Download or read book Simple and Efficient Estimation of Parameters of Non Gaussian Autoregressive Processes written by Steven M. Kay and published by . This book was released on 1986 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new technique for the estimation of autoregressive filter parameters of a non-Gaussian autoregressive process is proposed. The probability density function of the driving noise is assumed to be known. The new technique is a two-stage procedure motivated by maximum likelihood estimation. It is computationally much simpler than the maximum likelihood estimator and does not suffer from convergence problems. Computer simulations indicate that unlike the least squares or linear prediction estimators, the proposed estimator is nearly efficient, even for moderately sized data records. By a slight modification the proposed estimator can also be used in the case when the parameters of the driving noise probability density function are not known. Keywords: Parameter estimation; Autoregressive processes; Non Gaussian processes; Maximum likelihood estimator; Weighted least squares; Efficiency robustness.
Download or read book Efficient Estimation of Parameters for Non Gaussian Autoregressive Processes written by Debasis Sengupta and published by . This book was released on 1986 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of estimating the parameters of a non-Gaussian autoregressive process is addressed. Departure of the driving noise from Gaussianity is shown to have the potential of improving the accuracy of the estimation of the parameters. While the standard linear prediction techniques are computationally efficient, they show a substantial loss of efficiency when applied to non-Gaussian processes. A maximum likelihood estimator is proposed for more precise estimation of the parameters of these processes coupled with a realistic non-Gaussian model for the driving noise. The performance is compared to that of the linear prediction estimator and as expected the maximum likelihood estimator displays a marked improvement.
Download or read book Estimation of Parameters of Non Gaussian Non Zero Mean Autoregressive Processes with Application to Optimal Detection in Colored Noise written by Debasis Sengupta and published by . This book was released on 1988 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem addressed in this paper is that of estimating signal and noise parameters from a mixture of Non-Gaussian autoregressive (AR) noise with partially known deterministic signal. Two models are considered in order to examine different kinds of additive mixing. The Cramer-Rao bounds to the joint estimation of the signal amplitude and the noise parameters are presented. A computationally efficient estimator, which was previously proposed for estimation in the absence of signal, is extended for the two models under consideration. The proposed method essentially consists of two stages of least squares (LS) estimation which is motivated by the maximum likelihood estimation (MLE). The technique is then applied to the problem of detecting a signal known except for amplitude in colored non-Gaussian noise. Two slightly different mixing models are used and a generalized likelihood ratio test (GLRT), coupled with the proposed estimation scheme, is used to solve the problems. The results of computer simulations are presented as an evidence of the validity of the theoretical predictions of performance. (KR).
Download or read book Efficient Estimation of Autoregression Parameters and Innovation Distributions for Semiparametric Integer Valued Ar P Models written by Feike C. Drost and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters: a vector of autoregression coefficients and a probability distribution on the nonnegative integers, called an immigration or innovation distribution. Traditionally, parametric models are considered where the innovation distribution is assumed to belong to a parametric family. This paper instead considers a more realistic semiparametric INAR(p) model where there are essentially no restrictions on the innovation distribution. We provide an (semiparametrically) efficient estimator of both the autoregression parameters and the innovation distribution.
Download or read book Efficient Estimation of the Semiparametric Spatial Autoregressive Model written by and published by . This book was released on 2008 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of parameters of interest. These estimates are as efficient as ones based on a correct form, in particular they are more efficient than pseudo-Gaussian maximum likelihood estimates at non-Gaussian distributions. Two different adaptive estimates are considered. One entails a stringent condition on the spatial weight matrix, and is suitable only when observations have substantially many quot;neighboursquot;. The other adaptive estimate relaxes this requirement, at the expense of alternative conditions and possible computational expense. A Monte Carlo study of finite sample performance is included.
Download or read book Technical Reports Awareness Circular TRAC written by and published by . This book was released on 1987-02 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book ICASSP 87 written by and published by . This book was released on 1987 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Estimating Parameters in Autoregressive Models in Non Normal Situations written by Moti L. Tiku and published by . This book was released on 2018 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: The estimation of coefficients in a simple regression model with autocorrelated errors is considered. The underlying distribution is assumed to be symmetric, one of Student's t family for illustration. Closed form estimators are obtained and shown to be remarkably efficient and robust. Skew distributions will be considered in a future paper.
Download or read book Biometrika written by D. M. Titterington and published by . This book was released on 2001 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: The year 2001 marks the centenary of Biometrika, one of the world's leading academic journals in statistical theory and methodology. In celebration of this, the book brings together two sets of papers from the journal. The first comprises seven specially commissioned articles (authors: D.R. Cox, A.C. Davison, Anthony C. Atkinson and R.A. Bailey, David Oakes, Peter Hall, T.M.F. Smith, and Howell Tong). These articles review the history of the journal and the most important contributions made by appearing in the journal in a number of important areas of statitisical activity, including general theory and methodology, surveys and time sets. In the process the papers describe the general development of statistical science during the twentieth century. The second group of ten papers are a selection of particularly seminal articles form the journal's first hundred years. The book opens with an introduction by the editors Professor D.M. Titterington and Sir David Cox.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1991 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Estimation of Autoregression Parameters and Innovation Distributions for Semiparametric Integer valued AR p Models written by Feike Cornelis Drost and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Parameter Estimation of Nearly Non stationary Autoregressive Processes written by Michiel J.L. de Hoon and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Government Reports Announcements Index written by and published by . This book was released on 1987 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Universally Efficient Estimation of the First order Autoregressive Parameter and Universal Data Compression written by Neri Merhav and published by . This book was released on 1988 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Estimation of Systems of Autoregressive moving Average Processes written by Charles R. Nelson and published by . This book was released on 1973 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Download or read book Estimation and Detection for Non Gaussian Processes Using Autoregressive and Other Models written by Debasis Sengupta and published by . This book was released on 1986 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:
