scholarly journals Fitting Residual Error Structures for Growth Models in SAS PROC MCMC

2016 ◽  
Vol 77 (4) ◽  
pp. 587-612 ◽  
Author(s):  
Daniel McNeish
Keyword(s):  
Bayesian Methods ◽  
Variance Components ◽  
Applied Research ◽  
Growth Models ◽  
Residual Error ◽  
Small Samples ◽  
Standard Errors ◽  
Covariance Structures ◽  
Biased Estimates ◽  
Selection Of

In behavioral sciences broadly, estimating growth models with Bayesian methods is becoming increasingly common, especially to combat small samples common with longitudinal data. Although M plus is becoming an increasingly common program for applied research employing Bayesian methods, the limited selection of prior distributions for the elements of covariance structures makes more general software more advantages under certain conditions. However, as a disadvantage of general software’s software flexibility, few preprogrammed commands exist for specifying covariance structures. For instance, PROC MIXED has a few dozen such preprogrammed options, but when researchers divert to a Bayesian framework, software offer no such guidance and requires researchers to manually program these different structures, which is no small task. As such the literature has noted that empirical papers tend to simplify their covariance matrices to circumvent this difficulty, which is not desirable because such a simplification will likely lead to biased estimates of variance components and standard errors. To facilitate wider implementation of Bayesian growth models that properly model covariance structures, this article overviews how to generally program a growth model in SAS PROC MCMC and then demonstrates how to program common residual error structures. Full annotated SAS code and an applied example are provided.

scholarly journals Gradient boosting for linear mixed models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Colin Griesbach ◽  
Benjamin Säfken ◽  
Elisabeth Waldmann
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Selection Procedure ◽  
Classification Theory ◽  
Gradient Boosting ◽  
Random Structure ◽  
Boosting Algorithm ◽  
The One ◽  
Biased Estimates ◽  
Selection Of

Abstract 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. Current boosting approaches also offer methods accounting for random effects and thus enable prediction of mixed models for longitudinal and clustered data. However, these approaches include several flaws resulting in unbalanced effect selection with falsely induced shrinkage and a low convergence rate on the one hand and biased estimates of the random effects on the other hand. We therefore propose a new boosting algorithm which explicitly accounts for the random structure by excluding it from the selection procedure, properly correcting the random effects estimates and in addition providing likelihood-based estimation of the random effects variance structure. The new algorithm offers an organic and unbiased fitting approach, which is shown via simulations and data examples.

scholarly journals Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rama K. Vasudevan ◽  
Maxim Ziatdinov ◽  
Lukas Vlcek ◽  
Sergei V. Kalinin
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Bayesian Methods ◽  
Deep Neural Networks ◽  
Applied Research ◽  
Modern Science ◽  
Generative Models ◽  
Knowledge Development ◽  
Physical Constraints

AbstractDeep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

DOES THIS SOUND FAMILIAR?

PEDIATRICS ◽  
1989 ◽  
Vol 83 (3) ◽  
pp. A78-A78
Author(s):  
Student
Keyword(s):  
Small Samples ◽  
The Law ◽  
Selection Of

A study of the statistical intuitions of experience research psychologists revealed a lingering belief in what may be called the "law of small numbers," according to which even small samples are highly representative of the populations from which they are drawn. The responses of these investigators reflected the expectation that a valid hypothesis about a population will be represented by a statistically significant result in a sample with little regard for its size. As a consequence researchers put too much faith in the results of small samples and grossly overestimated the replicability of such results. In the actual conduct of research, this bias leads to the selection of samples of inadequate size and to overinterpretation of findings.

scholarly journals Selection of Tuning Parameters, Solution Paths and Standard Errors for Bayesian Lassos

2017 ◽  
Vol 12 (3) ◽  
pp. 753-778 ◽  
Author(s):  
Vivekananda Roy ◽  
Sounak Chakraborty
Keyword(s):  
Standard Errors ◽  
Tuning Parameters ◽  
Selection Of

scholarly journals Genetic effect in leaf and xylem transcriptome variations among Eucalyptus urophylla x grandis hybrids in field conditions

2018 ◽  
Vol 67 (1) ◽  
pp. 57-65
Author(s):  
Alexandre Vaillant ◽  
Astrid Honvault ◽  
Stéphanie Bocs ◽  
Maryline Summo ◽  
Garel Makouanzi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Variance Components ◽  
Genetic Effect ◽  
Field Conditions ◽  
Eucalyptus Urophylla ◽  
Rna Seq ◽  
Intermediate Phenotypes ◽  
False Discovery ◽  
Hybrid Clones ◽  
Selection Of

Abstract To assess the genetic and environmental components of gene-expression variation among trees we used RNA-seq technology and Eucalyptus urophylla x grandis hybrid clones tested in field conditions. Leaf and xylem transcriptomes of three 20 month old clones differing in terms of growth, repeated in two blocks, were investigated. Transcriptomes were very similar between ramets. The number of expressed genes was significantly (P<0.05) higher in leaf (25,665±634) than in xylem (23,637±1,241). A pairwise clone comparisons approach showed that 4.5 to 14 % of the genes were diffe­rentially expressed (false discovery rate [FDR]<0.05) in leaf and 7.1 to 16 % in xylem. An assessment of among clone variance components revealed significant results in leaf and xylem in 3431 (248) genes (at FDR<0.2) and 160 (3) (at FDR<0.05), respectively. These two complementary approa­ches displayed correlated results. A focus on the phenylpro­panoid, cellulose and xylan pathways revealed a large majo­rity of low expressed genes and a few highly expressed ones, with RPKM values ranging from nearly 0 to 600 in leaf and 10,000 in xylem. Out of the 115 genes of these pathways, 45 showed differential expression for at least one pair of geno­type, five of which displaying also clone variance compo­nents. These preliminary results are promising in evaluating whether gene expression can serve as possible ‘intermediate phenotypes’ that could improve the accuracy of selection of grossly observable traits.

scholarly journals Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

2016 ◽  
Vol 59 (2) ◽  
pp. 243-248 ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Abderrahmen Ben Gara ◽  
Hedi Hammami ◽  
Mohamed Amine Ferchichi ◽  
Boulbaba Rekik
Keyword(s):  
Dairy Cattle ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Bayesian Procedure ◽  
Milk Traits ◽  
Residual Variances ◽  
Estimation Of Variance

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.

On Describing an Incidence Sample

1982 ◽  
Vol 7 (4) ◽  
pp. 311-331 ◽  
Author(s):  
Gwyneth M. Boodoo
Keyword(s):  
Variance Components ◽  
Generalizability Theory ◽  
Standard Errors ◽  
Eighth Grade ◽  
Eighth Grade Students ◽  
Competency Based ◽  
Mean And Variance ◽  
The Mean ◽  
Using Data ◽  
Variance Estimates

Parameters used to describe an incidence sample are estimated using the theory of generalized symmetric means and generalizability theory. The former is used to compute estimates of the mean and variance components in an ANOVA framework, while the latter is used in obtaining generalizability coefficients. Standard errors of the variance estimates are obtained. The procedure is illustrated using data from two competency-based tests given to eighth grade students in mathematics and reading.

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