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Maximum Likelihood Estimation For An Autoregressive Process With Missing Observations
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Book Synopsis Maximum Likelihood Estimation for an Autoregressive Process with Missing Observations by : Suan-Boon Tan
Download or read book Maximum Likelihood Estimation for an Autoregressive Process with Missing Observations written by Suan-Boon Tan and published by . This book was released on 1979 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three methods are proposed for estimation of the parameters of an autogressive process of order p with missing observations. These methods are based on the maximum likelihood approach and use the EM algorithm, the Newton-Raphson method and the method of scoring, which are applied to the likelihood equations. Finally, comparison on those methods is also discussed. (Author).
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 Estimation in Autoregressive Processes with Missing Obs Ervation by : Suan-Boon Tan
Download or read book Maximum Likelihood Estimation in Autoregressive Processes with Missing Obs Ervation written by Suan-Boon Tan and published by . This book was released on 1981 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On the Problem of Missing Measurements in the Estimation of Economic Relationships by : Jan Kmenta
Download or read book On the Problem of Missing Measurements in the Estimation of Economic Relationships written by Jan Kmenta and published by . This book was released on 1977 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Exact Maximum Likelihood Estimation of Observation-driven Econometric Models by : Francis X. Diebold
Download or read book Exact Maximum Likelihood Estimation of Observation-driven Econometric Models written by Francis X. Diebold and published by . This book was released on 1996 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.
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 1986 with total page 1162 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation for Constrained Or Missing Data Models by : Stanford University. Department of Statistics
Download or read book Maximum Likelihood Estimation for Constrained Or Missing Data Models written by Stanford University. Department of Statistics and published by . This book was released on 1993 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation in a Linear Model with Serially Correlated Errors when Observations are Missing by : Tom Wansbeek
Download or read book Maximum Likelihood Estimation in a Linear Model with Serially Correlated Errors when Observations are Missing written by Tom Wansbeek and published by . This book was released on 1980 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Developments in Statistics by : Paruchuri R. Krishnaiah
Download or read book Developments in Statistics written by Paruchuri R. Krishnaiah and published by Academic Press. This book was released on 2014-06-28 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developments in Statistics, Volume 4 reviews developments in the theory and applications of statistics, covering topics such as time series, identifiability and model selection, and missing data. The application of structured exploratory data analysis to human genetics, specifically, the mode of inheritance, is also considered. Comprised of four chapters, this volume begins with an introduction to spectrum parameter estimation in time series analysis, restricting the discussion to the simplest univariate (that is, scalar) real-valued time series X(t). An accurate formulation of the general problem is presented. The accuracy of different consistent estimates obtained for large but fixed values of T (maximum likelihood estimates, Whittle's estimates, and simplified asymptotically efficient estimates) is also compared. The next chapter deals with identifiability and modeling in econometrics, focusing on the theoretical framework relating realization theory, identification, and parametrization. The realization theory is illustrated on various levels of generality by means of examples related to econometrics, along with some advanced applications of system theory. The book also examines inference on parameters of multivariate normal populations when some data are missing before concluding with an evaluation of structured exploratory data as applied to the study of the mode of inheritance. This monograph will be of interest to students and practitioners of statistics.
Book Synopsis AN APPROACH TO ESTIMATION AND SELECTION IN LINEAR MIXED MODELS WITH MISSING DATA by : Yi-Ching Lee
Download or read book AN APPROACH TO ESTIMATION AND SELECTION IN LINEAR MIXED MODELS WITH MISSING DATA written by Yi-Ching Lee and published by . This book was released on 2019 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the case of analyzing multilevel, correlated, or longitudinal data, linear mixed models are often incorporated. Such models can be thought of an extension of linear models in the sense that the additional random components are introduced to capture the dependency in observations. In practice, missing data occur in many disciplines, especially in the area of longitudinal studies where observations are taken repeatedly over time on samples in an experiment. Our primary goal in the dissertation is to propose an approach to estimation and model selection in linear mixed models when missing data present. The dissertation pays particular attention to the multivariate normal models. With such models, we propose an approach that incorporates the missingness in an indicator matrix and develop likelihood-based estimators under two specific covariance structures: compound symmetric and first-order autoregressive (AR(1)). Distinguishing from the existing maximum likelihood estimation (MLE) that relies on Newton-Raphson (NR), Expectation-Maximization (EM), or Fisher algorithms for obtaining the final estimates, we implement matrix theories to circumvent the difficulties in the estimation process imposed by the inversion and the determinant of the variance-covariance matrix. Numerous simulations are conducted in evaluations of the proposed approach. For instance, in the study of the comparison between the proposed method and MLE, the former yields better estimates in the variance component with the compound symmetric covariance and presents remarkable improvements in estimating both the variance and the autocorrelation components in AR(1). In the study of investigating the model selection performance using the proposal estimation approach with the Schwarz Information Criterion (SIC) serving as the selection criterion, the simulation results demonstrate that the proposed approach to estimation performs effectively with a moderate amount of missing proportion regardless of the missing behaviors, missing completely at random (MCAR) or missing not at random (MNAR). Two real data applications are provided for revealing the performance of the proposed approach in practice. In evidence of the developed method, the conducted simulations, and the applications, we provide the concluding remarks and the future research directions as the closing of the dissertation.
Book Synopsis Time Series Analysis of Irregularly Observed Data by : E. Parzen
Download or read book Time Series Analysis of Irregularly Observed Data written by E. Parzen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the support of the Office of Naval Research Program on Statistics and Probability (Dr. Edward J. Wegman, Director), The Department of Statistics at Texas A&M University hosted a Symposium on Time Series Analysis of Irregularly Observed Data during the period February 10-13, 1983. The symposium aimed to provide a review of the state of the art, define outstanding problems for research by theoreticians, transmit to practitioners recently developed algorithms, and stimulate interaction between statisticians and researchers in subject matter fields. Attendance was limited to actively involved researchers. This volume contains refereed versions of the papers presented at the Symposium. We would like to express our appreciation to the many colleagues and staff members whose cheerful help made the Symposium a successful happening which was enjoyed socially and intellectually by all participants. I would like to especially thank Dr. Donald W. Marquardt whose interest led me to undertake to organize this Symposium. This volume is dedicated to the world wide community of researchers who develop and apply methods of statistical analysis of time series. r:;) \J Picture Caption Participants in Symposium on Time Series Analysis of Irregularly Observed Data at Texas A&M University, College Station, Texas, February 10-13, 1983 First Row: Henry L. Gray, D. W. Marquardt, P. M. Robinson, Emanuel Parzen, Julia Abrahams, E. Masry, H. L. Weinert, R. H. Shumway.
Book Synopsis Missing Data in Multilevel Vector Autoregressive Model by : Linying Ji
Download or read book Missing Data in Multilevel Vector Autoregressive Model written by Linying Ji and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data are inevitable in longitudinal studies. Some missingness occurs by design, but most others are unplanned and may have profound consequences on inferential results. Numerous previous studies have evaluated different missing data handling techniques in cross-sectional and longitudinal panel data. However the impact of different kinds of missingness and strategies to handle missingness in multivariate, multilevel time-series data is less studied. It has been shown that ignoring the clustered structure in handling missing data when performing multiple imputation (MI) for cross-sectional data will lead to different variance and covariance properties of the imputed data, and thus the model estimation results are very likely to be biased (Grund, Lüdtke, & Robitzsch, 2018; van Buuren, 2018). In this dissertation project, I conducted a series of simulation studies to evaluate the performances of different multilevel MI methods in the context of Bayesian multilevel vector autoregressive models (MVARs) under different sample size, intraclass correlation, and missing data conditions. The missing data handling methods considered included a Bayesian equivalent of full-information maximum likelihood approach (BFIML), single-level MI, multilevel MI with joint modeling approach or full conditional specification approach, and a hybrid approach that integrated multilevel MI and BFIML method. Simulation results suggested that in fitting MVAR model under the parameter settings considered, researchers can effectively treat both MAR and, to a certain extent, MNAR missingness, with appropriate multilevel MI approach alone, or in combination with BFIML method. Specifically, for random-intercept only MVAR models with MAR missing data, the multilevel MI approaches performed as well as BFIML. For MNAR missingness, the multilevel MI and BFIML hybrid approach performed better. I also identified groups of parameters (i.e., random-intercept parameters) in the MVAR model that were more prone to biased estimates with MNAR missingness using the approaches considered. Finally, I discussed possible ways of resolving these limitations.
Book Synopsis An MCEM Algorithm for the State-space Model with Missing Observations by : Damaris Santana
Download or read book An MCEM Algorithm for the State-space Model with Missing Observations written by Damaris Santana and published by . This book was released on 2001 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Introduction to Time Series and Forecasting by : Peter J. Brockwell
Download or read book Introduction to Time Series and Forecasting written by Peter J. Brockwell and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.
Book Synopsis A Stochastic EM Estimator in the Presence of Missing Data-theory and Applications by : Eddie H. S. Ip
Download or read book A Stochastic EM Estimator in the Presence of Missing Data-theory and Applications written by Eddie H. S. Ip and published by . This book was released on 1994 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models by : Craig K. Enders
Download or read book The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models written by Craig K. Enders and published by . This book was released on 1999 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt:
