Han Du
Biographic Data
| ID | 903494 |
|---|---|
| NAME | Han Du |
| GIVEN NAMES | Han |
| FAMILY NAME | Du |
| SIGNATURE | DU H |
| AFFILIATIONS | University of California, Los Angeles |
| ORCID | 0000-0001-7538-7789 |
| VERIFIED | Yes |
| TOTAL WORKS | 22 |
| TOTAL CITATIONS | 16 |
| AUTHOR COUNT | 22 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Advancing Psychological Research With Random Forests
Contemporary psychological research increasingly involves machine-learning techniques, including random forests, for their capability in analyzing complex, high-dimensional data sets and modeling nonlinear predictive relations. In this article, we provide a comprehensive review of random-forest methods in psychological research. We begin by introducing the fundamental concepts of decision trees, followed by the theoretical framework of random for…
Demystifying Posterior Distributions
Bayesian statistics have gained significant traction across various fields over the past few decades. Bayesian statistics textbooks often provide both code and the analytical forms of parameters for simple models. However, they often omit the process of deriving posterior distributions or limit it to basic univariate examples focused on the mean and variance. Additionally, these resources frequently assume a strong background in linear algebra an…
Exploring the influence of block environmental characteristics on land surface temperature and its spatial heterogeneity for a high-density city
Synergistic effects of Lianhuaqingwen in combination with Oseltamivir and Baloxavir against seasonal influenza virus
Estimating the Weight Matrix in Distributionally Weighted Least Squares Estimation
Real data are unlikely to be exactly normally distributed. Ignoring non-normality will cause misleading and unreliable parameter estimates, standard error estimates, and model fit statistics. For non-normal data, researchers have proposed a distributionally-weighted least squares (DLS) estimator to combines the normal theory based generalized least squares estimation (GLSN) and WLS. The key in DLS is to select an optimal weight as to compute a we…
Weight stigma as a stressor
Covid-19 Increased Mortality Salience, Collectivism, and Subsistence Activities
How does a life-threatening pandemic affect a culture? The Theory of Social Change, Cultural Evolution, and Human Development predicts that danger, as indicated by rising death rates and narrowing social worlds, shifts human psychology and behavior toward that found in small-scale, collectivistic, and rural subsistence ecologies. In particular, mortality salience, collectivism, and engagement in subsistence activities should increase as death rat…
Extended Unbiased Distribution Free Estimator With Mean Structures
To handle the nonnormal data issue, Browne proposed an unbiased distribution free (DF) estimator (Γ^DFU) and an asymptotically distribution free estimator (Γ^ADF) of the covariance matrix of sample variances/covariances Γ to calculate robust test statistics and robust standard errors. However, Γ^DFU is ignored in methodological and substantive research, and has not been extended to models with mean structures. To improve robust standard errors an…
Bootstrap-Based Between-Study Heterogeneity Tests in Meta-Analysis
Meta-analysis combines pertinent information from existing studies to provide an overall estimate of population parameters/effect sizes, as well as to quantify and explain the differences between studies. However, testing between-study heterogeneity is one of the most challenging tasks in meta-analysis research. Existing methods for testing heterogeneity, such as the Q test and likelihood ratio (LR) test, have been criticized for their failure to…
Distributionally-Weighted Least Squares in Growth Curve Modeling
Growth curve modeling is commonly used in psychological, educational, and social science research. The mainstream estimators for growth curve modeling are based on normal theory, but real data are unlikely to be exactly normally distributed. To improve estimation and inference with non-normal data, various estimators have been proposed. Among these estimators, the asymptotically distribution free (ADF) estimator does not need to rely on any distr…
Year Old Unbiased Distribution Free Estimator Reliably Improves SEM Statistics for Nonnormal Data
In structural equation modeling, researchers conduct goodness-of-fit tests to evaluate whether the specified model fits the data well. With nonnormal data, the standard goodness-of-fit test statistic T does not follow a chi-square distribution. Comparing T to χdf2 can fail to control Type I error rates and lead to misleading model selection conclusions. To better evaluate model fit, researchers have proposed various robust test statistics, but no…
A Bayesian Latent Variable Selection Model for Nonignorable Missingness
Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and failing to accommodate incomplete covariates with interactions, non-li…
Buprenorphine reduces methamphetamine intake and drug seeking behavior via activating nociceptin/orphanin FQ peptide receptor in rats
Buprenorphine, which has been approved for the treatment of opioid dependence, reduces cocaine consumption by co-activating μ-opioid receptors and nociceptin/orphanin FQ peptide (NOP) receptors. However, the role of buprenorphine in methamphetamine (METH) reinforcement and drug-seeking behavior remains unclear. This study investigated the effects of buprenorphine on METH self-administration and reinstatement of METH-seeking behavior in rats. We f…
Arima model for predicting chronic kidney disease and estimating its economic burden in China
The number of patients with CKD and the economic burden of CKD will continue to rise in China. The number of patients with CKD in China would increase by 2.6 million (1.6%) per year on average from 2020 to 2025. Meanwhile, the total economic burden of CKD in China would increase by an average of $3.1 billion per year. The ARIMA model is applicable to predict the number of patients with CKD. This study provides a new perspective for more comprehen…
The open access usage advantage
Testing Variance Components in Linear Mixed Modeling Using Permutation
Inference of variance components in linear mixed modeling (LMM) provides evidence of heterogeneity between individuals or clusters. When only nonnegative variances are allowed, there is a boundary (i.e., 0) in the variances’ parameter space, and regular inference statistical procedures for such a parameter could be problematic. The goal of this article is to introduce a practically feasible permutation method to make inferences about variance com…
A Fully Conditional Specification Approach to Multilevel Multiple Imputation with Latent Cluster Means
"A Fully Conditional Specification Approach to Multilevel Multiple Imputation with Latent Cluster Means." Multivariate Behavioral Research, 54(1), pp. 149–150
Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data
Intraindividual variability can be measured by the intraindividual standard deviation ([Formula: see text]), intraindividual variance ([Formula: see text]), estimated hth-order autocorrelation coefficient ([Formula: see text]), and mean square successive difference ([Formula: see text]). Unresolved issues exist in the research on reliabilities of intraindividual variability indicators: (1) previous research only studied conditions with 0 autocorr…
A Comparison of Multilevel Imputation Schemes for Random Coefficient Models
Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random within-cluster covariance matrices to preserve cluster-specific associations is a promising alternative for rand…
Investigating Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data
"Investigating Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data." Multivariate Behavioral Research, 52(1), pp. 120–121
Racial/Ethnic Discrimination and Mental Health in Mexican-Origin Youths and Their Parents
A Bayesian Power Analysis Procedure Considering Uncertainty in Effect Size Estimates from a Meta-analysis
In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose a hybrid Bayesian power analysis procedure that models uncertainty in the effect size estimates from a meta-analysis. We use obse…
A Bayesian Power Analysis Procedure Considering Uncertainty in Effect Size Estimates from a Meta-analysis
In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose a hybrid Bayesian power analysis procedure that models uncertainty in the effect size estimates from a meta-analysis. We use obse…
Investigating Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data
"Investigating Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data." Multivariate Behavioral Research, 52(1), pp. 120–121
Racial/Ethnic Discrimination and Mental Health in Mexican-Origin Youths and Their Parents
Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data
Intraindividual variability can be measured by the intraindividual standard deviation ([Formula: see text]), intraindividual variance ([Formula: see text]), estimated hth-order autocorrelation coefficient ([Formula: see text]), and mean square successive difference ([Formula: see text]). Unresolved issues exist in the research on reliabilities of intraindividual variability indicators: (1) previous research only studied conditions with 0 autocorr…
A Comparison of Multilevel Imputation Schemes for Random Coefficient Models
Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random within-cluster covariance matrices to preserve cluster-specific associations is a promising alternative for rand…
A Fully Conditional Specification Approach to Multilevel Multiple Imputation with Latent Cluster Means
"A Fully Conditional Specification Approach to Multilevel Multiple Imputation with Latent Cluster Means." Multivariate Behavioral Research, 54(1), pp. 149–150
Testing Variance Components in Linear Mixed Modeling Using Permutation
Inference of variance components in linear mixed modeling (LMM) provides evidence of heterogeneity between individuals or clusters. When only nonnegative variances are allowed, there is a boundary (i.e., 0) in the variances’ parameter space, and regular inference statistical procedures for such a parameter could be problematic. The goal of this article is to introduce a practically feasible permutation method to make inferences about variance com…
The open access usage advantage
Distributionally-Weighted Least Squares in Growth Curve Modeling
Growth curve modeling is commonly used in psychological, educational, and social science research. The mainstream estimators for growth curve modeling are based on normal theory, but real data are unlikely to be exactly normally distributed. To improve estimation and inference with non-normal data, various estimators have been proposed. Among these estimators, the asymptotically distribution free (ADF) estimator does not need to rely on any distr…
Year Old Unbiased Distribution Free Estimator Reliably Improves SEM Statistics for Nonnormal Data
In structural equation modeling, researchers conduct goodness-of-fit tests to evaluate whether the specified model fits the data well. With nonnormal data, the standard goodness-of-fit test statistic T does not follow a chi-square distribution. Comparing T to χdf2 can fail to control Type I error rates and lead to misleading model selection conclusions. To better evaluate model fit, researchers have proposed various robust test statistics, but no…
A Bayesian Latent Variable Selection Model for Nonignorable Missingness
Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and failing to accommodate incomplete covariates with interactions, non-li…
Buprenorphine reduces methamphetamine intake and drug seeking behavior via activating nociceptin/orphanin FQ peptide receptor in rats
Buprenorphine, which has been approved for the treatment of opioid dependence, reduces cocaine consumption by co-activating μ-opioid receptors and nociceptin/orphanin FQ peptide (NOP) receptors. However, the role of buprenorphine in methamphetamine (METH) reinforcement and drug-seeking behavior remains unclear. This study investigated the effects of buprenorphine on METH self-administration and reinstatement of METH-seeking behavior in rats. We f…
Arima model for predicting chronic kidney disease and estimating its economic burden in China
The number of patients with CKD and the economic burden of CKD will continue to rise in China. The number of patients with CKD in China would increase by 2.6 million (1.6%) per year on average from 2020 to 2025. Meanwhile, the total economic burden of CKD in China would increase by an average of $3.1 billion per year. The ARIMA model is applicable to predict the number of patients with CKD. This study provides a new perspective for more comprehen…
Extended Unbiased Distribution Free Estimator With Mean Structures
To handle the nonnormal data issue, Browne proposed an unbiased distribution free (DF) estimator (Γ^DFU) and an asymptotically distribution free estimator (Γ^ADF) of the covariance matrix of sample variances/covariances Γ to calculate robust test statistics and robust standard errors. However, Γ^DFU is ignored in methodological and substantive research, and has not been extended to models with mean structures. To improve robust standard errors an…
Bootstrap-Based Between-Study Heterogeneity Tests in Meta-Analysis
Meta-analysis combines pertinent information from existing studies to provide an overall estimate of population parameters/effect sizes, as well as to quantify and explain the differences between studies. However, testing between-study heterogeneity is one of the most challenging tasks in meta-analysis research. Existing methods for testing heterogeneity, such as the Q test and likelihood ratio (LR) test, have been criticized for their failure to…
Estimating the Weight Matrix in Distributionally Weighted Least Squares Estimation
Real data are unlikely to be exactly normally distributed. Ignoring non-normality will cause misleading and unreliable parameter estimates, standard error estimates, and model fit statistics. For non-normal data, researchers have proposed a distributionally-weighted least squares (DLS) estimator to combines the normal theory based generalized least squares estimation (GLSN) and WLS. The key in DLS is to select an optimal weight as to compute a we…
Weight stigma as a stressor
Covid-19 Increased Mortality Salience, Collectivism, and Subsistence Activities
How does a life-threatening pandemic affect a culture? The Theory of Social Change, Cultural Evolution, and Human Development predicts that danger, as indicated by rising death rates and narrowing social worlds, shifts human psychology and behavior toward that found in small-scale, collectivistic, and rural subsistence ecologies. In particular, mortality salience, collectivism, and engagement in subsistence activities should increase as death rat…
Exploring the influence of block environmental characteristics on land surface temperature and its spatial heterogeneity for a high-density city
Synergistic effects of Lianhuaqingwen in combination with Oseltamivir and Baloxavir against seasonal influenza virus
Advancing Psychological Research With Random Forests
Contemporary psychological research increasingly involves machine-learning techniques, including random forests, for their capability in analyzing complex, high-dimensional data sets and modeling nonlinear predictive relations. In this article, we provide a comprehensive review of random-forest methods in psychological research. We begin by introducing the fundamental concepts of decision trees, followed by the theoretical framework of random for…
Demystifying Posterior Distributions
Bayesian statistics have gained significant traction across various fields over the past few decades. Bayesian statistics textbooks often provide both code and the analytical forms of parameters for simple models. However, they often omit the process of deriving posterior distributions or limit it to basic univariate examples focused on the mean and variance. Additionally, these resources frequently assume a strong background in linear algebra an…
Mathematics (14 works) · Statistics (13 works) · Econometrics (10 works) · Computer Science (8 works) · Statistical Methods and Bayesian Inference (7 works) · Medicine (5 works) · Psychology (5 works) · Statistical hypothesis testing (5 works) · Bayesian probability (4 works) · Psychometric Methodologies and Testing (4 works)