BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Nitha K. U. (University of Calicut, India), Krishnarani S. D. (University of Calicut, India)
Tytuł
On Autoregressive Processes with Lindley-Distributed Innovations: Modeling and Simulation
Źródło
Statistics in Transition, 2024, vol. 25, nr 3, s. 31-47, tab., wykr., bibliogr. 23 poz.
Słowa kluczowe
Statystyka, Innowacje, Symulacja, Metody statystyczne
Statistics, Innovations, Simulation, Statistical methods
Uwagi
summ.
Abstrakt
In this paper, we develop an autoregressive process of order one, assuming that the innovation random variable has a Lindley distribution. The key properties of the process are investigated. Five distinct estimation techniques are used to estimate the parameters and simulation studies are conducted. The stationarity of the process is tested using a unit root test. The application of the proposed process to the analysis of time series data is demonstrated using real data sets. Based on some important statistical measures, the analysis of the data sets reveals that the proposed model fits well, and the errors are independent and Lindley-distributed. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. Algarni, A., (2021). On a new generalized Lindley distribution: properties, estimation and applications. PLOS ONE, 16(2).
  2. Altun, E., (2019a). A new generalization of geometric distribution with properties and applications. Communications in Statistics Simulation and Computation, 17(5), pp. 1481-1495.
  3. Altun, E., (2019b). Two-sided Lindley distribution with inference and applications. Journal of the Indian Society for Probability and Statistics, 20, pp. 255-279.
  4. Andel, J., (1988). On AR(1) processes with exponential white noise. Communications in Statistics - Theory and Methods, 17(5), pp. 1481-1495.
  5. Asgharzadeh, A., Bakouch, H. S., Nadarajah, S., Sharafi, F., (2016). A new weighted Lindley distribution with application. Brazilian Journal of Probability and Statistics, 30, pp. 1-27.
  6. Bakouch, H. S., Popovi´c, B. V., (2016). Lindley first-order autoregressive model with applications. Communications in Statistics - Theory and Methods, 45(17), pp. 4988-5006.
  7. Beghriche, A., Zeghdoudi, H., Raman, V., Chouia, S., (2022). New polynomial exponential distribution: properties and applications. Statistics in Transition New Series, 23(3), pp. 95-112.
  8. Bell, C. B., Smith, E. P., (1986). Inference for non-negative autoregressive schemes. Communications in Statistics-Theory and Methods, 15(8), pp. 2267-2293.
  9. Bhati, D., Malik, M., Vaman, H., (2015). Lindley-exponential distribution: properties and applications. Metron, 73(3), pp. 335-357.
  10. Ekhosuehi, N., Opone, F., (2018). A three-parameter generalized Lindley distribution: properties and application. Statistica, 78(3), pp. 233-249.
  11. Elbatal, I., Merovci, F., Elgarhy, M., (2013). A new generalized Lindley distribution. Mathematical Theory and Modeling, 3(13), pp. 30-47.
  12. Gaver, D. P., Lewis, P. A. W., (1980). First order autoregressive gamma sequences and point processes. Advances and Applications in Probability, 12, pp. 727-745.
  13. Ghasami, S., Khodadadi, Z., Maleki, M., (2020). Autoregressive processes with generalized hyperbolic innovations. Communications in Statistics-Simulation and Computation, 49(12), pp. 3080-3092.
  14. Ghitany, M. E., Atieh, B., Nadarajah, S., (2008). Lindley distribution and its application. Mathematics and Computers in Simulation, 78, pp. 493-506.
  15. Hamed, D., Alzaghal, A., (2021). A new class of Lindley distribution: properties and applications. Journal of Statistical Distributions and Applications, 8(11).
  16. Hutton, J. L., (1990). Non-negative time series models for dry river flow. Journal of Applied Probability, 27, pp. 171-182.
  17. Jenny, N. L., Vance, L. M., (1992). Non-linear time series modelling and distributional flexibility. Journal of Time Series Analysis, 65, pp. 65-84.
  18. Oluyede, B., Yang, T., (2015). A new class of generalized Lindley distribution with applications. Journal of Statistical Computation and Simulation, 85(10), pp. 2072-2100.
  19. Sankaran, M., (1970). The discrete Poisson-Lindley distribution. Biometrics, 26(1), pp. 145-149.
  20. Sharafi, M., Nematollahi, A. R., (2016). AR(1) model with skew-normal innovations. Metrika, 79(8), pp. 1011-1029.
  21. Tiku, M. L., Wong, W. K., Bian, G., (2000). Time series models with non-normal innovations symmetric location-scale distributions. Journal of Time Series Analysis, 21(5), pp. 571-596.
  22. Wilks, S. S., (1938). The large-sample distribution of the likelihood ratio for testing composite hypotheses. The Annals of Mathematical Statistics, 9(1), pp. 60-62.
  23. Zeghdoudi, H., Bouchahed, L., (2018). A new and unified approach in generalizing Lindley's distribution with applications. Statistics in Transition New Series, 19(1), pp. 61-74.
Cytowane przez
Pokaż
ISSN
1234-7655
Język
eng
URI / DOI
http://dx.doi.org/doi.org/10.59170/stattrans-2024-026
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu