Abstract
Health services data often contain a high proportion of zeros. In studies examining patient hospitalization rates, for instance, many patients will have no hospitalizations, resulting in a count of zero. When the number of zeros is greater or less than expected under a standard count model, the data are said to be zero modified relative to the standard model. More precisely, the data are zero inflated if there is an overabundance of zeros, and zero deflated if there are fewer zeros than expected. A similar phenomenon arises with semicontinuous data, which are characterized by a spike at zero followed by a right-skewed continuous distribution of positive values. When dealing with zero-modified count and semicontinuous data, flexible two-part mixture distributions are often needed to accommodate both the excess zeros and the skewed distribution of nonzero values. A broad array of two-part models has been introduced over the past three decades to accommodate such data. These include hurdle models, zero-inflated models, and two-part semicontinuous models. While these models differ in their distributional assumptions, they each incorporate a two-part structure in which the zero and nonzero observations are modeled in distinct but related ways. This chapter describes recent developments in two-part modeling of zero-modified count and semicontinuous data and highlights their application in health services research.
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References
Agarwal DK, Gelfand AE, Citron-Pousty S. Zero-inflated models with application to spatial count data. Environ Ecol Stat. 2002;9(4):341–55. Available from http://www.ingentaconnect.com/content/klu/eest/2002/00000009/00000004/05102063
Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;19(6):716–23.
Albert P, Follman D. Shared-parameter models. In: Fitzmaurice G, Davidian M, Ver-beke G, Molenberghs G, editors. Longitudinal data analysis. Boca Raton: Chapman & Hall/CRC Press; 2009. p. 433–52.
Albert JM, Wang W, Nelson S. Estimating overall exposure effects for zero-inflated regression models with application to dental caries. Stat Methods Med Res. 2011. Available from http://smm.sagepub.com/content/early/2011/09/08/0962280211407800.abstract
Ando T. Bayesian model selection and statistical modeling. Boca Raton: Chapman Hall/CRC Press; 2010.
Arab A, Holan SH, Wikle CK, Wildhaber ML. Semiparametric bivariate zero-inflated Poisson models with application to studies of abundance for multiple species. ArXiv e-prints. 2011. Available from http://arxiv.org/abs/1105.3169v1
Basu A, Manning WG. Estimating lifetime or episode-of-illness costs under censoring. Health Econ. 2010;19(9):1010–28. https://doi.org/10.1002/hec.1640.
Berger JO, Pericchi LR. The intrinsic Bayes factor for model selection and prediction. J Am Stat Assoc. 1996;91(433):109–22. Available from http://www.jstor.org/stable/2291387
Blough DK, Madden CW, Hornbrook MC. Modeling risk using generalized linear models. J Health Econ. 1999;18(2):153–71. Available from http://www.sciencedirect.com/science/article/pii/S0167629698000320
Buntin MB, Zaslavsky AM. Too much ado about two-part models and transformation?: comparing methods of modeling Medicare expenditures. J Health Econ. 2004;23(3):525–42. Available from http://www.sciencedirect.com/science/article/pii/S0167629604000220
Buu A, Johnson NJ, Li R, Tan X. New variable selection methods for zero-inflated count data with applications to the substance abuse field. Stat Med. 2011;30(18):2326–40. https://doi.org/10.1002/sim.4268.
Cameron AC, Trivedi PK. Regression analysis of count data. No. 9780521635677 in Cambridge Books. Cambridge University Press; 1998. Available from http://ideas.repec.org/b/cup/cbooks/9780521635677.html
Celeux G, Forbes F, Robert CP, Titterington DM. Deviance information criteria for missing data models. Bayesian Anal. 2006;1(4):651–74.
Consul P. Generalized Poisson distributions: properties and applications. New York: Marcel Dekker; 1989.
Cooper NJ, Sutton AJ, Mugford M, Abrams KR. Use of Bayesian Markov chain Monte Carlo methods to model cost-of-illness data. Med Decis Mak. 2003;23(1):38–53. Available from http://mdm.sagepub.com/content/23/1/38.abstract
Cooper NJ, Lambert PC, Abrams KR, Sutton AJ. Predicting costs over time using Bayesian Markov chain Monte Carlo methods: an application to early inflammatory polyarthritis. Health Econ. 2007;16(1):37–56. https://doi.org/10.1002/hec.1141.
Dalrymple ML, Hudson IL, Ford RPK. Finite mixture, zero-inflated Poisson and hurdle models with application to SIDS. Comput Stat Data Anal. 2003;41(3–4):491–504. https://doi.org/10.1016/S0167-9473(02)00187-1.
Deb P, Munkin MK, Trivedi PK. Bayesian analysis of the two-part model with endogeneity: application to health care expenditure. J Appl Econ. 2006;21(7):1081–99. https://doi.org/10.1002/jae.891.
DeSantis SM, Bandyopadhyay D. Hidden Markov models for zero-inflated Poisson counts with an application to substance use. Stat Med. 2011;30(14):1678–94. https://doi.org/10.1002/sim.4207.
Dobbie MJ, Welsh AH. Modelling correlated zero-inflated count data. Aust N Z J Stat. 2001;43(4):431–44. https://doi.org/10.1111/1467-842X.00191.
Duan N. Smearing estimate: a nonparametric retransformation method. J Am Stat Assoc. 1983;78(383):605–10. Available from http://www.jstor.org/stable/2288126
Duan N, Manning J Willard G, Morris CN, Newhouse JP. A comparison of alternative models for the demand for medical care. J Bus Econ Stat. 1983;1(2):115–26. Available from http://www.jstor.org/stable/1391852
Fahrmeir L, Osuna EL. Structured additive regression for overdispersed and zero-inflated count data. Appl Stoch Model Bus Ind. 2006;22(4):351–69. https://doi.org/10.1002/asmb.631.
Ferguson TS. A bayesian analysis of some nonparametric problems. Ann Stat. 1973;1(2):209–30. Available from http://www.jstor.org/stable/2958008
Fournier DA, Skaug HJ, Ancheta J, Ianelli J, Magnusson A, Maunder MN, et al. AD model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim Methods Softw. 2012;27(2):233–49. https://doi.org/10.1080/10556788.2011.597854.
Gelfand AE, Dey DK. Bayesian model choice: asymptotics and exact calculations. J R Stat Soc Ser B Stat Methodol. 1994;56(3):501–14. Available from http://www.jstor.org/stable/2346123
Gelfand AE, Smith AFM. Sampling-based approaches to calculating marginal densities. J Am Stat Assoc. 1990;85(410):398–409. Available from http://www.jstor.org/stable/2289776
Gelman A, li Meng X, Stern H. Posterior predictive assessment of model fitness via realized discrepancies. Stat Sin. 1996;6:733–807.
Ghosh P, Albert PS. A Bayesian analysis for longitudinal semicontinuous data with an application to an acupuncture clinical trial. Comput Stat Data Anal. 2009;53(3):699–706. https://doi.org/10.1016/j.csda.2008.09.011.
Ghosh SK, Mukhopadhyay P, Lu JC. Bayesian analysis of zero-inflated regression models. J Stat Plann Infer. 2006;136(4):1360–75. Available from http://www.sciencedirect.com/science/article/pii/S0378375804004008
Ghosh S, Gelfand AE, Zhu K, Clark JS. The k-ZIG: flexible modeling for zero-inflated counts. Biometrics. 2012;68(3):878–85. https://doi.org/10.1111/j.1541-0420.2011.01729.x.
Green W. Accounting for excess zeros and sample selection in Poisson and negative binomial regression models. Working paper EC-94-10, Department of Economics. New York: New York University; 1994.
Gschlößl S, Czado C. Modelling count data with overdispersion and spatial effects. Stat Pap. 2008;49:531–52. https://doi.org/10.1007/s00362-006-0031-6.
Gupta PL, Gupta RC, Tripathi RC. Analysis of zero-adjusted count data. Comput Stat Data Anal. 1996;23(2):207–18. Available from http://EconPapers.repec.org/RePEc:eee:csdana:v:23:y:1996:i:2:p:207-218
Hadfield JD. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw. 2010;33(2):1–22. Available from http://www.jstatsoft.org/v33/i02/
Hall DB. Zero-inflated poisson and binomial regression with random effects: a case study. Biometrics. 2000;56(4):1030–9. https://doi.org/10.1111/j.0006-341X.2000.01030.x.
Hall DB, Zhang Z. Marginal models for zero inflated clustered data. Stat Model. 2004;4(3):161–80. Available from http://smj.sagepub.com/content/4/3/161.abstract
Hasan MT, Sneddon G. Zero-inflated Poisson regression for longitudinal data. Commun Stat – SimulCompu. 2009;38(3):638–53.
Hasan MT, Sneddon G, Ma R. Pattern-mixture zero-inflated mixed models for longitudinal unbalanced count data with excessive zeros. Biom J. 2009;51(6):946–60. Available from https://doi.org/10.1002/bimj.200900093
Hatfield LA, Boye ME, Carlin BP. Joint modeling of multiple longitudinal patient-reported outcomes and survival. J Biopharm Stat. 2011;21(5):971–91. Available from http://www.tandfonline.com/doi/abs/10.1080/10543406.2011.590922
Heilbron DC. Zero-altered and other regression models for count data with added zeros. Biom J. 1994;36(5):531–47. https://doi.org/10.1002/bimj.4710360505.
Hilbe J. HNBLOGIT: stata module to estimate negative binomial-logit hurdle regression; 2005a. Statistical Software Components, Boston College Department of Economics. Available from http://ideas.repec.org/c/boc/bocode/s456401.html
Hilbe J. HPLOGIT: stata module to estimate Poisson-logit hurdle regression. Statistical Software Components, Boston College Department of Economics; 2005b. Available from http://ideas.repec.org/c/boc/bocode/s456405.html
Hsu CH. Joint modelling of recurrence and progression of adenomas: a latent variable approach. Stat Model. 2005;5(3):201–15. Available from http://smj.sagepub.com/content/5/3/201.abstract
Jackman S. pscl: classes and methods for R developed in the political science computational laboratory. Stanford: Stanford University; 2012. R package version 1.04.4. Available from http://pscl.stanford.edu/
Jones AM. Models for health care. In: Hendry D, Clements M, editors. Oxford handbook of economic forecasting. Oxford: Oxford University Press; 2011. p. 625–54.
Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90(430):773–95. Available from http://www.jstor.org/stable/2291091
Kim S, Chang CC, Kim K, Fine M, Stone R. BLUP(REMQL) estimation of a correlated random effects negative binomial hurdle model. Health Serv Outcome Res Methodol. 2012;12:302–19. https://doi.org/10.1007/s10742-012-0083-0.
Lam KF, Xue H, Bun CY. Semiparametric analysis of zero-inflated count data. Biometrics. 2006;62(4):996–1003. https://doi.org/10.1111/j.1541-0420.2006.00575.x.
Lambert D. Zero-inflated poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1–14. Available from http://www.jstor.org/stable/1269547
Li CS, Lu JC, Park J, Kim K, Brinkley PA, Peterson JP. Multivariate zero-inflated Poisson models and their applications. Technometrics. 1999;41(1):29–38. https://doi.org/10.2307/1270992.
Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13–22. Available from http://biomet.oxfordjournals.org/content/73/1/13.abstract
Lillard LA, Panis CWA. Multiprocess multilevel modelling, version 2, user’s guide and reference manual. Los Angeles: EconoWare; 1998–2003.
Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. Hoboken: Wiley; 2002.
Liu H. Growth curve models for zero-inflated count data: an application to smoking behavior. Struct Equ Model Multidiscip J. 2007;14(2):247–79. https://doi.org/10.1080/10705510709336746.
Liu L. Joint modeling longitudinal semi-continuous data and survival, with application to longitudinal medical cost data. Stat Med. 2009;28(6):972–86. Available from https://doi.org/10.1002/sim.3497
Liu L, Ma JZ, Johnson BA. A multi-level two-part random effects model, with application to an alcohol-dependence study. Stat Med. 2008;27(18):3528–39. Available from https://doi.org/10.1002/sim.3205
Liu L, Strawderman RL, Cowen ME, Shih YCT. A flexible two-part random effects model for correlated medical costs. J Health Econ. 2010;29(1):110–23. Available from http://www.sciencedirect.com/science/article/pii/S0167629609001386
Liu L, Strawderman RL, Johnson BA, O’Quigley JM. Analyzing repeated measures semi-continuous data, with application to an alcohol dependence study. Stat Methods Med Res. 2012. Available from http://smm.sagepub.com/content/early/2012/04/01/0962280212443324.abstract
Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput. 2000;10(4):325–37. https://doi.org/10.1023/A:1008929526011.
Majumdar A, Gries C. Bivariate zero-inflated regression for count data: a Bayesian approach with application to plant counts. Int J Biostat. 2010;6(1):27. Available from http://ideas.repec.org/a/bpj/ijbist/v6y2010i1n27.html
Manning WG. The logged dependent variable, heteroscedasticity, and the retransformation problem. J Health Econ. 1998;17(3):283–95. Available from http://www.sciencedirect.com/science/article/pii/S0167629698000253
Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461–94. Available from http://www.sciencedirect.com/science/article/pii/S0167629601000868
Manning W, Morris C, Newhouse J, Orr L, Duan N, Keeler E, et al. A two-part model of the demand for medical care: preliminary results from the health insurance study. In: van der Gaag J, Perlman M, editors. Health, economics, and health economics. Amsterdam: North-Holland; 1981. p. 103–23.
Manning WG, Basu A, Mullahy J. Generalized modeling approaches to risk adjustment of skewed outcomes data. J Health Econ. 2005;24(3):465–88. Available from http://www.sciencedirect.com/science/article/pii/S0167629605000056
Maruotti A. A two-part mixed-effects pattern-mixture model to handle zero-inflation and incompleteness in a longitudinal setting. Biom J. 2011;53(5):716–34. Available from https://doi.org/10.1002/bimj.201000190
Millar RB. Comparison of hierarchical Bayesian models for overdispersed count data using DIC and Bayes’ factors. Biometrics. 2009;65(3):962–9. https://doi.org/10.1111/j.1541-0420.2008.01162.x.
Min Y, Agresti A. Random effect models for repeated measures of zero-inflated count data. Stat Model. 2005;5(1):1–19. Available from http://smj.sagepub.com/content/5/1/1.abstract
Moulton LH, Halsey NA. A mixture model with detection limits for regression analyses of antibody response to vaccine. Biometrics. 1995;51(4):1570–8. Available from http://www.jstor.org/stable/2533289
Mullahy J. Specification and testing of some modified count data models. J Econ. 1986;33(3):341–65. Available from http://www.sciencedirect.com/science/article/pii/0304407686900023
Muthén BO. Two-part growth mixture modeling; 2001. Unpublished Manuscript. Available from http://pages.gseis.ucla.edu/faculty/muthen/articles/Article_094.pdf
Muthén BO, Muthén LK. Mplus (Version 7). Muthén & Muthén; 1998–2012.
Mwalili SM, Lesaffre E, Declerck D. The zero-inflated negative binomial regression model with correction for misclassification: an example in caries research. Stat Methods Med Res. 2008;17(2):123–39. Available from http://smm.sagepub.com/content/17/2/123.abstract
Neelon BH, OMalley AJ, Normand SLT. A Bayesian model for repeated measures zero inflated count data with application to outpatient psychiatric service use. Stat Model. 2010;10(4):421–39. Available from http://smj.sagepub.com/content/10/4/421.abstract
Neelon B, O’Malley AJ, Normand SLT. A bayesian two-part latent class model for longitudinal medical expenditure data: assessing the impact of mental health and substance abuse parity. Biometrics. 2011;67(1):280–9. Available from https://doi.org/10.1111/j.1541-0420.2010.01439.x.
Neelon B, Ghosh P, Loebs PF. A spatial Poisson hurdle model for exploring geographic variation in emergency department visits. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2012; Published online ahead of print. Available from https://doi.org/10.1111/j.1467-985X.2012.01039.x
Olsen MK, Schafer JL. A two-part random-effects model for semicontinuous longitudinal data. J Am Stat Assoc. 2001;96(454):730–45. https://doi.org/10.1198/016214501753168389.
Pan W. Akaike’s information criterion in generalized estimating equations. Biometrics. 2001;57(1):120–5. Available from http://www.jstor.org/stable/2676849
Park RE. Estimation with heteroscedastic error terms. Econometrica. 1966;34(4):888. Available from http://www.jstor.org/stable/1910108
Patil GP. Maximum likelihood estimation for generalized power series distributions and its application to a truncated binomial distribution. Biometrika. 1962;49(1–2):227–37. Available from http://biomet.oxfordjournals.org/content/49/1-2/227.short
Preisser JS, Stamm JW, Long DL. Review and recommendations for zero-inflated count regression modeling of dental caries indices in epidemiological studies. Caries Res. 2012;46:413–23.
R Development Core Team. R: a language and environment for statistical computing. Vienna; 2012. ISBN 3-900051-07-0. Available from http://www.R-project.org/
Rabe-Hesketh S, Skrondal A, Pickles A. Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects. J Econ. 2005;128(2):301–23. Available from http://www.sciencedirect.com/science/article/pii/S0304407604001599
Raftery AM, Newton MA, Satagopan JM, Krivitsky PN. Estimating the integrated likelihood via posterior simulation using the harmonic mean identity. In: Bernardo JM, Bayarri MJ, Berger JO, Dawid AP, Heckerman D, Smith AFM, et al., editors. Bayesian statistics 8. Oxford: Oxford University Press; 2007. p. 1–45.
Rathbun S, Fei S. A spatial zero-inflated poisson regression model for oak regeneration. Environ Ecol Stat. 2006;13:409–26. https://doi.org/10.1007/s10651-006-0020-x.
Ridout M, Demétrio C, Hinde J. Models for count data with many zeros. Proceedings from the International Biometric Conference, Cape Town; 1998. Available from https://www.kent.ac.uk/smsas/personal/msr/webfiles/zip/ibc_fin.pdf
Ridout M, Hinde J, DemAtrio CGB. A score test for testing a zero-inflated Poisson regression model against zero-inflated negative binomial alternatives. Biometrics. 2001;57(1):219–23. Available from https://doi.org/10.1111/j.0006-341X.2001.00219.x
Rodrigues J. Bayesian analysis of zero-inflated distributions. Commun Stat Theory Methods. 2003;32(2):281–9. Available from http://www.tandfonline.com/doi/abs/10.1081/STA-120018186
Roeder K, Lynch KG, Nagin DS. Modeling uncertainty in latent class membership: a case study in criminology. J Am Stat Assoc. 1999;94(447):766–76. Available from http://www.jstor.org/stable/2669989
Rosen O, Jiang W, Tanner M. Mixtures of marginal models. Biometrika. 2000;87(2):391–404. Available from http://biomet.oxfordjournals.org/content/87/2/391.abstract
SAS 9.1.3 Help and Documentation. Cary; 2000–2004. Available from: http://sas.com/
Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–4. Available from http://www.jstor.org/stable/2958889
Silva FF, Tunin KP, Rosa GJM, Silva MVBd, Azevedo ALS, Verneque RdS, et al. Zero-inflated Poisson regression models for QTL mapping applied to tickresistance in a Gyr x Holstein F2 population. Genet Mol Biol; 2011;34:575–82. Available from http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572011000400008&nrm=iso
Skaug H, Fournier D, Nielsen A, Magnusson A, Bolker B. glmmADMB: generalized linear mixed models using AD Model Builder; 2012. R package version 0.7.2.12. Available from http://glmmadmb.r-forge.r-project.org
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol. 2002;64(4):583–639. https://doi.org/10.1111/1467-9868.00353.
Stata Statistical Software: Release 12. College Station; 2011. Available from http://stata.com/
Su L, Tom BDM, Farewell VT. Bias in 2-part mixed models for longitudinal semicontinuous data. Biostatistics. 2009;10(2):374–89. Available from http://biostatistics.oxfordjournals.org/content/10/2/374.abstract
Su L, Brown S, Ghosh P, Taylor K. Modelling household debt and financial assets: a Bayesian approach to a bivariate two-part model; 2012.
Tobin J. Estimation of relationships for limited dependent variables. Econometrica. 1958;26(1):24–36. Available from http://www.jstor.org/stable/1907382
Tooze JA, Grunwald GK, Jones RH. Analysis of repeated measures data with clumping at zero. Stat Methods Med Res. 2002;11(4):341–55. Available from http://smm.sagepub.com/content/11/4/341.abstract
Ver Hoef JM, Jansen JK. Spacetime zero-inflated count models of harbor seals. Environmetrics. 2007;18(7):697–712. Available from https://doi.org/10.1002/env.873
Vuong QH. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica. 1989;57(2):307–33. Available from http://www.jstor.org/stable/1912557
Walhin JF, Bivariate ZIP. Models. Biom J. 2001;43(2):147–60. Available from https://doi.org/10.1002/1521-4036(200105)43:2<147::AID-BIMJ147> 3.0.CO;2-5
Welsh AH, Zhou XH. Estimating the retransformed mean in a heteroscedastic two-part model. J Stat PlannInfer. 2006;136(3):860–81. Available from http://www.sciencedirect.com/science/article/pii/S0378375804003337
Williamson JM, Lin HM, Lyles RH. Power calculations for ZIP and ZINB models. J Data Sci. 2007;5:519–34. Available from http://www.jds-online.com/v5-4
Winkelmann R. Econometric analysis of count data. 5th ed. Berlin: Springer; 2008. Available from http://gso.gbv.de/DB=2.1/CMD?ACT=SRCHA&SRT=YOP&IKT=1016&TRM=ppn+368353176&sourceid=fbw_bibsonomy
Wu MC, Carroll RJ. Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics. 1988;44(1):175–88. Available from http://www.jstor.org/stable/2531905
Xiang L, Lee AH, Yau KKW, McLachlan GJ. A score test for overdispersion in zero-inflated poisson mixed regression model. Stat Med. 2007;26(7):1608–22. Available from https://doi.org/10.1002/sim.2616
Xie H, McHugo G, Sengupta A, Clark R, Drake R. A method for analyzing longitudinal outcomes with many zeros. Ment Health Serv Res. 2004;6:239–46. https://doi.org/10.1023/B:MHSR.0000044749.39484.1b. Available from https://doi.org/10.1023/B:MHSR.0000044749.39484.1b
Yau KKW, Lee AH. Zero-inflated Poisson regression with random effects to evaluate an occupational injury prevention programme. Stat Med. 2001;20(19):2907–20. Available from https://doi.org/10.1002/sim.860
Zeileis A, Kleiber C, Jackman S. Regression models for count data in R. J Stat Softw. 2008;27(8):1–25. Available from http://www.jstatsoft.org/v27/i08/
Zhang M, Strawderman RL, Cowen ME, Wells MT. Bayesian inference for a two-part hierarchical model: an application to profiling providers in managed health care. J Am Stat Assoc. 2006;101(475):934–45. Available from http://www.jstor.org/stable/27590773
Zurr AF, Saveliev AA, Ieno EN. Zero inflated models and generalized linear mixed models with R. Newburgh: Highland Statistics Ltd; 2012. Available from http://www.highstat.com/book4.htm
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Neelon, B., O’Malley, A.J. (2017). Two-Part Models for Zero-Modified Count and Semicontinuous Data. In: Sobolev, B., Gatsonis, C. (eds) Methods in Health Services Research. Health Services Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6704-9_17-1
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