New latent variable model with varying-coefficients

References

• Andriyana, Y., I. Gijbels, and A. Verhasselt. 2018. Quantile regression in varying-coefficient models: Non-crossing quantile curves and heteroscedasticity. Statistical Papers 59 (4):1589–621. doi:10.1007/s00362-016-0847-7.
   Web of Science ®Google Scholar
• Arntzen, J. 1989. Environmental pressure and adaptation in rural Botswana. Unpubl. Ph.D. diss., Free University.
   Google Scholar
• Assuno, R. M. 2003. Space varying coefficient models for small area data. Environmetrics 14 (5):453–73.
   Web of Science ®Google Scholar
• Becker J. M., Klein K., Wetzels M. 2012. Formative hierarchical latent variable models in PLS-SEM: recommendations and guidelines. Long Range Plan 45(5–6):359–94.
   Web of Science ®Google Scholar
• Biller, C., and L. Fahrmeir. 2001. Bayesian varying-coefficient models using adaptive regression splines. Statistical Modelling 1 (3):195–211. doi:10.1177/1471082X0100100303.
   Web of Science ®Google Scholar
• Bollen, K. A. 1989. Structural equations with latent variables. New York: Wiley,
   Google Scholar
• Cai, Z., J. Fan, and R. Li. 2000. Efficient estimation and inferences for varying-coefficient models. Journal of the American Statistical Association 95 (451):888–902. doi:10.1080/01621459.2000.10474280.
   Web of Science ®Google Scholar
• Chatelin Y. M., Esposito V. V., Tenenhaus M. 2002. State-of-art on PLS path modeling through the available software.
   Google Scholar
• Cao, X. L., Y. M. Cheng, and W. G. Wu. 2022. Study on the priorities for development of China Certified Emission Reduction (CCER) forest carbon sink projects under context of carbon neutrality goals. Journal of Statistics and Information 37 (5):103–14.
   Google Scholar
• Carrión-Flores, C. E., and R. Innes. 2010. Environmental innovation and environmental performance. Journal of Environmental Economics and Management 59 (1):27–42. doi:10.1016/j.jeem.2009.05.003.
   Web of Science ®Google Scholar
• Che, W., Y. Zhang, C. Lin, Y. H. Fung, J. C. H. Fung, and A. K. H. Lau. 2023. Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data. Journal of Environmental Sciences (China) 125:513–23. doi:10.1016/j.jes.2022.02.041.36375934
   PubMedGoogle Scholar
• Chen X. R., Wan A. T. K., Zhou Y. 2015. Efficient quantile regression analysis with missing observations. J Am Stat Assoc 110(510):723–741.
   Web of Science ®Google Scholar
• Cheng H. 2020. A class of new partial least square algorithms for first and higher order models. Commun Stat Simul Comput 51(8):4349–4371.
   Web of Science ®Google Scholar
• Cheng, H. 2022. Composite quantile estimation in PLS-SEM for environment sustainable development evaluation.Environment, Development and Sustainability25:6249–68.
   Google Scholar
• Cheng, H. 2023a. Quantile varying-coefficient structural equation models. Statistical Methods & Applications 2023:1–37.
   Google Scholar
• Cheng, H. 2023b. Quantile-based PLS-SEM with bag of little bootstraps. Communications in Statistics - Theory and Methods 2023:1–19.
   Google Scholar
• Cheng, H. 2023c. Environmental effect evaluation: A quantile-type path-modeling approach. Sustainability 15 (5):4399. doi:10.3390/su15054399.
   Web of Science ®Google Scholar
• Cheng, H., and R. M. Pei. 2022. Visualization analysis of functional dynamic effects of globalization talent flow on international cooperation. Journal of Statistics and Information 37 (11):107–16.
   Google Scholar
• Chiang, C.-T., J. A. Rice, and C. O. Wu. 2001. Smoothing spline estimation for varying coefficient models with repeatedly measured dependent variables. Journal of the American Statistical Association 96 (454):605–19. doi:10.1198/016214501753168280.
   Web of Science ®Google Scholar
• Chiou, J.-M., Y. Ma, and C.-L. Tsai. 2012. Functional random effect time-varying coefficient model for longitudinal data. Stat 1 (1):75–89. doi:10.1002/sta4.10.23645939
   PubMedGoogle Scholar
• Chin W. W., Marcolin B. L., Newsted P. R. 2003. A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic- mail emotion/adoption study. Inf Syst Res 14:189–217.
   Web of Science ®Google Scholar
• Ciavolino E., Nitti M. 2013a. Using the hybrid two-step estimation approach for the identification of second-order latent variable models. J Appl Stat 40(3):508–526.
   Web of Science ®Google Scholar
• Ciavolino E., Nitti M. 2013b. Simulation study for PLS path modeling with high-order construct: a job satisfaction model evidence. Adv Dyn Model Econ Soc Syst. 185–207.
   Google Scholar
• Ciavolino E., Al-Nasser A. D. 2009. Comparing generalized maximum entropy and partial least squares methods for structural equation models. J Nonparametric Stat 21(8):1017–1036.
   Web of Science ®Google Scholar
• Claes C., Peter H., Anders H. W. 1999. Robustness of partial least squares method for estimating latent variable quality structures. J Appl Stat 26(4):435–446.
   Web of Science ®Google Scholar
• Cleveland W. S., Grosse E., Shyu W. M. 1993. Local regression models. In: Chambers JM, Hastie TJ (eds) Statistical models in Wadsworth/Brooke-Cole. Pacic Grove, CA, pp. 309–376.
   Google Scholar
• Davino C., Esposito V. V. 2016. Quantile composite-based path modelling. Adv Data Anal Classif 10(4):491–520.
   Web of Science ®Google Scholar
• Davino C., Dolce P., Taralli S. 2017. Quantile composite-based model: a recent advance in PLS-PM. Partial Least Squares Path Modeling. Methodological Issues and Applications. Springer International Publishing AG, Basic Concepts, pp. 81–108.
   Google Scholar
• Dolce P., Davino C., Vistocco D. 2021. Quantile composite-based path modeling: algorithms, properties and applications.
   Google Scholar
• Edwards, J. R., and R. P. Bagozzi. 2000. On the nature and direction of relationships between constructs and measures. Psychological Methods 5 (2):155–74. doi:10.1037/1082-989x.5.2.155.10937327
   PubMed Web of Science ®Google Scholar
• Eubank, R. L., C. Huang, Y. M. Maldonado, N. Wang, S. Wang, and R. J. Buchanan. 2004. Smoothing spline estimation in varying-coefficient models. Journal of the Royal Statistical Society Series B: Statistical Methodology 66 (3):653–67. doi:10.1111/j.1467-9868.2004.B5595.x.
   Web of Science ®Google Scholar
• Fan, J., and I. Gijbels. 1996. Local polynomial modeling and its applications. London: Chapman & Hall.
   Google Scholar
• Fan, J., and T. Huang. 2005. Profile likelihood inferences on semiparametric varying-coefficient partially linear models. Bernoulli 11:1031–57.
   Web of Science ®Google Scholar
• Fan, J., Q. Yao, and Z. Cai. 2003. Adaptive varying-coefficient linear models. Journal of the Royal Statistical Society Series B: Statistical Methodology 65 (1):57–80. doi:10.1111/1467-9868.00372.
   Web of Science ®Google Scholar
• Fan, J., and W. Zhang. 2008. Statistical methods with varying coefficient models. Statistics and Its Interface 1 (1):179–95. doi:10.4310/sii.2008.v1.n1.a15.18978950
   PubMed Web of Science ®Google Scholar
• Fang, D. 2014. Impact assessment of air pollution on the population health in major cities of China. unpubl Master diss., Nanjing University, Nanjing, China.
   Google Scholar
• Feng, C., M. Wang, G.-C. Liu, and J.-B. Huang. 2017. Green development performance and its influencing factors: A global perspective. Journal of Cleaner Production 144:323–33. doi:10.1016/j.jclepro.2017.01.005.
   Web of Science ®Google Scholar
• Gao, J., and L. Kong, 2015. cqrReg: quantile, composite quantile regression and regularized versions, R package version 1.2, https://CRAN.R-project.org/package=cqrReg.
   Google Scholar
• Greenstone, M., and R. Hanna. 2011. Environmental regulations, air and water pollution, and infant mortality in India. Social Science Electronic Publishing 104 (10):1573–1576. doi:10.2139/ssrn.1907924.
   Google Scholar
• Guinot C., Latreille J., Tenenhaus M. 2001. PLS path modeling and multiple table analysis. Application to the cosmetic habits of women in Ile-de-France. Chemom Intell Lab Syst 58(2):247–259.
   Google Scholar
• Hastie T, Tibshirani R. 1993. Generalized additive models. Chapman & Hall, London.
   Google Scholar
• Henseler J., Chin W. W. 2010. A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Struct Equ Model 17(1):82–109.
   Web of Science ®Google Scholar
• Hintze, J. L., Nelson, R. D. 1998. Violin Plots: A Box-Plot Density Trace Synergism. Am. Stat. 52:181–184.
   Web of Science ®Google Scholar
• Jarvis, C., S. MacKenzie, and P. Podsakoff. 2003. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research 30 (2):199–218. doi:10.1086/376806.
   Web of Science ®Google Scholar
• Jeong, S., M. Park, and T. Park. 2017. Analysis of binary longitudinal data with time-varying effects. Computational Statistics & Data Analysis 112:145–53. doi:10.1016/j.csda.2017.03.007.
   Web of Science ®Google Scholar
• Kaklauskas, A., E. Herrera-Viedma, V. Echenique, E. K. Zavadskas, I. Ubarte, A. Mostert, V. Podvezko, A. Binkyte, and A. Podviezko. 2018. Multiple criteria analysis of environmental sustainability and quality of life in post-Soviet states. Ecological Indicators 89:781–807. doi:10.1016/j.ecolind.2017.12.070.
   Web of Science ®Google Scholar
• Jeong, H., J.-S. Ryu, and K. Ra. 2022. Characteristics of potentially toxic elements and multi-isotope signatures (Cu, Zn, Pb) in non-exhaust traffic emission sources. Environmental Pollution (Barking, Essex: 1987) 292 (Pt A):118339. doi:10.1016/j.envpol.2021.118339.
   PubMedGoogle Scholar
• Johansson, C., M. Norman, and L. Burman. 2009. Road traffic emission factors for heavy metals. Atmospheric Environment 43 (31):4681–8. doi:10.1016/j.atmosenv.2008.10.024.
   Web of Science ®Google Scholar
• Liu, D. D. 2016. Statistical analysis of risk of air pollution on people health. Master diss., Ji’nan University, Guangzhou, China.
   Google Scholar
• Lu, T., and Y. Huang. 2017. Bayesian inference on mixed-effects varying-coefficient joint models with skew- t distribution for longitudinal data with multiple features. Statistical Methods in Medical Research 26 (3):1146–64. doi:10.1177/0962280215569294.25670749
   PubMed Web of Science ®Google Scholar
• Lv, J., and Y. Zhang. 2012. Effect of signal coordination on traffic emission. Transportation Research Part D: Transport and Environment 17 (2):149–53. doi:10.1016/j.trd.2011.10.005.
   Web of Science ®Google Scholar
• Masry, E., and J. Fan. 1997. Local polynomial estimation of regression functions for mixing processes. Scandinavian Journal of Statistics 24 (2):165–79. doi:10.1111/1467-9469.00056.
   Web of Science ®Google Scholar
• Meng, M., C. Shao, Y. D. Wong, and Jie. Zhang. 2016. Multimodal traffic assignment with traffic emission effects. Proceedings of the Institution of Civil Engineers- Engineering Sustainability 169 (3):114–22. doi:10.1680/jensu.14.00046.
   Web of Science ®Google Scholar
• Pearce, D. W., and R. K. Turner, 1990. Economics of natural resources and the environment. New York: Harvester Wheatsheaf.
   Google Scholar
• Pearce, D. W., E. B. Barbier, and A. Markandya. 1990. Sustainable development: Economics and environment in the third world. Aldershot: Edward Elgar.
   Google Scholar
• Reinartz B., Ballmann J. 2009. Shock Waves. Springer, Berlin, pp. 1099–1104.
   Google Scholar
• Ringle C. M., Wende S., Becker J. M. 2015. SmartPLS 3. SmartPLS GmbH, Boenningstedt.
   Google Scholar
• Robert W. G., Bruce R. K., Herman O. A. W. 1979. Partial least squares path modeling with latent variables. Anal Chim Acta 112(4):417–421.
   Google Scholar
• Schramm, G., and J. J. Warford. 1989. Environmental management and economic development, Washington, DC/Baltimore: Johns Hopkins University Press.
   Google Scholar
• Senturk, D., and H.-G. Muller. 2008. Generalized varying coefficient models for longitudinal data. Biometrika 95 (3):653–66. doi:10.1093/biomet/asn006.
   Web of Science ®Google Scholar
• Senturk, D., and H. G. Muller. 2010. Functional varying coefficient models for longitudinal data. Journal of the American Statistical Association 105:1256–64.
   Web of Science ®Google Scholar
• Serban, N. 2011. A space-time varying coefficient model: the equity of service accessibility. The Annals of Applied Statistics 5:2024–51.
   Google Scholar
• Shao, J. 2003. Mathematical statistics. New York: Springer.
   Google Scholar
• Sosa, J., and L. Buitrago. 2022. Time-varying coefficient model estimation through radial basis functions. Journal of Applied Statistics 49 (10):2510–34. doi:10.1080/02664763.2021.1910938.35757039
   PubMed Web of Science ®Google Scholar
• Sosa, J., and L. G. Diaz. 2012. Random time-varying coefficient model estimation through radial basis functions. Revista Colombiana de Estadística 35:167–84.
   Google Scholar
• Tang, Y., H. J. Wang, and Z. Zhu. 2013. Variable selection in quantile varying coefficient models with longitudinal data. Computational Statistics & Data Analysis 57 (1):435–49. doi:10.1016/j.csda.2012.07.015.
   Web of Science ®Google Scholar
• Voelkle M. C., Oud J. H. L., Oertzen T. V., Lindenberger U. 2012. Maximum likelihood dynamic factor modeling for arbitrary N and T using SEM. Struct Equ Model 19(3):329–350.
   Web of Science ®Google Scholar
• Wang Y., Feng X. N., Song X. Y. 2016. Bayesian quantile structural equation models. Struct Equ Model 23:1–13.
   Web of Science ®Google Scholar
• Wang, H., and Y. Xia. 2009. Shrinkage estimation of the varying coefficient model. Journal of the American Statistical Association 104 (486):747–57. doi:10.1198/jasa.2009.0138.
   Web of Science ®Google Scholar
• Wang, H. J., Z. Zhu, and J. Zhou. 2009. Quantile regression in partially linear varying coefficient models. Annals of Statistics 37:3841–66.
   Google Scholar
• Wang, L., H. Li, and J. Z. Huang. 2008. Variable selection in nonparametric varying-coefficient models for analysis of repeated measurements. Journal of the American Statistical Association 103 (484):1556–69. doi:10.1198/016214508000000788.20054431
   PubMed Web of Science ®Google Scholar
• Wang, X., G. F. Birch, and E. Liu. 2022. Traffic emission dominates the spatial variations of metal contamination and ecological-health risks in urban park soil. Chemosphere 297:134155. doi:10.1016/j.chemosphere.2022.134155
   PubMed Web of Science ®Google Scholar
• Wei, C. H., S. J. Wang, and Y. N. Su. 2022. Local GMM estimation in spatial varying coefficient geographocally weighted autoregressive model. Journal of Statistics and Information 37 (11):3–13.
   Google Scholar
• Wei, F., J. Huang, and H. Li. 2011. Variable selection and estimation in high-dimensional varying-coefficient models. Statistica Sinica 21:1515.
   Google Scholar
• Wold H. O. A. 1982. Soft modeling: the basic design and some extensions. Syst Under Indirect Obs II 1–54.
   Google Scholar
• Wold H. 1985. Partial least squares. In: Kotz S, Johnson NL (eds) Encyclopedia of statistical sciences, vol 6. Wiley, New York.
   Google Scholar
• Zhang W. Y., Lee S. Y. 2009. Nonlinear dynamical structural equation models. Quant Financ 9(3):305–314.
   Web of Science ®Google Scholar
• Zivin, J. G., and M. Neidell. 2012. The impact of pollution on worker productivity. The American Economic Review 102 (7):3652–73. doi:10.1257/aer.102.7.3652.26401055
   PubMed Web of Science ®Google Scholar
• Zhou Y., Wan A. T. K., Wang X. 2008. Estimating equation inference with missing data. J Am Stat Assoc 103:1187–1199.
   Web of Science ®Google Scholar
• Zou, H., and M. Yuan. 2008. Composite quantile regression and the oracle model selection theory. The Annals of Statistics 36 (3):1108–26.
   Web of Science ®Google Scholar