A comparison between Speckman and Bayesian estimation method of a semiparametric balanced longitudinal data model
DOI:
https://doi.org/10.33095/kpscqv37Keywords:
Bayesian, Speckman, Nadaraya &Watson, Semi-parametric, Balanced longitudinal data.Abstract
This paper aims to use semi-parametric regression to balanced longitudinal data model, where the parametric regression models suffer from the problem of strict constraints, while non-parametric regression models, despite their flexibility, suffer from the problem of the curse of dimensionality. Consequently, semi-parametric regression is an ideal solution to get rid of the problems that parametric and non-parametric regression suffer from. The great advantage of this model is that it contains all the positive features included in the previous two models, such as containing strict restrictions in its parametric component, complete flexibility in its non-parametric component, and clarity of the interaction between its parametric and non-parametric components.
Based on the above, two methods were used to estimate a semi-parametric balanced longitudinal data model. The first is the Bayesian estimating method; the second is the Speckman method, which estimated the unknown nonparametric smoothing function by employing the kernel smoothing Nadaraya-Watson method. The Aim was to make a comparison between the Bayesian estimation method and the classical estimation method. Three different sample sizes were used in the simulation studies: 50, 100, and 200. The study results showed that the Bayesian estimating method is best at low variance levels (1,5), whereas the Speckman method is best at high variance level (10).
Paper type: A Research derived from Dissertation Ph.D.
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References:
Abd-Alhafez,A. and Rashid, D. (2013), Comparison Robust M Estimate With Cubic Smoothing Spline For time –Varying Coefficient Model For Balance Longitudinal Data. Journal of Economics and Administrative Sciences, Vol 19,No.72,pp:398-413.
Abd-Alrazzaq, Ali(2015), Estimating Missing Data In Panel data Model with Practical Application. Master's thesis, University of Baghdad, Baghdad.
Abd-Alreda, M.A. and Fadam,E. (2019), Nonparametric and Semiparametric Bayesian Estimation in survival function analysis, Journal of Economics and Administrative Sciences, Vol 25,No.112,pp:461-480.
Burhan,Y. and Hammoud,M. (2018), Comparison between the methods estimate nonparametric and semiparametric transfer function model in time series the Using simulation. Journal of Economics and Administrative Sciences, Vol 24,No.106,pp:375-391.
Castelein, A., Fok, D., and Paap, R. (2020), Heterogeneous Variable Selection in Nonlinear Panel data Models: A Semi-Parametric Bayesian Approach, (No. TI 2020-061/III). Tinbergen Institute Discussion Paper.
Conklin, M. (2001), Monte Carlo Methods in Bayesian Computation, Technometrics, Vol 43,No.2, pp:240-240.
Ferguson, T. S. (1973), A Bayesian analysis of some nonparametric problems. The annals of statistics, pp:209-230.
Green. W. (2002), Econometric Analysis. 5ed, Prentice Hall, New Jersey.
Hamza , R. K. and Jaafar, M. (2018), Comparison of estimations methods of the entropy function to the random coefficients for two models: the general regression and Swamy of the panel data", Journal of Economics and Administrative Sciences, Vol 25,No.110,pp:371-391.
Hardle,W., Muller, M., Sperlich, S., and Werwatz, A. (2004), Nonparametric and semiparametric models. Berlin: Springer, Vol. 1.
Jochmann, M., and León‐González, R. (2004), Estimating the demand for health care with Longitudinal data: a semiparametric Bayesian approach. Health Economics, Vol 13,No.10, pp:1003-1014.
Kamel, R. and Aboudi,E. (2021), Comparison Of Some Methods For Estimating a Semi-parametric Model For Longitudinal Data, Journal of Economics and Administrative Al-Mustansiriya, Vol 28,No.127,pp:261-249.
Khalil, Y. and Fadam,E. (2016), Compare to the conditional logistic regression models with fixed and mixed effects for longitudinal data, Journal of Economics and Administrative Sciences, Vol 23,No.98,pp:406-429.
Kleinman, K. P., and Ibrahim, J. G. (1998), A semiparametric Bayesian approach to the random effects model. Biometrics, PP: 921-938.
Laird, N. M. and Ware, J. H. (1982), Random-effects models for longitudinal data. Biometrics No.38, pp:963-974.
Li, Q., and Stengos, T. (1996), Semiparametric estimation of partially linear Longitudinal data models. Journal of econometrics, Vol 71,No.1, pp:389-397.
Liu, S., You, J., and Lian, H. (2017), Estimation and model identification of longitudinal data time –varying nonparametric models. Journal of Multivariate Analysis, No.156,pp:116-136.
Mohaisen, A. J., & Abdulhussein, A. M. (2014). Fuzzy sets in semiparametric Bayes Regression. Basrah Journal of Science (A), 32(1), 141-167.
Nauef Al-Qazaz, Q. N., and Ali, A. H. (2022), Bayesian inference of fractional brownian motion of multivariate stochastic differential equations. International Journal of Nonlinear Analysis and Applications, Vol 13,No.1, pp:2425-2454.
Nayef Al-Qazaz, Q. N., and Shawkat, L. N. (2022), Using some methods to estimate the parameters of the Multivariate Skew Normal (MSN) distribution function with missing data. International Journal of Nonlinear Analysis and Applications, Vol13,No.1, pp:2333-2350.
Robert, C. P., Casella, G., and Casella, G. (1999), Monte Carlo statistical method. New York: Springer. Vol. 2 .
Robinson, P. M. (1988). Root-N-consistent semiparametric regression. Econometrica: Journal of the Econometric Society, No.56, pp:931-954.
Rodriguez-Poo,J.M., and Sobern,A.(2017), Nonparametric and semiparametric Longitudinal data models:Recent developments. Journal of Economic Surveys, vol31,No.4, pp:923-960.
Sadiq, N. J.(2015), Estimate the Regression Model of Longitudinal Data with Drop-outs in Response Variable with Application in Medical field.Ph.D. thesis, University of Baghdad, Baghdad.
Shaker,A. and Nayef,Q. (2016), Comparison Semiparametric Bayesian Method with Classical Method for Estimating Systems Reliability using Simulation Procedure. Journal of Economics and Administrative Sciences, Vol 23,No.97,pp:378-393.
Speckman, P. (1988). Kernel smoothing in partial linear models. Journal of the Royal Statistical Society: Series B (Methodological), Vol.50, No.3, pp:413-436.
Tanner MA, Wong W. ,(1987), The calculation of posterior distributions by data augmentation (with discussion). Journal Amer Statist Assoc ; No.82: pp:528–550.
Tukey, J. W. (1961), Curves as parameters, and touch estimation. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability. Volume 1: Contributions to the Theory of Statistics, University of California Press, No. 4, pp: 681-695.
Wang, H. (2014), Essays on Semiparametric Ridge-Type Shrinkage Estimation, Model Averaging and Nonparametric Panel Data Model Estimation. University of California, Riverside.
West, M., Muller, P., and Escobar, M. D. (1994), Hierarchical priors and mixture models, with applications in regression and density estimation. In Aspects of Uncertainty: A Tribute to D. V. Lindley, A. F. M. Smith and P. R. Freeman (eds). London: Wiley.
Zeger, S. L., Diggle, P. J., Heagerty, P. and Liang, K. Y., (2002), Analysis of longitudinal data. Second edition, oxford university press, New York, No.69.
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