Article
Automation & Control Systems
Johan Alenlov, Arnaud Doucet, Fredrik Lindsten
Summary: The pseudo-marginal HMC algorithm proposed in this paper combines the advantages of both HMC and pseudo-marginal schemes by controlling the precision parameter N to approximate the likelihood and efficiently sample the marginal posterior of parameters in high-dimensional scenarios. Results from experiments show that the PM-HMC algorithm can significantly outperform standard HMC and pseudo-marginal MH schemes.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Genetics & Heredity
Qianqian Song, Jing Su, Lance D. Miller, Wei Zhang
Summary: In gene expression profiling studies, specifically in single-cell RNA sequencing analyses, accurately identifying and clustering co-expressed genes is essential for understanding cell identity and function. Existing methods for single-cell data often fail to accurately identify co-expressed genes, but the scLM algorithm tailored for single-cell data proves to be effective in detecting biologically significant gene clusters and can cluster multiple single-cell datasets simultaneously. Results from simulation and experimental data show that scLM outperforms existing methods and provides novel biological insights for mechanism discovery and understanding complex biosystems like cancer.
GENOMICS PROTEOMICS & BIOINFORMATICS
(2021)
Article
Biology
Qi Zheng
Summary: The Luria-Delbruck protocol has been the preferred method for determining microbial mutation rates for nearly eight decades. Statistical methods for mutation rate comparison using fluctuation assay data have been recently developed and rigorously applied. Three methods for constructing intervals for mutation rate fold change are proposed and evaluated by large-scale simulations.
MATHEMATICAL BIOSCIENCES
(2021)
Article
Statistics & Probability
Junru Ren, Wenhao Gui
Summary: This paper discusses statistical inference for competing risks model using generalized Rayleigh distribution and progressive Type-II censoring, considering different or common parameters for latent lifetime distributions. Maximum likelihood estimates and Bayesian estimates are obtained, while Bootstrap methods and hypothesis testing are also carried out. Performance evaluation is done through Monte Carlo simulation and real data, and optimal censoring scheme issue is addressed.
COMPUTATIONAL STATISTICS
(2021)
Article
Public, Environmental & Occupational Health
Richard Kiplimo, Mathew Kosgei, Ann Mwangi, Elizabeth Onyango, Morris Ogero, Joseph Koske
Summary: The study utilized a joint model to analyze the association between longitudinally measured sputum smear results and time to experiencing unfavorable outcomes among TB patients. The findings suggest that men, previously treated, TB/HIV co-infected, and severely malnourished TB patients are at a higher risk of unfavorable outcomes.
FRONTIERS IN PUBLIC HEALTH
(2021)
Article
Engineering, Mechanical
P. L. Green, L. J. Devlin, R. E. Moore, R. J. Jackson, J. Li, S. Maskell
Summary: This paper discusses the optimization of the 'L-kernel' in Sequential Monte Carlo samplers to improve performance, resulting in reduced variance of estimates and fewer resampling requirements.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Mathematics
Shen-Ming Lee, Phuoc-Loc Tran, Truong-Nhat Le, Chin-Shang Li
Summary: In this study, we investigate the association between a sensitive characteristic and observed binary random variables using Warner's randomized response technique and a latent class model. We provide an EM algorithm to estimate the parameters and utilize the likelihood ratio test to identify the significant variables. We also use a latent class model to predict individuals' sensitive or non-sensitive group based on observed binary variables.
Article
Computer Science, Interdisciplinary Applications
DanHua ShangGuan
Summary: The Monte Carlo method is a powerful tool in many research fields, but the increasing complexity of physical models and mathematical models requires efficient algorithms to overcome the computational cost.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Review
Ecology
Ken Newman, Ruth King, Victor Elvira, Perry Valpine, Rachel S. McCrea, Byron J. T. Morgan
Summary: State-space models are a valuable tool for quantitative ecologists in analyzing time-series data. They offer flexibility and intuitive structures in describing the dynamics of complex systems, making model specification simpler. These models consist of system and observation processes, and there is a trade-off between model complexity and the fitting process.
METHODS IN ECOLOGY AND EVOLUTION
(2023)
Article
Engineering, Mechanical
Adolphus Lye, Alice Cicirello, Edoardo Patelli
Summary: This tutorial paper reviews the use of advanced Monte Carlo sampling methods in Bayesian model updating for engineering applications, introducing different methods and comparing their performance. Three case studies demonstrate the advantages and limitations of these sampling techniques in parameter identification, posterior distribution sampling, and stochastic identification of model parameters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Mathematics
Tzong-Ru Tsai, Yuhlong Lio, Wei-Chen Ting
Summary: An expectation-maximization (EM) likelihood estimation procedure is proposed for obtaining maximum likelihood estimates of parameters in a mixture distributions model with unknown mixture proportions based on type-I hybrid censored samples. Three bootstrap methods are utilized for constructing confidence intervals of the model parameters, and Monte Carlo simulations are conducted to evaluate the performance of the proposed methods. Simulation results demonstrate that the proposed methods can effectively provide reliable point and interval estimation results, with three examples illustrating their applications.
Article
Multidisciplinary Sciences
Joas S. Santos, Francisco Cribari-Neto
Summary: This paper focuses on the potential misleading results of hypothesis testing when the sample size is small. To address this issue, a Bartlett-corrected likelihood ratio test is proposed. Monte Carlo simulations are conducted to compare the performance of the corrected test with the standard likelihood ratio test in finite samples. The results indicate that the corrected test has excellent control of the type I error frequency. An empirical application is also presented and discussed.
ADVANCED THEORY AND SIMULATIONS
(2023)
Article
Biology
D. Vats, F. B. Goncalves, K. Latuszynski, G. O. Roberts
Summary: The paper introduces a new family of Markov chain Monte Carlo acceptance probabilities that are not based on the ratio of the target density at contested points, providing two stable Bernoulli factories. The efficiency of the methods relies on obtaining reasonable local upper or lower bounds on the target density, applicable to Bayesian inference for diffusions and Markov chain Monte Carlo on constrained spaces. The resulting Barker's algorithms are exact and computationally more efficient than current state-of-the-art methods.
Article
Astronomy & Astrophysics
Alessandro Laio, Uriel Luviano Valenzuela, Marco Serone
Summary: We introduce an approach to find approximate numerical solutions of truncated bootstrap equations for conformal field theories in arbitrary dimensions. This method uses a stochastic search guided by an action S, which is the logarithm of the truncated bootstrap equations for a single scalar field correlator. The method looks for approximate solutions that correspond to local minima of S, even if they are far from the extremality region.
Article
Psychology, Multidisciplinary
Oliver Luedtke, Esther Ulitzsch, Alexander Robitzsch
Summary: The article discusses different Bayesian estimators for stabilizing parameter estimates in confirmatory factor analysis models, with simulation studies showing that the EAP method produces more accurate estimates of latent correlations and outperforms other Bayesian estimators in terms of root mean squared error. It is recommended to use the four-parameter beta distribution as a prior distribution to improve the stability of parameter estimates.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Mathematics, Interdisciplinary Applications
Sunghoon Kim, Ashley Stadler Blank, Wayne S. DeSarbo, Jeroen K. Vermunt
Summary: The study introduces a hierarchical clustering model based on NFL fan survey data, which reveals the dispersion characteristics between teams and fans more effectively. The results show that NFL fans can be divided into three non-contiguous team segments, with differences in consumer behavior being observed.
JOURNAL OF CLASSIFICATION
(2022)
Article
Statistics & Probability
F. J. Clouth, S. Pauws, F. Mols, J. K. Vermunt
Summary: The bias-adjusted three-step LCA is extended to incorporate IPW, separating the estimation of the measurement model from the treatment effect estimation. This new approach solves conceptual issues and makes it easier for model selection and the use of multiple imputation. The implementation of this method in Latent GOLD is evaluated in a simulation study and illustrated with data of prostate cancer patients.
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
(2022)
Article
Obstetrics & Gynecology
Michela Dalmartello, Jeroen Vermunt, Eva Negri, Fabio Levi, Carlo La Vecchia
Summary: This study found that BMI trajectories in adult life are associated with endometrial cancer risk, with longer exposure to overweight and obesity across a lifetime increasing the risk. Weight during adulthood also plays an important role in EC risk.
BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY
(2022)
Article
Emergency Medicine
Roos Johanna Maria Havermans, Felix Johannes Clouth, Koen Willem Wouter Lansink, Jeroen Kornelis Vermunt, Mariska Adriana Cornelia de Jongh, Leonie de Munter
Summary: This study identified different physical recovery trajectories using Latent Markov Models (LMMs) and predicted these recovery states based on individual patient characteristics. Most patients recovered quickly, with only about a quarter experiencing severe problems after 1 month.
EUROPEAN JOURNAL OF TRAUMA AND EMERGENCY SURGERY
(2022)
Correction
Mathematics, Interdisciplinary Applications
Sunghoon Kim, Ashley Stadler Blank, Wayne S. DeSarbo, Jeroen K. Vermunt
JOURNAL OF CLASSIFICATION
(2022)
Article
Mathematics, Interdisciplinary Applications
Leonie V. D. E. Vogelsmeier, Jeroen K. Vermunt, Anne Bulow, Kim De Roover
Summary: This paper introduces a simplified method for estimating the latent Markov factor analysis (LMFA) and facilitates the exploration of covariate effects. A real data example demonstrates the empirical value of this method.
MULTIVARIATE BEHAVIORAL RESEARCH
(2023)
Article
Psychology, Educational
E. Damiano D'Urso, Jesper Tijmstra, Jeroen K. Vermunt, Kim De Roover
Summary: Assessing the measurement model of self-report scales is crucial for obtaining valid measurements of individuals' latent psychological constructs. This study found that the acquiescence response style has an impact on the measurement results, especially in balanced scales. The use of informed rotation approaches can help resolve this issue.
EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
(2023)
Article
Psychology, Educational
Martijn Schoenmakers, Jesper Tijmstra, Jeroen Vermunt, Maria Bolsinova
Summary: This article examines two frequently used item response theory (IRT) models, the multidimensional nominal response model (MNRM) and the IRTree model. The study reveals conceptual differences between these models, which result in different conclusions about the size and presence of differences in substantive trait between groups. A simulation study shows that the IRTree model and MNRM can drastically differ in their conclusions when groups differ in their average extreme response style (ERS). An empirical example is provided and implications for the future use of both models and the conceptualization of ERS are discussed.
EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
(2023)
Article
Mathematics, Interdisciplinary Applications
F. J. Clouth, S. Pauws, J. K. Vermunt
Summary: This article presents an extension of the bias-adjusted three-step latent class analysis with inverse propensity weighting (IPW) to account for differential item function (DIF) caused by treatment or exposure variables. The proposed method includes treatment with its direct effect on the class indicators in the step-one model and incorporates IPW in the step-three model to adjust for classification errors that differ across treatment groups. DIF caused by confounders used to create the propensity scores is found to be less problematic. The newly proposed approach is demonstrated using synthetic and real-life data examples and implemented in Latent GOLD program.
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
(2023)
Article
Psychology, Multidisciplinary
Brenda De Wit-De Visser, Madeleine Rijckmans, Jeroen K. Vermunt, Arno van Dam
Summary: ASPD and ASB have significant impacts on individuals, their environment, and society, with no evidence-based treatments currently available. Contradictory research findings on therapy effectiveness and underlying factors of ASB further fuel the debate on the accuracy of the conceptualization of ASPD and the homogeneity of this population.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Psychology, Multidisciplinary
Mihai A. A. Constantin, Noemi K. Schuurman, Jeroen K. K. Vermunt
Summary: We present a general method for sample size computations in the context of cross-sectional network models. The method is an automated Monte Carlo algorithm that iteratively concentrates computations on relevant sample sizes to find the optimal size. It requires inputs of a hypothesized network structure or desired characteristics, an estimation performance measure and target value, and a statistic and target value to reach the performance measure. The method includes a Monte Carlo simulation, curve-fitting, and stratified bootstrapping steps to provide sample size recommendations with uncertainty quantification. Rating: 7/10
PSYCHOLOGICAL METHODS
(2023)
Article
Psychology, Mathematical
Leonie V. D. E. Vogelsmeier, Jeroen K. Vermunt, Kim De Roover
Summary: Intensive longitudinal data (ILD) have gained popularity in studying within-person dynamics in psychological constructs. Before exploring the dynamics, it is important to examine whether the measurement model (MM) is consistent across subjects and time, in order to ensure the constructs have the same meaning. Latent Markov factor analysis (LMFA) can be used to investigate MM differences in ILD, and the new user-friendly software package lmfa makes it easier for researchers to perform this analysis.
BEHAVIOR RESEARCH METHODS
(2023)
Article
Psychology, Mathematical
Jonas M. B. Haslbeck, Jeroen K. Vermunt, Lourens J. Waldorp
Summary: Gaussian mixture models (GMMs) are widely used for exploring heterogeneity in multivariate continuous data, but their performance in estimating GMMs for ordinal data is uncertain. In this study, we investigate this by simulating data from various GMMs, thresholding them in ordinal categories, and evaluating recovery performance. We find that the number of components can be reliably estimated with enough ordinal categories and variables, but the estimates of component model parameters are biased regardless of sample size.
BEHAVIOR RESEARCH METHODS
(2023)
Article
Social Sciences, Mathematical Methods
Laura Boeschoten, Sander Scholtus, Jacco Daalmans, Jeroen K. Vermunt, Ton de Waal
SURVEY METHODOLOGY
(2022)
Article
Psychology, Mathematical
E. Damiano D'Urso, Kim De Roover, Jeroen K. Vermunt, Jesper Tijmstra
Summary: The study compared the performance of scale- and item-level approaches based on multiple group categorical confirmatory factor analysis and multiple group item response theory in testing measurement invariance with ordinal data. Results showed that, in general, MG-CCFA-based approaches outperformed MG-IRT-based approaches at the scale level. The best performing approach at the item level depends on the tested parameter, with likelihood ratio test providing the best trade-off when testing loadings equivalence and chi(2) test outperforming others when testing thresholds equivalence.
BEHAVIOR RESEARCH METHODS
(2022)
