arXiv subjects

Nilotpal Sanyal

Publications and source records attributed to Nilotpal Sanyal.

7 recordsLinked to original sources

arXiv2026

Robust and Sparse Group Dynamic Causal Modeling via Student-t Parametric Empirical Bayes and Nonlocal Priors

Dynamic causal modeling (DCM) estimates directed effective connectivity, while parametric empirical Bayes (PEB) supports group inference using subject-specific posterior summaries. Standard PEB relies on Gaussian models and continuous shrinkage, making it sensitive to atypical estimates and unable to distinguish negligible from nonzero effects. We develop a robust and sparse group-DCM extension combining a Student-t likelihood with a spike-and-slab prior using a nonlocal product-moment (pMOM) slab. A normal--gamma representation of the Student-t distribution yields weights that downweight atypical subject--parameter combinations. The pMOM slab vanishes at zero, sharpening coefficient selection and yielding inclusion probabilities. We propagate first-level posterior uncertainty through block-covariance pre-whitening and estimate the model using an EM--ReML algorithm that updates weights, inclusion probabilities, effects, and variance components. A simulation study showed that Student-t weighting provided the main protection against contamination, the nonlocal prior contributed most under strong sparsity, and their combination was most beneficial when contamination and sparsity occurred together. In an openly shared mixed-gambles fMRI application, we found mild heterogeneity, concentrated inclusion support on three intrinsic self-connections, and close directional agreement with standard SPM-PEB. Our framework therefore adds interpretable element-level robustness diagnostics and sparse coefficient selection to hierarchical DCM while retaining first-level uncertainty.

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arXiv2026

Competing-Risk Cure Models: A Comprehensive Systematic Review of Methodological Literature

Competing-risk cure models describe time-to-event populations with individuals immune to all event types or an event of interest, yet literature is fragmented across model families. We review 26 papers across five axes: cure definition/scope; decomposition/cure mechanism; latency; dependence, censoring, and masked causes; and estimation. We distinguish global from cause-specific cure and incidence--latency mixtures from vertical susceptibility factorizations, latent competing-causes/zero-count constructions, defective-survival models, and zero-inflated mixture or cumulative incidence function (CIF) formulations. We compare parametric, piecewise-constant, PH, AFT, transformation, CIF-based, nonparametric, and partially specified latency models for right/interval censoring, clustering, and masked causes. Mixture formulations dominate, but similar names can mask different estimands, cure mechanisms, latent-risk/censoring assumptions, and regression interpretations. Latent-failure dependence is modeled less often than cure or latency; failure--censoring dependence, within-cluster association, and masked causes occur in smaller subsets. Estimation spans likelihood and expectation-maximization (EM), including neural-network M-steps, estimating equations, inverse-probability-of-censoring weighting, Bayesian computation, and copula-graphic estimation. A reproducible defective-Gompertz analysis of public bone-marrow-transplant data shows that fitted tail probabilities require model- and endpoint-specific interpretation, not all-method comparison. Reproducibility remains limited: most implementations use custom code, few offer repository access, and no widely adopted, clearly licensed R/Python framework unifies the constructions. This taxonomy supports transparent model selection/reporting, estimation-method comparison, and needs for theory, software, benchmarking, and reproducible applications.

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arXiv2026

Resolution-Adaptive Compact-Support Priors for Bayesian Wavelet Denoising

We propose a resolution-adaptive Bayesian wavelet denoising method for noisy one-dimensional signals. Its central innovation is a spike-and-slab prior whose continuous slab mixes a compactly supported Wendland-type polynomial density with the more dispersed semicircle density. A low-dimensional empirical-Bayes trend produces data-adaptive mixture weights by resolution, while a data-adaptive support scale controls the common bounded interval. Thus, the method combines sparsity, explicit support control, and interpretable resolution-dependent shrinkage. Under squared-error loss, we derive the posterior-mean estimator and establish symmetry, boundedness, continuity, and limiting properties. We define fixed-hyperparameter bias, variance, and risk and develop an empirical-Bayes fitting procedure. Under a Laplace working likelihood, the Wendland contribution has finite-sum expressions, while the semicircle contribution is evaluated by stable one-dimensional integration. Simulations with the Bumps, Blocks, Doppler, and HeaviSine signals compare Gaussian- and Laplace-likelihood versions with universal thresholding, false-discovery-rate (FDR) thresholding, cross-validation (CV), Stein's unbiased risk estimate (SURE), the Bayesian adaptive multiresolution shrinker (BAMS), and a nonlocal-prior (NLP) method. In the primary Gaussian-error study, WS--Gaussian was the best non-NLP method in 24 of 36 cells, including 11 of 12 low-SNR cells, with a much more favorable computational profile than WS--Laplace. A real seismic acceleration record from the 2008 Chino Hills earthquake illustrates attenuation of rapid fluctuations and preservation of the dominant event. A semi-synthetic study using the processed trace as surrogate truth showed improvement over the noisy observation at lower and moderate SNRs, but not at the highest SNR.

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arXiv2026

Comparative Review of Modern Competing Risk Methods in High-dimensional Settings

Competing risk analysis accounts for multiple mutually exclusive events, improving risk estimation over traditional survival analysis. Despite methodological advancements, a comprehensive comparison of competing risk methods, especially in high-dimensional settings, remains limited. This study evaluates penalized regression (LASSO, SCAD, MCP), boosting (CoxBoost, CB), random forest (RF), and deep learning (DeepHit, DH) methods for competing risk analysis through extensive simulations, assessing variable selection, estimation accuracy, discrimination, and calibration under diverse data conditions. Our results show that, under the considered settings, CB provides strong control of false discoveries, stable estimation, and competitive discriminative ability, particularly in high-dimensional settings, while MCP and SCAD provide improved calibration in $n>p$ scenarios. RF and DH are effective at capturing nonlinear effects, but in the present implementation, they tend to exhibit weaker performance, with RF identifying broader variable sets and DH showing limited calibration accuracy. We further illustrate the application of these methods through an analysis of a melanoma gene expression dataset with survival outcomes. This study provides comparative evidence and preliminary guidelines for selecting competing risk models in high-dimensional settings and outlines important directions for future research.

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arXiv2025

Nonlocal Prior Mixture-Based Bayesian Wavelet Regression with Application to Noisy Imaging and Audio Data

We propose a novel Bayesian wavelet regression approach using a three-component spike-and-slab prior for wavelet coefficients, combining a point mass at zero, a moment (MOM) prior, and an inverse moment (IMOM) prior. This flexible prior supports small and large coefficients differently, offering advantages for highly dispersed data where wavelet coefficients span multiple scales. The IMOM prior's heavy tails capture large coefficients, while the MOM prior is better suited for smaller non-zero coefficients. Further, our method introduces innovative hyperparameter specifications for mixture probabilities and scale parameters, including generalized logit, hyperbolic secant, and generalized normal decay for probabilities, and double exponential decay for scaling. Hyperparameters are estimated via an empirical Bayes approach, enabling posterior inference tailored to the data. Extensive simulations demonstrate significant performance gains over two-component wavelet methods. Applications to electroencephalography and noisy audio data illustrate the method's utility in capturing complex signal characteristics. We implement our method in an R package, NLPwavelet (>= 1.1).

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arXiv2025

High-dimensional iterative variable selection for accelerated failure time models

We propose an iterative variable selection method for the accelerated failure time model using high-dimensional survival data. Our method pioneers the use of the recently proposed structured screen-and-select framework for survival analysis. We use the marginal utility as the measure of association to inform the structured screening process. For the selection steps, we use Bayesian model selection based on non-local priors. We compare the proposed method with a few well-known methods. Assessment in terms of true positive rate and false discovery rate shows the usefulness of our method. We have implemented the method within the R package GWASinlps.

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arXiv2022

Iterative variable selection for high-dimensional data with binary outcomes

We propose an iterative variable selection scheme for high-dimensional data with binary outcomes. The scheme adopts a structured screen-and-select framework and uses non-local prior-based Bayesian model selection within the same. The structured screening is based on the association of the independent variables with the outcome which is measured in terms of the maximum marginal likelihood estimator. Performance comparison with several well-known methods in terms of true positive rate and false discovery rate shows that our proposed method stands to be a competitive alternative for sparse high-dimensional variable selection with binary outcomes. The method has been implemented within the R package GWASinlps.

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