Changbao Wu
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View article: Estimation of Treatment Harm Rate via Partitioning
Estimation of Treatment Harm Rate via Partitioning Open
In causal inference with binary outcomes, there is a growing interest in estimation of treatment harm rate (THR), which is a measure of treatment risk and reveals treatment effect heterogeneity in a subpopulation. The THR is generally non-…
View article: Suicides in China's scientific community: A call for a public health response
Suicides in China's scientific community: A call for a public health response Open
The rising number of suicide cases in Chinese academia are not isolated incidents but rather reflect systemic issues within the academic and sociopolitical environment. A public health response that enhances our understanding of root cause…
View article: Sample empirical likelihood methods for causal inference
Sample empirical likelihood methods for causal inference Open
Causal inference plays a crucial role in understanding the true impact of interventions, medical treatments, policies, or actions, enabling informed decision making and providing insights into the underlying mechanisms that shape our world…
View article: Pseudo-empirical likelihood methods for causal inference
Pseudo-empirical likelihood methods for causal inference Open
View article: Statistical Inference with Nonignorable Non-Probability Survey Samples
Statistical Inference with Nonignorable Non-Probability Survey Samples Open
Statistical inference with non-probability survey samples is an emerging topic in survey sampling and official statistics and has gained increased attention from researchers and practitioners in the field. Much of the existing literature, …
View article: Sample Empirical Likelihood Methods for Causal Inference
Sample Empirical Likelihood Methods for Causal Inference Open
Causal inference is crucial for understanding the true impact of interventions, policies, or actions, enabling informed decision-making and providing insights into the underlying mechanisms that shape our world. In this paper, we establish…
View article: Pseudo-Empirical Likelihood Methods for Causal Inference
Pseudo-Empirical Likelihood Methods for Causal Inference Open
Causal inference problems have remained an important research topic over the past several decades due to their general applicability in assessing a treatment effect in many different real-world settings. In this paper, we propose two infer…
View article: Protocol for validating an algorithm to identify neurocognitive disorders in Canadian Longitudinal Study on Aging participants: an observational study
Protocol for validating an algorithm to identify neurocognitive disorders in Canadian Longitudinal Study on Aging participants: an observational study Open
Introduction In population-based research, disease ascertainment algorithms can be as accurate as, and less costly than, performing supplementary clinical examinations on selected participants to confirm a diagnosis of a neurocognitive dis…
View article: Colquhounia Root Tablet Promotes Autophagy and Inhibits Apoptosis in Diabetic Nephropathy by Suppressing CD36 Expression In Vivo and In Vitro
Colquhounia Root Tablet Promotes Autophagy and Inhibits Apoptosis in Diabetic Nephropathy by Suppressing CD36 Expression In Vivo and In Vitro Open
Background/Aims. Accumulating clinical evidence suggests that Colquhounia root tablet (CRT) has the potential to alleviate diabetic nephropathy (DN); however, the exact mechanism of action remains unclear. Here, we report the effects of CR…
View article: Augmented two-step estimating equations with nuisance functionals and complex survey data
Augmented two-step estimating equations with nuisance functionals and complex survey data Open
Statistical inference in the presence of nuisance functionals with complex survey data is an important topic in social and economic studies. The Gini index, Lorenz curves and quantile shares are among the commonly encountered examples. The…
View article: A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series
A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series Open
Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which sh…
View article: A hierarchical expected improvement method for Bayesian optimization
A hierarchical expected improvement method for Bayesian optimization Open
The Expected Improvement (EI) method, proposed by Jones et al. (1998), is a widely-used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficient black-box optimization. However, one key drawback of EI …
View article: A Hierarchical Expected Improvement Method for Bayesian Optimization
A Hierarchical Expected Improvement Method for Bayesian Optimization Open
The Expected Improvement (EI) method, proposed by Jones, Schonlau, andWelch, is a widely used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficient black-box optimization. However, one key drawba…
View article: Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference
Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference Open
Summary We provide a critical review on calibration methods developed in three different areas: survey sampling, missing data analysis and causal inference. We highlight the connections and variations of calibration techniques used in miss…
View article: Semiparametric empirical likelihood inference with estimating equations under density ratio models
Semiparametric empirical likelihood inference with estimating equations under density ratio models Open
The density ratio model (DRM) provides a flexible and useful platform for combining information from multiple sources. In this paper, we consider statistical inference under two-sample DRMs with additional parameters defined through and/or…
View article: Combining Non-Probability and Probability Survey Samples Through Mass Imputation
Combining Non-Probability and Probability Survey Samples Through Mass Imputation Open
Analysis of non-probability survey samples requires auxiliary information at the population level. Such information may also be obtained from an existing probability survey sample from the same finite population. Mass imputation has been u…
View article: Semiparametric inference on Gini indices of two semicontinuous populations under density ratio models
Semiparametric inference on Gini indices of two semicontinuous populations under density ratio models Open
The Gini index is a popular inequality measure with many applications in social and economic studies. This paper studies semiparametric inference on the Gini indices of two semicontinuous populations. We characterize the distribution of ea…
View article: Semiparametric empirical likelihood inference with estimating equations under density ratio models
Semiparametric empirical likelihood inference with estimating equations under density ratio models Open
The density ratio model (DRM) provides a flexible and useful platform for combining information from multiple sources. In this paper, we consider statistical inference under two-sample DRMs with additional parameters defined through and/or…
View article: On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
On Prediction Properties of Kriging: Uniform Error Bounds and Robustness Open
Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict…
View article: Modelling Complex Survey Data Using R, SAS, SPSS and Stata: A Comparison Using CLSA Datasets
Modelling Complex Survey Data Using R, SAS, SPSS and Stata: A Comparison Using CLSA Datasets Open
The R software has become popular among researchers due to its flexibility and open-source nature. However, researchers in the fields of public health and epidemiological studies are more customary to commercial statistical softwares such …
View article: Empirical Likelihood Inference With Public-Use Survey Data
Empirical Likelihood Inference With Public-Use Survey Data Open
Public-use survey data are an important source of information for researchers in social science and health studies to build statistical models and make inferences on the target finite population. This paper presents two general inferential…
View article: Semiparametric Inference of the Youden Index and the Optimal Cutoff Point under Density Ratio Models
Semiparametric Inference of the Youden Index and the Optimal Cutoff Point under Density Ratio Models Open
The Youden index is a popular summary statistic for receiver operating characteristic curve. It gives the optimal cutoff point of a biomarker to distinguish the diseased and healthy individuals. In this paper, we propose to model the distr…
View article: Calibration for Computer Experiments With Binary Responses and Application to Cell Adhesion Study
Calibration for Computer Experiments With Binary Responses and Application to Cell Adhesion Study Open
Calibration refers to the estimation of unknown parameters which are present in computer experiments but not available in physical experiments. An accurate estimation of these parameters is important because it provides a scientific unders…
View article: Empirical likelihood inference with public-use survey data
Empirical likelihood inference with public-use survey data Open
Public-use survey data are an important source of information for researchers in social sciences and health studies to build statistical models and make inferences on the target finite population. This paper presents two general inferentia…
View article: On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
On Prediction Properties of Kriging: Uniform Error Bounds and Robustness Open
Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict…
View article: Secondhand smoke exposure and support for smoke-free policies in cities and rural areas of China from 2009 to 2015: a population-based cohort study (the ITC China Survey)
Secondhand smoke exposure and support for smoke-free policies in cities and rural areas of China from 2009 to 2015: a population-based cohort study (the ITC China Survey) Open
Objectives To examine trends in smoking prevalence in key venues (workplaces, restaurants, bars) and in public support for comprehensive smoke-free laws, with comparisons between cities and rural areas in China. Design Data are from Waves …
View article: A hierarchical expected improvement method for Bayesian optimization
A hierarchical expected improvement method for Bayesian optimization Open
The Expected Improvement (EI) method, proposed by Jones et al. (1998), is a widely-used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficient black-box optimization. However, one key drawback of EI …
View article: Corrigendum to: Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA)
Corrigendum to: Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA) Open
View article: Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA)
Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA) Open
View article: Comprehensive comparisons of major sequential design procedures for sensitivity testing
Comprehensive comparisons of major sequential design procedures for sensitivity testing Open
The main goal of a good sensitivity testing procedure is to quickly determine at which stimulus level a specified fraction of test items fail. Based on a previous study, two procedures, D-optimal and 3pod, appear to be most promising. In t…