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**Empirical Bayesian smoothing splines for signals with correlated errors: methods and applications.**
*(English)*
Zbl 1369.62006

Göttingen: Univ. Göttingen, Fakultät für Mathematik und Informatik (Diss.). vi, 122 p. (2016).

The origin of the idea of the presented dissertation goes back to E. T. Whittaker [Proc. R. Soc. Edinburgh 44, 77–83 (1924; JFM 49.0374.01)].

The chapters are 1. Introduction, 2. Demmler-Reinsch basis, 3. Smoothing splines with correlated errors, 4. Extensions of smoothing splines with correlated errors, 5. Applications with subsections 5.1. Nonparametric price transmission and 5.2. Stem cell dynamics, 6. Software.

Author’s abstract: Smoothing splines is a well established method in non-parametric statistics, although the selection of the smoothness degree of the regression function is rarely addressed and, instead, a two times differentiable function, i.e. cubic smoothing spline, is assumed. For a general regression function there is no known method that can identify the smoothness degree under the presence of correlated errors. This apparent disregard in the literature can be justified because the condition number of the solution increases with the smoothness degree of the function, turning the estimation unstable. In this thesis we introduce an exact expression for the Demmler-Reinsch basis constructed as the solution of an ordinary differential equation, so that the estimation can be carried out for an arbitrary smoothness degree, and under the presence of correlated errors, without affecting the condition number of the solution. We provide asymptotic properties of the proposed estimators and conduct simulation experiments to study their finite sample properties. We expect this new approach to have a direct impact on related methods that use smoothing splines as a building block. In this direction, we present extensions of the method to signal extraction and functional principal component analysis. The empirical relevance to our findings in these areas of statistics is shown in applications for agricultural economics and biophysics. R packages of the implementation of the developed methods are also provided.

The chapters are 1. Introduction, 2. Demmler-Reinsch basis, 3. Smoothing splines with correlated errors, 4. Extensions of smoothing splines with correlated errors, 5. Applications with subsections 5.1. Nonparametric price transmission and 5.2. Stem cell dynamics, 6. Software.

Author’s abstract: Smoothing splines is a well established method in non-parametric statistics, although the selection of the smoothness degree of the regression function is rarely addressed and, instead, a two times differentiable function, i.e. cubic smoothing spline, is assumed. For a general regression function there is no known method that can identify the smoothness degree under the presence of correlated errors. This apparent disregard in the literature can be justified because the condition number of the solution increases with the smoothness degree of the function, turning the estimation unstable. In this thesis we introduce an exact expression for the Demmler-Reinsch basis constructed as the solution of an ordinary differential equation, so that the estimation can be carried out for an arbitrary smoothness degree, and under the presence of correlated errors, without affecting the condition number of the solution. We provide asymptotic properties of the proposed estimators and conduct simulation experiments to study their finite sample properties. We expect this new approach to have a direct impact on related methods that use smoothing splines as a building block. In this direction, we present extensions of the method to signal extraction and functional principal component analysis. The empirical relevance to our findings in these areas of statistics is shown in applications for agricultural economics and biophysics. R packages of the implementation of the developed methods are also provided.

Reviewer: Hans-Jürgen Schmidt (Potsdam)

### MSC:

62-02 | Research exposition (monographs, survey articles) pertaining to statistics |

62G08 | Nonparametric regression and quantile regression |

62G20 | Asymptotic properties of nonparametric inference |

65D07 | Numerical computation using splines |

62P20 | Applications of statistics to economics |

62-04 | Software, source code, etc. for problems pertaining to statistics |