Search · four archives
Search · four archives
12 papers · ranked by Valyu relevance
Soonil Kwon, Dai Wang, Xiuqing Guo
Genome-wide association studies usually involve several hundred thousand of single-nucleotide polymorphisms (SNPs). Conventional approaches face challenges when there are enormous number of SNPs but a relatively small number of samples and, in some cases, are not feasible. We introduce here an iterative Bayesian…
Gao Wang, Abhishek Sarkar, Peter Carbonetto, Matthew Stephens
We introduce a simple new approach to variable selection in linear regression, and to quantifying uncertainty in selected variables. The approach is based on a new model – the “Sum of Single Effects” (SuSiE) model – which comes from writing the sparse vector of regression coefficients as a sum of “single-effect”…
Wei Cheng, Sohini Ramachandran, Lorin Crawford
In this paper, we propose a new approach for variable selection using a collection of Bayesian neural networks with a focus on quantifying uncertainty over which variables are selected. Motivated by fine-mapping applications in statistical genetics, we refer to our framework as an “ensemble of single-effect neural…
Xitong Liang, Samuel Livingstone, Jim Griffin, Geert Verdoolaege + 1 more
'Ricardo Sandes Ehlers'] Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions to the marginal likelihood. The Reversible Jump Markov Chain Monte…
Kun Fan, Xiaoxi Li, Shejuty Devnath, Brock Olson + 2 more
Robust variable selection methods have emerged as powerful tools for dissecting high-dimensional gene-environment interactions in longitudinal studies, owing to their ability to accommodate intra-cluster correlations, capture structured sparsity, and handle heavy-tailed repeated measures. Despite these advantages…
Jacob Williams, Marco A. R. Ferreira, Tieming Ji
Background Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical power to…
Sierra A. Bainter, Thomas G. McCauley, Mahmoud M. Fahmy, Zachary T. Goodman + 2 more
'Zachary T. Goodman' 'Lauren B. Kupis' 'J. Sunil Rao'] In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network…
Yong Li, Hefei Liu, Rubing Li, Lei Shi
Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model prediction accuracy and interpretation effect. redThis study introduces…
Daniel F. Linder, Viral Panchal
In this paper we describe a Bayesian hierarchical model termed ‘PMMLogit’ for classification and model selection in high-dimensional settings with binary phenotypes as outcomes. Posterior computation in the logistic model is known to be computationally demanding due to its non-conjugacy with common priors. We combine a…
Anna Genell, Szilard Nemes, Gunnar Steineck, Paul W Dickman
Background Automatic variable selection methods are usually discouraged in medical research although we believe they might be valuable for studies where subject matter knowledge is limited. Bayesian model averaging may be useful for model selection but only limited attempts to compare it to stepwise regression have…
François Rousset, Raphaël Leblois, Arnaud Estoup, Jean-Michel Marin
Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface…
Junyang Qian, Wenfei Du, Yosuke Tanigawa, Matthew Aguirre + 3 more
Since its first proposal in statistics (1), the lasso has been an effective method for simultaneous variable selection and estimation. A number of packages have been developed to solve the lasso efficiently. However as large datasets become more prevalent, many algorithms are constrained by efficiency or memory bounds.…