%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/


%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/


%% Created for Guy Nason at 2011-12-23 13:40:57 +0000 


%% Saved with string encoding Unicode (UTF-8) 


@string{acha = {App. Comp. Harm. Anal.}}

@string{aer = {Amer.\ Econ. Rev.}}

@string{aism = {Ann. Inst. Statist. Math.}}

@string{ajm = {Am. J. Math}}

@string{annstat = {Ann. Statist.}}

@string{annmstat = {Ann.\ Math.\ Stat.}}

@string{appear = {To appear}}

@string{appstat = {Appl. Statist.}}

@string{astrophysj={Astrophys. J.}}

@string{ass={Astrophys. Space Sci.}}

@string{automatica={Automatica}}

@string{bcs = {Biometrics}}

@string{bernoulli = {Bernoulli}}

@string{bka = {Biometrika}}

@string{cct = {Contr. Clin. Trials}}

@string{cm = {Colloq. Math.}}

@string{cpam = {Comm. Pure Appl. Math.}}

@string{cras = {Compt. Rend. Acad. Sci. Paris A}}

@string{crasi = {Compt. Rend. Acad. Sci. Paris I}}

@string{csda = {Comp. Stat. Data Anal.}}

@string{cssa = {Comm. Stat. - Sequential Analysis}}

@string{cssc = {Comm. Stat. - Simulation and Computation}}

@string{cstm = {Comm. Stat. - Theory and Methods}}

@string{dsp = {Digit.\ Signal Process.}}

@string{ejs = {Elec. J. Stat.}}

@string{eka = {Econometrica}}

@string{el = {Econ. Lett.}}

@string{er = {Econ. Rev.}}

@string{et = {Econ. Th.}}

@string{ejasp={Eurasip J. Adv. Sig. Pr.}}

@string{geop = {Geophysics}}

@string{ier={Int.\ Econ.\ Rev.}}

@string{ijf={Int. J. Forecasting}}


@string{ijw = {Int. J. Wavelets, Multiresolution, Inf. Process.}}

@string{ijtaf={Int. J. Theor. Appl. Fin.}}

@string{isds = {Duke University, Institute of Statistics and Decision Sciences}}

@string{isr = {Intl. Statist. Rev.}}

@string{itassp = {IEEE Trans. Acoust., Speech}}

@string{itc = {IEEE Trans. Comput.}}

@string{itec={IEEE Trans.\ Energ.\ Conv.}}

@string{itip = {IEEE Trans. Im. Proc.}}

@string{itit = {IEEE Trans.\ Inf. Th.}}

@string{itim={IEEE Trans.\ Instrum. Meas.}}

@string{itopaami = {IEEE Trans. Patt. Anal. and Mach. Intell.}}

@string{itr = {IEEE Trans. Rel.}}

@string{itsp = {IEEE Trans. Sig. Proc}}

@string{ituffc = {IEEE Trans. Ultra.\ Ferro.\ Freq.\ Cont.}}

@string{jae = {J. Appl. Econ.}}

@string{jaec = {J. Appl. Econometrics}}

@string{jams = {J. Am. Math. Soc.}}

@string{jap = {J. Appl. Prob.}}

@string{japs = {J. Appl. Stat.}}

@string{jas = {J. Appl. Statist.}}

@string{jasa = {J. Am. Statist. Ass.}}

@string{jastp={J. Atmos. Sol-Terr.\ Phy.}}

@string{jb = {J. Bus.}}

@string{jbes = {J. Bus. Econ. Stat.}}

@string{jc = {J. Complex.}}

@string{jcn = {J. Complex Netw.}}

@string{jcgs = {J. Comp. Graph. Stat.}}

@string{je={J.\ Econometrics}}

@string{jet = {J. Econ. Th.}}

@string{jf = {J. Finance}}

@string{jfore={J. Forecasting}}

@string{jiss = {J. Italian Stat. Soc.}}

@string{jma = {J. Mult. Anal.}}

@string{jns = {J. Nonparam. Statist.}}

@string{jors={J. Oper. Res. Soc.}}

@string{jpe = {J. Polit. Econ.}}

@string{jps={J. Prob. Stat.}}


@string{jrssa = {J. R. Statist. Soc. A}}


@string{jrssb = {J. R. Statist. Soc. {\rm B}}}

@string{jrssc = {J. R. Statist. Soc. {\rm C}}}

@string{jrssd = {Statistician}}

@string{jrsss = {J. R. Statist. Soc. Suppl.}}

@string{jspi = {J. Statist. Plan. Inf.}}

@string{jscs={J. Statist. Comp. Simul.}}

@string{jss = {J. Statist. Soft.}}

@string{jts = {J. Time Ser. Anal.}}

@string{jtse = {J. Time Ser. Econom.}}

@string{lmj = {Lithuanian Math.\ J.}}

@string{mms = {Multiscale Model.\ and Simul.}}

@string{mri = {Mag. Res. Imag.}}

@string{nrm = {Nat. Rev. Microbiol.}}

@string{nm={Numer. Math.}}

@string{oe = {Optical Engineering}}

@string{oup = {Oxford University Press}}

@string{pa = {Physica A}}

@string{pd = {Physica D: Nonlinear Phenomena}}


@string{pams = {Proc.\ Am.\ Math.\ Soc.}}

@string{phd = {{PhD} Thesis}}

@string{pieee = {Proceedings of the IEEE}}

@string{pm={Psychol. Methods}}

@string{ptrf = {Probability Theory and Related Fields}}

@string{qjrms={Quart.\ J.\ Roy.\ Met.\ Soc.}}

@string{qje={Quart.\ J. Econ.}}

@string{res={Rev.\ Econ.\ Stud.}}

@string{roes = {Rev.\ Econ.\ Stat.}}

@string{rofs = {Rev.\ Fin.\ Stud.}}

@string{sanka = {Sankhy\={a} {\rm A}}}

@string{sankb = {Sankhy\={a} {\rm B}}}

@string{sc = {Statistics and Computing}}

@string{sd = {Statistics and Decisions}}

@string{se={Sol. Energy}}

@string{siamrev = {SIAM Rev.}}

@string{sjma = {SIAM J.\ Math.\ Anal.}}

@string{sjs = {Scand.\ J.\ Stat.}}

@string{sjsc = {SIAM J.\ Sci.\ Comput.}}

@string{sm = {Stat. Med.}}

@string{sp = {Sig. Proc.}}

@string{spl = {Stat. Prob. Lett.}}

@string{ss = {Statist. Sci.}}

@string{stat = {Stat}}

@string{statsin = {Statistica Sinica}}

@string{statsurv = {Statistics Surveys}}

@string{stprap = {Stoch. Process. Appl.}}

@string{sindae = {Stud. Nonlinear Dyn. E.}}

@string{submitted = {Submitted for publication}}

@string{tams = {Trans. Am. Math. Soc.}}

@string{tas = {Am. Statist.}}

@string{tej = {The Econometrics J.}}

@string{tres = {The Review of Economics and Statistics}}



%% Created for Galyardt at 2014-08-21 22:49:39 -0400 


%% Saved with string encoding Unicode (UTF-8) 



@Article{gnar_paper,
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    journal = jss,
    year = {2020},
    volume = {96},
    pages = {1--36},
    doi = {10.18637/jss.v096.i05},
}


@Manual{r_language,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2022},
    url = {https://www.R-project.org/},
    note = "Version {R} 4.3.2"
  }


@Manual{viridis_lib,
    title = {{viridis} - Colorblind-Friendly Color Maps for R},
    author = {Garnier, S. and Ross, N. and Rudis, B. and Sciaini, M. and Pedro~Camargo, A.
                and Scherer, C.},
    year = {2023},
    note = {{R} package version 0.6.4},
    url = {https://sjmgarnier.github.io/viridis/},
    doi = {10.5281/zenodo.4679424}
  }


@Book{ggplot_lib,
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    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org},
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  @Manual{gnar_package, 
    title = {GNAR: Methods for Fitting Network Time Series Models.},
    author = {Knight, M. and Leeming, K. and Nason, G. P. and Nunes, M.},
    year = {2023},
    note = {{R} package version 1.1.1},
    url = {https://CRAN.R-project.org/package=GNAR}
    }

@Manual{sna,
    title = {sna: Tools for Social Network Analysis},
    author = {Butts, C. T.},
    year = {2023},
    note = {{R} package version 2.7-1},
    url = {https://CRAN.R-project.org/package=sna},
  }

@article{dijkstra1959note,
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  pages={269--271},
  year={1959},
  publisher={Springer}
} 


@InProceedings{wu2019simplifying,
  title = 	 {Simplifying Graph Convolutional Networks},
  author =       {Wu, Felix and Souza, Amauri and Zhang, Tianyi and Fifty, Christopher and Yu, Tao and Weinberger, Kilian},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {6861--6871},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/wu19e/wu19e.pdf},
  url = 	 {https://proceedings.mlr.press/v97/wu19e.html}
}


@misc{liu2019higherorder,
      title={Higher-order Weighted Graph Convolutional Networks}, 
      author={Liu, S. and Chen, L. and Dong, H. and Wang, Z. and Wu, D. and Huang, Z.},
      year={2019},
      note={arXiv:1911.04129}
}


@Article{graph_series, 
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        volume = {51}, 
        pages = {157--172}, 
        year = {2000}
}

@Article{graph_lasso, 
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    pages = "432–441",
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@Book{brockwell_davis, 
    title = {Time Series: Theory and Methods},
    year = {2006}, 
    publisher = {Springer-Verlag},
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    edition = {2nd},
    author = {Brockwell, P. J. and Davis, R. A.}
}

@book{statLearn,
        title={The Elements of Statistical Learning},
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        doi={10.1007/b94608}, 
        edition={2nd}
}

@book{wainwright_2019, 
    author={Wainwright, M. J.}, 
    title={High-Dimensional Statistics: A Non-Asymptotic Viewpoint}, 
    DOI={10.1017/9781108627771}, 
    publisher={Cambridge University Press},
    address={Cambridge},
    year={2019}, 
}

@Article{ggplot,
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    pages = {144-161},
}

@article{DALLAKYAN2022107557,
title = {Time series graphical lasso and sparse {VAR} estimation},
journal = csda,
volume = {176},
pages = {107557},
year = {2022},
issn = {0167-9473},
doi = {https://doi.org/10.1016/j.csda.2022.107557},
url = {https://www.sciencedirect.com/science/article/pii/S0167947322001372},
author = {Dallakyan, A. and Kim, R. and Pourahmadi, M.}
}


@inproceedings{aic, 
        author ={Akaike, H.}, 
        year = {1973}, 
        title = {Information Theory and An Extension of the Maximum Likelihood Principle}, 
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        pages = {267--281}, 
        publisher = {Academiai Kiado, Budapest Hungary} 
}

@Book{luth, 
    title = {New Introduction to Multiple Time Series Analysis}, 
    author = {L\"{u}tkepohl, H.}, 
    publisher = {Springer-Verlag},
    address={Berlin}, 
    year = {2005}
}

@incollection{gnar_arxiv,
    author = {Nunes, M. and Knight, M. and Nason, G.},
    booktitle = {Modeling and Stochastic Learning for Forecasting in High Dimensions},
    title = {Modelling And Prediction of Time Series Arising On a Graph},
    publisher = {Springer-Verlag},
    address={New York},
    volume = {217}, 
    series = {Lecture Notes in Statistics},
    pages = {183-192},
    editor = {Antoniadis, A. and Poggi, J-M. and Brossat, X.},
    year = {2015}
}

@misc{gnar_org,
    author = {Marina Knight and Guy Nason and Matthew Nunes},
    title = {Modelling, Detrending and Decorrelation of Network Time Series}, 
    year={2016},
    note={arXiv:1603.03221}
}

@Article{bic, 
        title = {Estimating the Dimension of a Model}, 
        author = {Schwarz, G.}, 
        year = {1978}, 
        journal = annstat, 
        volume = {6}, 
        pages = {461--464}
    }

@Book{sna_book, 
    author = {Kolaczyk, E.},
    title = {Statistical Analysis of Network Data: Methods and Models},
    publisher ={Springer-Verlag},
    address={New York},
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}

@Article{varsart,
  title = {{VAR}, {SVAR} and {SVEC} Models: Implementation Within {R} Package {vars}},
  author = {Pfaff, B.},
  journal = jss,
  year = {2008},
  volume = {27},
  number = {4},
  url = {https://www.jstatsoft.org/v27/i04/},
}

@Book{vars,
  title = {Analysis of Integrated and Cointegrated Time Series with R},
  author = {Pfaff, B.},
  publisher = {Springer},
  edition = {2nd},
  address = {New York},
  year = {2008},
  url = {https://www.pfaffikus.de},
}

@Manual{varspack,
    title = {vars: VAR, SVAR and SVEC Models},
    author = {Pfaff, B.},
    year = {2023},
    note = {{R} package version 1.5.9},
    url = {https://CRAN.R-project.org/package=vars},
  }

@Manual{sparsevar,
    title = {sparsevar: Sparse VAR/VECM Models Estimation},
    author = {Vazzoler, S.},
    year = {2021},
    note = {{R} package version 0.1.0},
    url = {https://CRAN.R-project.org/package=sparsevar},
  }

@Manual{fcst_pack,
  title = {forecast: Forecasting functions for time series and linear models},
  author = {Hyndman, R. J. and Athanasopoulos, G. and Bergmeir, C. and Caceres, G. and Chhay, L. and O'Hara-Wild, M. and Petropoulos, F. and Razbash, S. and Wang, E. and Yasmeen, F.},
  year = {2023},
  note = {{R} package version 8.21},
  url = {https://pkg.robjhyndman.com/forecast/},
}

@Article{fcst_art,
  title = {Automatic time series forecasting: the forecast package for {R}},
  author = {Hyndman, R. J. and Khandakar, Y.},
  journal = jss,
  volume = {26},
  pages = {1--22},
  year = {2008},
  doi = {10.18637/jss.v027.i03},
}

@Article{igraph,
    title = {The igraph software package for complex network research},
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    url = {https://igraph.org},
  }

@book{brillinger, 
    author = {Brillinger, D. R.},
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@Book{lauritzen, 
    author = {Lauritzen, S. L.}, 
    title = {Graphical Models},
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    address={Oxford},
    year = {2004} 
}


@incollection{Songsiri200989,
	author = {Songsiri, J. and Dahl, J. and Vandenberghe, L.},
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	year = {2009},
	booktitle = {Convex Optimization in Signal Processing and Communications},
    editor={Palomar, D.P. and Eldar, Y. C.},
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	doi = {10.1017/CBO9780511804458.004},
	publisher = {Elsevier},
}

@Article{network_time_series_overview,
author = {Silva, V. F. and Silva, M. E. and Ribeiro, P. and Silva, F.},
title = {Time series analysis via network science: Concepts and algorithms},
journal = {WIREs Data Mining and Knowledge Discovery},
volume = {11},
number = {3},
pages = {e1404},
keywords = {mapping methods, multivariate time series, network science, univariate time series},
doi = {https://doi.org/10.1002/widm.1404},
url = {https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1404},
eprint = {https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1404},
year = {2021}
}

@Book{ts_forecast_book,
author = {Shumway, R. H.},
edition = {4th},
isbn = {9783319524528},
keywords = {Statistics},
language = {eng},
publisher = {Springer},
address = {New York},
title = {Time Series Analysis and Its Applications With R Examples },
year = {2017},
}

@misc{owidcoronavirus,
    author = {Mathieu, E. and Ritchie, R. and Rodés-Guirao, L. and Appel, C. and Giattino, C. and Hasell, J. and  Macdonald, B. and Dattani, S. and Beltekian, D. and Ortiz-Ospina, E. and Roser, M.},
    title = {Coronavirus Pandemic ({COVID-19})},
    journal = {Our World in Data},
    year = {2022},
    note = {https://ourworldindata.org/coronavirus (accessed {September 9th, 2024})}
}

@article{Zhu17,
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    journal=annstat,
    volume=45,
    pages={1096--1123}}

@article{NasonWei22,
    author={Nason, G.P. and Wei, J.},
    title={Quantifying the economic response to {COVID}-19 mitigations and death rates via forecasting Purchasing Managers’ Indices using Generalised Network Autoregressive models with exogenous variables (with discussion)},
    journal=jrssa,
    volume=185,
    year=2022,
    pages={1778--1792}}


@misc{ArmillottaFokianos21,
      title={Poisson Network Autoregression}, 
      author={Armillotta, M. and Fokianos, K.},
      year={2021},
      note={arXiv:2104.06296}}

@article{ArmillottaFokianos24,
    author={Armillotta, M. and Fokianos, K.},
    title = {Count network autoregression},
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    pages = {584-612},
    keywords = {Generalized linear models, increasing dimension, link function, multi-variate count time series, quasi-likelihood},
    doi = {https://doi.org/10.1111/jtsa.12728},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jtsa.12728},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/jtsa.12728},
    year = "2024"
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@article{Zhou20,
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    pages={1--18}}

@article{Zhu19,
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    pages={345--358}}

@article{Mantziou23,
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    number = {6},
    pages = {cnad039},
    year = {2023},
    month = {11}
}



@article{partialcorrnetworks,
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@misc{defra,
    author="DEFRA",
    year="2011",
    title="Foot and mouth control strategy for {Great Britain}",
    note="{D}epartment for Environment and Rural Affairs, The Scottish Government,
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}

@phdthesis{leeming,
    author="Leeming, K.",
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    title="New methods in time series analysis",
    school="University of Bristol, Bristol, UK"}

@book{chatfield,
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@article{rasines,
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@Article{carbayes,
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  }

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url = {https://www.sciencedirect.com/science/article/pii/S1877584514000203},
author = {Alastair Rushworth and Duncan Lee and Richard Mitchell},
keywords = {Air pollution, Gaussian Markov Random Fields, Respiratory disease, Spatio-temporal autocorrelation}
}

@article{nar_local_local,
author = {Hang Yin and Abolfazl Safikhani and George Michailidis},
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number = {4},
pages = {579--589},
year = {2023},
publisher = {Taylor \& Francis},
doi = {10.1080/00401706.2023.2203184},


URL = { 
    
        https://doi.org/10.1080/00401706.2023.2203184
    
    

},
eprint = { 
    
        https://doi.org/10.1080/00401706.2023.2203184
    
    

}

}

@book{gelman2013bayesian,
  added-at = {2022-03-31T02:40:53.000+0200},
  address = {Boca Raton, Florida},
  author = {Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Dunson, David B. and Vehtari, Akti and Rubin, Donald B.},
  biburl = {https://www.bibsonomy.org/bibtex/237fd188593cb6f131eadacf86f7bd012/gdmcbain},
  edition = {Third},
  interhash = {d22f1da4df2934f61f1d4ef42c32e3be},
  intrahash = {37fd188593cb6f131eadacf86f7bd012},
  isbn = {9781439840955 1439840954},
  keywords = {62-01-statistics-instructional-exposition 62-07-data-analysis 62f15-bayesian-inference 62jxx-linear-inference-regression 62pxx-applications-of-statistics 65c40-numerical-analysis-or-methods-applied-to-Markov-chains},
  publisher = {CRC},
  refid = {966614951},
  series = {Chapman \& Hall/CRC Texts in Statistical Science Series},
  timestamp = {2024-08-02T07:15:35.000+0200},
  title = {Bayesian Data Analysis},
  url = {https://stat.columbia.edu/~gelman/book/},
  year = {2013}
}

