Results 31 to 40 of about 25,265 (258)
Advanced Intelligent Discovery, EarlyView.scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
American Journal of Botany, EarlyView.Abstract Premise In semiarid ecosystems such as the Brazilian Caatinga, physical seed dormancy (PY) is critical for plant persistence when water is unpredictable, but how soil moisture and temperature regulate PY release and seed longevity in species of native Fabaceae that have different growth forms is unknown. Methods Seeds of the herbaceous species
Humberto Araújo Almeida +2 more
wiley +1 more source
Journal of Statistical Software, 2017 Rgbp is an R package that provides estimates and verifiable confidence intervals for random effects in two-level conjugate hierarchical models for overdispersed Gaussian, Poisson, and binomial data.
Hyungsuk Tak, Joseph Kelly, Carl Morris
doaj +1 more source
Testing Overdispersion in CUBE Models
, 2016 A practical problem with large-scale survey data is the possible presence of overdispersion. It occurs when the data display more variability than is predicted by the variance-mean relationship. This article describes a probability distribution generated
IANNARIO, MARIA
core +1 more source
American Journal of Industrial Medicine, EarlyView.ABSTRACT Background The impact of presumptive coverage policies for work‐related mental health disorders has not been extensively studied to date. Using data from the Association of Workers’ Compensation Boards of Canada (AWCBC), our objectives were to describe the evolution of compensated mental health conditions, and to explore the association ...
Quentin Durand‐Moreau +3 more
wiley +1 more source
Overdispersion at the Binomial and Multinomial Distribution [PDF]
, 2011 Overdispersion is a widely discussed phenomenon in case of binomial and Poisson distributed data. We analysed multinomial data with varying multinomial parameters as a part of attribute gauge study.
Láng, Zsolt +2 more
core +1 more source
Scaling limits for infinite-server systems in a random environment
Stochastic Systems, 2017 This paper studies the effect of an overdispersed arrival process on the performance of an infinite-server system. In our setup, a random environment is modeled by drawing an arrival rate Λ from a given distribution every Δ time units, yielding an i.i.d.
Mariska Heemskerk +2 more
doaj +1 more source
Australian Journal of Social Issues, EarlyView.ABSTRACT Most adults with neurological disability rely on general practitioners for managing and preventing health complications. This study analysed patterns of general practice service utilisation among National Disability Insurance Scheme (NDIS) participants with neurological disability and compared these with people who are not NDIS participants ...
Stacey Oliver +8 more
wiley +1 more source
CONWAY-MAXWELL POISSON REGRESSION MODELING OF INFANT MORTALITY IN SOUTH SULAWESI
Media StatistikaOverdispersion is a common problem in count data that can lead to inaccurate parameter estimates in Poisson regression models. Quasi-Poisson and negative binomial regressions are often used to address overdispersion but have limitations, especially with ...
Oktaviana Oktaviana +4 more
doaj +1 more source
ISPRS International Journal of Geo-Information, 2020 Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects.
Jianguo Chen +4 more
doaj +1 more source