Results 91 to 100 of about 93,893 (298)

ANALYTICAL APPROACH OF GENERALIZED LINEAR MODELS FOR HANDLING OVERDISPERSION IN POVERTY DATA OF INDONESIA

Barekeng
Poverty is one of the complex phenomena that occurs in Indonesia. Various socio-economic variables in Indonesia influence poverty, which we can mathematically model using the Generalized Linear Model (GLM) framework. In this study, we modeled data on the
Restu Arisanti, Resa Septiani Pontoh, Sri Winarni, Fellita Odelia Wibowo, Hanifah Khairunnisa, Raissheva Andika Pratama   +5 more
doaj   +1 more source

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source

Fast Bayesian Variable Selection in Binomial and Negative Binomial Regression

, 2021
18 pages; this work is superseded by arXiv:2208 ...
openaire   +2 more sources

An Integrated and Robust Deep Learning Framework for Denoising and Analyzing Single‐Cell Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
Single‐cell Spatial Transcriptomics Analysis and Denoising Engine is introduced as a unified deep learning framework that jointly performs denoising, clustering, and gene prioritization in spatial transcriptomics. By integrating linear and nonlinear representations within a dual‐channel architecture, it improves robustness and accuracy, uncovers ...
Yaxuan Cui, Yang Cui, Ruheng Wang, Xin Zeng, Zheyong Zhu, Ding Yi, Ruihua Guo, Tetsuya Sakurai, Kenta Nakai, Xiucai Ye, Leyi Wei, Yong Chen   +11 more
wiley   +1 more source

Parameter Estimation of Mixed Geographically Weighted Bivariate Zero-Inflated Negative Binomial Regression Model

Jambura Journal of Mathematics
The Bivariate Zero-Inflated Negative Binomial (BZINBR) regression model is commonly used to analyze two correlated count response variables characterized by overdispersion and excess zeros.
Mawadah Putri Islamiati, Purhadi Purhadi, Wibawati Wibawati   +2 more
doaj   +1 more source

Negative binomial regression in dose-effect relationships

Ufa Mathematical Journal, 2022
Summary: This paper is devoted to problem on estimating the distribution function and its quantiles in the dose-effect relationships with nonparametric negative binomial regression. Most of the mathematical researches on dose-response relationships concerned models with binomial regression, in particular, models with binary data.
openaire   +2 more sources

Incidence-rate ratios from negative binomial regression models predicting inflammation.

, 2023
Incidence-rate ratios from negative binomial regression models predicting inflammation.
Jennifer C. Cornman (17512285), Dana A. Glei (1939324), Maxine Weinstein (1939333), Jacob Witt (17512288)   +3 more
core   +1 more source

High‐elevation endemic plants predicted to lose habitat from changing climate in Washington State

American Journal of Botany, EarlyView.
Abstract Premise High‐elevation plants face unique challenges from potential climate change impacts that will likely require upslope migration into increasingly smaller suitable habitat. This situation is particularly acute for endemic species that by definition occupy small geographic ranges.
Nicholas L. Gjording, Erik W. Ertsgaard, David E. Giblin, Iris H. Dimpsey, Owen B. Bard   +4 more
wiley   +1 more source

Robust Negative Binomial Regression via the Kibria–Lukman Strategy: Methodology and Application

Mathematics
Count regression models, particularly negative binomial regression (NBR), are widely used in various fields, including biometrics, ecology, and insurance.
Adewale F. Lukman, Olayan Albalawi, Mohammad Arashi, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Rasha A. Farghali   +5 more
doaj   +1 more source

Do not log-transform count data

, 2010
1. Ecological count data (e.g., number of individuals or species) are often log-transformed to satisfy parametric test assumptions. 2. Apart from the fact that generalized linear models are better suited in dealing with count data, a log ...
Robert B. O’hara, Johan Kotze, Robert B. O'Hara, D. Johan Kotze   +3 more
core