### Abstract

We propose and analyse fully data-driven methods for inference about the mean function of a Gaussian process from a sample of independent trajectories of the process, observed at random time points and corrupted by additive random error. Our methods are based on thresholded least squares estimators relative to an approximating function basis. The variable threshold levels are determined from the data and the resulting estimates adapt to the unknown sparsity of the mean function relative to the approximating basis. These results are obtained via novel oracle inequalities, which are further used to derive the rates of convergence of our mean estimates. In addition, we construct confidence balls that adapt to the unknown regularity of the mean and covariance function of the stochastic process. They are easy to compute since they do not require explicit estimation of the covariance operator of the process. A simulation study shows that the new method performs very well in practice and is robust against large variations that may be introduced by the random-error terms.

Original language | English |
---|---|

Pages (from-to) | 531-538 |

Number of pages | 8 |

Journal | Journal of the Royal Statistical Society. Series B: Statistical Methodology |

Volume | 73 |

Issue number | 4 |

DOIs | |

State | Published - 1 Sep 2011 |

### Fingerprint

### Keywords

- Adaptive inference
- Confidence balls
- Functional data
- Non-parametric mean estimation
- Oracle inequalities
- Stochastic processes
- Thresholded estimators

### Cite this

*Journal of the Royal Statistical Society. Series B: Statistical Methodology*,

*73*(4), 531-538. https://doi.org/10.1111/j.1467-9868.2010.00768.x

}

*Journal of the Royal Statistical Society. Series B: Statistical Methodology*, vol. 73, no. 4, pp. 531-538. https://doi.org/10.1111/j.1467-9868.2010.00768.x

**Adaptive inference for the mean of a gaussian process in functional data.** / Bunea, Florentina; Ivanescu, Andrada E.; Wegkamp, Marten H.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Adaptive inference for the mean of a gaussian process in functional data

AU - Bunea, Florentina

AU - Ivanescu, Andrada E.

AU - Wegkamp, Marten H.

PY - 2011/9/1

Y1 - 2011/9/1

N2 - We propose and analyse fully data-driven methods for inference about the mean function of a Gaussian process from a sample of independent trajectories of the process, observed at random time points and corrupted by additive random error. Our methods are based on thresholded least squares estimators relative to an approximating function basis. The variable threshold levels are determined from the data and the resulting estimates adapt to the unknown sparsity of the mean function relative to the approximating basis. These results are obtained via novel oracle inequalities, which are further used to derive the rates of convergence of our mean estimates. In addition, we construct confidence balls that adapt to the unknown regularity of the mean and covariance function of the stochastic process. They are easy to compute since they do not require explicit estimation of the covariance operator of the process. A simulation study shows that the new method performs very well in practice and is robust against large variations that may be introduced by the random-error terms.

AB - We propose and analyse fully data-driven methods for inference about the mean function of a Gaussian process from a sample of independent trajectories of the process, observed at random time points and corrupted by additive random error. Our methods are based on thresholded least squares estimators relative to an approximating function basis. The variable threshold levels are determined from the data and the resulting estimates adapt to the unknown sparsity of the mean function relative to the approximating basis. These results are obtained via novel oracle inequalities, which are further used to derive the rates of convergence of our mean estimates. In addition, we construct confidence balls that adapt to the unknown regularity of the mean and covariance function of the stochastic process. They are easy to compute since they do not require explicit estimation of the covariance operator of the process. A simulation study shows that the new method performs very well in practice and is robust against large variations that may be introduced by the random-error terms.

KW - Adaptive inference

KW - Confidence balls

KW - Functional data

KW - Non-parametric mean estimation

KW - Oracle inequalities

KW - Stochastic processes

KW - Thresholded estimators

UR - http://www.scopus.com/inward/record.url?scp=79961039284&partnerID=8YFLogxK

U2 - 10.1111/j.1467-9868.2010.00768.x

DO - 10.1111/j.1467-9868.2010.00768.x

M3 - Article

AN - SCOPUS:79961039284

VL - 73

SP - 531

EP - 538

JO - Journal of the Royal Statistical Society. Series B: Statistical Methodology

JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology

SN - 1369-7412

IS - 4

ER -