Abramowitz M, Stegun IA (1972) Handbook of mathematical functions. Number 55 in National Bureau of Standards: Applied Mathematics, 10 edn. U.S. Government Printing Office, Washington
Agresti A (2013)Categorical data analysis. Number 792 in Wiley Series in Probability and Statistics, 3 edn. Wiley, Hoboken, ISBN 978-0-470-46363-5
D.W. Bacon D.G. Watts Estimating the transition between two intersecting straight lines Biometrika 1971 58 3 525 534 10.1093/biomet/58.3.525
B. Bader J. Yan X. Zhang Automated threshold selection for extreme value analysis via ordered goodness-of-fit tests with adjustment for false discovery rate Ann Appl Stat 2018 12 1 310 329 3773395 10.1214/17-AOAS1092
M. Bee D.J. Dupuis L. Trapin Realized peaks over threshold: a time-varying extreme value approach with high-frequency-based measures J Financ Economet 2019 17 2 254 283 10.1093/jjfinec/nbz003
A. Brezger S. Lang Generalized structured additive regression based on Bayesian P-Splines Comput Stat Data Anal 2006 50 4 967 991 2210741 10.1016/j.csda.2004.10.011
H.J. Brockmann Satellite male groups in horseshoe crabs, Limulus polyphemus Ethology 1996 102 1 1 21 10.1111/j.1439-0310.1996.tb01099.x
P.-C. Bürkner brms: an R package for Bayesian multilevel models using Stan J Stat Softw 2017 80 1 1 28 10.18637/jss.v080.i01
V. Chavez-Demoulin P. Embrechts M. Hofert An extreme value approach for modeling operational risk losses depending on covariates J Risk Insur 2016 83 3 735 776 10.1111/jori.12059
V. Choulakian M.A. Stephens Goodness-of-fit tests for the generalized pareto distribution Technometrics 2001 43 4 478 484 1938678 10.1198/00401700152672573
C. Dugas Y. Bengio F. Bélisle C. Nadeau R. Garcia Incorporating second-order functional knowledge for better option pricing Adv Neural Inf Process Syst 2001 13 451 457
P.K. Dunn G.K. Smyth Randomized quantile residuals J Comput Graph Stat 1996 5 3 236 244 10.1080/10618600.1996.10474708
P.H.C. Eilers B.D. Marx Flexible smoothing with B-splines and penalties Stat Sci 1996 11 2 89 102 1435485 10.1214/ss/1038425655
L. Fahrmeir T. Kneib S. Lang B.D. Marx Regression 2013 Berlin Springer 10.1007/978-3-642-34333-9
D. Gamerman Sampling from the posterior distribution in generalized linear mixed models Stat Comput 1997 7 57 68 10.1023/A:1018509429360
A. Groll J. Hambuckers T. Kneib N. Umlauf Lasso-type penalization in the framework of generalized additive models for location, scale and shape Comput Stat Data Anal 2019 140 59 73 3979313 10.1016/j.csda.2019.06.005
Hambuckers J, Groll A, Kneib T (2018a) Understanding the economic determinants of the severity of operational losses: a regularized generalized pareto regression approach. J Appl Economet 33(6):898–935
Hambuckers J, Kneib T, Langrock R, Silbersdorff A (2018b) A Markov-switching generalized additive model for compound Poisson processes, with applications to operational loss models. Quantitative Financ 18(10):1679–1698
T. Hastie R. Tibshirani Generalized additive models Stat Sci 1986 1 3 297 310 858512 10.1214/ss/1177013604
B. Hofner T. Kneib T. Hothorn A unified framework of constrained regression Stat Comput 2016 26 1–2 1 14 3439355 10.1007/s11222-014-9520-y
H. Ichimura Semiparametric least squares (SLS) and weighted SLS estimation of single-index models J Economet 1993 58 1–2 71 120 1230981 10.1016/0304-4076(93)90114-K
C. Kleiber A. Zeileis Visualizing count data regressions using rootograms Am Stat 2016 70 3 296 303 3535517 10.1080/00031305.2016.1173590
N. Klein T. Kneib Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach Stat Comput 2016 26 4 841 860 3515025 10.1007/s11222-015-9573-6
N. Klein T. Kneib S. Lang Bayesian generalized additive models for location, scale, and shape for zero-inflated and overdispersed count data J Am Stat Assoc 2015 110 509 405 419 3338512 10.1080/01621459.2014.912955
S. Lang A. Brezger Bayesian P-Splines J Comput Graph Stat 2004 13 1 183 212 2044877 10.1198/1061860043010
Liu Q Furber S (2016) Noisy softplus: a biology inspired activation function. In: Hirose A, Ozawa S, Doya K, Ikeda K, Lee M, Liu D (eds) Neural Information Processing (ICONIP), volume 9950 of Lecture Notes in Computer Science. Springer, Cham, pp. 405–412. https://doi.org/10.1007/978-3-319-46681-1_49
McCullagh P, Nelder J (1989) Generalized linear models. Number 37 in monographs on statistics and applied probability, 2 edn. Chapman & Hall/CRC, Boca Raton. ISBN 978-0-203-75373-6
F. Nielsen K. Sun Guaranteed bounds on information-theoretic measures of univariate mixtures using piecewise Log-Sum-Exp inequalities Entropy 2016 18 12 442 467 3594086 10.3390/e18120442
I. Ntzoufras P. Dellaportas J.J. Forster Bayesian variable and link determination for generalised linear models J Stat Plan Inference 2003 111 1–2 165 180 1955879 10.1016/S0378-3758(02)00298-7
D. Pregibon Goodness of link tests for generalized linear models Appl Stat 1980 29 1 15 10.2307/2346405
R Core Team R: a language and environment for statistical computing 2022 Vienna R Foundation for Statistical Computing
R.A. Rigby D.M. Stasinopoulos Generalized additive models for location, scale and shape (with discussion) J R Stat Soc 2005 54 3 507 554 10.1111/j.1467-9876.2005.00510.x
E. Spiegel T. Kneib F. Otto-Sobotka Generalized additive models with flexible response functions Stat Comput 2019 29 1 123 138 3905544 10.1007/s11222-017-9799-6
D.J. Spiegelhalter N.G. Best B.P. Carlin A. van der Linde Bayesian measures of model complexity and fit J R Stat Soc Ser B 2002 64 4 583 639 1979380 10.1111/1467-9868.00353
M.A. Stephens EDF statistics for goodness of fit and some comparisons J Am Stat Assoc 1974 69 347 730 737 10.1080/01621459.1974.10480196
N. Umlauf T. Kneib A primer on Bayesian distributional regression Stat Model 2018 18 3–4 219 247 3799716 10.1177/1471082X18759140
N. Umlauf N. Klein A. Zeileis BAMLSS: bayesian additive models for location, scale, and shape (and beyond) J Comput Graph Stat 2018 27 3 612 627 3863762 10.1080/10618600.2017.1407325
C.H. Weiß F. Zhu A. Hoshiyar Softplus INGARCH model Stat Sin 2021 10.5705/ss.202020.0353
Y. Yu D. Ruppert Penalized spline estimation for partially linear single-index models J Am Stat Assoc 2002 97 460 1042 1054 1951258 10.1198/016214502388618861
Y. Yu C. Wu Y. Zhang Penalised spline estimation for generalised partially linear single-index models Stat Comput 2017 27 2 571 582 3599690 10.1007/s11222-016-9639-0
Zheng H, Yang Z, Liu W, Liang J, Li Y (2015) Improving deep neural networks using softplus units. In: 2015 international joint conference on neural networks (IJCNN). IEEE, Killarney, Ireland, pp 1–4. https://doi.org/10.1109/IJCNN.2015.7280459
D. Zuras M. Cowlishaw A. Aiken M. Applegate D. Bailey S. Bass D. Bhandarkar M. Bhat D. Bindel S. Boldo et al. IEEE standard for floating-point arithmetic IEEE Std 2008 754–2008 1 70