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Existence Of Maximum Likelihood Estimators In Autoregressive And Moving Average Models
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Book Synopsis Existence of Maximum Likelihood Estimators in Autoregressive and Moving Average Models by : STANFORD UNIV CA DEPT OF STATISTICS.
Download or read book Existence of Maximum Likelihood Estimators in Autoregressive and Moving Average Models written by STANFORD UNIV CA DEPT OF STATISTICS. and published by . This book was released on 1980 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is given a sufficient condition on the observations from a scalar autoregressive process such that the maximum likelihood estimate exists and corresponds to a stationary process. A sufficient condition is given for the likelihood function to fail to have a maximum. In a moving average model the maximum likelihood estimates always exist. Some results are obtained for the autoregressive moving average model and vector models. It is shown that the solution to the sample Yule-Walker equations in the autoregressive case yield a stationary process. (Author).
Book Synopsis An Efficient Algorithm for Maximum Likelihood Estimation for Autoregressive Moving Average Model by : Pham Dinh Tuan
Download or read book An Efficient Algorithm for Maximum Likelihood Estimation for Autoregressive Moving Average Model written by Pham Dinh Tuan and published by . This book was released on 1986 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models by : Rusdu Saracoglu
Download or read book The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models written by Rusdu Saracoglu and published by . This book was released on 1977 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of the Autoregressive Coefficients and Moving Average Covariances of Vector Autoregressive Moving Average Models by : Fereydoon Ahrabi
Download or read book Maximum Likelihood Estimation of the Autoregressive Coefficients and Moving Average Covariances of Vector Autoregressive Moving Average Models written by Fereydoon Ahrabi and published by . This book was released on 1979 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is to derive asymptotically efficient estimates for the autoregressive matrix coefficients and moving average covariance matrices of the vector autoregressive moving average (VARMA) models in both time and frequency domains. To do this we shall apply the Newton-Raphson and scoring methods to the maximum likelihood equations derived from modified likelihood functions under the Gaussian Assumption.
Book Synopsis Maximum Likelihood Estimation of Multivariate Autoregressive-Moving Average Models by : M. S. Phadke
Download or read book Maximum Likelihood Estimation of Multivariate Autoregressive-Moving Average Models written by M. S. Phadke and published by . This book was released on 1977 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms for computing the exact likelihood function of n successive observation vectors from an s-variate autoregressive moving average process of order (p, q) are developed. A quasi-Newton method is used to maximize the likelihood function with respect to the parameters of the process. Monte Carlo simulations are performed to compare the parameter estimates obtained by maximizing the exact likelihood function versus those obtained by maximizing various approximate forms of the likelihood function. (Author).
Book Synopsis The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models by : Rüşdü Saracoglu
Download or read book The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models written by Rüşdü Saracoglu and published by . This book was released on 1977 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: "No abstract available"--Federal Reserve Bank of Minneapolis web site.
Book Synopsis Estimation by Maximum Likelihood in Autorgressive Moving Average Models in the Time and Frequency Domains by : Stanford University. Department of Statistics
Download or read book Estimation by Maximum Likelihood in Autorgressive Moving Average Models in the Time and Frequency Domains written by Stanford University. Department of Statistics and published by . This book was released on 1975 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Vector Autoregressive Moving Average Models by : Greg Reinsel
Download or read book Maximum Likelihood Estimation of Vector Autoregressive Moving Average Models written by Greg Reinsel and published by . This book was released on 1976 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: A method is presented for the estimation of the parameters in the vector autoregressive moving average time series model. The estimation procedure is derived from the maximum likelihood approach and is based on Newton-Raphson techniques applied to the likelihood equations. The resulting two-step Newton-Raphson procedure is computationally simple, involving only generalized least squares estimation in the second step. This Newton-Raphson estimator is shown to be asymptotically efficient and to possess a limiting multivariate normal distribution. (Author).
Book Synopsis Maximum Likelihood Estimation for Vector Autoregressive Moving Average Models by : STANFORD UNIV CALIF DEPT OF STATISTICS.
Download or read book Maximum Likelihood Estimation for Vector Autoregressive Moving Average Models written by STANFORD UNIV CALIF DEPT OF STATISTICS. and published by . This book was released on 1978 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: The vector autoregressive moving average model is a multivariate stationary stochastic process where the unobservable multivariate process consists of independently identically distributed random vectors. The coefficient matrices and the covariance matrix are to be estimated from an observed sequence. Under the assumption of normality the method of maximum likelihood is applied to likelihoods suitably modified for techniques in the frequency and time domains. Newton-Raphson and scoring iterative methods are presented.
Book Synopsis Exact Maximum Likelihood Estimation of an Arma(1, 1) Model with Incomplete Data by : Chunsheng Ma
Download or read book Exact Maximum Likelihood Estimation of an Arma(1, 1) Model with Incomplete Data written by Chunsheng Ma and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a first-order autoregressive and first-order moving average model with nonconsecutively observed or missing data, the closed form of the exact likelihood function is obtained, and the exact maximum likelihood estimation of parameters is derived in the stationary case.
Book Synopsis Maximum Likelihood Based Techniques in Identifying ARMA Models by : Michel Riad Nehme
Download or read book Maximum Likelihood Based Techniques in Identifying ARMA Models written by Michel Riad Nehme and published by . This book was released on 2005 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this thesis is to investigate some preliminary identification techniq ues in time series Autoregressive Moving Average, ARMA, models. In particular, w e take a look at the sample auto- correlation estimate as the primary identifica tion quantity for specifying a tentative model and propose a maximum likelihood estimator as an alternative estimator to the sample ones. It is shown empiricall y that the likelihood based technique performs more or less the same for large l ength series that follow the autoregressive model of order oe, AR(1). While, fo r short to moderate length AR(1) series, the maximum likelihood shows improved efficiency in comparison to the moment estimate. In chapter one, the popular Box and Jenkins ARMA models are introduced. For this class of models the general behavior and some properties are derived and discus sed for some specific ARMA processes. In chapter two, the identification techniques that are used to select a tentativ e model are presented and some diagnostic checks for the adequacy of the fitted model are listed. In particular, the portmanteau test for the presence of serial correlation is considered and some modifications that exist in the literature a re reviewed. In chapter three, we propose a modification for the Hasza maximum likelihood est imation of the first lag autocorrelation to the lag k- autocorrelation. This met hod requires the Newton Raphson to obtain recursively the estimate and its varia nce a by product of the algorithm. An empirical study is conducted to compare th e proposed estimate to the sample moment one. Finally, in chapter four, further directions for investigation of more efficient identification techniques are examined and left for future work.
Book Synopsis Rank-Based Estimation for Autoregressive Moving Average Time Series Models by : Beth Andrews
Download or read book Rank-Based Estimation for Autoregressive Moving Average Time Series Models written by Beth Andrews and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We establish asymptotic normality and consistency for rank-based estimators of autoregressive-moving average model parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449-1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation.
Book Synopsis Scientific and Technical Aerospace Reports by :
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1987 with total page 1390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Book Synopsis Asymptotic Properties of Some Estimators in Moving Average Models by : Stanford University. Department of Statistics
Download or read book Asymptotic Properties of Some Estimators in Moving Average Models written by Stanford University. Department of Statistics and published by . This book was released on 1975 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author considers estimation procedures for the moving average model of order q. Walker's method uses k sample autocovariances (k> or = q). Assume that k depends on T in such a way that k nears infinity as T nears infinity. The estimates are consistent, asymptotically normal and asymptotically efficient if k = k (T) dominates log T and is dominated by (T sub 1/2). The approach in proving these theorems involves obtaining an explicit form for the components of the inverse of a symmetric matrix with equal elements along its five central diagonals, and zeroes elsewhere. The asymptotic normality follows from a central limit theorem for normalized sums of random variables that are dependent of order k, where k tends to infinity with T. An alternative form of the estimator facilitates the calculations and the analysis of the role of k, without changing the asymptotic properties.
Book Synopsis Maximum Likelihood Estimation of Parameters of an Autoregressive Process with Moving Average Residuals and Other Covariance Matrices with Linear Structure by : STANFORD UNIV CALIF DEPT OF STATISTICS.
Download or read book Maximum Likelihood Estimation of Parameters of an Autoregressive Process with Moving Average Residuals and Other Covariance Matrices with Linear Structure written by STANFORD UNIV CALIF DEPT OF STATISTICS. and published by . This book was released on 1973 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Exact Maximum Likelihood Function of Multivariate Autoregressive Moving Average Models by : Des Francis Nicholls
Download or read book The Exact Maximum Likelihood Function of Multivariate Autoregressive Moving Average Models written by Des Francis Nicholls and published by . This book was released on 1978 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimators for Arma and ARFIMA Models by : Michael A. Hauser
Download or read book Maximum Likelihood Estimators for Arma and ARFIMA Models written by Michael A. Hauser and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Monte Carlo study is undertaken to analyze the small sample properties of two time domain and two frequency domain estimators for low order autoregressive fractionally integrated moving average Gaussian models, ARFIMA (p,d,q). More specifically (1,0,0), (0,0,1), (0,d,0), various (2,0,0), (0,0,2), and (1,d,0), (0,d,1) models. The time domain estimators are the exact maximum likelihood for demeaned data, EML, the associated modified profile likelihood, MPL. The frequency domain estimators are the Whittle estimator with, WLT, and without tapered data, WL. The length of the series investigated is 100. First the pile-up effect of the estimators is documented. Then the estimators are compared with respect to their mean square error, bias, and empirical confidence level. We conclude that the tapered version of the Whittle likelihood is a reliable estimator for ARMA and ARFIMA models. Despite of small losses in performance in case of "well-behaved" models it performs well for more "difficult" models. The modified profile likelihood has certain advantages over the WLT but is computationally more demanding, especially for long series. In relation to the EML, the MPL is essentially equivalent or slightly more favorable for ARMA models. For fractionally integrated models, in particular, both the WLT and the MPL dominate clearly the EML. The WL cannot be recommended in general. There are serious deficiencies for large ranges of parameters, especially for MA, and AR(2) models with roots "close" to +1 or -1. The EML exhibits a potential large negative bias of the fractional integration parameter, which tends to be enlarged if AR or MA parameters are present in the model. Generally, caution is required for ARMA(1,1) models where the polynomials have almost canceling roots, and with respect to inference for models with a MA root close to +1. This is especially the case for the time domain estimators, EML and MPL.
