IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v188y2022ics0047259x21000841.html
   My bibliography  Save this article

From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas

Author

Listed:
  • Li, Yehua
  • Qiu, Yumou
  • Xu, Yuhang

Abstract

Functional data analysis (FDA), which is a branch of statistics on modeling infinite dimensional random vectors resided in functional spaces, has become a major research area for Journal of Multivariate Analysis. We review some fundamental concepts of FDA, their origins and connections from multivariate analysis, and some of its recent developments, including multi-level functional data analysis, high-dimensional functional regression, and dependent functional data analysis. We also discuss the impact of these new methodology developments on genetics, plant science, wearable device data analysis, image data analysis, and business analytics. Two real data examples are provided to motivate our discussions.

Suggested Citation

  • Li, Yehua & Qiu, Yumou & Xu, Yuhang, 2022. "From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  • Handle: RePEc:eee:jmvana:v:188:y:2022:i:c:s0047259x21000841
    DOI: 10.1016/j.jmva.2021.104806
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X21000841
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jmva.2021.104806?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Menafoglio, Alessandra & Petris, Giovanni, 2016. "Kriging for Hilbert-space valued random fields: The operatorial point of view," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 84-94.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
    3. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    4. Xinghao Qiao & Shaojun Guo & Gareth M. James, 2019. "Functional Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 211-222, January.
    5. Shan Yu & Guannan Wang & Li Wang & Chenhui Liu & Lijian Yang, 2020. "Estimation and Inference for Generalized Geoadditive Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 761-774, April.
    6. Jingyuan Liu & Runze Li & Rongling Wu, 2014. "Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 266-274, March.
    7. Crainiceanu, Ciprian M. & Staicu, Ana-Maria & Di, Chong-Zhi, 2009. "Generalized Multilevel Functional Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1550-1561.
    8. Xiao Wang & Hongtu Zhu, 2017. "Generalized Scalar-on-Image Regression Models via Total Variation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1156-1168, July.
    9. Reiss Philip T. & Huang Lei & Mennes Maarten, 2010. "Fast Function-on-Scalar Regression with Penalized Basis Expansions," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-30, August.
    10. Morris, Jeffrey S. & Vannucci, Marina & Brown, Philip J. & Carroll, Raymond J., 2003. "Wavelet-Based Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 573-583, January.
    11. Haochang Shou & Vadim Zipunnikov & Ciprian M. Crainiceanu & Sonja Greven, 2015. "Structured functional principal component analysis," Biometrics, The International Biometric Society, vol. 71(1), pages 247-257, March.
    12. Cardot, Hervé & Sarda, Pacal, 2005. "Estimation in generalized linear models for functional data via penalized likelihood," Journal of Multivariate Analysis, Elsevier, vol. 92(1), pages 24-41, January.
    13. Haozhe Zhang & Yehua Li, 2020. "Unified Principal Component Analysis for Sparse and Dense Functional Data under Spatial Dependency," Papers 2006.13489, arXiv.org, revised Jun 2021.
    14. Anders Brix & Peter J. Diggle, 2001. "Spatiotemporal prediction for log‐Gaussian Cox processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 823-841.
    15. Liu, Bitao & Müller, Hans-Georg, 2009. "Estimating Derivatives for Samples of Sparsely Observed Functions, With Application to Online Auction Dynamics," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 704-717.
    16. Fan, Zhaohu & Reimherr, Matthew, 2017. "High-dimensional adaptive function-on-scalar regression," Econometrics and Statistics, Elsevier, vol. 1(C), pages 167-183.
    17. Haocheng Li & John Staudenmayer & Raymond J. Carroll, 2014. "Hierarchical functional data with mixed continuous and binary measurements," Biometrics, The International Biometric Society, vol. 70(4), pages 802-811, December.
    18. Song, Jun & Li, Bing, 2021. "Nonlinear and additive principal component analysis for functional data," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
    19. Ana-Maria Staicu & Ciprian M. Crainiceanu & Daniel S. Reich & David Ruppert, 2012. "Modeling Functional Data with Spatially Heterogeneous Shape Characteristics," Biometrics, The International Biometric Society, vol. 68(2), pages 331-343, June.
    20. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    21. Lan Zhou & Jianhua Z. Huang & Raymond J. Carroll, 2008. "Joint modelling of paired sparse functional data using principal components," Biometrika, Biometrika Trust, vol. 95(3), pages 601-619.
    22. Kishor, N. Kundan & Morley, James, 2015. "What factors drive the price–rent ratio for the housing market? A modified present-value analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 235-249.
    23. Anders Brix & Jesper Moller, 2001. "Space‐time Multi Type Log Gaussian Cox Processes with a View to Modelling Weeds," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(3), pages 471-488, September.
    24. He, Guozhong & Müller, Hans-Georg & Wang, Jane-Ling, 2003. "Functional canonical analysis for square integrable stochastic processes," Journal of Multivariate Analysis, Elsevier, vol. 85(1), pages 54-77, April.
    25. Zhu, Hanbing & Li, Rui & Zhang, Riquan & Lian, Heng, 2020. "Nonlinear functional canonical correlation analysis via distance covariance," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    26. Wonyul Lee & Michelle F. Miranda & Philip Rausch & Veerabhadran Baladandayuthapani & Massimo Fazio & J. Crawford Downs & Jeffrey S. Morris, 2019. "Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, With Application to Glaucoma Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 495-513, April.
    27. Mirshani, Ardalan & Reimherr, Matthew, 2021. "Adaptive function-on-scalar regression with a smoothing elastic net," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    28. Yao, Fang & Sue-Chee, Shivon & Wang, Fan, 2017. "Regularized partially functional quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 39-56.
    29. Zhou, Lan & Huang, Jianhua Z. & Martinez, Josue G. & Maity, Arnab & Baladandayuthapani, Veerabhadran & Carroll, Raymond J., 2010. "Reduced Rank Mixed Effects Models for Spatially Correlated Hierarchical Functional Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 390-400.
    30. Yuhang Xu & Yehua Li & Dan Nettleton, 2018. "Nested Hierarchical Functional Data Modeling and Inference for the Analysis of Functional Plant Phenotypes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 593-606, April.
    31. Daniel R. Kowal & David S. Matteson & David Ruppert, 2017. "A Bayesian Multivariate Functional Dynamic Linear Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 733-744, April.
    32. Alan Gelfand & Alexandra Schmidt & Sudipto Banerjee & C. Sirmans, 2004. "Nonstationary multivariate process modeling through spatially varying coregionalization," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 263-312, December.
    33. Hui Huang & Yehua Li & Yongtao Guan, 2014. "Joint Modeling and Clustering Paired Generalized Longitudinal Trajectories With Application to Cocaine Abuse Treatment Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1412-1424, December.
    34. Lijie Gu & Li Wang & Wolfgang Härdle & Lijian Yang, 2014. "A simultaneous confidence corridor for varying coefficient regression with sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 806-843, December.
    35. Xiaoxiao Sun & Pang Du & Xiao Wang & Ping Ma, 2018. "Optimal Penalized Function-on-Function Regression Under a Reproducing Kernel Hilbert Space Framework," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1601-1611, October.
    36. Li, Yehua & Hsing, Tailen, 2007. "On rates of convergence in functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 98(9), pages 1782-1804, October.
    37. Yehua Li & Yongtao Guan, 2014. "Functional Principal Component Analysis of Spatiotemporal Point Processes With Applications in Disease Surveillance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1205-1215, September.
    38. Nerini, David & Monestiez, Pascal & Manté, Claude, 2010. "Cokriging for spatial functional data," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 409-418, February.
    39. Peter Hall & Mohammad Hosseini‐Nasab, 2006. "On properties of functional principal components analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 109-126, February.
    40. Jesper Møller & Rasmus P. Waagepetersen, 2007. "Modern Statistics for Spatial Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 643-684, December.
    41. Shin, Hyejin & Lee, Seokho, 2015. "Canonical correlation analysis for irregularly and sparsely observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 1-18.
    42. Jeff Goldsmith & Vadim Zipunnikov & Jennifer Schrack, 2015. "Generalized multilevel function-on-scalar regression and principal component analysis," Biometrics, The International Biometric Society, vol. 71(2), pages 344-353, June.
    43. A. J. Baddeley & J. Møller & R. Waagepetersen, 2000. "Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 54(3), pages 329-350, November.
    44. Nicoleta Serban & Huijing Jiang, 2012. "Multilevel Functional Clustering Analysis," Biometrics, The International Biometric Society, vol. 68(3), pages 805-814, September.
    45. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    46. Hongtu Zhu & Jianqing Fan & Linglong Kong, 2014. "Spatially Varying Coefficient Model for Neuroimaging Data With Jump Discontinuities," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1084-1098, September.
    47. Honglang Wang & Ping‐Shou Zhong & Yuehua Cui & Yehua Li, 2018. "Unified empirical likelihood ratio tests for functional concurrent linear models and the phase transition from sparse to dense functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(2), pages 343-364, March.
    48. Peter Hall & Hans‐Georg Müller & Fang Yao, 2008. "Modelling sparse generalized longitudinal observations with latent Gaussian processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 703-723, September.
    49. Xia, Xiaochao & Yang, Hu & Li, Jialiang, 2016. "Feature screening for generalized varying coefficient models with application to dichotomous responses," Computational Statistics & Data Analysis, Elsevier, vol. 102(C), pages 85-97.
    50. Bosl Noh & Anindita Bandyopadhyay & Wendy Ann Peer & Edgar P. Spalding & Angus S. Murphy, 2003. "Enhanced gravi- and phototropism in plant mdr mutants mislocalizing the auxin efflux protein PIN1," Nature, Nature, vol. 423(6943), pages 999-1002, June.
    51. Fang Yao & Thomas C. M. Lee, 2006. "Penalized spline models for functional principal component analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 3-25, February.
    52. Jeffrey S. Morris & Raymond J. Carroll, 2006. "Wavelet‐based functional mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 179-199, April.
    53. Raymond K. W. Wong & Yehua Li & Zhengyuan Zhu, 2019. "Partially Linear Functional Additive Models for Multivariate Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 406-418, January.
    54. Nicoleta Serban & Ana-Maria Staicu & Raymond J. Carroll, 2013. "Multilevel Cross-Dependent Binary Longitudinal Data," Biometrics, The International Biometric Society, vol. 69(4), pages 903-913, December.
    55. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, October.
    56. Wang, Jiangyan & Cao, Guanqun & Wang, Li & Yang, Lijian, 2020. "Simultaneous confidence band for stationary covariance function of dense functional data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    57. Yehua Li & Naisyin Wang & Raymond J. Carroll, 2013. "Selecting the Number of Principal Components in Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1284-1294, December.
    58. Dauxois, J. & Pousse, A. & Romain, Y., 1982. "Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference," Journal of Multivariate Analysis, Elsevier, vol. 12(1), pages 136-154, March.
    59. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    60. Qian Jiang & Hansheng Wang & Yingcun Xia & Guohua Jiang, 2013. "On a Principal Varying Coefficient Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 228-236, March.
    61. Jin Tang & Yehua Li & Yongtao Guan, 2016. "Generalized Quasi-Likelihood Ratio Tests for Semiparametric Analysis of Covariance Models in Longitudinal Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 736-747, April.
    62. Lin Zhang & Veerabhadran Baladandayuthapani & Hongxiao Zhu & Keith A. Baggerly & Tadeusz Majewski & Bogdan A. Czerniak & Jeffrey S. Morris, 2016. "Functional CAR Models for Large Spatially Correlated Functional Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 772-786, April.
    63. Alexander Aue & Diogo Dubart Norinho & Siegfried Hörmann, 2015. "On the Prediction of Stationary Functional Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 378-392, March.
    64. Cao, Guanqun & Wang, Li, 2018. "Simultaneous inference for the mean of repeated functional data," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 279-295.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Febrero-Bande, Manuel & González-Manteiga, Wenceslao & Prallon, Brenda & Saporito, Yuri F., 2023. "Functional classification of bitcoin addresses," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haozhe Zhang & Yehua Li, 2020. "Unified Principal Component Analysis for Sparse and Dense Functional Data under Spatial Dependency," Papers 2006.13489, arXiv.org, revised Jun 2021.
    2. Gertheiss, Jan & Goldsmith, Jeff & Staicu, Ana-Maria, 2017. "A note on modeling sparse exponential-family functional response curves," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 46-52.
    3. Jeff Goldsmith & Vadim Zipunnikov & Jennifer Schrack, 2015. "Generalized multilevel function-on-scalar regression and principal component analysis," Biometrics, The International Biometric Society, vol. 71(2), pages 344-353, June.
    4. Yukun Zhang & Haocheng Li & Sarah Kozey Keadle & Charles E. Matthews & Raymond J. Carroll, 2019. "A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 465-476, July.
    5. Li, Meng & Wang, Kehui & Maity, Arnab & Staicu, Ana-Maria, 2022. "Inference in functional linear quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    6. Liu, Yanghui & Li, Yehua & Carroll, Raymond J. & Wang, Naisyin, 2022. "Predictive functional linear models with diverging number of semiparametric single-index interactions," Journal of Econometrics, Elsevier, vol. 230(2), pages 221-239.
    7. Haocheng Li & John Staudenmayer & Raymond J. Carroll, 2014. "Hierarchical functional data with mixed continuous and binary measurements," Biometrics, The International Biometric Society, vol. 70(4), pages 802-811, December.
    8. Fang Yao & Yichao Wu & Jialin Zou, 2016. "Probability-enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 1-22, March.
    9. Ghosal, Rahul & Maity, Arnab, 2022. "A Score Based Test for Functional Linear Concurrent Regression," Econometrics and Statistics, Elsevier, vol. 21(C), pages 114-130.
    10. Kyunghee Han & Pantelis Z Hadjipantelis & Jane-Ling Wang & Michael S Kramer & Seungmi Yang & Richard M Martin & Hans-Georg Müller, 2018. "Functional principal component analysis for identifying multivariate patterns and archetypes of growth, and their association with long-term cognitive development," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    11. Philip T. Reiss & Jeff Goldsmith & Han Lin Shang & R. Todd Ogden, 2017. "Methods for Scalar-on-Function Regression," International Statistical Review, International Statistical Institute, vol. 85(2), pages 228-249, August.
    12. Yehua Li & Yongtao Guan, 2014. "Functional Principal Component Analysis of Spatiotemporal Point Processes With Applications in Disease Surveillance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1205-1215, September.
    13. Mengfei Ran & Yihe Yang, 2022. "Optimal Estimation of Large Functional and Longitudinal Data by Using Functional Linear Mixed Model," Mathematics, MDPI, vol. 10(22), pages 1-28, November.
    14. Fang Yao & Yichao Wu & Jialin Zou, 2016. "Probability-enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 1-22, March.
    15. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.
    16. Centofanti, Fabio & Fontana, Matteo & Lepore, Antonio & Vantini, Simone, 2022. "Smooth LASSO estimator for the Function-on-Function linear regression model," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    17. Ruzong Fan & Hong-Bin Fang, 2022. "Stochastic functional linear models and Malliavin calculus," Computational Statistics, Springer, vol. 37(2), pages 591-611, April.
    18. Weicheng Zhu & Sheng Xu & Catherine C. Liu & Yehua Li, 2023. "Minimax powerful functional analysis of covariance tests with application to longitudinal genome‐wide association studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 266-295, March.
    19. Craig, Sarah J.C. & Kenney, Ana M. & Lin, Junli & Paul, Ian M. & Birch, Leann L. & Savage, Jennifer S. & Marini, Michele E. & Chiaromonte, Francesca & Reimherr, Matthew L. & Makova, Kateryna D., 2023. "Constructing a polygenic risk score for childhood obesity using functional data analysis," Econometrics and Statistics, Elsevier, vol. 25(C), pages 66-86.
    20. Kehui Chen & Pedro Delicado & Hans-Georg Müller, 2017. "Modelling function-valued stochastic processes, with applications to fertility dynamics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 177-196, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jmvana:v:188:y:2022:i:c:s0047259x21000841. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.