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:
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 Masters Theses in the Pure and Applied Sciences written by Wade H. Shafer and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Masters Theses in the Pure and Applied Sciences was first conceived, published, and disseminated by the Center for Information and Numerical Data Analysis and Synthesis (CINDAS) * at Purdue University in 1957, starting its coverage of theses with the academic year 1955. Beginning with Volume 13, the printing and dissemination phases of the activity were transferred to University Microfilms/Xerox of Ann Arbor, Michigan, with the thougtit that such an arrangement would be more beneficial to the academic and general scientific and technical community. After five years of this joint undertaking we had concluded that it was in the interest of all con cerned if the printing and distribution of the volumes were handled by an interna tional publishing house to assure improved service and broader dissemination. Hence, starting with Volume 18, Masters Theses in the Pure and Applied Sciences has been disseminated on a worldwide basis by Plenum Publishing Cor poration of New York, and in the same year the coverage was broadened to include Canadian universities. All back issues can also be ordered from Plenum. We have reported in Volume 31 (thesis year 1986) a total of 11 ,480 theses titles trom 24 Canadian and 182 United States universities. We are sure that this broader base tor these titles reported will greatly enhance the value ot this important annual reterence work. While Volume 31 reports theses submitted in 1986, on occasion, certain univer sities do re port theses submitted in previousyears but not reported at the time.
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 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 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 Estimation and Prediction for Non Gaussian Autoregressive Processes written by Pradipta Sarkar and published by . This book was released on 1997 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: For normal errors the spline method is only moderately inferior to the least squares method. In the Monte Carlo study confidence intervals for predictions were constructed using normal distribution theory and using the distribution estimated by the spline method. For chi-square and mixture of normals error distributions, coverages of the intervals based on the spline method are superior to the coverages of the intervals based on the normal distribution. For normal errors the performance of the spline intervals is very close to that of intervals based on the normal distribution.
Download or read book Bayesian Outlier Detection in Non Gaussian Autoregressive Time Series written by Maria Eduarda Silva and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.
Download or read book Topics in Non Gaussian Signal Processing written by Edward J. Wegman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-Gaussian Signal Processing is a child of a technological push. It is evident that we are moving from an era of simple signal processing with relatively primitive electronic cir cuits to one in which digital processing systems, in a combined hardware-software configura. tion, are quite capable of implementing advanced mathematical and statistical procedures. Moreover, as these processing techniques become more sophisticated and powerful, the sharper resolution of the resulting system brings into question the classic distributional assumptions of Gaussianity for both noise and signal processes. This in turn opens the door to a fundamental reexamination of structure and inference methods for non-Gaussian sto chastic processes together with the application of such processes as models in the context of filtering, estimation, detection and signal extraction. Based on the premise that such a fun damental reexamination was timely, in 1981 the Office of Naval Research initiated a research effort in Non-Gaussian Signal Processing under the Selected Research Opportunities Program.
Download or read book Singular Non gaussian Measures in Detection and Estimation Theory written by Percy A. Pierre and published by . This book was released on 1968 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: If a mathematical model of a signal detection problem is such that there exists a detector which achieves zero error, the model is called singular. Such models are usually not acceptable. In this paper, we present various sufficient conditions for singular detection and estimation. For the case of a known signal, second moment conditions are given which imply singularity of detection in the most general kind of noise. For the case of random signals, no such general result exists. (Author).
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Download or read book On estimating non causal non minimum phase arma models of non gaussian processes written by Georgios B. Giannakis and published by . This book was released on 1988 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On the Three Step Non Gaussian Quasi Maximum Likelihood Estimation of Heavy Tailed Double Autoregressive Models written by Dong Li and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note considers a three-step non-Gaussian quasi-maximum likelihood estimation (TS-NGQMLE) of the double autoregressive model with its asymptotics, which improves efficiency of the GQMLE and circumvents inconsistency of the NGQMLE when the innovation is heavy-tailed. Under mild conditions, the estimator not only can achieve consistency and asymptotic normality regardless of density misspecification of the innovation, but also outperforms the existing estimators, such as the GQMLE and the (weighted) least absolute deviation estimator, when the innovation is indeed heavy-tailed.
Download or read book Learning Kernel Classifiers written by Ralf Herbrich and published by MIT Press. This book was released on 2022-11-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Download or read book Studies in Neural Data Science written by Antonio Canale and published by Springer. This book was released on 2018-12-28 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on June 25–27, 2017 in Siena (Italy), the research groups focused on recent multimodality imaging datasets measuring brain function and structure, and proposed a wide variety of methods for network analysis, spatial inference, graphical modeling, multiple testing, dynamic inference, data fusion, tensor factorization, object-oriented analysis and others. The results of their studies are gathered here, along with a final contribution by Michele Guindani and Marina Vannucci that opens new research directions in this field. The book offers a valuable resource for all researchers in Data Science and Neuroscience who are interested in the promising intersections of these two fundamental disciplines.
Download or read book Application of Optimization in Production Logistics Inventory Supply Chain Management and Block Chain written by Biswajit Sarkar and published by MDPI. This book was released on 2020-04-23 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment.
