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A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression [article]

Eran Treister and Javier S. Turek and Irad Yavneh
2016 arXiv   pre-print
This framework is applied for solving two problems: (1) the sparse inverse covariance estimation problem, and (2) l1-regularized logistic regression.  ...  A multilevel framework is presented for solving such l1 regularized sparse optimization problems efficiently.  ...  Application of the multilevel framework to sparse inverse covariance estimation.  ... 
arXiv:1607.00315v1 fatcat:32v7bkfjy5hbza5ldzgkc4gq5i

Modeling item–item similarities for personalized recommendations on Yahoo! front page

Deepak Agarwal, Liang Zhang, Rahul Mazumder
2011 Annals of Applied Statistics  
In fact, we build a per-item regression model based on a rich set of user covariates and estimate individual user affinity to items by introducing a latent random vector for each user.  ...  Our approach is based on a novel multilevel hierarchical model that we refer to as a User Profile Model with Graphical Lasso (UPG).  ...  The authors would like to thank the anonymous reviewers and the Editor for their helpful comments and suggestions that improved the presentation of the paper.  ... 
doi:10.1214/11-aoas475 fatcat:uabzn2ybz5eipgl3ick2yfr63u

Propensity score weighting for a continuous exposure with multilevel data

Megan S. Schuler, Wanghuan Chu, Donna Coffman
2016 Health Services & Outcomes Research Methodology  
Recent work has extended propensity score methods to a multilevel setting, primarily focusing on binary exposures.  ...  ., matching, weighting, subclassification) provide a statistical approach for balancing dissimilar exposure groups on baseline covariates.  ...  This work was funded by awards P50 DA010075, P50 DA039838, and T32 DA017629 from the National Institute on Drug Abuse and K01 ES025437 from the National Institutes of Health Big Data to Knowledge initiative  ... 
doi:10.1007/s10742-016-0157-5 pmid:27990097 pmcid:PMC5157938 fatcat:4cnygspbyneqzfx4xzak6emsoe

Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models With Factor Structures

Minjeong Jeon, Sophia Rabe-Hesketh
2012 Journal of educational and behavioral statistics  
Such models are useful in educational research, for example, for estimation of value-added teacher or school effects with persistence parameters and for analysis of large-scale assessment data using multilevel  ...  Here, w mij is a vector of covariates and known constants associated with the random effect or latent variable mj and m is a vector of coefficients that we will refer to as factor loadings.  ...  regression model with category-specific intercept and covariates s x i .  ... 
doi:10.3102/1076998611417628 fatcat:cwlbxgio4rh7pchbxgasmhtuem

Scalable computation for Bayesian hierarchical models [article]

Omiros Papaspiliopoulos, Timothée Stumpf-Fétizon, Giacomo Zanella
2021 arXiv   pre-print
For each class we develop a framework for scalable computation.  ...  We provide a number of negative (for crossed) and positive (for nested) results for the scalability (or lack thereof) of methods based on sparse linear algebra, which are relevant also to Laplace approximation  ...  Acknowledgements The development of belief propagation for nested multilevel models first appeared in the unpublished technical report Papaspiliopoulos and Zanella (2017) by two of the  ... 
arXiv:2103.10875v2 fatcat:7hl3vzw5wbaireozk7oizamwma

Adjustment for Biased Sampling Using NHANES Derived Propensity Weights [article]

Olivia M. Bernstein, Brian G. Vegetabile, Christian R. Salazar, Joshua D. Grill, Daniel L. Gillen
2021 arXiv   pre-print
We create a combined dataset of C2C and NHANES subjects and compare different approaches (logistic regression, covariate balancing propensity score, entropy balancing, and random forest) for estimating  ...  To address questions about generalizability of estimated associations we estimate propensity for self-selection into the convenience sample weights using data from the National Health and Nutrition Examination  ...  CRS was supported by the National Institute on Aging of the National Institute of Health under a diversity supplement to award AG059407 and an Alzheimer's Association research fellowship AARFD-20-682432  ... 
arXiv:2104.10298v1 fatcat:ti3bqqtvvfgxbp564nw2j2owwa

A Marginal Approach to Reduced-Rank Penalized Spline Smoothing With Application to Multilevel Functional Data

Huaihou Chen, Yuanjia Wang, Myunghee Cho Paik, H. Alex Choi
2013 Journal of the American Statistical Association  
This data has a natural multilevel structure with treatment cycles nested within subjects and measurements nested within cycles.  ...  In this work, we propose marginal approaches to fit multilevel functional data through penalized spline generalized estimating equation  ...  Acknowledgements Wang's research is supported by various NIH grants AG031113-01A2 and NS073670-01.  ... 
doi:10.1080/01621459.2013.826134 pmid:24497670 pmcid:PMC3909538 fatcat:rim33mskafdmtchhwtf5smzcuu

Bayesian Analysis of Genetic Interactions in Case-control Studies, with Application to Adiponectin Genes and Colorectal Cancer Risk

Nengjun Yi, Virginia G. Kaklamani, Boris Pasche
2010 Annals of Human Genetics  
We propose a novel method to interpret and visualize models with multiple interactions by computing the average predictive probability.  ...  Our Bayesian models use Student-t prior distributions with different shrinkage parameters for different types of effects, allowing reliable estimates of main effects and interactions and hence increasing  ...  Acknowledgments This work was supported in part by the following research grants: NIH 2R01GM069430-06, NIH R01 GM077490, NCI CA137000, NCI CA112520, NCI CA108741 and the Walter Mander Foundation, Chicago  ... 
doi:10.1111/j.1469-1809.2010.00605.x pmid:20846215 pmcid:PMC3005151 fatcat:np53qup6mjd7hb3skv5p4r3qy4

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

Peter C. Austin
2011 Multivariate Behavioral Research  
Randomized controlled trials (RCTs) are considered the gold standard approach for estimating the effects of treatments, interventions, and exposures (hereafter referred to as treatments) on outcomes.  ...  Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data.  ...  is using a logistic regression model).  ... 
doi:10.1080/00273171.2011.568786 pmid:21818162 pmcid:PMC3144483 fatcat:ivghxwsul5hjvcssqrretk55ta

Parametric G-computation for Compatible Indirect Treatment Comparisons with Limited Individual Patient Data [article]

Antonio Remiro-Azócar, Anna Heath, Gianluca Baio
2022 arXiv   pre-print
When adjusting for covariates, one must integrate or average the conditional estimate over the relevant population to recover a compatible marginal treatment effect.  ...  The approach views the covariate adjustment regression as a nuisance model and separates its estimation from the evaluation of the marginal treatment effect of interest.  ...  ACKNOWLEDGMENTS The authors thank the peer reviewers of a previous article of theirs. 17 Their comments were extremely insightful and helped improved the underlying motivation of this article.  ... 
arXiv:2108.12208v3 fatcat:fp44wrmghratvbnfwwpiszftbm

A lag functional linear model for prediction of magnetization transfer ratio in multiple sclerosis lesions

Gina-Maria Pomann, Ana-Maria Staicu, Edgar J. Lobaton, Amanda F. Mejia, Blake E. Dewey, Daniel S. Reich, Elizabeth M. Sweeney, Russell T. Shinohara
2016 Annals of Applied Statistics  
Two procedures are proposed to estimate the regression parameter functions: 1) an approach that ensures smoothness for each value of time using generalized cross-validation; and 2) a global smoothing approach  ...  We propose a lag functional linear model to predict a response using multiple functional predictors observed at discrete grids with noise.  ...  Meyer et al. (2015) proposes a multilevel, wavelet-based, Bayesian function-on-function regression framework.  ... 
doi:10.1214/16-aoas981 fatcat:dh23eq7s6fcd7eoknlrima3lme

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
The machine learning algorithms covered are Gaussian models, linear regression, logistic regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian  ...  Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  to logistic regression.  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Bayesian Functional Principal Components Analysis via Variational Message Passing [article]

Tui H. Nolan, Jeff Goldsmith, David Ruppert
2021 arXiv   pre-print
In this article, we develop a Bayesian approach, which aims to determine the Karhunen-Lo\'eve decomposition directly without the need to smooth and estimate a covariance surface.  ...  We present the computational details, a set of simulations for assessing accuracy and speed and an application to United States temperature data.  ...  For instance, the updates for the optimal posterior density functions of the coefficients in a linear regression model will differ from those in a linear logistic regression model.  ... 
arXiv:2104.00645v1 fatcat:mm4olnxhlbgbni2hiabzonpbaq

Bayesian Item Response Modeling in R with brms and Stan [article]

Paul-Christian Bürkner
2020 arXiv   pre-print
We demonstrate how to use the R package brms together with the probabilistic programming language Stan to specify and fit a wide range of Bayesian IRT models using flexible and intuitive multilevel formula  ...  Common IRT model classes that can be specified natively in the presented framework include 1PL and 2PL logistic models optionally also containing guessing parameters, graded response and partial credit  ...  The non-linear multilevel formula syntax of brms allows for a flexible yet concise specification of multidimensional IRT models, with an arbitrary number of person or item covariates and multilevel structure  ... 
arXiv:1905.09501v3 fatcat:ybgszq32frfl5atfn6fzaguc5q

An analytics approach to designing patient centered medical homes

Saeede Ajorlou, Issac Shams, Kai Yang
2014 Health Care Management Science  
Another common application in multilevel analysis is related to random slopes that appear when combining regression equations of higher levels with the lower levels to form a compound representation [  ...  Note that here we use the same set of effects for the both response regression, but this may change in other applications with a bivariate response.  ... 
doi:10.1007/s10729-014-9287-x pmid:24942633 fatcat:n2r6thntg5azvbkummopwyas5q
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