Michael D Hunter
Biographic Data
| ID | 6630004 |
|---|---|
| NAME | Michael D Hunter |
| GIVEN NAMES | Michael D |
| FAMILY NAME | Hunter |
| SIGNATURE | HUNTER M D |
| AFFILIATIONS | Pennsylvania State University |
| ORCID | 0000-0002-3651-6709 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Extending reliability to intensive longitudinal data with the Kalman filter
Reliability is central to how researchers approach measurement in standard, group‐based analyses of single‐time‐point data, yet this critical aspect is often overlooked in the analysis of repeated observations. Since its inception, reliability has been a between‐person concept, but we redevelop this notion for within‐person designs by proposing a new coefficient of reliability for single‐subject designs. This coefficient shares the same general d…
Integrated Trend and Lagged Modeling of Multi-Subject, Multilevel, and Short Time Series
Trends represent systematic intra-individual variations that occur over slower time scales that, if unaccounted, are known to yield biases in estimation of momentary change patterns captured by time series models. The applicability of detrending methods has rarely been assessed in the context of multi-level longitudinal panel data, namely, nested data structures with relatively few measurements. This paper evaluated the efficacy of a series of tw…
Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other Considerations
Dynamic Structural Equation Models (DSEMs) integrate multilevel modeling, time series analysis, and structural equation modeling within a Bayesian estimation framework, offering a versatile tool for analyzing intensive longitudinal data (ILD). However, the impact of measurement structure misspecification in DSEMs, especially under varying reliability conditions and model complexities, remains underexplored. Our Monte Carlo simulation revealed tha…
State Space Mixture Modeling: Finding People with Similar Patterns of Change
Increasingly, behavioral scientists encounter data where several individuals were measured on multiple variables over numerous occasions. Many current methods combine these data, assuming all individuals are randomly equivalent. An extreme alternative assumes no one is randomly equivalent. We propose state space mixture modeling as one possible compromise. State space mixture modeling assumes that unknown groups of people exist who share the same…
Structured Estimation of Heterogeneous Time Series
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. introduced the multi-VAR approach for simultaneously estimating multiple-subject multivariate time series characterized by common and individualizing features using penalized estimation. This approach differs from many popular modeling approaches for multiple-subject time series in that qualitati…
Unsupervised Model Construction in Continuous-Time
Many of the advancements reconciling individual- and group-level results have occurred in the context of a discrete-time modeling framework. Discrete-time models are intuitive and offer relatively simple interpretations for the resulting dynamic structures; however, they do not possess the flexibility of models fitted in the continuous-time framework. We introduce ct-gimme, a continuous-time extension of the group iterative multiple model estimat…
Products of Variables in Structural Equation Models
A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Products of Variables Model (PoV). Some useful and practical features of PoV models include estimation of interactions between latent variables, latent variable moderators, manifest mode…
Association of Covid-19 With Achieving Time-to-Surgery Benchmarks in Patients With Musculoskeletal Trauma
In this cohort study, there was no association between meeting time-to-surgery benchmarks in either open fracture or closed femur/hip fracture during the COVID-19 pandemic compared with before the pandemic. This is counter to concerns that the unprecedented challenges associated with managing the COVID-19 pandemic would be associated with clinically significant delays in acute management of urgent surgical cases and suggests that many hospital sy…
A Method of Correcting Estimation Failure in Latent Differential Equations with Comparisons to Kalman Filtering
Studies have used the latent differential equation (LDE) model to estimate the parameters of damped oscillation in various phenomena, but it has been shown that correct, non-zero parameter estimates are only obtained when the latent series exhibits little or no process noise. Consequently, LDEs are limited to modeling deterministic processes with measurement error rather than those with random behavior in the true latent state. The reasons for th…
A Diagnostic Procedure for Detecting Outliers in Linear State–Space Models
Outliers can be more problematic in longitudinal data than in independent observations due to the correlated nature of such data. It is common practice to discard outliers as they are typically regarded as a nuisance or an aberration in the data. However, outliers can also convey meaningful information concerning potential model misspecification, and ways to modify and improve the model. Moreover, outliers that occur among the latent variables (i…
Representing Sudden Shifts in Intensive Dyadic Interaction Data Using Differential Equation Models with Regime Switching
A growing number of social scientists have turned to differential equations as a tool for capturing the dynamic interdependence among a system of variables. Current tools for fitting differential equation models do not provide a straightforward mechanism for diagnosing evidence for qualitative shifts in dynamics, nor do they provide ways of identifying the timing and possible determinants of such shifts. In this paper, we discuss regime-switching…
Modular Open-Source Software for Item Factor Analysis
This article introduces an item factor analysis (IFA) module for OpenMx, a free, open-source, and modular statistical modeling package that runs within the R programming environment on GNU/Linux, Mac OS X, and Microsoft Windows. The IFA module offers a novel model specification language that is well suited to programmatic generation and manipulation of models. Modular organization of the source code facilitates the easy addition of item models, i…
Maintained Individual Data Distributed Likelihood Estimation (MIDDLE)
Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belo…
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Modular Open-Source Software for Item Factor Analysis
This article introduces an item factor analysis (IFA) module for OpenMx, a free, open-source, and modular statistical modeling package that runs within the R programming environment on GNU/Linux, Mac OS X, and Microsoft Windows. The IFA module offers a novel model specification language that is well suited to programmatic generation and manipulation of models. Modular organization of the source code facilitates the easy addition of item models, i…
Maintained Individual Data Distributed Likelihood Estimation (MIDDLE)
Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belo…
Representing Sudden Shifts in Intensive Dyadic Interaction Data Using Differential Equation Models with Regime Switching
A growing number of social scientists have turned to differential equations as a tool for capturing the dynamic interdependence among a system of variables. Current tools for fitting differential equation models do not provide a straightforward mechanism for diagnosing evidence for qualitative shifts in dynamics, nor do they provide ways of identifying the timing and possible determinants of such shifts. In this paper, we discuss regime-switching…
A Method of Correcting Estimation Failure in Latent Differential Equations with Comparisons to Kalman Filtering
Studies have used the latent differential equation (LDE) model to estimate the parameters of damped oscillation in various phenomena, but it has been shown that correct, non-zero parameter estimates are only obtained when the latent series exhibits little or no process noise. Consequently, LDEs are limited to modeling deterministic processes with measurement error rather than those with random behavior in the true latent state. The reasons for th…
A Diagnostic Procedure for Detecting Outliers in Linear State–Space Models
Outliers can be more problematic in longitudinal data than in independent observations due to the correlated nature of such data. It is common practice to discard outliers as they are typically regarded as a nuisance or an aberration in the data. However, outliers can also convey meaningful information concerning potential model misspecification, and ways to modify and improve the model. Moreover, outliers that occur among the latent variables (i…
Association of Covid-19 With Achieving Time-to-Surgery Benchmarks in Patients With Musculoskeletal Trauma
In this cohort study, there was no association between meeting time-to-surgery benchmarks in either open fracture or closed femur/hip fracture during the COVID-19 pandemic compared with before the pandemic. This is counter to concerns that the unprecedented challenges associated with managing the COVID-19 pandemic would be associated with clinically significant delays in acute management of urgent surgical cases and suggests that many hospital sy…
Products of Variables in Structural Equation Models
A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Products of Variables Model (PoV). Some useful and practical features of PoV models include estimation of interactions between latent variables, latent variable moderators, manifest mode…
State Space Mixture Modeling: Finding People with Similar Patterns of Change
Increasingly, behavioral scientists encounter data where several individuals were measured on multiple variables over numerous occasions. Many current methods combine these data, assuming all individuals are randomly equivalent. An extreme alternative assumes no one is randomly equivalent. We propose state space mixture modeling as one possible compromise. State space mixture modeling assumes that unknown groups of people exist who share the same…
Structured Estimation of Heterogeneous Time Series
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. introduced the multi-VAR approach for simultaneously estimating multiple-subject multivariate time series characterized by common and individualizing features using penalized estimation. This approach differs from many popular modeling approaches for multiple-subject time series in that qualitati…
Unsupervised Model Construction in Continuous-Time
Many of the advancements reconciling individual- and group-level results have occurred in the context of a discrete-time modeling framework. Discrete-time models are intuitive and offer relatively simple interpretations for the resulting dynamic structures; however, they do not possess the flexibility of models fitted in the continuous-time framework. We introduce ct-gimme, a continuous-time extension of the group iterative multiple model estimat…
Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other Considerations
Dynamic Structural Equation Models (DSEMs) integrate multilevel modeling, time series analysis, and structural equation modeling within a Bayesian estimation framework, offering a versatile tool for analyzing intensive longitudinal data (ILD). However, the impact of measurement structure misspecification in DSEMs, especially under varying reliability conditions and model complexities, remains underexplored. Our Monte Carlo simulation revealed tha…
Extending reliability to intensive longitudinal data with the Kalman filter
Reliability is central to how researchers approach measurement in standard, group‐based analyses of single‐time‐point data, yet this critical aspect is often overlooked in the analysis of repeated observations. Since its inception, reliability has been a between‐person concept, but we redevelop this notion for within‐person designs by proposing a new coefficient of reliability for single‐subject designs. This coefficient shares the same general d…
Integrated Trend and Lagged Modeling of Multi-Subject, Multilevel, and Short Time Series
Trends represent systematic intra-individual variations that occur over slower time scales that, if unaccounted, are known to yield biases in estimation of momentary change patterns captured by time series models. The applicability of detrending methods has rarely been assessed in the context of multi-level longitudinal panel data, namely, nested data structures with relatively few measurements. This paper evaluated the efficacy of a series of tw…
Computer Science (9 works) · Mathematics (9 works) · Statistics (9 works) · Mental Health Research Topics (8 works) · Artificial Intelligence (7 works) · Econometrics (6 works) · Artificial Intelligence (5 works) · Monte Carlo method (4 works) · Algorithm (3 works) · Applied Mathematics (3 works)