14 papers · ranked by Valyu relevance
Fentaw Abegaz, Davar Abedini, Lemeng Dong, Johan A. Westerhuis + 3 more
In microbiome studies, addressing the unique characteristics of sequence data-such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality-is crucial for accurate analysis. In addition, integrating experimental design elements into microbiome data analysis is important for…
Mats Najström, Remi Chamoun, Karin Lindqvist
Introduction Traditional masculinity norms have been consistently linked to aggression in men, yet relatively few studies have examined how specific masculinity dimensions relate to distinct forms of aggression within structural and social contexts. Drawing on hegemonic masculinity and gender role strain theory, this…
Pekka Korhonen, Francis K.C. Hui, Jenni Niku, Sara Taskinen + 2 more
Background Over the past decade, joint species distribution models (JSDMs) and model-based ordination have emerged as powerful tools for the analysis of community ecology data. Generalized linear latent variable models (GLLVMs) offer a flexible framework for multivariate analysis of a wide range of data types, based on…
Yusrianti Hanike, Purhadi, Achmad Choiruddin
Regression modeling for multivariate count data often struggles with assumption of overdispersion and correlation among response variables. To address these issues, this study proposes a new model called Multivariate Correlated Poisson Generalized Inverse Gaussian Regression (MCPGIGR), which integrates random effects…
Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long + 1 more
Motivated by the CATHGEN data, we develop a new statistical method for simultaneous variable selection and parameter estimation in the context of generalized partly linear models for data with high-dimensional covariates. The method is referred to as the broken adaptive ridge (BAR) estimator, which is an approximation…
Georgios A. Manios, Dionysios Kandylas, Athanasios Kylonis, Pantelis G. Bagos + 1 more
Genome-Wide Association Studies (GWAS) have transformed human genetics by identifying thousands of loci associated with complex traits and diseases. Yet, individual GWAS are often underpowered, and traditional meta-analysis methods - though widely used in tools such as METAL, GWAMA, and PLINK- typically analyze one…
Maksym Hrachov, Hans-Peter Piepho, Niaz Md. Farhat Rahman, Waqas Ahmed Malik
Key message Several seemingly distinct regression methods are closely related. Environmental covariates delivered improved prediction, and a new approach improves estimation of prediction variance. Abstract In plant breeding and variety testing, there is an increasing interest in making use of environmental information…
Sarah S. Ji, Benjamin B. Chu, Hua Zhou, Kenneth Lange + 1 more
Copulas, generalized estimating equations, and generalized linear mixed models promote the analysis of grouped data where non-normal responses are correlated. Unfortunately, parameter estimation remains challenging in these three frameworks. Based on prior work of Tonda, we derive a new class of probability density…
Sören Budig, Charlotte Vogel, Frank Schaarschmidt
Overdispersion, a common issue in clustered multinomial data, can lead to biased standard errors and compromised statistical inference if not adequately addressed. This study describes a comprehensive procedure for constructing multiple comparisons of interest and applying multiplicity adjustments in the analysis of…
Matias Bermann, Andres Legarra, Ignacy Misztal, Daniela Lourenco + 1 more
The landscape of animal and plant breeding is rapidly evolving. Companies now invest millions in collecting extensive data on health, disease, welfare, and fertility, which are key fitness traits relevant for sustainable production. Likewise, genomic-based predictions or GWAS-based searches for causal variants in…
Youjia Li, Liyang Su, Qingquan Zhang, Zhonghua Liu
Background Granulomatous lobular mastitis (GLM) frequently mimics ductal carcinoma in situ (DCIS) in clinical presentation and imaging characteristics, leading to misdiagnosis and unnecessary aggressive interventions. This study aimed to develop and validate a practical nomogram for differentiating GLM from DCIS.…
Mareen Pigorsch, Ludwig A. Hothorn, Frank Konietschke
Although count data are collected in many experiments, their analysis remains challenging, especially in small sample sizes. Until now, linear or generalized linear models in Poisson or Negative Binomial distributional families have often been used. However, these data frequently show signs of over-, underdispersion…
Fengling Hu, Jiayi Tong, Margaret Gardner, Andrew A. Chen + 7 more
We first describe the pooled GAMLSS framework. GAMLSS-family distributions are a generalized set of probability distributions defined by at least two and up to four parameters. The first two parameters pertain to the mean and variance, while the third and fourth parameters describe skewness and kurtosis, respectively.…
Noorie Hyun, Abisola E Idu, Andrea J. Cook, Jennifer F. Bobb
Background/Aims Evaluating heterogeneity of treatment effects (HTE) across subgroups is common in both randomized trials and observational studies. Although several statistical challenges of HTE analyses including low statistical power and multiple comparisons are widely acknowledged, issues specific to clustered data…