arXiv subjects

Mohammad Javad Davoudabadi

Publications and source records attributed to Mohammad Javad Davoudabadi.

5 recordsLinked to original sources

arXiv2026

A Sparse-Group Pliable Lasso

The sparse-group pliable Lasso (SGPL) extends the pliable Lasso and group pliable Lasso by combining sparse-group regularization with a predictor-level coupling penalty, enabling simultaneous group-level selection, within-group sparsity, and hierarchical structure between main effects and interactions. We propose a blockwise coordinate descent algorithm for fitting the SGPL that exploits the convexity and structure of the objective function, establish convexity and Karush--Kuhn--Tucker optimality conditions, and prove that the algorithm converges to a global minimizer. Simulation studies demonstrate competitive predictive performance and smaller interaction estimation error than the pliable Lasso and group pliable Lasso, albeit with the expected precision--recall trade-off in support recovery. We further illustrate the proposed method using a Parkinson's disease gut microbiome study and an adrenocortical carcinoma (ACC) copy-number dataset from The Cancer Genome Atlas. The Parkinson's application identifies interpretable interactions between microbial abundances and dietary variables, while the ACC application illustrates that the effectiveness of group-structured regularization depends on how well the prespecified grouping reflects the underlying signal structure.

stat.ME↗

arXiv2026

Moment Propagation

Mean-field variational Bayes is a fast and scalable approach to approximate Bayesian inference, but its independence assumptions often lead to underestimated posterior uncertainty. We introduce moment propagation (MP), a framework for improving marginal posterior approximations by propagating conditional posterior moment information between parameter blocks and matching these moments within convenient approximating families. In conjugate settings, MP identifies variance terms omitted by mean-field approximations and uses them to construct corrected marginal updates. We develop both mean-field and Gaussian variants of MP, the latter using conditional Gaussian structure to obtain low-dimensional local updates for non-conjugate models. We establish consistency and asymptotic variance correctness for two-component models, and show that MP recovers exact marginal posteriors for linear regression and multivariate normal models. We also derive algorithms for multivariate normal models with missing data, probit regression, generalized linear models, and Bayesian Lasso regression. Numerical experiments show that MP, particularly Gaussian MP, can substantially improve marginal posterior accuracy over standard variational approximations while remaining much faster than MCMC.

stat.CO↗

arXiv2025

The Lasso Distribution: Properties, Sampling Methods, and Applications in Bayesian Lasso Regression

In this paper, we introduce a new probability distribution, the Lasso distribution. We derive several fundamental properties of the distribution, including closed-form expressions for its moments and moment-generating function. Additionally, we present an efficient and numerically stable algorithm for generating random samples from the distribution, facilitating its use in both theoretical and applied settings. We establish that the Lasso distribution belongs to the exponential family. A direct application of the Lasso distribution arises in the context of an existing Gibbs sampler, where the full conditional distribution of each regression coefficient follows this distribution. This leads to a more computationally efficient and theoretically grounded sampling scheme. To facilitate the adoption of our methodology, we provide an R package, BayesianLasso, available on CRAN, implementing the proposed methods. Our findings offer new insights into the probabilistic structure underlying the Lasso penalty and provide practical improvements in Bayesian inference for high-dimensional regression problems.

stat.CO↗

arXiv2024

Innovative Approaches in Soil Carbon Sequestration Modelling for Better Prediction with Limited Data

Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. The model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. Modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (SOC) components. In this study, we introduce two novel SOC models and a new RothC-like model and investigate how the SOC components and complexity of the SOC models affect the SOC prediction in the presence of small and sparse time series data. We develop model selection methods that can identify the soil carbon model with the best predictive performance, in light of the available data. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of sparse time series data, and our simpler models can produce more accurate predictions. The published version of this study is available in Scientific Reports (https://www.nature.com/articles/s41598-024-53516-z/<10.1038/s41598-024-53516-z>)

stat.CO↗

arXiv2022

The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings

Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, is the most active component of the soil organic carbon (SOC) that regulates bio-geochemical processes in terrestrial ecosystems. Some studies in the literature ignore the effect of microbial population growth on carbon decomposition rates. In reality, we might expect that the decomposition rate should be related to the population of microbes in the soil and have a positive relationship with the size of the microbial biomass pool. In this study, we explore the effect of microbial population growth on the accuracy of modelling soil carbon sequestration by developing and comparing two soil carbon models that consider a carrying capacity and limit to the growth of the microbial pool. We apply our models to three datasets, two small and one large datasets, and we select the best model in terms of having the best predictive performance through two model selection methods. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of both small and large time-series datasets, and our simpler model can produce more accurate predictions. We conclude that considering the microbial population growth in a soil carbon model improves the accuracy of a model in the presence of a large dataset.

stat.AP↗