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Sequential Parameter Estimation In Exponential Autoregressive Processes
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Book Synopsis Sequential Parameter Estimation in Exponential Autoregressive Processes by : M. R. Chernick
Download or read book Sequential Parameter Estimation in Exponential Autoregressive Processes written by M. R. Chernick and published by . This book was released on 1982 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper generalizes the sequential estimation of autoregressive parameters as given in Gaver and Lewis (1980) of pth order processes EAR(p) and GAR(p). The first two moments of the stopping time distribution are computed for the EAR(2) process, a procedure for estimating the paramter lambda is given, and a simulation is given to show how large the stopping time can be for various alpha 1 and alpha 2.
Book Synopsis On Sequential Maximum Likelihood Estimation for Exponential Families of Stochastic Processes by : Michael Sørensen
Download or read book On Sequential Maximum Likelihood Estimation for Exponential Families of Stochastic Processes written by Michael Sørensen and published by . This book was released on 1985 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 1989 with total page 896 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 Sequential Estimation in Exponential-type Processes Under Random Initial Conditions by : R. Magiera
Download or read book Sequential Estimation in Exponential-type Processes Under Random Initial Conditions written by R. Magiera and published by . This book was released on 1988 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sequential Estimation of Parameters in a First Order Autoregressive Model by : Tharuvai Narayanaswami Sriram
Download or read book Sequential Estimation of Parameters in a First Order Autoregressive Model written by Tharuvai Narayanaswami Sriram and published by . This book was released on 1986 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation for a First-Order Bifurcating Autoregressive Process with Exponential Errors by : Jin Zhou
Download or read book Maximum Likelihood Estimation for a First-Order Bifurcating Autoregressive Process with Exponential Errors written by Jin Zhou and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exact and asymptotic distributions of the maximum likelihood estimator of the autoregressive parameter in a first-order bifurcating autoregressive process with exponential innovations are derived. The limit distributions for the stationary, critical and explosive cases are unified via a single pivot using a random normalization. The pivot is shown to be asymptotically exponential for all values of the autoregressive parameter.
Book Synopsis Parameter Estimation for Exponential Signals in Colored Noise Using the Pseudo-autoregressive (PAR) Model by : Tong-Zhang Kou
Download or read book Parameter Estimation for Exponential Signals in Colored Noise Using the Pseudo-autoregressive (PAR) Model written by Tong-Zhang Kou and published by . This book was released on 1995 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Autoregressive Model Inference in Finite Samples by : Hans Einar Wensink
Download or read book Autoregressive Model Inference in Finite Samples written by Hans Einar Wensink and published by . This book was released on 1996 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series by : K. Dzhaparidze
Download or read book Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series written by K. Dzhaparidze and published by Springer Science & Business Media. This book was released on 1986 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: . . ) (under the assumption that the spectral density exists). For this reason, a vast amount of periodical and monographic literature is devoted to the nonparametric statistical problem of estimating the function tJ( T) and especially that of leA) (see, for example, the books [4,21,22,26,56,77,137,139,140,]). However, the empirical value t;; of the spectral density I obtained by applying a certain statistical procedure to the observed values of the variables Xl' . . . , X , usually depends in n a complicated manner on the cyclic frequency). . This fact often presents difficulties in applying the obtained estimate t;; of the function I to the solution of specific problems rela ted to the process X . Theref ore, in practice, the t obtained values of the estimator t;; (or an estimator of the covariance function tJ~( T» are almost always "smoothed," i. e. , are approximated by values of a certain sufficiently simple function 1 = 1
Book Synopsis Parameter Estimation for Noncausal and Heavy Tailed Autoregressive Processes by : Matthew Calder
Download or read book Parameter Estimation for Noncausal and Heavy Tailed Autoregressive Processes written by Matthew Calder and published by . This book was released on 1998 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Estimation in Reliability and Life Span Models by : A Clifford Cohen
Download or read book Parameter Estimation in Reliability and Life Span Models written by A Clifford Cohen and published by CRC Press. This book was released on 2020-07-26 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers an applications-oriented treatment of parameter estimation from both complete and censored samples; contains notations, simplified formats for estimates, graphical techniques, and numerous tables and charts allowing users to calculate estimates and analyze sample data quickly and easily. Anno
Download or read book NBS Special Publication written by and published by . This book was released on 1970 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Theory and Method Abstracts by :
Download or read book Statistical Theory and Method Abstracts written by and published by . This book was released on 2001 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Digital Spectral Analysis by : S. Lawrence Marple, Jr.
Download or read book Digital Spectral Analysis written by S. Lawrence Marple, Jr. and published by Courier Dover Publications. This book was released on 2019-03-20 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed to offer a broad perspective on spectral estimations techniques and their implementation, this text provides theoretical background and review material in linear systems, Fourier transforms, matrix algebra, random processes, and statistics. 1987 edition.
Book Synopsis Maximum Likelihood Estimation of Higher-Order Integer-Valued Autoregressive Processes by : Ruijun Bu
Download or read book Maximum Likelihood Estimation of Higher-Order Integer-Valued Autoregressive Processes written by Ruijun Bu and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 70-722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) specification with binomial thinning and Poisson innovations, we examine both the asymptotic efficiency and finite sample properties of the ML estimator in relation to the widely used conditional least squares (CLS) and YuleWalker (YW) estimators. We conclude that, if the Poisson assumption can be justified, there are substantial gains to be had from using ML especially when the thinning parameters are large.
Download or read book Technical Abstract Bulletin written by and published by . This book was released on 1967 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Inference for Discrete Time Stochastic Processes by : M. B. Rajarshi
Download or read book Statistical Inference for Discrete Time Stochastic Processes written by M. B. Rajarshi and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.
