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A hyperspherical transformation forecasting model for compositional data

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  • Wang, Huiwen
  • Liu, Qiang
  • Mok, Henry M.K.
  • Fu, Linghui
  • Tse, Wai Man

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  • Wang, Huiwen & Liu, Qiang & Mok, Henry M.K. & Fu, Linghui & Tse, Wai Man, 2007. "A hyperspherical transformation forecasting model for compositional data," European Journal of Operational Research, Elsevier, vol. 179(2), pages 459-468, June.
    • Handle: RePEc:eee:ejores:v:179:y:2007:i:2:p:459-468
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    References listed on IDEAS

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    1. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    2. Jane Fry & Tim Fry & Keith McLaren, 2000. "Compositional data analysis and zeros in micro data," Applied Economics, Taylor & Francis Journals, vol. 32(8), pages 953-959.
    3. Noel Capon & John U. Farley & Scott Hoenig, 1990. "Determinants of Financial Performance: A Meta-Analysis," Management Science, INFORMS, vol. 36(10), pages 1143-1159, October.
    4. Allan G. B. Fisher, 1939. "Production, Primary, Secondary And Tertiary," The Economic Record, The Economic Society of Australia, vol. 15(1), pages 24-38, June.
    5. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
    6. Billheimer D. & Guttorp P. & Fagan W.F., 2001. "Statistical Interpretation of Species Composition," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1205-1214, December.
    7. Katz, Jonathan N. & King, Gary, 1999. "A Statistical Model for Multiparty Electoral Data," American Political Science Review, Cambridge University Press, vol. 93(1), pages 15-32, March.
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    Cited by:

    1. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    2. Terence C. Mills, 2009. "Forecasting obesity trends in England," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 107-117, January.
    3. Javier Palarea-Albaladejo & Josep Martín-Fernández & Jesús Soto, 2012. "Dealing with Distances and Transformations for Fuzzy C-Means Clustering of Compositional Data," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 144-169, July.
    4. Yigang Wei & Zhichao Wang & Huiwen Wang & Yan Li & Zhenyu Jiang, 2019. "Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-42, April.
    5. Wang, Dieter & Andrée, Bo Pieter Johannes & Chamorro, Andres Fernando & Spencer, Phoebe Girouard, 2022. "Transitions into and out of food insecurity: A probabilistic approach with panel data evidence from 15 countries," World Development, Elsevier, vol. 159(C).
    6. Zhang, Kai & Yin, Kedong & Yang, Wendong, 2022. "Predicting bioenergy power generation structure using a newly developed grey compositional data model: A case study in China," Renewable Energy, Elsevier, vol. 198(C), pages 695-711.
    7. Joscha Krause & Jan Pablo Burgard & Domingo Morales, 2022. "Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 65-96, February.
    8. Joanna Morais & Christine Thomas-Agnan & Michel Simioni, 2018. "Using compositional and Dirichlet models for market share regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1670-1689, July.
    9. Raharjo, Hendry & Xie, Min & Brombacher, Aarnout C., 2009. "On modeling dynamic priorities in the analytic hierarchy process using compositional data analysis," European Journal of Operational Research, Elsevier, vol. 194(3), pages 834-846, May.
    10. Chuanbin Zhou & Shijun Ma & Xiao Yu & Zhuqi Chen & Jingru Liu & Li Yan, 2022. "A comparison study of bottom‐up and top‐down methods for analyzing the physical composition of municipal solid waste," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 240-251, February.
    11. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
    12. Colignatus, Thomas, 2018. "Comparing the Aitchison distance and the angular distance for use as inequality or disproportionality measures for votes and seats," MPRA Paper 84334, University Library of Munich, Germany, revised 03 Feb 2018.
    13. Morais, Joanna & Simioni, Michel & Thomas-Agnan, Christine, 2016. "A tour of regression models for explaining shares," TSE Working Papers 16-742, Toulouse School of Economics (TSE).
    14. Wang,Dieter & Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Spencer,Phoebe Girouard, 2020. "Stochastic Modeling of Food Insecurity," Policy Research Working Paper Series 9413, The World Bank.
    15. Huiwen Wang & Liying Shangguan & Rong Guan & Lynne Billard, 2015. "Principal component analysis for compositional data vectors," Computational Statistics, Springer, vol. 30(4), pages 1079-1096, December.

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