21 papers · ranked by Valyu relevance
Heinz Holling, Katrin Jansen, Walailuck Böhning, Dankmar Böhning + 2 more
'Susan Martin' 'Patarawan Sangnawakij'] The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies.…
M. Gabriela M. Gomes
Selection acting on unobserved heterogeneity is a fundamental issue in the mathematics of populations. As recognised in disciplines as diverse as demography [11, 23, 24], ecology [10, 9], evolution [21] and epidemiology [1, 4, 5], in any population, individuals differ in many characteristics and it is essential that…
Kristian Thorlund, Lehana Thabane, Edward J Mills
Background Multiple treatment comparison (MTC) meta-analyses are commonly modeled in a Bayesian framework, and weakly informative priors are typically preferred to mirror familiar data driven frequentist approaches. Random-effects MTCs have commonly modeled heterogeneity under the assumption that the between-trial…
Gerhard Tutz, Ingrid Mauerer
Different voters behave differently, different governments make different decisions, or different organizations are ruled differently. Many research questions important to political scientists concern choice behavior, which involves dealing with nominal-scale dependent variables. Drawing on the principle of maximum…
Jeppe B Schroll, Rasmus Moustgaard, Peter C Gøtzsche
Background Dealing with heterogeneity in meta-analyses is often tricky, and there is only limited advice for authors on what to do. We investigated how authors addressed different degrees of heterogeneity, in particular whether they used a fixed effect model, which assumes that all the included studies are estimating…
Stephanie Wankowicz, James Fraser
In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting…
Stephanie Wankowicz, James Fraser
In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting…
Anna K Sonesson, Jørgen Ødegård, Lars Rönnegård
Background Canalization is defined as the stability of a genotype against minor variations in both environment and genetics. Genetic variation in degree of canalization causes heterogeneity of within-family variance. The aims of this study are twofold: (1) quantify genetic heterogeneity of (within-family) residual…
Stephane Hess, Sander van Cranenburgh
Models allowing for random heterogeneity, such as mixed logit and latent class, are generally observed to obtain superior model fit and yield detailed insights into unobserved preference heterogeneity. Using theoretical arguments and two case studies on revealed and stated choice data, this paper highlights that these…
Gerhard Tutz
A common framework is provided that comprises classical ordinal item response models as the cumulative, sequential and adjacent categories models as well as nominal response models and item response tree models. The taxonomy is based on the ways binary models can be seen as building blocks of the various models. In…
E. Sell-Kubiak, C. Kasper, A. Lepori, J.P. Gutiérrez + 3 more
Uniformity of production traits is desired for different traits in livestock species, including the uniformity of within-litter birth weights in piglets. Birth weight (BW) in pigs is associated with increased vitality and survival until weaning. However, as uniformity of BW increases, the importance of initial weight…
Diego Vallarino
Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as multinomial logit (MNL) and mixed logit models, impose rigid parametric assumptions that limit their…
Kim Luijken, Rolf H. H. Groenwold, Ben Van Calster, Ewout W. Steyerberg + 1 more
'Ewout W. Steyerberg' 'Maarten van Smeden'] It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out-of-sample performance can be hampered when predictors are measured differently at…
Cameron Pellett, Rubén Valbuena
Understanding the effect of heterogeneity is fundamental to numerous fields. In community ecology, classical theory postulates that habitat heterogeneity determines niche dimensionality and drives biodiversity. However, disparate heterogeneity-diversity relationships have been empirically observed, generating…
Xiao Wang, Zhong-Yun Li, Ling Zeng, An-Qiang Zhang + 3 more
'Wei Gu' 'Jian-Xin Jiang'] Unfortunately, the original version of this article [1] contained an error. A sentence in the statistical analysis section was written incorrectly. It was written wrongly as: “If there was significant heterogeneity, we choose a fixed model; if there was no heterogeneity, we choose a random…
Ramalingam Shanmugam, Gerald Ledlow, Karan P. Singh
In this paper, heterogeneity is formally defined, and its properties are explored. We define and distinguish observable versus non-observable heterogeneity. It is proposed that heterogeneity among the vulnerable is a significant factor in the contagion impact of COVID-19, as demonstrated with incidence rates on a…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas + 5 more
'Christopher De' 'James Grimmelmann' 'Jon Kleinberg' 'Siddhartha Sen' 'Baobao Zhang'] | A. Feder Cooper,∗ | Katherine Lee, | Madiha Zahrah Choksi, | Solon Barocas, | | --- | --- | --- | --- | | The GenLaw Center | The GenLaw Center | Cornell University | Microsoft Research | | Cornell University | Cornell University |…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…