21 papers · ranked by Valyu relevance
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…
Abdul-Karim Iddrisu, Emmanuel A. Amikiya, Dominic Otoo
Background: Coronavirus disease 2019 (COVID-19) is a pandemic that has affected the daily life, governments and economies of many countries all over the globe. Ghana is currently experiencing a surge in the number of cases with a corresponding increase in the cumulative confirmed cases and deaths. The surge in cases…
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)…
Roberto Di Mari, Salvatore Ingrassia, Antonio Punzo
In generalized linear models (GLMs), measures of lack of fit are typically defined as the deviance between two nested models, and a deviance-based R2 is commonly used to evaluate the fit. In this paper, we extend deviance measures to mixtures of GLMs, whose parameters are estimated by maximum likelihood (ML) via the EM…
Peng Ding
| | Acronyms | | xiii | | --- | --- | --- | --- | | | Symbols | | xv | | | Useful R packages | | xvii | | Preface | | | xix | | I | Introduction | | 1 | | 1 | Motivations for Statistical Models | | 3 | | 1.1 | | Data and statistical models | 3 | | 1.2 | | Why linear models? | 5 | | 2 | | Ordinary Least Squares (OLS)…
Tien-Wen Lee
The General Linear Model (GLM) has been widely used in research, where error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. A modified GLM was proposed to explicitly model biological variance and non-biological…
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…
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…
Camila Ferreira Azevedo, Luis Felipe Ventorim Ferrão, Juliana Benevenuto, Marcos Deon Vilela de Resende + 3 more
Most genomic prediction methods are based on assumptions of normality due to their simplicity, robustness, and ease of implementation. However, in plant and animal breeding, target traits are often collected as categorical data, thus violating the normality assumption, which could affect the prediction of breeding…
Mona Mahmoud Abo El Nasr, Alaa A. Abdelmegaly, Doaa A. Abdo
This paper provides a comprehensive analysis of linear regression models, focusing on addressing multicollinearity challenges in breast cancer patient data. Linear regression methodologies, including GAM, Beta, GAM Beta, Ridge, and Beta Ridge, are compared using two statistical criteria. The study, conducted with R…
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…
Guilherme Parreira da Silva, Henrique Aparecido Laureano, Ricardo Rasmussen Petterle, Paulo Justiniano Ribeiro + 1 more
'Ricardo Rasmussen Petterle' 'Paulo Justiniano Ribeiro' 'Wagner Hugo Bonat'] Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely…
Rex Parsons, Oliver Jayasinghe, Nicole White, Prasad Chunduri + 1 more
The complexity, volume, and importance of time series data across various research domains highlight the necessity for tools that can efficiently analyze, visualize, and extract insights. Cosinor modeling is a widely used methodology to estimate or compare rhythmic characteristics in time series datasets. Time series…
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…
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…
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…
Fengling Hu, Jiayi Tong, Margaret Gardner, Andrew A. Chen + 6 more
There is growing interest in estimating population reference ranges across age and sex to better identify atypical clinically-relevant measurements throughout the lifespan. For this task, the World Health Organization recommends using Generalized Additive Models for Location, Scale, and Shape (GAMLSS) which can model…
Xiaqiong Wang, Yalu Wen
Linear mixed models have long been the method of choice for risk prediction analysis on high-dimensional genomic data. However, it remains computationally challenging to simultaneously model a large amount of genetic variants that can be noise or have predictive effects of complex forms. In this work, we have developed…
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…
Matthijs van Veelen
The generality of Hamilton’s rule^1,2^ is much debated^3–14^. In this paper, I show that this debate can be resolved by constructing a general version of Hamilton’s rule, which allows for a large variety of ways in which the fitness of an individual can depend on the social behaviour of oneself and of others. For this…
Andrew McCluskey
The use of mathematical transformations to reduce non-linear functions to linear problems, which can be tackled with analytical linear regression, is commonplace in the chemistry curriculum. The linearization procedure, however, assumes an incorrect statistical model for real experimental data; leading to biased…