Bayesian Semi-Parametric Spatial Dispersed Count Model for Precipitation Analysis

• URL: http://arxiv.org/abs/2503.19117v1
• Date: Mon, 24 Mar 2025 20:13:55 GMT
• Title: Bayesian Semi-Parametric Spatial Dispersed Count Model for Precipitation Analysis
• Authors: Mahsa Nadifar, Andriette Bekker, Mohammad Arashi, Abel Ramoelo,
• Abstract summary: Method combines non-parametric techniques with an adapted dispersed count model based on renewal theory.<n>Applying it to lung and bronchus cancer mortality data from Iowa, emphasizing environmental and demographic factors.<n>This application highlights the significance of our methodology in public health research.
• Score: 0.5399800035598186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The appropriateness of the Poisson model is frequently challenged when examining spatial count data marked by unbalanced distributions, over-dispersion, or under-dispersion. Moreover, traditional parametric models may inadequately capture the relationships among variables when covariates display ambiguous functional forms or when spatial patterns are intricate and indeterminate. To tackle these issues, we propose an innovative Bayesian hierarchical modeling system. This method combines non-parametric techniques with an adapted dispersed count model based on renewal theory, facilitating the effective management of unequal dispersion, non-linear correlations, and complex geographic dependencies in count data. We illustrate the efficacy of our strategy by applying it to lung and bronchus cancer mortality data from Iowa, emphasizing environmental and demographic factors like ozone concentrations, PM2.5, green space, and asthma prevalence. Our analysis demonstrates considerable regional heterogeneity and non-linear relationships, providing important insights into the impact of environmental and health-related factors on cancer death rates. This application highlights the significance of our methodology in public health research, where precise modeling and forecasting are essential for guiding policy and intervention efforts. Additionally, we performed a simulation study to assess the resilience and accuracy of the suggested method, validating its superiority in managing dispersion and capturing intricate spatial patterns relative to conventional methods. The suggested framework presents a flexible and robust instrument for geographical count analysis, offering innovative insights for academics and practitioners in disciplines such as epidemiology, environmental science, and spatial statistics.

Related papers

• Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood [0.0]
  This study introduces an integrated framework for predictive causal inference designed to overcome limitations in conventional single model approaches.<n> Specifically, we combine a Hidden Markov Model for spatial health state estimation with a Multi Task and Multi Graph Convolutional Network (MTGCN) for capturing temporal outcome trajectories.<n>To demonstrate its utility, we focus on clinical domains such as cancer, dementia, Parkinson disease, where treatment effects are challenging to observe directly.
  arXiv  Detail & Related papers  (2025-07-11T03:11:15Z)
• LLM-based Evaluation Policy Extraction for Ecological Modeling [22.432508855430797]
  evaluating ecological time series is critical for benchmarking model performance in many important applications.<n>Traditional numerical metrics fail to capture domain-specific temporal patterns critical to ecological processes.<n>We propose a novel framework that integrates metric learning with large language model (LLM)-based natural language policy extraction.
  arXiv  Detail & Related papers  (2025-05-20T01:02:29Z)
• Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models [8.798959872821962]
  This paper outlines an approach in the domain of federated survival analysis, specifically the Cox Proportional Hazards (CoxPH) model. We present an FL approach that employs feature-based clustering to enhance model accuracy across synthetic datasets and real-world applications.
  arXiv  Detail & Related papers  (2024-07-20T18:34:20Z)
• Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications [0.0]
  This study explores model adaptation and generalization by utilizing synthetic data. We employ quantitative measures such as Kullback-Leibler divergence, Jensen-Shannon distance, and Mahalanobis distance to assess data similarity. Our findings suggest that utilizing statistical measures, such as the Mahalanobis distance, to determine whether model predictions fall within the low-error "interpolation regime" or the high-error "extrapolation regime" provides a complementary method for assessing distribution shift and model uncertainty.
  arXiv  Detail & Related papers  (2024-05-03T10:05:31Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• BARTSIMP: flexible spatial covariate modeling and prediction using Bayesian additive regression trees [0.0]
  In this paper, we investigate the combination of a Gaussian process spatial model with a Bayesian Additive Regression Tree (BART) model. The computational burden of the approach is reduced by combining Markov chain Monte Carlo with the Integrated Nested Laplace Approximation (INLA) technique. We then use the model to predict anthropometric responses in Kenya, with the data collected via a complex sampling design.
  arXiv  Detail & Related papers  (2023-09-23T05:35:17Z)
• Simulation-based Inference for Cardiovascular Models [43.55219268578912]
  We use simulation-based inference to solve the inverse problem of mapping waveforms back to plausible physiological parameters.<n>We perform an in-silico uncertainty analysis of five biomarkers of clinical interest.<n>We study the gap between in-vivo and in-silico with the MIMIC-III waveform database.
  arXiv  Detail & Related papers  (2023-07-26T02:34:57Z)
• Deep Variational Lesion-Deficit Mapping [0.3914676152740142]
  We introduce a comprehensive framework for lesion-deficit model comparison. We show that our model outperforms established methods by a substantial margin across all simulation scenarios. Our analysis justifies the widespread adoption of this approach.
  arXiv  Detail & Related papers  (2023-05-27T13:49:35Z)
• Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots [0.0]
  A data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. A non-intrusive reduced-order model for geometrical parameters is constructed using proper decomposition. A radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model.
  arXiv  Detail & Related papers  (2023-02-21T21:18:17Z)
• Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications [60.26261850082012]
  We introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. We employ association rules as new representations using association rule mining based on the original features to increase model interpretability.
  arXiv  Detail & Related papers  (2022-09-29T17:44:14Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Spatial machine-learning model diagnostics: a model-agnostic distance-based approach [91.62936410696409]
  This contribution proposes spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as novel model-agnostic assessment and interpretation tools. The SPEPs and SVIPs of geostatistical methods, linear models, random forest, and hybrid algorithms show striking differences and also relevant similarities. The novel diagnostic tools enrich the toolkit of spatial data science, and may improve ML model interpretation, selection, and design.
  arXiv  Detail & Related papers  (2021-11-13T01:50:36Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.