Abstract
Population-corrected rates are often used in statistical documents that show the features of a municipality. In addition, it is important to determine changes of the features over time, and for this purpose, data collection is continually carried out by census. In the present study, we propose a method for analyzing the spatio-temporal effects on rates by adaptive fused lasso. For estimation, the coordinate descent algorithm, which is known to have better estimation accuracy and speed than the algorithm used in genlasso in the R software package, is used for optimization. Based on the results of the real data analysis for the crime rates in the Kinki region of Japan in 1995-2008, the proposed method can be applied to spatio-temporal proportion data analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arnold, T., Tibshirani, R.: Path algorithm for generalized lasso problems. R package version 1.5. (2020). https://cran.r-project.org/web/packages/genlasso/genlasso.pdf
Brunsdon, C., Fotheringham, S., Charlton, M.: Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr. Anal. 28, 281–298 (1996). https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Friedman, J., Hastie, T., Höfling, H., Tibshirani, R.: Pathwise coordinate optimization. Ann. Appl. Stat. 1, 302–332 (2007). https://doi.org/10.1214/07-AOAS131
McCullagh, P., Nelder, J.A.: Monographs on statistics and applied probability. In: Generalized Linear Models, 2nd edn. Chapman & Hall/CRC (1989)
Ohishi, M., Fukui, K., Okamura, K., Itoh, Y., Yanagihara, H.: Estimation for spatial effects by using the fused lasso, pp. 1–28. TR-No. 19-07, Hiroshima Statistical Research Group, Hiroshima (2019). https://www.math.sci.hiroshima-u.ac.jp/stat/TR/TR19/TR19-07.pdf
Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978). https://doi.org/10.1214/aos/1176344136
Statistics Bureau of Japan.: e-Stat, portal site of official statistics of Japan. https://www.e-stat.go.jp (2021)
Tibshirani, R., Saunders, M., Rosset, S.: Sparsity and smoothness via the fused lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 91–108 (2005). https://doi.org/10.1111/j.1467-9868.2005.00490.x
Tibshirani, R., Taylor, J.: The solution path of the generalized lasso. Ann. Stat. 39, 1335–1371 (2011). https://doi.org/10.1214/11-AOS878
Zou, H.: The adaptive lasso and its oracle properties. J. Am. Stat. Assoc. 101, 1418–1429 (2006). https://doi.org/10.1198/016214506000000735
Acknowledgements
The present study was supported by Grant-in-Aid for Scientific Research (B) (KAKENHI 20H04151). The Radiation Effects Research Foundation (RERF), Hiroshima and Nagasaki, Japan is a public interest foundation funded by the Japanese Ministry of Health, Labor and Welfare (MHLW) and the US Department of Energy (DOE). This publication was supported by RERF. The views of the authors do not necessarily reflect those of the two governments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yamamura, M., Ohishi, M., Yanagihara, H. (2021). Spatio-Temporal Adaptive Fused Lasso for Proportion Data. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_40
Download citation
DOI: https://doi.org/10.1007/978-981-16-2765-1_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2764-4
Online ISBN: 978-981-16-2765-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)