C. Agrell, Gaussian processes with linear operator inequality constraints, Journal of Machine Learning Research, vol.20, pp.1-36, 2019.

Y. Aït-sahalia and J. Duarte, Nonparametric option pricing under shape restrictions, Journal of Econometrics, vol.116, issue.1-2, pp.9-47, 2003.

F. Alizadeh and D. Goldfarb, Second-order cone programming, Mathematical Programming, vol.95, issue.1, pp.3-51, 2003.

N. Aronszajn, Theory of reproducing kernels, Transactions of the American Mathematical Society, vol.68, pp.337-404, 1950.

N. S. Aybat and Z. Wang, A parallel method for large scale convex regression problems, Conference on Decision and Control (CDC), pp.5710-5717, 2014.

F. Bach and M. Jordan, Kernel independent component analysis, Journal of Machine Learning Research, vol.3, pp.1-48, 2002.

J. A. Bagnell and A. Farahmand, Learning positive functions in a Hilbert space, NIPS Workshop on Optimization, 2015.

F. Balabdaoui, C. Durot, and H. Jankowski, Least squares estimation in the monotone single index model, Bernoulli, vol.25, issue.4B, pp.3276-3310, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01713041

A. Berlinet and C. Thomas-agnan, Reproducing Kernel Hilbert Spaces in Probability and Statistics, 2004.

R. Blundell, J. L. Horowitz, and M. Parey, Measuring the price responsiveness of gasoline demand: economic shape restrictions and nonparametric demand estimation, Quantitative Economics, vol.3, pp.29-51, 2012.

C. Carmeli, E. D. Vito, A. Toigo, and V. Umanitá, Vector valued reproducing kernel Hilbert spaces and universality, Analysis and Applications, vol.8, pp.19-61, 2010.

S. Chatterjee, A. Guntuboyina, and B. Sen, On risk bounds in isotonic and other shape restricted regression problems, Annals of Statistics, vol.43, issue.4, pp.1774-1800, 2015.

Y. Chen and R. J. Samworth, Generalized additive and index models with shape constraints, Journal of the Royal Statistical Society -Statistical Methodology, Series B, vol.78, issue.4, pp.729-754, 2016.

D. Chetverikov, A. Santos, and A. M. Shaikh, The econometrics of shape restrictions, Annual Review of Economics, vol.10, issue.1, pp.31-63, 2018.

M. Delecroix, M. Simioni, and C. Thomas-agnan, Functional estimation under shape constraints, Journal of Nonparametric Statistics, vol.6, issue.1, pp.69-89, 1996.
URL : https://hal.archives-ouvertes.fr/hal-02691995

A. M. Fink, Kolmogorov-Landau inequalities for monotone functions, Journal of Mathematical Analysis and Applications, vol.90, pp.251-258, 1982.

S. Flaxman, Y. W. Teh, and D. Sejdinovic, Poisson intensity estimation with reproducing kernels, Electronic Journal of Statistics, vol.11, issue.2, pp.5081-5104, 2017.

J. Freyberger and B. Reeves, Inference under shape restrictions, 2018.

K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf, Kernel measures of conditional dependence, Advances in Neural Information Processing Systems (NIPS), pp.498-496, 2008.

A. Guntuboyina and B. Sen, Nonparametric shape-restricted regression, Statistical Science, vol.33, issue.4, pp.568-594, 2018.

G. Hall, Optimization over nonnegative and convex polynomials with and without semidefinite programming, 2018.

Q. Han, T. Wang, S. Chatterjee, and R. J. Samworth, Isotonic regression in general dimensions, Annals of Statistics, vol.47, issue.5, pp.2440-2471, 2019.

Q. Han and J. A. Wellner, Multivariate convex regression: global risk bounds and adaptation, 2016.

T. Hofmann, B. Schölkopf, and A. J. Smola, Kernel methods in machine learning, The Annals of Statistics, vol.36, issue.3, pp.1171-1220, 2008.

J. L. Horowitz and S. Lee, Nonparametric estimation and inference under shape restrictions, Journal of Econometrics, vol.201, pp.108-126, 2017.

J. Hu, M. Kapoor, W. Zhang, S. R. Hamilton, and K. R. Coombes, Analysis of doseresponse effects on gene expression data with comparison of two microarray platforms, Bioinformatics, vol.21, issue.17, pp.3524-3529, 2005.

A. L. Johnson and D. R. Jiang, Shape constraints in economics and operations research, vol.33, pp.527-546, 2018.

A. Keshavarz, Y. Wang, and S. Boyd, Imputing a convex objective function, IEEE Multi-Conference on Systems and Control, pp.613-619, 2011.

A. Koppel, K. Zhang, H. Zhu, and T. Ba?ar, Projected stochastic primal-dual method for constrained online learning with kernels, IEEE Transactions on Signal Processing, vol.67, issue.10, pp.2528-2542, 2019.

A. Lewbel, Shape-invariant demand functions, The Review of Economics and Statistics, vol.92, issue.3, pp.549-556, 2010.

Q. Li and J. S. Racine, Nonparametric Econometrics, 2007.

R. Luss, S. Rossett, and M. Shahar, Efficient regularized isotonic regression with application to gene-gene interaction search, Annals of Applied Statistics, vol.6, issue.1, pp.253-283, 2012.

S. V. Malov, On finite-dimensional Archimedean copulas, Asymptotic Methods in Probability and Statistics with Applications, pp.19-35, 2001.

A. W. Marshall, I. Olkin, A. , and B. C. , Inequalities: Theory of Majorization and Its Applications, 2011.

J. Mattingley and S. Boyd, CVXGEN: A code generator for embedded convex optimization, Optimization and Engineering, vol.12, issue.1, pp.1-27, 2012.

R. L. Matzkin, Semiparametric estimation of monotone and concave utility functions for polychotomous choice models, Econometrica, vol.59, issue.5, pp.1315-1327, 1991.

R. Mazumder, A. Choudhury, G. Iyengar, and B. Sen, A computational framework for multivariate convex regression and its variants, Journal of the American Statistical Association, vol.114, issue.525, pp.318-331, 2019.

A. J. Mcneil and J. Neslehová, , 2009.

, Multivariate Archimedean copulas, d-monotone functions and 1 -norm symmetric distributions, Annals of Statistics, vol.37, issue.5B, pp.3059-3097

M. C. Meyer, A framework for estimation and inference in generalized additive models with shape and order restrictions, Statistical Science, vol.33, issue.4, pp.595-614, 2018.

C. Micchelli, Y. Xu, and H. Zhang, Universal kernels, Journal of Machine Learning Research, vol.7, pp.2651-2667, 2006.

F. Nicol, Functional principal component analysis of aircraft trajectories, International Conference on Interdisciplinary Science for Innovative Air Traffic Management (ISIATM), 2013.
URL : https://hal.archives-ouvertes.fr/hal-00867957

D. Papp and F. Alizadeh, Shape-constrained estimation using nonnegative splines, Journal of Computational and Graphical Statistics, vol.23, issue.1, pp.211-231, 2014.

N. Pya and S. N. Wood, Shape constrained additive models, Statistics and Computing, vol.25, pp.543-559, 2015.

S. Saitoh and Y. Sawano, Theory of Reproducing Kernels and Applications, 2016.

M. Sangnier, O. Fercoq, and F. Buc, Joint quantile regression in vector-valued RKHSs, Advances in Neural Information Processing Systems (NIPS), pp.3693-3701, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01272327

A. Shapiro, D. Dentcheva, and A. Ruszczynski, Lectures on Stochastic Programming: Modeling and Theory, 2014.

X. Shi, M. Shum, and W. Song, Estimating semi-parametric panel multinomial choice models using cyclic monotonicity, Econometrica, vol.86, issue.2, pp.737-761, 2018.

D. Simchi-levi, X. Chen, and J. Bramel, The Logic of Logistics: Theory, Algorithms, and Applications for Logistics Management, 2014.

C. Simon-gabriel and B. Schölkopf, Kernel distribution embeddings: Universal kernels, characteristic kernels and kernel metrics on distributions, Journal of Machine Learning Research, vol.19, issue.44, pp.1-29, 2018.

B. Sriperumbudur, K. Fukumizu, and G. Lanckriet, Universality, characteristic kernels and RKHS embedding of measures, Journal of Machine Learning Research, vol.12, pp.2389-2410, 2011.

B. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G. Lanckriet, Hilbert space embeddings and metrics on probability measures, Journal of Machine Learning Research, vol.11, pp.1517-1561, 2010.

I. Steinwart, On the influence of the kernel on the consistency of support vector machines, Journal of Machine Learning Research, vol.6, issue.3, pp.67-93, 2001.

I. Steinwart and A. Christmann, Support Vector Machines, 2008.

Z. Szabó and B. K. Sriperumbudur, Characteristic and universal tensor product kernels, Journal of Machine Learning Research, vol.18, issue.233, pp.1-29, 2018.

I. Takeuchi, Q. Le, T. Sears, and A. Smola, Nonparametric quantile estimation, Journal of Machine Learning Research, vol.7, pp.1231-1264, 2006.

D. M. Topkis, Supermodularity and complementarity, 1998.

B. A. Turlach, Shape constrained smoothing using smoothing splines, Computational Statistics, vol.20, pp.81-104, 2005.

H. R. Varian, The nonparametric approach to production analysis, Econometrica, vol.52, issue.3, pp.579-597, 1984.

G. Wahba, Spline Models for Observational Data, SIAM, CBMS-NSF Regional Conference Series in Applied Mathematics, 1990.

Y. Wang, Smoothing Splines -Methods and Applications, 2011.

J. Wu, M. C. Meyer, and J. D. Opsomer, Penalized isotonic regression, Journal of Statistical Planning and Inference, vol.161, pp.12-24, 2015.

X. Wu and R. Sickles, Semiparametric estimation under shape constraints, Econometrics and Statistics, vol.6, pp.74-89, 2018.

D. Yagi, Y. Chen, A. L. Johnson, and T. Kuosmanen, Shape-constrained kernel-weighted least squares: Estimating production functions for Chilean manufacturing industries, Journal of Business & Economic Statistics, vol.38, issue.1, pp.43-54, 2020.

D. Zhou, Derivative reproducing properties for kernel methods in learning theory, Journal of Computational and Applied Mathematics, vol.220, pp.456-463, 2008.