### Abstract

Lingua originale | English |
---|---|

Numero di pagine | 30 |

Rivista | Stochastic Environmental Research and Risk Assessment |

Stato di pubblicazione | Published - 2019 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Environmental Engineering
- Environmental Chemistry
- Safety, Risk, Reliability and Quality
- Water Science and Technology
- Environmental Science(all)

### Cita questo

*Stochastic Environmental Research and Risk Assessment*.

**Some properties of local weighted second-order statistics for spatio-temporal point processes.** / Siino, Marianna; Adelfio, Giada; Rodríguez-Cortés, Francisco J.; Mateu, Jorge.

Risultato della ricerca: Article

*Stochastic Environmental Research and Risk Assessment*.

}

TY - JOUR

T1 - Some properties of local weighted second-order statistics for spatio-temporal point processes

AU - Siino, Marianna

AU - Adelfio, Giada

AU - Rodríguez-Cortés, Francisco J.

AU - Mateu, Jorge

PY - 2019

Y1 - 2019

N2 - Diagnostics of goodness-of-fit in the theory of point processes are often considered through the transformation of data into residuals as a result of a thinning or a rescaling procedure. We alternatively consider here second-order statistics coming from weighted measures. Motivated by Adelfio and Schoenberg (2009) for the temporal and spatial cases, we consider an extension to the spatio-temporal context in addition to focussing on local characteristics. In particular, our proposed method assesses goodness-of-fit of spatio-temporal models by using local weighted secondorder statistics, computed after weighting the contribution of each observed point by the inverse of the conditional intensity function that identifies the process. Weighted second-order statistics directly apply to data without assuming homogeneity nor transforming the data into residuals, eliminating thus the sampling variability due to the use of a transforming procedure. We provide some characterisations and show a number of simulation studies.

AB - Diagnostics of goodness-of-fit in the theory of point processes are often considered through the transformation of data into residuals as a result of a thinning or a rescaling procedure. We alternatively consider here second-order statistics coming from weighted measures. Motivated by Adelfio and Schoenberg (2009) for the temporal and spatial cases, we consider an extension to the spatio-temporal context in addition to focussing on local characteristics. In particular, our proposed method assesses goodness-of-fit of spatio-temporal models by using local weighted secondorder statistics, computed after weighting the contribution of each observed point by the inverse of the conditional intensity function that identifies the process. Weighted second-order statistics directly apply to data without assuming homogeneity nor transforming the data into residuals, eliminating thus the sampling variability due to the use of a transforming procedure. We provide some characterisations and show a number of simulation studies.

UR - http://hdl.handle.net/10447/385126

M3 - Article

JO - Stochastic Environmental Research and Risk Assessment

JF - Stochastic Environmental Research and Risk Assessment

SN - 1436-3240

ER -