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pro vyhledávání: '"Qiu, Hongxiang"'
Autor:
Qiu, Hongxiang
Objectives: Highly flexible nonparametric estimators have gained popularity in causal inference and epidemiology. Popular examples of such estimators include targeted maximum likelihood estimators (TMLE) and double machine learning (DML). TMLE is oft
Externí odkaz:
http://arxiv.org/abs/2408.10091
Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in oth
Externí odkaz:
http://arxiv.org/abs/2306.16406
To infer the treatment effect for a single treated unit using panel data, synthetic control methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is
Externí odkaz:
http://arxiv.org/abs/2210.02014
Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small-sample corrections have been proposed
Externí odkaz:
http://arxiv.org/abs/2209.04364
Predicting sets of outcomes -- instead of unique outcomes -- is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructing prediction sets with statistical guarantees, adapting to unknown cov
Externí odkaz:
http://arxiv.org/abs/2203.06126
Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened up
Externí odkaz:
http://arxiv.org/abs/2201.06669
Autor:
Bobb, Jennifer F., Idu, Abisola E., Qiu, Hongxiang, Yu, Onchee, Boudreau, Denise M., Wartko, Paige D., Matthews, Abigail G., McCormack, Jennifer, Lee, Amy K., Campbell, Cynthia I., Saxon, Andrew J., Liu, David S., Altschuler, Andrea, Samet, Jeffrey H., Northrup, Thomas F., Braciszewski, Jordan M., Murphy, Mark T., Arnsten, Julia H., Cunningham, Chinazo O., Horigian, Viviana E., Szapocznik, José, Glass, Joseph E., Caldeiro, Ryan M., Tsui, Judith I., Burganowski, Rachael P., Weinstein, Zoe M., Murphy, Sean M., Hyun, Noorie, Bradley, Katharine A.
Publikováno v:
In Drug and Alcohol Dependence 1 August 2024 261
Autor:
Qiu, Hongxiang, Luedtke, Alex
Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution. However, when this knowledge is too vague to express with a single prior, an alternative approach is need
Externí odkaz:
http://arxiv.org/abs/2012.05465
Suppose that we wish to estimate a finite-dimensional summary of one or more function-valued features of an underlying data-generating mechanism under a nonparametric model. One approach to estimation is by plugging in flexible estimates of these fea
Externí odkaz:
http://arxiv.org/abs/2003.01856
Publikováno v:
Journal of Causal Inference, Vol 10, Iss 1, Pp 480-493 (2022)
Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened up
Externí odkaz:
https://doaj.org/article/04dbd0458bd540fd8abfa42dc7cb1e41