Estimation and detection of flexible trends

By Hans Visser. 2004

55 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a8521c730387e68d8d98d8f38d66801c56372c86

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. I. Tulp
2. L. Bolle
3. A. Rijnsdorp
4. A. Strien
5. L. Soldaat
6. M. Roomen
7. Hans Visser

In Economics and Econometrics

1. I. Tulp
2. L. Bolle
3. A. Rijnsdorp
4. A. Strien
5. L. Soldaat
6. M. Roomen
7. Arthur C. Petersen
8. G. Powney

In Signal Processing

1. I. Tulp
2. L. Bolle
3. A. Rijnsdorp
4. A. Strien
5. L. Soldaat
6. M. Roomen

Citations in the corpus

• Signals from the shallows: in search of common patterns in long-term trends in Dutch estuarine and coastal fish (2008, methodology): “nl for food types and Yang (1982) for biogeographic guild.”
• On the relation between weather-related disaster impacts, vulnerability and climate change (2014, background): “The IRW trend can therefore be seen as a natural extension of the straight line, in which the full uncertainty information is retained (Visser 2004; see Appendix B of ESM for details).”
• The State of the UK's Butterflies 2011 (2011, methodology): “…25 UK Priority Species -71% 1990-2019 11 Butterfly indicators 38 Brereton et al. 2011, www.jncc.gov.uk/our-work/uk-biodiversity-indicators-2021 39 Visser 2004 40 following the approach of Dennis et al. 2019 41 Note that the approach used here to construct multi-species indicators across the UK…”
• The decline of Afro-Palaearctic migrants and an assessment of potential causes (2014, unknown): “The non-linear solid lines are statistically smoothed trends derived from the software TRENDSPOTTER, which uses structural time series analysis and the Kalman filter to fit flexible trends (Visser 2004, 2005), and the dashed lines are the 95% confidence limits on the smooth trend.”
• Smoothing and trend detection in waterbird monitoring data using structural time-series analysis and the Kalman filter (2007, methodology): “In such cases, an alternative smoothing method from the class of structural time-series analysis, in combination with the Kalman filter, may be helpful (Visser 2004, 2005, 2007).”