Results 11 to 20 of about 1,409,985 (359)
Polygenic modeling with bayesian sparse linear mixed models. [PDF]
PLoS Genetics, 2013 Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies.
Xiang Zhou +2 more
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Critical evaluation of linear regression models for cell-subtype specific methylation signal from mixed blood cell DNA. [PDF]
PLoS ONE, 2018 Epigenome-wide association studies seek to identify DNA methylation sites associated with clinical outcomes. Difference in observed methylation between specific cell-subtypes is often of interest; however, available samples often comprise a mixture of ...
Daniel W Kennedy +8 more
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iForest - Biogeosciences and Forestry, 2016 The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area.
López-Serrano Pablito M +6 more
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Model Selection in Linear Mixed Models [PDF]
, 2013 Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other ...
Müller, Samuel +2 more
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Forest Systems, 2016 Aim of the study: The main objective of this study was to test Geographically Weighted Regression (GWR) for developing height-diameter curves for forests on a large scale and to compare it with Linear Mixed Models (LMM).
María Quirós Segovia +2 more
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Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models [PDF]
2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP), 2021 Mixed linear regression (MLR) model is among the most exemplary statistical tools for modeling non-linear distributions using a mixture of linear models. When the additive noise in MLR model is Gaussian, Expectation-Maximization (EM) algorithm is a widely-used algorithm for maximum likelihood estimation of MLR parameters.
Barazandeh, Babak +3 more
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Brain Sciences, 2017 Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants.
Tess K. Koerner, Yang Zhang
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The Consistency of Quantile Regression in Linear Mixed Models
, 2019 Quantiles are parameters of a distribution, which are of location and of scale character at the same time. The median, as a location parameter, is even robust and outperforms the mean, whenever there are outliers or extreme values in the data. In linear models quantile regression was firstly introduced by Koenker and Bassett [1978].
Beate Weidenhammer
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Gradient boosting for linear mixed models [PDF]
The International Journal of Biostatistics, 2020 Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory.
C. Griesbach +2 more
semanticscholar +1 more source
Scientific Reports, 2021 Repeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling
Ann L. Oberg +13 more
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