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+++ b/glmmbib.html
@@ -46,6 +46,17 @@
2008.
(doi:10.1016/j.csda.2008.02.033)
+
+[Anderson et al.,
+ 2022]Sean C. Anderson, Eric J.
+ Ward, Philina A. English, Lewis A. K. Barnett,
+ and James T. Thorson.
+sdmTMB: An R package for fast, flexible, and user-friendly generalized
+ linear mixed effects models with spatial and spatiotemporal random fields,
+ March 2022.
+(doi:10.1101/2022.03.24.485545)
+
[Angrist and Pischke,
2009]Joshua D. Angrist and
@@ -169,6 +180,17 @@
Statistics and Computing, 21(2):173–183, April 2011.
(doi:10.1007/s11222-009-9157-4)
+
+
+[Bellio et al.,
+ 2023]Ruggero Bellio, Swarnadip
+ Ghosh, Art B. Owen, and Cristiano Varin.
+Scalable Estimation of Probit
+ Models with Crossed Random Effects, August 2023.
+arXiv:2308.15681 [stat].
+(doi:10.48550/arXiv.2308.15681)
+
[Belshe et al.,
@@ -293,6 +315,25 @@
loglinear mixed models.
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+
+[Breiman,
+ 2001]Leo Breiman.
+Statistical Modeling: The
+ Two Cultures.
+Statistical Science, 16(3):199–215, August 2001.
+There are two cultures in the use of statistical modeling to reach conclusions
+ from data. One assumes that the data are generated by a given stochastic data
+ model. The other uses algorithmic models and treats the data mechanism as
+ unknown. The statistical community has been committed to the almost exclusive
+ use of data models. This commitment has led to irrelevant theory,
+ questionable conclusions, and has kept statisticians from working on a large
+ range of interesting current problems. Algorithmic modeling, both in theory
+ and practice, has developed rapidly in fields outside statistics. It can be
+ used both on large complex data sets and as a more accurate and informative
+ alternative to data modeling on smaller data sets. If our goal as a field is
+ to use data to solve problems, then we need to move away from exclusive
+ dependence on data models and adopt a more diverse set of tools.
+
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@@ -308,6 +349,21 @@
Proceedings of the second Seattle symposium in biostatistics:
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+
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+[Bridge
+ et al.]Helen Bridge, Katy E.
+ Morgan, and Chris Frost.
+Negative
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+ magnitude: How do such ânon-regularâ random intercept and slope models
+ arise, and what should be done when they do?.
+Statistics in Medicine, n/a(n/a).
+_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.10070.
+(doi:10.1002/sim.10070)
+
@@ -367,6 +423,25 @@
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+In Proceedings of the Twelth International Conference on
+ Artificial Intelligence and Statistics, pages 73–80. PMLR,
+ April 2009.
+ISSN: 1938-7228.
+
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+ 1992]John M. Chambers and Trevor J.
+ Hastie.
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+Num Pages: 32.
+
+
+[Efron, 1986]B. Efron.
+Why
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+The American Statistician, 40(1):1–5, February 1986.
+Publisher: Taylor & Francis _eprint:
+ https://www.tandfonline.com/doi/pdf/10.1080/00031305.1986.10475342.
+(doi:10.1080/00031305.1986.10475342)
+
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Statistics & Probability Letters, 76(8):764–772, April 2006.
(doi:10.1016/j.spl.2005.10.036)
+
+
+[Freitas et al.,
+ 2016]Carla Freitas, Esben M.
+ Olsen, Halvor Knutsen, Jon Albretsen, and
+ Even Moland.
+Temperature-associated habitat selection in a cold-water marine fish.
+Journal of Animal Ecology, 85(3):628–637, 2016.
+(doi:10.1111/1365-2656.12458)
+
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2003.
(doi:10.1016/S0167-9473(02)00290-6)
+
+
+[Gao and Owen,
+ 2017]K. Gao and A. B. Owen.
+Estimation and Inference for Very
+ Large Linear Mixed Effects Models.
+arXiv:1610.08088 [stat], May 2017.
+arXiv: 1610.08088.
+
+
+
+[Gao and Owen,
+ 2017]Katelyn Gao and Art Owen.
+Efficient moment
+ calculations for variance components in large unbalanced crossed random
+ effects models.
+Electronic Journal of Statistics, 11(1):1235–1296, 2017.
+(doi:10.1214/17-EJS1236)
+
+
+[Gao and Owen,
+ 2020]Katelyn Gao and Art B. Owen.
+Estimation and Inference for
+ Very Large Linear Mixed Effects Models.
+Statistica Sinica, 30(4):1741–1771, 2020.
+Publisher: Institute of Statistical Science, Academia Sinica.
+
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Statistics in Medicine, 27(15):2865–2873, July 2008.
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+
+[Gelman,
+ 2008]Andrew Gelman.
+Objections
+ to Bayesian statistics.
+Bayesian Analysis, 3:445–450, 2008.
+(doi:10.1214/08-BA318)
+
+
+
+[Ghandwani et al.,
+ 2023]Disha Ghandwani, Swarnadip
+ Ghosh, Trevor Hastie, and Art B. Owen.
+Scalable solution to crossed random
+ effects model with random slopes, September 2023.
+arXiv:2307.12378 [stat].
+(doi:10.48550/arXiv.2307.12378)
+
+
+
+[Ghosh et al.,
+ 2022]Swarnadip Ghosh, Trevor
+ Hastie, and Art B. Owen.
+Scalable
+ logistic regression with crossed random effects.
+Electronic Journal of Statistics, 16(2):4604–4635, January 2022.
+Publisher: Institute of Mathematical Statistics and Bernoulli Society.
+(doi:10.1214/22-EJS2047)
+
+
+
+[Ghosh et al.,
+ 2022]Swarnadip Ghosh, Trevor
+ Hastie, and Art B. Owen.
+Backfitting
+ for large scale crossed random effects regressions.
+The Annals of Statistics, 50(1):560–583, February 2022.
+Publisher: Institute of Mathematical Statistics.
+(doi:10.1214/21-AOS2121)
+
[Goldman and Whelan,
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Problems in Inference for Additive and Linear Mixed Models.
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+
+[Griewank and Walther,
+ 2003]Andreas Griewank and Andrea
+ Walther.
+Introduction to Automatic Differentiation.
+Proc. Appl. Math. Mech, 2(1):45–49, 2003.
+(doi:10.1002/pamm.200310012)
+
[Hadfield,
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@@ -813,6 +990,15 @@
Models, 2018.
R package version 0.2.0.
+
+[He et al., 2019]Hua He,
+ Hui Zhang, Peng Ye, and Wan
+ Tang.
+A test of inflated zeros for Poisson regression models.
+Statistical methods in medical research, 28(4):1157–1169, April
+ 2019.
+(doi:10.1177/0962280217749991)
+
+[Heiling et al.,
+ 2024]Hillary M Heiling, Naim U
+ Rashid, Quefeng Li, Xianlu L Peng,
+ Jen Jen Yeh, and Joseph G Ibrahim.
+Efficient computation of
+ high-dimensional penalized generalized linear mixed models by latent factor
+ modeling of the random effects.
+Biometrics, 80(1):ujae016, March 2024.
+(doi:10.1093/biomtc/ujae016)
+
+
+
+[Heiling et al.,
+ 2024]Hillary M. Heiling, Naim U.
+ Rashid, Quefeng Li, and Joseph G. Ibrahim.
+glmmPen: High Dimensional
+ Penalized Generalized Linear Mixed Models, April 2024.
+arXiv:2305.08204 [stat].
+(doi:10.48550/arXiv.2305.08204)
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+
+[Madar, 2015]Vered
+ Madar.
+Direct formulation to Cholesky decomposition of a general nonsingular
+ correlation matrix.
+Statistics & Probability Letters, 103:142–147, August 2015.
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+
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+[Tanaka and Hui,
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+ Hui.
+Symbolic Formulae for Linear Mixed Models.
+In Hien Nguyen, editor, Statistics and Data
+ Science, Communications in Computer and Information Science,
+ pages 3–21, Singapore, 2019. Springer.
+(doi:10.1007/978-981-15-1960-4_1)
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