Data Smoothing Structural Equation Modeling to Study Quality of Life and Model Selection

References

• Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrox and F. Csaki, (Eds.), Second International Symposium on Information Theory (pp. 267–281). Academiai Kiado.
   Google Scholar
• Ayebo, A., & Kozubowski, T. (2003). An asymmetric generalization of gaussian and laplace laws. Journal of Probability and Statistical Science, 1, 187–210.
   Google Scholar
• Basulto-Elias, G. (2016). Kernel deconvolution density estimation [PhD thesis, Iowa State University].
   Google Scholar
• Bentler, P. M., & Dudgeon, P. (1996). Covariance structure analysis: Statistical practice, theory, and directions. Annual Review of Psychology, 47, 563–592.
   PubMed Web of Science ®Google Scholar
• Bollen, K. (1989). Structural Equations with Latent Variables. John Wiley.
   Google Scholar
• Box, G., & Cox, D. (1964). An analysis of transformations. Journal of the Royal Statistical Society, Series B (Methodological), 26, 211–243. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
   Web of Science ®Google Scholar
• Bozdogan, H. (1988). Icomp: A new model-selection criteria. In H. H Bock, (Ed.), Classification and Related Methods of Data Analysis
   Google Scholar
• Bozdogan, H. (1990). On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models. Communications in Statistics - Theory and Methods, 19, 221–278. https://doi.org/10.1080/03610929008830199
   Web of Science ®Google Scholar
• Bozdogan, H. (1994). Choosing the Number of Clusters, Subset Selection of Variables, and Outlier Detection in the Standard Mixture-Model Cluster Analysis. In E. Diday, (Ed.), New Approaches in Classification and Data Analysis (pp. 169–177). Springer-Verlag.
   Google Scholar
• Bozdogan, H. (2000). Akaike’s Information Criterion and Recent Developments in Information Complexity. Journal of Mathematical Psychology, 44, 62–91. https://doi.org/10.1006/jmps.1999.1277
   PubMed Web of Science ®Google Scholar
• Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62–83. https://doi.org/10.1111/j.2044-8317.1984.tb00789.x
   PubMed Web of Science ®Google Scholar
• Browne, M. W., & Shapiro, A. (1988). Robustness of normal theory methods in the analysis of linear latent variate models. British Journal of Mathematical and Statistical Psychology, 41, 193–208. https://doi.org/10.1111/j.2044-8317.1988.tb00896.x
   Web of Science ®Google Scholar
• Charpentier, A., & Flachaire, E. (2016). Log-transform kernel density estimation of income distribution. L'Actualité Économique, 91, 141–159. https://doi.org/10.7202/1036917ar
   Google Scholar
• Delaigle, A., & Van Keilegom, I. (2021). Deconvolution with unknown error distribution. In G. Yi, A. Delaigle, & P. Gustafson (Eds.), Handbook of Measurement Error Models (245–270). Chapman and Hall/CRC.
   Google Scholar
• Erçelik, E., & Nadar, M. (2021). A new kernel estimator based on scaled inverse chi-squared density function. American Journal of Mathematical and Management Sciences, 40, 306–319. https://doi.org/10.1080/01966324.2020.1854138
   Google Scholar
• Fan, X., & Fan, X. (2005). Using SAS for monte carlo simulation research in SEM. Structural Equation Modeling, 12, 299–333.
   Web of Science ®Google Scholar
• Fang, K., Kotz, S., & Ng, K. (1990). Symmetric Multivariate and Related Distributions. Chapman and Hall.
   Google Scholar
• Gao, S., Mokhtarian, P., & Johnston, R. (2008). Non-normality of data in structural equation models. Transportation Research Board’s 87th Annual Meeting.
   Google Scholar
• Gifi, A. (1989). Nonlinear Multivariate Analysis. Wiley.
   Google Scholar
• Gomez, E., Gomez-Villegas, M., & Marin, J. (1998). A multivariate generalization of the power exponential family of distributions. Communications in Statistics - Theory and Methods, 27, 589–600.
   Web of Science ®Google Scholar
• Huba, G., & Harlow, L. (1987). Robust structural equation models: Implications for developmental psychology. Child Development, 58, 147–166. https://doi.org/10.2307/1130297
   Web of Science ®Google Scholar
• Kullback, A., & Leibler, R. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79–86. https://doi.org/10.1214/aoms/1177729694
   Google Scholar
• Lee, S. (2007). Structural Equation Modeling: A Bayesian Approach, chapter Structural equation models with continuous and ordered categorical variables. John Wiley and Sons.
   Google Scholar
• Long, J. (1983). Confirmatory Factor Analysis. Sage Publications.
   Google Scholar
• Magnus, J., & Neudecker, H. (1988). Matrix Differential Calculus with Applications in Statistis and Econometrics. Wiley.
   Google Scholar
• Magnus, J. (1988). Linear Structures. Charles Griffin & Company and Oxford University Press.
   Google Scholar
• Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33, 1065–1076. https://doi.org/10.1214/aoms/1177704472
   Google Scholar
• Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. The Annals of Mathematical Statistics, 27, 832–837. https://doi.org/10.1214/aoms/1177728190
   Google Scholar
• Scott, D. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley & Sons.
   Google Scholar
• Shapiro, A., & Browne, M. W. (1987). Analysis of covariance structures under elliptical distributions. Journal of the American Statistical Association, 82, 1092–1097. https://doi.org/10.1080/01621459.1987.10478544
   Web of Science ®Google Scholar
• Silverman, B. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall.
   Google Scholar
• Stoelting, R. (2002). Structural equation modeling/path analysis. http://userwww.sfsu.edu/efc/classes/biol710/path/SEMwebpage.htm
   Google Scholar
• Van Emden, M. (1971). An analysis of complexity. In Mathematical Centre Tracts. (Vol 35). Mathematisch Centrum.
   Google Scholar
• Wand, M. P., & Jones, M. C. (1993). Comparison of smoothing parameterizations in bivariate kernel density estimation. Journal of the American Statistical Association, 88, 520–528. https://doi.org/10.1080/01621459.1993.10476303
   Web of Science ®Google Scholar
• Wang, X.-F., Wang, B., Wang, M. X.-F., Wang, X. F., & Wang, B. (2021). Package ‘decon’.
   Google Scholar
• Yuan, K., & Bentler, P. (1998). Structural equation modeling with robust covariances. Sociological Methodology, 28, 363–396. https://doi.org/10.1111/0081-1750.00052
   Web of Science ®Google Scholar
• Yuan, K. (2005). Fit indices versus test statistics. Multivariate Behavioral Research, 40, 115–148. https://doi.org/10.1207/s15327906mbr4001_5
   PubMed Web of Science ®Google Scholar
• Yuan, K.-H., Bentler, P. M., & Chan, W. (2004). Structural equation modeling with heavy tailed distributions. Psychometrika, 69, 421–436. https://doi.org/10.1007/BF02295644
   Web of Science ®Google Scholar