of Bipolar Disorders.

Multiply robust estimation in nonparametric regression with missing data

Yilun Sun, Lu Wang, Peisong Han, 2020 .

A further study of the multiply robust estimator in missing data analysis

Peisong Han, Peisong Han, 2014 .

Calibration and Multiple Robustness When Data Are Missing Not At Random

Peisong Han, 2018 .

Improving main analysis by borrowing information from auxiliary data

Peisong Han, Chixiang Chen, Fanfan He, 2021, Statistics in medicine.

Exploring the big data paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS) (preprint)

S. Richardson, W. Dempsey, Peisong Han, 2023, 2306.14940.

Lunar cycling in sleep and mood in individuals with bipolar disorder

M. McInnis, Peisong Han, H. Burgess, 2022, International Journal of Bipolar Disorders.

Daily ratings of mood and sleep duration over 3.3 years are associated with the lunar illumination cycle in a rapid cycling bipolar patient

M. McInnis, Peisong Han, H. Burgess, 2021, Bipolar disorders.

Robust estimation for moment condition models with data missing not at random

Shu Yang, Peisong Han, Wei Li, 2020 .

Cohort Profile Update: The Heinz C. Prechter Longitudinal Study of Bipolar Disorder.

Amy L. Cochran, Paul M. Jenkins, M. McInnis, 2023, International journal of epidemiology.

Data integration: exploiting ratios of parameter estimates from a reduced external model.

J. M. Taylor, Peisong Han, Kyuseong Choi, 2022, Biometrika.

Estimating the marginal hazard ratio by simultaneously using a set of propensity score models: A multiply robust approach

Peisong Han, Sengwee Toh, Di Shu, 2021, Statistics in medicine.

Improving estimation efficiency for two-phase, outcome-dependent sampling studies

Peisong Han, Menglu Che, J. Lawless, 2022, Electronic Journal of Statistics.

A unified empirical likelihood approach for testing MCAR and subsequent estimation

Peisong Han, Shixiao Zhang, Changbao Wu, 2018, Scandinavian Journal of Statistics.

Understanding the Tradeoffs Between Travel Burden and Quality of Care for In-center Hemodialysis Patients

Garrett W. Gremel, Peisong Han, R. Hirth, 2021, Medical care.