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  journal={Nature Communications},
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  publisher={Nature Publishing Group UK London}
}
@article{balaj2024effects,
  title={Effects of education on adult mortality: a global systematic review and meta-analysis},
  author={Balaj, Mirza and Henson, Claire A and Aronsson, Amanda and Aravkin, Aleksandr and Beck, Kathryn and Degail, Claire and Donadello, Lorena and Eikemo, Kristoffer and Friedman, Joseph and Giouleka, Anna and others},
  journal={The Lancet Public Health},
  year={2024},
  publisher={Elsevier}
}
@article{flor2024health,
  title={Health effects associated with exposure to secondhand smoke: a Burden of Proof study},
  author={Flor, Luisa S and Anderson, Jason A and Ahmad, Noah and Aravkin, Aleksandr and Carr, Sinclair and Dai, Xiaochen and Gil, Gabriela F and Hay, Simon I and Malloy, Matthew J and McLaughlin, Susan A and others},
  journal={Nature medicine},
  pages={1--19},
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}
@article{spencer2023health,
  title={Health effects associated with exposure to intimate partner violence against women and childhood sexual abuse: a burden of proof study},
  author={Spencer, Cory N and Khalil, Mariam and Herbert, Molly and Aravkin, Aleksandr Y and Arrieta, Alejandra and Baeza, Mar{\'\i}a Jose and Bustreo, Flavia and Cagney, Jack and Calderon-Anyosa, Renzo JC and Carr, Sinclair and others},
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@article{dai2022health,
  title={Health effects associated with smoking: a Burden of Proof study},
  author={Dai, Xiaochen and Gil, Gabriela F and Reitsma, Marissa B and Ahmad, Noah S and Anderson, Jason A and Bisignano, Catherine and Carr, Sinclair and Feldman, Rachel and Hay, Simon I and He, Jiawei and others},
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@article{razo2022effects,
  title={Effects of elevated systolic blood pressure on ischemic heart disease: a Burden of Proof study},
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@article{stanaway2022health,
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@inproceedings{johannessen2020splipy,
  title={Splipy: B-spline and NURBS modelling in python},
  author={Johannessen, Kjetil Andr{\'e} and Fonn, Eivind},
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}

@article{zheng2021trimmed,
  title={Trimmed constrained mixed effects models: formulations and algorithms},
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}


@article{Buscemi2019Survey,
abstract = {Linear mixed-effects models are a class of models widely used for analyzing different types of data: longitudinal, clustered and panel data. Many fields, in which a statistical methodology is required, involve the employment of linear mixed models, such as biology, chemistry, medicine, finance and so forth. One of the most important processes, in a statistical analysis, is given by model_name selection. Hence, since there are a large number of linear mixed model_name selection procedures available in the literature, a pressing issue is how to identify the best approach to adopt in a specific case. We outline mainly all approaches focusing on the part of the model_name subject to selection (fixed and/or random), the dimensionality of models and the structure of variance and covariance matrices, and also, wherever possible, the existence of an implemented application of the methodologies set out.},
annote = {The most up-to-date literature review found on this issue.},
author = {Buscemi, Simona and Plaia, Antonella},
doi = {10.1007/s10182-019-00359-z},
file = {:Users/aksh/Documents/Papers/2020/Buscemi, Plaia/Model selection in linear mixed-effect models/Buscemi, Plaia - 2020 - Model selection in linear mixed-effect models.pdf:pdf},
issn = {1863-8171},
journal = {AStA Advances in Statistical Analysis},
keywords = {AIC,BIC,LASSO,Linear mixed model_name,MCP,MDL,Mixed model_name selection,Shrinkage methods},
mendeley-groups = {Feature/Effects Selection,Surveys Summaries Overviews},
month = {dec},
number = {4},
pages = {529--575},
publisher = {Springer Berlin Heidelberg},
title = {{Model selection in linear mixed-effect models}},
url = {https://doi.org/10.1007/s10182-019-00359-z http://link.springer.com/10.1007/s10182-019-00359-z},
volume = {104},
year = {2020}
}

@article{zheng2018unified,
  title={A unified framework for sparse relaxed regularized regression: SR3},
  author={Zheng, Peng and Askham, Travis and Brunton, Steven L and Kutz, J Nathan and Aravkin, Aleksandr Y},
  journal={IEEE Access},
  volume={7},
  pages={1404--1423},
  year={2018},
  publisher={IEEE},
  doi = {10.1109/ACCESS.2018.2886528}
}

@article{sholokhov2022relaxation,
  title={A Relaxation Approach to Feature Selection for Linear Mixed Effects Models},
  author={Sholokhov, Aleksei and Burke, James V and Santomauro, Damian F and Zheng, Peng and Aravkin, Aleksandr},
  journal={arXiv preprint arXiv:2205.06925},
  year={2022},
  doi={10.48550/arXiv.2205.06925}
}
@article{aravkin2022relaxationb,
  title={Analysis of Relaxation Methods for Feature Selection in Mixed Effects Models},
  author={Aravkin, Aleksandr and Burke, James and Sholokhov, Aleksei and Zheng, Peng},
  journal={arXiv preprint arXiv:2209.10575},
  year={2022},
  doi={10.48550/arXiv.2209.10575}
}

@article{baraldi2019basis,
  title={Basis pursuit denoise with nonsmooth constraints},
  author={Baraldi, Robert and Kumar, Rajiv and Aravkin, Aleksandr},
  journal={IEEE Transactions on Signal Processing},
  volume={67},
  number={22},
  pages={5811--5823},
  year={2019},
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  doi={10.1109/tsp.2019.2946029}
}

@article{murray2020global,
  title={Global burden of 87 risk factors in 204 countries and territories, 1990--2019: a systematic analysis for the Global Burden of Disease Study 2019},
  author={Murray, Christopher JL and Aravkin, Aleksandr Y and Zheng, Peng and Abbafati, Cristiana and Abbas, Kaja M and Abbasi-Kangevari, Mohsen and Abd-Allah, Foad and Abdelalim, Ahmed and Abdollahi, Mohammad and Abdollahpour, Ibrahim and others},
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  volume={396},
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  year={2020},
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}


@article{schelldorfer2014glmmlasso,
  title={Glmmlasso: an algorithm for high-dimensional generalized linear mixed models using L1-penalization},
  author={Schelldorfer, J{\"u}rg and Meier, Lukas and B{\"u}hlmann, Peter},
  journal={Journal of Computational and Graphical Statistics},
  volume={23},
  number={2},
  pages={460--477},
  year={2014},
  publisher={Taylor \& Francis},
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}


@article{li2020survey,
  title={A survey on sparse learning models for feature selection},
  author={Li, Xiaoping and Wang, Yadi and Ruiz, Rub{\'e}n},
  journal={IEEE transactions on cybernetics},
  year={2020},
  publisher={IEEE},
  doi={10.1109/TCYB.2020.2982445}
}

@article{miao2016survey,
  title={A survey on feature selection},
  author={Miao, Jianyu and Niu, Lingfeng},
  journal={Procedia Computer Science},
  volume={91},
  pages={919--926},
  year={2016},
  publisher={Elsevier},
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}

@article{Mendible2020,
abstract = {We develop an unsupervised machine learning algorithm for the automated discovery and identification of traveling waves in spatiotemporal systems governed by partial differential equations (PDEs). Our method uses sparse regression and subspace clustering to robustly identify translational invariances that can be leveraged to build improved reduced-order models (ROMs). Invariances, whether translational or rotational, are well known to compromise the ability of ROMs to produce accurate and/or low-rank representations of the spatiotemporal dynamics. However, by discovering translations in a principled way, data can be shifted into a coordinate systems where quality, low-dimensional ROMs can be constructed. This approach can be used on either numerical or experimental data with or without knowledge of the governing equations. We demonstrate our method on a variety of PDEs of increasing difficulty, taken from the field of fluid dynamics, showing the efficacy and robustness of the proposed approach.},
archivePrefix = {arXiv},
arxivId = {1911.00565},
author = {Mendible, Ariana and Brunton, Steven L. and Aravkin, Aleksandr Y. and Lowrie, Wes and Kutz, J. Nathan},
doi = {10.1007/s00162-020-00529-9},
eprint = {1911.00565},
file = {:Users/aksh/Documents/Papers/2020/Mendible et al/Dimensionality reduction and reduced-order modeling for traveling wave physics/Mendible et al. - 2020 - Dimensionality reduction and reduced-order modeling for traveling wave physics.pdf:pdf},
issn = {14322250},
journal = {Theoretical and Computational Fluid Dynamics},
keywords = {Data decomposition,Reduced-order modeling,Transported quantities,Traveling waves},
number = {4},
pages = {385--400},
title = {{Dimensionality reduction and reduced-order modeling for traveling wave physics}},
volume = {34},
year = {2020}
}

@article{levin2019proof,
  title={A Proof of Principle: Multi-Modality Radiotherapy Optimization},
  author={Levin, Roman and Aravkin, Aleksandr Y and Kim, Minsun},
  journal={arXiv preprint arXiv:1911.05182},
  year={2019},
  doi={10.48550/arXiv.1911.05182}
}

@inproceedings{sklearn_api,
  author    = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and
               Fabian Pedregosa and Andreas Mueller and Olivier Grisel and
               Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort
               and Jaques Grobler and Robert Layton and Jake VanderPlas and
               Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux},
  title     = {{API} design for machine learning software: experiences from the scikit-learn
               project},
  booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning},
  year      = {2013},
  pages = {108--122},
  doi = {10.48550/arXiv.1309.0238}
}

@article{schelldorfer2011estimation,
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  author={Schelldorfer, J{\"u}rg and B{\"u}hlmann, Peter and DE GEER, SARA VAN},
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  number={2},
  pages={197--214},
  year={2011},
  publisher={Wiley Online Library},
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}