arXiv subjects

Niloofar Ramezani

Publications and source records attributed to Niloofar Ramezani.

6 recordsLinked to original sources

arXiv2026

Power Estimation for Longitudinal Studies with Time Dependent Covariates Using Generalized Method of Moments

Longitudinal studies frequently incorporate covariates that evolve over time, creating complex dependence structures between outcomes and predictors. When covariates are time dependent, standard power analysis tools--largely developed for generalized estimating equations (GEE)--can yield misleading results because they do not account for the moment based structure required for valid marginal inference. Generalized Method of Moments (GMM) provides a flexible and efficient framework for estimating marginal effects in the presence of time dependent covariates, yet no practical tools exist for conducting power analysis under GMM. This paper introduces a modern, implementable framework for power estimation in longitudinal studies with time dependent covariates using GMM. Two complementary approaches are developed: a Wald based method that leverages the asymptotic normality of GMM estimators, and a distance metric method based on quadratic forms of sample and population moment conditions. Both approaches require only limited distributional assumptions and rely on valid moment conditions rather than full likelihood specification. We outline the theoretical foundations, provide step by step implementation guidance, and illustrate the methods using data from the Osteoarthritis Initiative. A simulation framework is presented for evaluating empirical performance. These methods fill a critical gap in the longitudinal modeling literature by offering applied researchers a practical, distribution light approach to power estimation when time dependent covariates are present and GMM is the preferred estimation technique.

stat.ME↗

arXiv2026

Penalized KLIC Model Selection for the Generalized Method of Moments in Longitudinal Data with Time-Dependent Covariates

Model selection plays an important role in longitudinal data analysis, especially when models are estimated using the generalized method of moments (GMM) in the presence of time-dependent covariates. In this setting, the number of valid moment conditions can grow quickly and may lead to over-parameterized models. The Kullback--Leibler Information Criterion (KLIC) has been proposed as a model-selection tool for this framework; however, the original KLIC criterion may favor overly complex models when the number of parameters or valid moment conditions increases. To address this limitation, this study proposes two penalized versions of KLIC that incorporate penalties based on both the number of model parameters and the number of valid moment conditions. The proposed criteria are referred to as the Moment--Parameter Product Penalty KLIC (MPPP--KLIC) and the Logarithmic Penalty KLIC (LP--KLIC). These criteria provide a theoretically motivated mechanism for balancing model fit and model complexity in GMM-based longitudinal models. Through an extensive simulation study involving both binary and continuous response settings, the proposed criteria are shown to improve the ability of KLIC to distinguish among competing models and to reduce the selection of over-parameterized models. The performance of the proposed methods is further illustrated using the Filipino Child Morbidity dataset, a longitudinal study of child health in the Philippines. The results show that the proposed penalized criteria provide stable and interpretable model rankings and consistently identify age as the most important predictor of child morbidity. Overall, the proposed penalized KLIC criteria offer practical and theoretically grounded tools for model selection in GMM-based longitudinal data analysis with time-dependent covariates.

stat.ME↗

arXiv2026

Semi-partitioned Generalized Method of Moments for Longitudinal Data with Lagged and Feedback Covariates

We propose a semi-partitioned Generalized Method of Moments (GMM) framework for analyzing longitudinal data with time-dependent covariates, within a marginal modeling paradigm. This approach addresses limitations of both aggregated and fully partitioned GMM models. Aggregated methods obscure temporal dynamics by assuming constant effects, while fully partitioned approaches offer temporal specificity at the cost of increased model complexity and instability--particularly with moderate sample sizes or deep lag structures. Our method distinguishes immediate from lagged effects by estimating contemporaneous coefficients separately and grouping lagged moment conditions into structured sets, while retaining flexibility in the lag-specific effects. This yields a model that is both statistically efficient and interpretable, capturing essential temporal variation while mitigating variance inflation and convergence challenges associated with full partitioning. The framework accommodates feedback, supports both continuous and binary outcomes, and utilizes the Broyden--Fletcher--Goldfarb--Shanno (BFGS) algorithm for reliable optimization. Through simulations, we demonstrate that the semi-partitioned GMM achieves coverage and competitive efficiency relative to fully partitioned models when the grouped-lag structure approximates the underlying lag pattern. Applications to clinical datasets on knee osteoarthritis and adolescent obesity confirm that the method recovers consistent, interpretable effects and avoids instability associated with finely grained partitioning.

stat.ME↗

arXiv2026

Modeling Covariate Feedback, Reversal, and Latent Traits in Longitudinal Data: A Joint Hierarchical Framework

Time-varying covariates in longitudinal studies frequently evolve through reciprocal feedback, undergo role reversal, and reflect unobserved individual heterogeneity. Standard statistical frameworks often assume fixed covariate roles and exogenous predictors, limiting their utility in systems governed by dynamic behavioral or physiological processes. We develop a hierarchical joint modeling framework that unifies three key features of such systems: (i) bidirectional feedback between a binary and a continuous covariate, (ii) role reversal in which these covariates become jointly modeled outcomes at a prespecified decision phase, and (iii) a shared latent trait influencing both intermediate covariates and a final binary endpoint. The model proceeds in three phases: a feedback-driven longitudinal process, a reversal phase in which the two covariates are jointly modeled conditional on the latent trait, and an outcome model linking a binary, decision-relevant endpoint to observed and latent components. Estimation is carried out using maximum likelihood and Bayesian inference, with Hamiltonian Monte Carlo supporting robust posterior estimation for models with latent structure and mixed outcome types. Simulation studies show that the model yields well calibrated coverage, small bias, and improved predictive performance compared to standard generalized linear mixed models, marginal approaches, and models that ignore feedback or latent traits. In an analysis of nationally representative U.S. panel data, the model captures the co-evolution of physical activity and body mass index and their joint influence, moderated by a latent behavioral resilience factor, on income mobility. The framework offers a flexible, practically implementable tool for analyzing longitudinal decision systems in which feedback, covariate role transition, and unmeasured capacity are central to prediction and intervention.

stat.ME↗

arXiv2026

Latent Moment Models for Recurrent Binary Outcomes: A Bayesian and Quasi-Distributional Approach

Recurrent binary outcomes within individuals, such as hospital readmissions, often reflect latent risk processes that evolve over time. Conventional methods like generalized linear mixed models and generalized estimating equations estimate average risk but fail to capture temporal changes in variability, asymmetry, and tail behavior. We introduce two statistical frameworks that model each binary event as the outcome of a thresholded value drawn from a time-varying latent distribution defined by its location, scale, skewness, and kurtosis. Rather than treating these four quantities as nonparametric moment estimators, we model them as interpretable latent moments within a flexible latent distributional family. The first, BLaS-Recurrent, is a Bayesian model using the sinh-arcsinh distribution (a parametric family that provides explicit control over asymmetry and tail weight) to estimate latent moment trajectories; the second, QuaD-Recurrent, is a quasi-distributional approach that maps simulated moment vectors to event probabilities using a flexible nonparametric surface. Both models support time-dependent covariates, serial correlation, and multiple membership structures. Simulation studies show improved calibration, interpretability, and robustness over standard models. Applied to ICU readmission data from the MIMIC-IV database, both approaches uncover clinically meaningful patterns in latent risk, such as right-skewed escalation and widening dispersion, that are missed by traditional methods. These models provide interpretable, distribution-sensitive tools for longitudinal binary outcomes in healthcare while explicitly acknowledging that latent "moments" summarize but do not uniquely determine the underlying distribution.

stat.ME↗

arXiv2025

Lasso Penalization for High-Dimensional Beta Regression Models: Computation, Analysis, and Inference

Beta regression is commonly employed when the outcome variable is a proportion. Since its conception, the approach has been widely used in applications spanning various scientific fields. A series of extensions have been proposed over time, several of which address variable selection and penalized estimation, e.g., with an $\ell_1$-penalty (LASSO). However, a theoretical analysis of this popular approach in the context of Beta regression with high-dimensional predictors is lacking. In this paper, we aim to close this gap. A particular challenge arises from the non-convexity of the associated negative log-likelihood, which we address by resorting to a framework for analyzing stationary points in a neighborhood of the target parameter. Leveraging this framework, we derive a non-asymptotic bound on the $\ell_1$-error of such stationary points. In addition, we propose a debiasing approach to construct confidence intervals for the regression parameters. A proximal gradient algorithm is devised for optimizing the resulting penalized negative log-likelihood function. Our theoretical analysis is corroborated via simulation studies, and a real data example concerning the prediction of county-level proportions of incarceration is presented to showcase the practical utility of our methodology.

stat.ME↗