Network Reconstruction From High-Dimensional Ordinary Differential Equations
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[1] Dario Floreano,et al. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods , 2011, Bioinform..
[2] R. Tibshirani,et al. Regression shrinkage and selection via the lasso: a retrospective , 2011 .
[3] A. Tsybakov,et al. Sparse recovery under matrix uncertainty , 2008, 0812.2818.
[4] Po-Ling Loh,et al. High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity , 2011, NIPS.
[5] G. DeAngelis,et al. Parallel Input Channels to Mouse Primary Visual Cortex , 2010, The Journal of Neuroscience.
[6] Hulin Wu,et al. Sieve Estimation of Constant and Time-Varying Coefficients in Nonlinear Ordinary Differential Equation Models by Considering Both Numerical Error and Measurement Error. , 2010, Annals of statistics.
[7] R. Tibshirani,et al. Linear Smoothers and Additive Models , 1989 .
[8] Yingcun Xia,et al. Asymptotic Behavior of Bandwidth Selected by the Cross-Validation Method for Local Polynomial Fitting , 2002 .
[9] M. Wells,et al. Variations and Fluctuations of the Number of Individuals in Animal Species living together , 2006 .
[10] D. Floreano,et al. Revealing strengths and weaknesses of methods for gene network inference , 2010, Proceedings of the National Academy of Sciences.
[11] Dario Floreano,et al. Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods , 2009, J. Comput. Biol..
[12] Hulin Wu,et al. Statistical methods for HIV dynamic studies in AIDS clinical trials , 2005, Statistical methods in medical research.
[13] M. Yuan,et al. Model selection and estimation in the Gaussian graphical model , 2007 .
[14] Byron M. Yu,et al. Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.
[15] Yanyuan Ma,et al. Quick and easy one‐step parameter estimation in differential equations , 2014 .
[16] Lorenz T. Biegler,et al. Nonlinear parameter estimation: A case study comparison , 1986 .
[17] Jiguo Cao,et al. On the selection of ordinary differential equation models with application to predator‐prey dynamical models , 2015, Biometrics.
[18] P. Bühlmann,et al. The group lasso for logistic regression , 2008 .
[19] Jonathan E. Taylor,et al. On model selection consistency of penalized M-estimators: a geometric theory , 2013, NIPS.
[20] J. Lafferty,et al. Sparse additive models , 2007, 0711.4555.
[21] J. Varah. A Spline Least Squares Method for Numerical Parameter Estimation in Differential Equations , 1982 .
[22] M. Benson,et al. Parameter fitting in dynamic models , 1979 .
[23] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[24] Tso-Jung Yen,et al. Discussion on "Stability Selection" by Meinshausen and Buhlmann , 2010 .
[25] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[26] Xiaohua Xia,et al. On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics , 2011, SIAM Rev..
[27] Chenlei Leng,et al. Unified LASSO Estimation by Least Squares Approximation , 2007 .
[28] L Wang,et al. Robust Estimation for Ordinary Differential Equation Models , 2011, Biometrics.
[29] Runze Li,et al. Variable Selection in Measurement Error Models. , 2010, Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability.
[30] Hongyu Zhao,et al. Asymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equations , 2009, 0903.3400.
[31] Alexandre B. Tsybakov,et al. Introduction to Nonparametric Estimation , 2008, Springer series in statistics.
[32] G. Michailidis,et al. Network Reconstruction Using Nonparametric Additive ODE Models , 2014, PloS one.
[33] W. Lim,et al. Defining Network Topologies that Can Achieve Biochemical Adaptation , 2009, Cell.
[34] Itai Dattner,et al. Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters , 2013 .
[35] I. Chou,et al. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. , 2009, Mathematical biosciences.
[36] Eugene M. Izhikevich,et al. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting , 2006 .
[37] N. Meinshausen,et al. Stability selection , 2008, 0809.2932.
[38] Volker Roth,et al. The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms , 2008, ICML '08.
[39] Hulin Wu,et al. Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling , 2014, Journal of the American Statistical Association.
[40] Jiguo Cao,et al. Parameter estimation for differential equations: a generalized smoothing approach , 2007 .
[41] Hulin Wu,et al. Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models , 2008, Journal of the American Statistical Association.
[42] Wulfram Gerstner,et al. Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition , 2014 .
[43] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[44] Quentin Clairon,et al. Parametric Estimation of Ordinary Differential Equations With Orthogonality Conditions , 2014, Journal of the American Statistical Association.
[45] M. Wand. Local Regression and Likelihood , 2001 .
[46] Jiguo Cao,et al. Estimating dynamic models for gene regulation networks , 2008, Bioinform..
[47] Martin J. Wainwright,et al. Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso) , 2009, IEEE Transactions on Information Theory.
[48] Hongzhe Li,et al. High-Dimensional ODEs Coupled With Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification , 2011, Journal of the American Statistical Association.
[49] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[50] Chris A. J. Klaassen,et al. √n-consistent parameter estimation for systems of ordinary differential equations : bypassing numerical integration via smoothing , 2010, 1007.3880.
[51] Guohua Pan,et al. Local Regression and Likelihood , 1999, Technometrics.
[52] Gábor Lugosi,et al. Concentration Inequalities - A Nonasymptotic Theory of Independence , 2013, Concentration Inequalities.
[53] Martin J. Wainwright,et al. Sharp thresholds for high-dimensional and noisy recovery of sparsity , 2006, ArXiv.
[54] S. Ellner,et al. Fitting population dynamic models to time-series data by gradient matching , 2002 .
[55] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[56] H. Weyl. Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung) , 1912 .
[57] Gábor Lugosi,et al. Concentration Inequalities , 2008, COLT.
[58] Robert Tibshirani,et al. STANDARDIZATION AND THE GROUP LASSO PENALTY. , 2012, Statistica Sinica.
[59] N. Brunel. Parameter estimation of ODE’s via nonparametric estimators , 2007, 0710.4190.
[60] Ali Shojaie,et al. Graph Estimation with Joint Additive Models. , 2013, Biometrika.
[61] C. Sbardella. High dimensional regression , 2011 .