References
- Adler, R., Feldman, R., & Taqqu, M. (1998). A practical guide to heavy tails: Statistical techniques and applications. Springer Science & Business Media.
- Agarwal, M., Tripathi, P. K., & Pareek, S. (2021). Forecasting infant mortality rate of India using ARIMA model: A comparison of Bayesian and classical approaches. Statistics and Applications, 19(2), 101–114.
- Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705
- Aston, J. (2006). Modeling macroeconomic time series via heavy tailed distributions. In H.-C. Ho, C.-K. Ing & T. L. Lai (Eds.), Time series and related topics (Vol. 52, pp. 138–149). Institute of Mathematical Statistics.
- Bradley, B. O., & Taqqu, M. S. (2003). Financial risk and heavy tails. In S. T. Rachev (Ed.), Handbook of heavy tailed distributions in finance (pp. 35–103). Elsevier.
- Brown, R. J. (2009). Thanks, Louis, you were right!! 100 years of heavy tails–the hypothesis that won’t go away. Real Estate Review, 38(2), 3.
- Chib, S., & Greenberg, E. (1994). Bayes inference in regression models with ARMA(p, q) errors. Journal of Econometrics, 64(1–2), 183–206. https://doi.org/10.1016/0304-4076(94)90063-9
- Claeskens, G., & Jansen, M. (2015). Model selection and model averaging. In J. D. Wright (Ed.), International encyclopedia of the social and behavioral sciences (2nd ed., Vol. 15, pp. 647–652). Elsevier.
- Durbin, J., & Koopman, S. J. (2000). Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives. Journal of the Royal Statistical Society Series B: Statistical Methodology, 62(1), 3–56. https://doi.org/10.1111/1467-9868.00218
- Foss, S., Korshunov, D., Zachary, S. (2011). An introduction to heavy-tailed and subexponential distributions (Vol. 6). Springer.
- Gonçalves, F. B., Prates, M. O., & Lachos, V. H. (2015). Robust Bayesian model selection for heavy-tailed linear regression using finite mixtures. arxiv preprint arxiv:1509.00331
- Jiang, Y. (2016). An exponential-squared estimator in the autoregressive model with heavy-tailed errors. Statistics and Its Interface, 9(2), 233–238. https://doi.org/10.4310/SII.2016.v9.n2.a10
- Juárez, M. A., & Steel, M. F. J. (2010). Model-based clustering of non-Gaussian panel data based on skew-t distributions. Journal of Business & Economic Statistics, 28(1), 52–66. https://doi.org/10.1198/jbes.2009.07145
- Kleibergen, F. R., & Hoek, H. (2000). Bayesian analysis of ARMA models. Tinbergen Institute. Discussion Paper (TI 2000-027/4).
- Liu, J., Kumar, S., & Palomar, D. P. (2019). Parameter estimation of heavy-tailed AR model with missing data via stochastic EM. IEEE Transactions on Signal Processing, 67(8), 2159–2172. https://doi.org/10.1109/TSP.2019.2899816
- Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business, 36(4), 394–419. https://doi.org/10.1086/294632
- Marriott, J., Ravishanker, N., Gelfand, A. E., & Pai, J. (1996). Bayesian analysis of ARMA processes: Complete sampling-based inference under exact likelihoods. In D. A. Berry, K. M. Chaloner & J. Geweke (Eds.), Bayesian analysis in statistics and econometrics: Essays in honor of Arnold Zellner (pp. 243–256). Wiley.
- Pruitt, R. C. (1987). Estimation of an autoregressive parameter when the innovations are heavy tailed. Technical report, University of Minnesota.
- Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136
- Smith, A. F. M., & Roberts, G. O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society. Series B (Methodological), 55(1), 3–23. https://doi.org/10.1111/j.2517-6161.1993.tb01466.x
- Tiku, M. L., Wong, W. K., Vaughan, D. C., & Bian, G. (1996). Time series models in non-normal situations: Symmetric innovations. Journal of Time Series Analysis, 21(5), 571–596. https://doi.org/10.1111/1467-9892.00199
- Tripathi, P. K., & Agarwal, M. (2021). Bayesian prediction of monthly gold prices using an EARSV model and its competitive component models. International Journal of Mathematics and Statistics, 22(3), 1–17.
- Tripathi, P. K., & Upadhyay, S. K. (2019). Bayesian analysis of extended autoregressive model with stochastic volatility. Journal of the Indian Society for Probability and Statistics, 20(1), 1–29. https://doi.org/10.1007/s41096-019-00060-z
- Tripathi, P. K., Mishra, R. K., & Upadhyay, S. K. (2018). Bayes and classical prediction of total fertility rate of India using autoregressive integrated moving average model. Journal of Statistics Applications & Probability, 7(2), 233–244. https://doi.org/10.18576/jsap/070202
- Tripathi, P. K., Ranjan, R., Pant, R., & Upadhyay, S. K. (2017). An approximate Bayes analysis of ARMA model for Indian GDP growth rate data. Journal of Statistics and Management Systems, 20(3), 399–419. https://doi.org/10.1080/09720510.2017.1293952
- Tripathi, P. K., Sen, R., & Upadhyay, S. K. (2021). A Bayes algorithm for model compatibility and comparison of ARMA(p, q) models. Statistics in Transition New Series, 22(2), 95–123. https://doi.org/10.21307/stattrans-2021-018
- Upadhyay, S. K., Vasishta, N., & Smith, A. F. M. (2001). Bayes inference in life testing and reliability via Markov chain Monte Carlo simulation. Sankhyā: The Indian Journal of Statistics, Series A (1961–2002), 63(1), 15–40.