21 papers · ranked by Valyu relevance
Anna Ly, Rune Haubo Bojesen Christensen, Douglas Bates, Martin Maechler + 1 more
The lme4 R package can be used to fit generalized linear mixed models (GLMMs), which extend the class of linear mixed models (LMMs). The two main extensions provided by GLMMs are (1) allowing for the conditional distribution of the response given the random effects to be non-Gaussian (e.g. binomial, Poisson) and (2)…
Rezzy Eko Caraka, Rung-Ching Chen, Su-Wen Huang, Shyue-Yow Chiou + 2 more
'Prana Ugiana Gio' 'Bens Pardamean'] Background In heart data mining and machine learning, dimension reduction is needed to remove multicollinearity. Meanwhile, it has been proven to improve the interpretation of the parameter model. In addition, dimension reduction can also increase the time of computing in high…
Ariel I. Mundo, John R. Tipton, Timothy J. Muldoon
In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA in terms of correlation and missing observations, both methodologies…
Christophe Coupé
As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such…
Murwan Siddig
—This paper aims to review the methodology behind the generalized linear models which are used in analyzing the actuarial situations instead of the ordinary multiple linear regression. We introduce how to assess the adequacy of the model which includes comparing nested models using the deviance and the scaled deviance.…
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
Oluwatobi Blessing Ojo, Siaka Lougue, Woldegebriel Assefa Woldegerima, Rongling Wu
'Rongling Wu'] TB is rated as one of the world’s deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with…
Anil Aktas Samur, Nesil Coskunfirat, Osman Saka, Søren Bentzen
Longitudinal data with binary repeated responses are now widespread among clinical studies and standard statistical analysis methods have become inadequate in the answering of clinical hypotheses. Instead of such conventional approaches, statisticians have started proposing better techniques, such as the Generalized…
Luca Maestrini, Francis K. C. Hui, A. H. Welsh
models Authors: ['Luca Maestrini' 'Francis K. C. Hui' 'A. H. Welsh'] Restricted maximum likelihood (REML) estimation is a widely accepted and frequently used method for fitting linear mixed models, with its principal advantage being that it produces less biased estimates of the variance components. However, the concept…
Jeffrey J. Gory, Peter F. Craigmile, Steven N. MacEachern
Two popular approaches for relating correlated measurements of a non-Gaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equations and to fit a generalized linear mixed model by introducing latent random variables. The first approach is effective for parameter…
Giovanny Covarrubias-Pazaran
Mixed models are a cornerstone in quantitative genetics to study the genetics of complex traits. A standard quantitative genetic model assumes that the effects of some random effects (e.g., individuals) are correlated based on their identity by descent and state. In addition, other relationships arise in the genotype…
Øystein Sørensen, Anders M. Fjell, Kristine B. Walhovd
We present generalized additive latent and mixed models (GALAMMs) for analysis of clustered data with responses and latent variables depending smoothly on observed variables. A scalable maximum likelihood estimation algorithm is proposed, utilizing the Laplace approximation, sparse matrix computation, and automatic…
Dylan G.E. Gomes
As (generalized) linear mixed-effects models (GLMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common ‘rule of thumb’ is that one needs at least five levels of a random effect. Having such few levels makes the estimation of the variance of random…
Priscilla Balestrucci, Marc O. Ernst, Alessandro Moscatelli
Psychophysical methods are widely used in neuroscience to investigate the quantitative relation between a physical property of the world and its perceptual representation provided by the senses. Recent studies introduced the Generalized Linear Mixed Model (GLMM) to fit the responses of multiple participants in…
Hans‐Peter Piepho
SUMMARY. Extensions of linear models are very commonly used in the analysis of biological data. Whereas goodness of fit measures such as the coefficient of determination (R2 ) or the adjusted R2 are well established for linear models, it is not obvious how such measures should be defined for generalized linear and…
Gillian Z. Heller, Kristy P. Robledo, Ian C. Marschner
Background The classical linear model is widely used in the analysis of clinical trials with continuous outcomes. However, required model assumptions are frequently not met, resulting in estimates of treatment effect that can be inefficient and biased. In addition, traditional models assess treatment effect only on the…
Pierre de Villemereuil, Holger Schielzeth, Shinichi Nakagawa, Michael B. Morrissey
Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioural traits, have inherently non-normal…
Authors not listed
We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into…
Kevin Robben, Christopher Cheatum
We report a comprehensive study of the efficacy of least-squares fitting of multidimensional spectra to generalized Kubo lineshape models and introduce a novel least-squares fitting metric, termed the Scale Invariant Gradient Norm (SIGN), that enables a highly reliable and versatile algorithm. The precision of…
Authors not listed
Hydration free energy (HFE) of molecules is a fundamental property having impor- tance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Ma- chine learning (ML) models are increasingly being used to predict HFE.…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…