پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 657 |
| تعداد مقالات | 9,640 |
| تعداد مشاهده مقاله | 68,658,776 |
| تعداد دریافت فایل اصل مقاله | 48,131,939 |
A proposed conditional method for estimating ARMA(1, 1) model | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 243، دوره 13، شماره 1، خرداد 2022، صفحه 3011-3020 اصل مقاله (328.27 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6032 | ||
| نویسنده | ||
| Lamyaa Mohammed Ali Hameed* | ||
| Department of Statistics, College of Administration and Economic, Baghdad University, Iraq. | ||
| تاریخ دریافت: 23 اردیبهشت 1400، تاریخ پذیرش: 14 مهر 1400 | ||
| چکیده | ||
| This paper aims to study the parameters estimation methods of the stationary mixed model (autoregressive-moving average) of low order ARMA (1, 1) regarding to time domain analysis in univariate time series. Using the approximating methods: Back Forecasting (BF), Classical Conditional Maximum Likelihood (CC) and Proposed Conditional Maximum Likelihood(PC). A comparison is done among the three methods by Mean Squared Error (MSE) using several simulation experiments; the obtained results from the empirical analysis indicate that the accuracy of the proposed conditional method is better than the classical conditional method. | ||
| کلیدواژه‌ها | ||
| ARMA model؛ Estimation؛ Conditional Maximum Likelihood؛ Back Forecasting؛ Sum Squared Error | ||
| مراجع | ||
|
[1] M. M. Ali, Analysis of Autoregressive- Moving Average Models: Estimation and Prediction, Biometrica, 64 (1977) 535-45. [2] D. J. Allcroft, A Spectral Estimator of ARMA Parameters from Thresholded Data, 2000, http://bioss.ac.uk. [3] C. F. Ansley, An Algorithm for the Exact Likelihood of a Mixed Autoregressive Moving Average Process, Biometrrka, 66(1) (1979) 59-65. [4] G. E. P. Box and G.M. Jenkins, Time Series Analysis Forecasting and Control, 2nd ed., Holden-Day, San Francisco, 1976. [5] R. A. Davis, T. M. Dunsmuir and S. B. Streett, Maximum Likelihood Estimation for an Observation Driven Model for Poisson Counts, National Center for Atmospheric Research, 2003. [6] S. De Waele & Broersen Modeling Radar Data with Time Series Models, Dept. of Applied Physics, Delft University of Technology, 2001, E-mail: S.dewaele@tn.tudelft.nl. [7] J. Durban, Estimation of Parameters in Time-Series Regression Models, J. Royal. Stat. Soc., Series B, 22(1960) 139-153. [8] J. D. Hamilton, Time Series Analysis, Princeton University Press Princeton, New Jersey, 1994. [9] E. J. Hannan, Recursive Estimation Based on ARMA Models, Ann. Stat., 8 (4) (1980) 762-777. [10] M. Horner, S. N. Pakzad and N. S. Gulgec, Parameter Estimation of Autoregressive-Exogenous and Autoregressive Models Subject to Missing Data Using Expectation Maximization, Front. Built Environ., 13 (2019) 2019, https://doi.org/10.3389/fbuil.2019.00109 [11] M. Karanasos, A New Method for Obtaining the Autocovariance of an ARMA Model: An Exact Form Solution, Econom. Theory, 16 (2)(2000) 280-282. [12] T. Krone, C. J. AlbersMarieke and M. E. Timmerman, A comparative simulation study of AR(1) estimators in short time series, Qual. Quantity, 51(1)(2017) 1-21. [13] H. Lei, Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter, Sensors, 15(10)(2015 ) 25277-25286, https://doi.org/10.3390/s151025277 [14] H. Lei, Y. Xia and X.Qin, Estimation of semi-varying coefficient time series models with ARMA errors, Ann. stat. ,V 44(4)(2016) 1618-1660. [15] S. Makridakis, S. C. Wheelwright and R. J. Hyndman, Forecasting Methods and Applications, 3rd ed, John Wiley & Sons, 1998. [16] A. I. Mcleod and Y. Zhang, Faster ARMA maximum likelihood estimation, Comput. Stat. Data Anal., 52(4) (2008) 2166-2176. [17] G. Melard, A Fast Algorithm for the Exact Likelihood of Autoregressive- Moving Average Models, Appl. Stat., 33(1)(1984) 104-114. [18] N. Muler, D. Pena and V. J. Yohai, Robust estimation for ARMA models, Ann. Stat. 37(2)(2009) 816-840. [19] P. Newbold, The Exact Likelihood Function For a Mixed Autoregressive Moving Average Process, Biometrika, 61(39)(1974) 423- 426. [20] R. Paul and D. Balmer, Simulation Modeling, Chartwell-Bratt (Publishing and Training) Ltd, 1993. [21] D. S. G Pollock, Time-series analysis, signal processing and dynamics, Academic Press, 1999. [22] W. W. S. Wei, Time Series Analysis Univariate and Multivariate Methods, Addison- Wesley Publishing Company, Inc, 1990. | ||
|
آمار تعداد مشاهده مقاله: 16,019 تعداد دریافت فایل اصل مقاله: 8,080 |
||