Model misspecification is typical in applied structural equation modeling (SEM). Traditional specification search methods, such as modification indices, search for misspecifications within the mode...

Ideal point estimation methods in the social sciences lack a principled approach for identifying multidimensional ideal points. We present a novel method for estimating multidimensional ideal point...

Published in Journal of the American Statistical Association (Just accepted, 2024)

In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful fram...

Published in Journal of the American Statistical Association (Just accepted, 2024)

Published in Journal of the American Statistical Association (Just accepted, 2024)

In this paper, we propose a new test for testing the equality of two population covariance matrices in the ultra-high dimensional setting that the dimension is much larger than the sizes of both of...

Published in Journal of the American Statistical Association (Just accepted, 2024)

Tensors, characterized as multidimensional arrays, are frequently encountered in modern scientific studies. Quantile regression has the unique capacity to explore how a tensor covariate influences ...

Causal mediation, pleiotropy, and replication analyses are three highly popular genetic study designs. Although these analyses address different scientific questions, the underlying statistical inf...

In multivariate functional data analysis, different functional covariates often exhibit homogeneity. The covariates with pronounced homogeneity can be analyzed jointly within the same group, offeri...

Severe thunderstorms cause substantial economic and human losses in the United States. Simultaneous high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are...

When a fluid flows over a solid surface, it creates a thin boundary layer where the flow velocity is influenced by the surface through viscosity, and can transition from laminar to turbulent at suf...

Probability surveys are a major source of population representative data for policy research and program evaluation. However, the data come with the added complications of being observational and s...

Model evaluation is a crucial step in SEM, consisting of two broad areas: global and local fit, where local fit indices are used to modify the original model. In the modification process, the modif...

Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a

Due to dependent happenings, vaccines can have different effects in populations. In addition to direct protective effects in the vaccinated, vaccination in a population can have indirect effects in the unvaccinated individuals. Vaccination can also reduce person-to-person transmission to vaccinated individuals or from vaccinated individuals compared with unvaccinated individuals. Design of vaccine

This study specifically focuses on addressing the challenges related to employing missing data techniques when estimating a conditional Latent Class Analysis (LCA) model. In the context of a condit...

Researchers seeking valid statistical inference in the presence of measurement error often apply approaches that ignore measurement error. This may result in biased estimates, inflated type I error...

Accurate model performance assessment in survival analysis is imperative for robust predictions and informed decision-making. Traditional residual diagnostic tools like martingale and deviance resi...

The Network Scale-up Methods (NSUM) are methods to estimate unknown populations based on indirect surveys in which the participants provide information about aggregated data of their acquaintances....

Cyclical phenomena are commonly observed in many areas of repeated measurements, especially with intensive longitudinal data. A typical example is circadian (24-hour) rhythm of physical measures su...

We consider the problem of structured tensor denoising in the presence of unknown permutations. Such data problems arise commonly in recommendation systems, neuroimaging, community detection, and m...

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financi...

This paper develops permutation versions of identification-robust tests in linear instrumental variables regression. Unlike the existing randomization and rank-based tests in which independence bet...

Differential privacy is widely considered the formal privacy for privacy-preserving data analysis due to its robust and rigorous guarantees, with increasingly broad adoption in public services, academia, and industry. Although differential privacy originated in the cryptographic context, in this review we argue that, fundamentally, it can be considered a pure statistical concept. We leverage Blackwell's

DNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingl...

In this paper, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for ...

We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series p is large in relation to the length of time series n. ...

We propose a mean functional that exists for arbitrary probability distributions and characterizes the Pareto distribution within the set of distributions with finite left endpoint. This is in shar...

Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecast...

This manuscript extensively reviews applications, extensions, and models derived from the Bayesian ideal point estimator. We focus our attention on studies conducted in the United States as well as...

Difficulties in reproducing results from scientific studies have lately been referred to as a reproducibility crisis. Scientific practice depends heavily on scientific training. What gets taught in the classroom is often practiced in labs, fields, and data analysis. The importance of reproducibility in the classroom has gained momentum in statistics education in recent years. In this article, we review

Latent Gaussian process (GP) models are flexible probabilistic non-parametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for l...

For grouped covariates, we propose a framework for boosting that allows for sparsity within and between groups. By using component-wise and group-wise gradient ridge boosting simultaneously with ad...

Published in Journal of the American Statistical Association (Just accepted, 2024)

Generative models based on latent variables, such as generative adversarial networks (GANs) and variational auto-encoders (VAEs), have gained lots of interests due to their impressive performance i...

Published in Journal of the American Statistical Association (Just accepted, 2024)

Generalized additive models are generalized linear models in which the linear predictor includes a sum of smooth functions of covariates, where the shape of the functions is to be estimated. They have also been generalized beyond the original generalized linear model setting to distributions outside the exponential family and to situations in which multiple parameters of the response distribution may

Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of variables are of...

Knowing whether vaccine protection wanes over time is important for health policy and drug development. However, quantifying waning effects is difficult. A simple contrast of vaccine efficacy at tw...

Published in Journal of the American Statistical Association (Just accepted, 2024)

Analyzing changes in network evolution is central to statistical network inference. We consider a dynamic network model in which each node has an associated time-varying low-dimensional latent vect...

For statistical analysis of network data, the β -model has emerged as a useful tool, thanks to its flexibility in incorporating nodewise heterogeneity and theoretical tractability. To generalize th...

Extreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change may affect the s...

Several methodological innovations have been advanced in the past decades that combine growth curve models (GCMs) with models of autoregressive (AR) processes. However, most of these approaches do ...

Phonetics is the scientific field concerned with the study of how speech is produced, heard, and perceived. It abounds with data, such as acoustic speech recordings, neuroimaging data, or articulatory data. In this article, we provide an introduction to different areas of phonetics (acoustic phonetics, sociophonetics, speech perception, articulatory phonetics, speech inversion, sound change, and speech

The Hawkes process is a model for counting the number of arrivals to a system that exhibits the self-exciting property—that one arrival creates a heightened chance of further arrivals in the near future. The model and its generalizations have been applied in a plethora of disparate domains, though two particularly developed applications are in seismology and in finance. As the original model is elegantly

Amid increasing awareness regarding opioid addiction, medical marijuana has emerged as a substitute to opioids for pain management. Concurrently, opioid manufacturers are putting significant resear...

This study investigated the performance of Bayesian fully-latent principal stratification (FLPS) models in estimating causal and principal effects in small-sample randomized control trials (RCTs) a...

Researchers often study dynamic processes of latent variables in everyday life, such as the interplay of positive and negative affect over time. An intuitive approach is to first estimate the measu...

With the rapid development of data acquisition and storage space, massive data sets exhibited with large sample size emerge increasingly and make more advanced statistical tools urgently need. To a...

Instrumental variables (IVs) are widely used to study the causal effect of an exposure on an outcome in the presence of unmeasured confounding. IVs require an instrument, a variable that (a) is associated with the exposure, (b) has no direct effect on the outcome except through the exposure, and (c) is not related to unmeasured confounders. Unfortunately, finding variables that satisfy conditions b

Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membe...

Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying c...

Published in Structural Equation Modeling: A Multidisciplinary Journal (Ahead of Print, 2024)

Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this paper, we introduce a new estimator based on t...

The parallel processor scheduling and bin packing problems with contiguity constraints are important in the field of combinatorial optimization because both problems can be used as components of effective exact decomposition approaches for several two-dimensional packing problems. In this study, we provide an extensive review of existing mathematical formulations for the two problems, together with

With the rapid development of quantum computers, researchers have shown quantum advantages in physics-oriented problems. Quantum algorithms tackling computational biology problems are still lacking...

Low-rank approximation is a popular strategy to tackle the “big n problem” associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial a...