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Statistical guarantees for sparse deep learning
Lederer, Johannes - In: AStA Advances in Statistical Analysis 108 (2023) 2, pp. 231-258
previous theories have neglected, such as multiple outputs, regularization, and ℓ2-loss. The guarantees have a mild dependence …
Persistent link: https://www.econbiz.de/10015165589
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Penalized likelihood and Bayesian function selection in regression models
Scheipl, Fabian; Kneib, Thomas; Fahrmeir, Ludwig - In: AStA Advances in Statistical Analysis 97 (2013) 4, pp. 349-385
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonlinear functions in models with additive predictors has been considered only more recently....
Persistent link: https://www.econbiz.de/10010698292
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Statistical guarantees for sparse deep learning
Lederer, Johannes - In: AStA Advances in Statistical Analysis 108 (2023) 2, pp. 231-258
previous theories have neglected, such as multiple outputs, regularization, and ℓ2-loss. The guarantees have a mild dependence …
Persistent link: https://www.econbiz.de/10015404242
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Testing monotonicity of pricing kernels
Golubev, Yuri; Härdle, Wolfgang; Timofeev, Roman - In: AStA Advances in Statistical Analysis 98 (2014) 4, pp. 305-326
agents’ utility function by testing the monotonicity of empirical pricing kernels (EPKs). A monotonically decreasing EPK …
Persistent link: https://www.econbiz.de/10010998857