arXiv subjects

Dandan Tang

Publications and source records attributed to Dandan Tang.

5 recordsLinked to original sources

arXiv2025

A Comparative Evaluation of a Conditional Median-Based Bayesian Growth Curve Modeling Approach with Missing Data

Longitudinal data are essential for studying within subject change and between subject differences in change. However, missing data, especially when the observed variables are nonnormal, remain a significant challenge in longitudinal analysis. Full information maximum likelihood estimation (FIML) and a two stage robust estimation (TSRE) are widely used to handle missing data, but their effectiveness may diminish with data skewness, high missingness rates, and nonignorable missingness. Recently, a robust median \textendash based Bayesian (RMB) approach for growth curve modeling (GCM) was proposed to handle nonnormal longitudinal data, yet its performance with missing data has not been fully investigated. This study fills that gap by using Monte Carlo simulations to evaluate RMB relative to FIML and TSRE. Overall, the RMB \textendash based GCM is shown to be a reliable option for managing both ignorable and nonignorable missing data across a variety of distributional scenarios. An empirical example illustrates the application of these methods.

stat.ME↗

arXiv2024

MIPD: A Multi-sensory Interactive Perception Dataset for Embodied Intelligent Driving

During the process of driving, humans usually rely on multiple senses to gather information and make decisions. Analogously, in order to achieve embodied intelligence in autonomous driving, it is essential to integrate multidimensional sensory information in order to facilitate interaction with the environment. However, the current multi-modal fusion sensing schemes often neglect these additional sensory inputs, hindering the realization of fully autonomous driving. This paper considers multi-sensory information and proposes a multi-modal interactive perception dataset named MIPD, enabling expanding the current autonomous driving algorithm framework, for supporting the research on embodied intelligent driving. In addition to the conventional camera, lidar, and 4D radar data, our dataset incorporates multiple sensor inputs including sound, light intensity, vibration intensity and vehicle speed to enrich the dataset comprehensiveness. Comprising 126 consecutive sequences, many exceeding twenty seconds, MIPD features over 8,500 meticulously synchronized and annotated frames. Moreover, it encompasses many challenging scenarios, covering various road and lighting conditions. The dataset has undergone thorough experimental validation, producing valuable insights for the exploration of next-generation autonomous driving frameworks.

cs.RO↗

arXiv2024

Evaluation of Missing Data Analytical Techniques in Longitudinal Research: Traditional and Machine Learning Approaches

Missing Not at Random (MNAR) and nonnormal data are challenging to handle. Traditional missing data analytical techniques such as full information maximum likelihood estimation (FIML) may fail with nonnormal data as they are built on normal distribution assumptions. Two-Stage Robust Estimation (TSRE) does manage nonnormal data, but both FIML and TSRE are less explored in longitudinal studies under MNAR conditions with nonnormal distributions. Unlike traditional statistical approaches, machine learning approaches do not require distributional assumptions about the data. More importantly, they have shown promise for MNAR data; however, their application in longitudinal studies, addressing both Missing at Random (MAR) and MNAR scenarios, is also underexplored. This study utilizes Monte Carlo simulations to assess and compare the effectiveness of six analytical techniques for missing data within the growth curve modeling framework. These techniques include traditional approaches like FIML and TSRE, machine learning approaches by single imputation (K-Nearest Neighbors and missForest), and machine learning approaches by multiple imputation (micecart and miceForest). We investigate the influence of sample size, missing data rate, missing data mechanism, and data distribution on the accuracy and efficiency of model estimation. Our findings indicate that FIML is most effective for MNAR data among the tested approaches. TSRE excels in handling MAR data, while missForest is only advantageous in limited conditions with a combination of very skewed distributions, very large sample sizes (e.g., n larger than 1000), and low missing data rates.

stat.AP↗

arXiv2024

Are the Signs of Factor Loadings Arbitrary in Confirmatory Factor Analysis? Problems and Solutions

The replication crisis in social and behavioral sciences has raised concerns about the reliability and validity of empirical studies. While research in the literature has explored contributing factors to this crisis, the issues related to analytical tools have received less attention. This study focuses on a widely used analytical tool - confirmatory factor analysis (CFA) - and investigates one issue that is typically overlooked in practice: accurately estimating factor-loading signs. Incorrect loading signs can distort the relationship between observed variables and latent factors, leading to unreliable or invalid results in subsequent analyses. Our study aims to investigate and address the estimation problem of factor-loading signs in CFA models. Based on an empirical demonstration and Monte Carlo simulation studies, we found current methods have drawbacks in estimating loading signs. To address this problem, three solutions are proposed and proven to work effectively. The applications of these solutions are discussed and elaborated.

stat.ME↗

arXiv2023

A Comparison of Full Information Maximum Likelihood and Machine Learning Missing Data Analytical Methods in Growth Curve Modeling

Missing data are inevitable in longitudinal studies. Traditional methods, such as the full information maximum likelihood (FIML), are commonly used to handle ignorable missing data. However, they may lead to biased model estimation due to missing not at random data that often appear in longitudinal studies. Recently, machine learning methods, such as random forests (RF) and K-nearest neighbors (KNN) imputation methods, have been proposed to cope with missing values. Although machine learning imputation methods have been gaining popularity, few studies have investigated the tenability and utility of these methods in longitudinal research. Through Monte Carlo simulations, this study evaluates and compares the performance of traditional and machine learning approaches (FIML, RF, and KNN) in growth curve modeling. The effects of sample size, the rate of missingness, and the missing data mechanism on model estimation are investigated. Results indicate that FIML is a better choice than the two machine learning imputation methods in terms of model estimation accuracy and efficiency.

stat.AP↗