IDEAS home Printed from https://ideas.repec.org/r/bla/jorssc/v52y2003i1p1-18.html
   My bibliography  Save this item

Geoadditive models

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Barbara Cafarelli & Alessio Pollice, 2005. "I modelli geo-additivi per l'analisi del grado di salinita' di un suolo," Quaderni DSEMS 17-2005, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
  2. Hartman, Linda & Hossjer, Ola, 2008. "Fast kriging of large data sets with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2331-2349, January.
  3. Axel Schaffer & Jan Rauland, 2011. "Regional efficiency in generating technological knowledge," ERSA conference papers ersa10p1108, European Regional Science Association.
  4. Alexander März & Nadja Klein & Thomas Kneib & Oliver Musshoff, 2016. "Analysing farmland rental rates using Bayesian geoadditive quantile regression," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(4), pages 663-698.
  5. Ren, Qian & Banerjee, Sudipto & Finley, Andrew O. & Hodges, James S., 2011. "Variational Bayesian methods for spatial data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3197-3217, December.
  6. Kandala, Ngianga-Bakwin & Magadi, Monica Akinyi & Madise, Nyovani Janet, 2006. "An investigation of district spatial variations of childhood diarrhoea and fever morbidity in Malawi," Social Science & Medicine, Elsevier, vol. 62(5), pages 1138-1152, March.
  7. Chiara Bocci & Emilia Rocco, 2014. "Estimates for geographical domains through geoadditive models in presence of incomplete geographical information," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 283-305, June.
  8. W. Brunauer & S. Lang & P. Wechselberger & S. Bienert, 2010. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," The Journal of Real Estate Finance and Economics, Springer, vol. 41(4), pages 390-411, November.
  9. Lee, Dae-Jin & Durbán, María, 2009. "P-spline anova-type interaction models for spatio-temporal smoothing," DES - Working Papers. Statistics and Econometrics. WS ws093312, Universidad Carlos III de Madrid. Departamento de Estadística.
  10. Eidsvik, Jo & Finley, Andrew O. & Banerjee, Sudipto & Rue, Håvard, 2012. "Approximate Bayesian inference for large spatial datasets using predictive process models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1362-1380.
  11. Jeffery Caroline & Ozonoff Al & White Laura Forsberg & Pagano Marcello, 2013. "Distance-Based Mapping of Disease Risk," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 265-290, May.
  12. Higgs, Megan Dailey & Hoeting, Jennifer A., 2010. "A clipped latent variable model for spatially correlated ordered categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1999-2011, August.
  13. Belitz, Christiane & Lang, Stefan, 2008. "Simultaneous selection of variables and smoothing parameters in structured additive regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 61-81, September.
  14. Liang, Hua, 2006. "Estimation in partially linear models and numerical comparisons," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 675-687, February.
  15. Andrew Finley & Sudipto Banerjee & Alan Gelfand, 2012. "Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes," Journal of Geographical Systems, Springer, vol. 14(1), pages 29-47, January.
  16. Lawrence Kazembe, 2009. "Modelling individual fertility levels in Malawian women: a spatial semiparametric regression model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(2), pages 237-255, July.
  17. Haonan Wang & Hari Iyer, 2007. "Application of local linear embedding to nonlinear exploratory latent structure analysis," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 199-225, June.
  18. Paciorek, Christopher J., 2007. "Computational techniques for spatial logistic regression with large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3631-3653, May.
  19. E. Andres Houseman & Brent Coull & James Shine, 2004. "A Nonstationary Negative Binomial Time Series with Time-Dependent Covariates: Enterococcus Counts in Boston Harbor," Harvard University Biostatistics Working Paper Series 1017, Berkeley Electronic Press.
  20. Lee, Dae-Jin & Durbán, María, 2009. "Smooth-CAR mixed models for spatial count data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2968-2979, June.
  21. Nakamura, Shohei, 2017. "Tenure Security Premium in Informal Housing Markets: A Spatial Hedonic Analysis," World Development, Elsevier, vol. 89(C), pages 184-198.
  22. Liang, Shengde & Banerjee, Sudipto & Bushhouse, Sally & Finley, Andrew O. & Carlin, Bradley P., 2008. "Hierarchical multiresolution approaches for dense point-level breast cancer treatment data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2650-2668, January.
  23. Sadler, Rohan J. & Hazelton, Martin & Boer, Matthias M. & Grierson, Pauline F., 2010. "Deriving state-and-transition models from an image series of grassland pattern dynamics," Ecological Modelling, Elsevier, vol. 221(3), pages 433-444.
  24. Bruno Scarpa, 2005. "Non parametric space-time modeling of SO2 in presence of many missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 67-82, February.
  25. Schaffer, Axel, 2011. "Appropriate policy measures to attract private capital in consideration of regional efficiency in using infrastructure and human capital," Working Paper Series in Economics 31, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
  26. Wolfgang Brunauer & Stefan Lang & Peter Wechselberger & Sven Bienert, 2008. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," Working Papers 2008-17, Faculty of Economics and Statistics, Universität Innsbruck.
  27. Ludwig Fahrmeir & Alexander Raach, 2007. "A Bayesian Semiparametric Latent Variable Model for Mixed Responses," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 327-346, September.
  28. Seya, Hajime & Yamagata, Yoshiki & Nakamichi, Kumiko, 2016. "Creation of municipality level intensity data of electricity in Japan," Applied Energy, Elsevier, vol. 162(C), pages 1336-1344.
  29. Kneib, Thomas, 2006. "Mixed model-based inference in geoadditive hazard regression for interval-censored survival times," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 777-792, November.
  30. Congdon, Peter, 2006. "A model for non-parametric spatially varying regression effects," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 422-445, January.
  31. Sobotka, Fabian & Kneib, Thomas, 2012. "Geoadditive expectile regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 755-767.
  32. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
  33. Lee, Dae-Jin & Durbán, María, 2008. "Smooth-car mixed models for spatial count data," DES - Working Papers. Statistics and Econometrics. WS ws085820, Universidad Carlos III de Madrid. Departamento de Estadística.
  34. Stefan Lang & Nikolaus Umlauf & Peter Wechselberger & Kenneth Harttgen & Thomas Kneib, 2012. "Multilevel structured additive regression," Working Papers 2012-07, Faculty of Economics and Statistics, Universität Innsbruck.
  35. Hossain, Md. Monir & Lawson, Andrew B., 2009. "Approximate methods in Bayesian point process spatial models," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2831-2842, June.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.