Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Evaluating information criteria for selecting spatial processes
https://orcid.org/0000-0003-3028-3374
Abstract Information criteria have been widely used in many quantitative applications as an effort to select the most appropriate model that describes well enough the unknown population behavior for a given dataset. Studies have shown that their performance depends on several elements and the selection of the best fitted model is not always the same for all criteria. For this purpose, this research evaluates the performance of the three most often used information criteria, such as the Akaike information criterion, the Bayesian information criterion and Hannan and Quinn information criterion, for selecting spatial processes, taking into account that the sample in spatial analysis is regarded as a realization of a spatial process that incorporates the spatial dependence between the observations. Using a Monte Carlo analysis for the three most frequently applied in practice spatial processes, such as the first-order spatial autoregressive process, SAR(1), the first-order spatial moving average process, SMA(1), and the mixed spatial autoregressive moving average process, SARMA(1, 1), this study finds that these information criteria can assist the analyst to select the true process, but their behavior depends on sample size as well as on the magnitude of the spatial parameters, leading occasionally to alternative competitive processes.
Evaluating information criteria for selecting spatial processes
https://orcid.org/0000-0003-3028-3374
Abstract Information criteria have been widely used in many quantitative applications as an effort to select the most appropriate model that describes well enough the unknown population behavior for a given dataset. Studies have shown that their performance depends on several elements and the selection of the best fitted model is not always the same for all criteria. For this purpose, this research evaluates the performance of the three most often used information criteria, such as the Akaike information criterion, the Bayesian information criterion and Hannan and Quinn information criterion, for selecting spatial processes, taking into account that the sample in spatial analysis is regarded as a realization of a spatial process that incorporates the spatial dependence between the observations. Using a Monte Carlo analysis for the three most frequently applied in practice spatial processes, such as the first-order spatial autoregressive process, SAR(1), the first-order spatial moving average process, SMA(1), and the mixed spatial autoregressive moving average process, SARMA(1, 1), this study finds that these information criteria can assist the analyst to select the true process, but their behavior depends on sample size as well as on the magnitude of the spatial parameters, leading occasionally to alternative competitive processes.
Evaluating information criteria for selecting spatial processes
https://orcid.org/0000-0003-3028-3374
Abstract Information criteria have been widely used in many quantitative applications as an effort to select the most appropriate model that describes well enough the unknown population behavior for a given dataset. Studies have shown that their performance depends on several elements and the selection of the best fitted model is not always the same for all criteria. For this purpose, this research evaluates the performance of the three most often used information criteria, such as the Akaike information criterion, the Bayesian information criterion and Hannan and Quinn information criterion, for selecting spatial processes, taking into account that the sample in spatial analysis is regarded as a realization of a spatial process that incorporates the spatial dependence between the observations. Using a Monte Carlo analysis for the three most frequently applied in practice spatial processes, such as the first-order spatial autoregressive process, SAR(1), the first-order spatial moving average process, SMA(1), and the mixed spatial autoregressive moving average process, SARMA(1, 1), this study finds that these information criteria can assist the analyst to select the true process, but their behavior depends on sample size as well as on the magnitude of the spatial parameters, leading occasionally to alternative competitive processes.
Evaluating information criteria for selecting spatial processes
Agiakloglou, Christos (Autor:in) / Tsimpanos, Apostolos (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BKL:
83.64$jRegionalwirtschaft
/
74.12
Stadtgeographie, Siedlungsgeographie
/
38.00$jGeowissenschaften: Allgemeines
/
38.00
Geowissenschaften: Allgemeines
/
83.64
Regionalwirtschaft
/
74.12$jStadtgeographie$jSiedlungsgeographie
RVK:
ELIB39
/
ELIB18
/
ELIB45
Lokalklassifikation FBW:
oek 4450
Evaluating information criteria for selecting spatial processes
| Springer Verlag | 2021
Criteria for Selecting a Management Contractor
| British Library Conference Proceedings | 1995
Selecting and evaluating CAD systems
| Tema Archiv | 1977
Criteria for Selecting the Medical - Use Polymers
| British Library Online Contents | 1998
Criteria for Selecting Strengthening Methods – Case Studies
| Wiley | 2023