Estimation of the Parameters of Mixtures Via Distance Between Densities Or Characteristic Functions PDF Download

Author: J. L. Bryant
Publisher:
ISBN:
Category :
Languages : en
Pages : 98

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Book Description
The integrated weighted distance between the sample characteristic function and the assumed characteristic function, or equivalently, the integrated distance between the smoothed assumed density and its kernel-estimate, is shown to be affective procedure for estimation of mixing proportions and for estimating all parameters of a modified compound Poisson distribution. These procedures are compared against their competitors in terms of efficiency, mean square error, and computational time. The characteristic function-based procedures are generally superior in terms of computation time for each of two types of procedures. The procedure introduced for the modified compound distribution is widely applicable since it is basically nonlinear modified x squared minimum. THe role of the sampling interval in estimating the parameters of the modified compound distribution is discussed and recommendations are made. Information matrices associated with this distribution are given for a spectrum of parameter values.

Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 892

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Author: Stan Z. Li
Publisher: Springer Science & Business Media
ISBN: 0387730028
Category : Computers
Languages : en
Pages : 1466

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With an A–Z format, this encyclopedia provides easy access to relevant information on all aspects of biometrics. It features approximately 250 overview entries and 800 definitional entries. Each entry includes a definition, key words, list of synonyms, list of related entries, illustration(s), applications, and a bibliography. Most entries include useful literature references providing the reader with a portal to more detailed information.

Author: Nathaniel A. Litton
Publisher:
ISBN:
Category :
Languages : en
Pages :

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This dissertation describes a minimum distance method for density estimation when the variable of interest is not directly observed. It is assumed that the underlying target density can be well approximated by a mixture of normals. The method compares a density estimate of observable data with a density of the observable data induced from assuming the target density can be written as a mixture of normals. The goal is to choose the parameters in the normal mixture that minimize the distance between the density estimate of the observable data and the induced density from the model. The method is applied to the deconvolution problem to estimate the density of Xi when the variable Yi=Xi+Zi, i=1 ..., n, is observed, and the density of Zi is known. Additionally, it is applied to a location random effects model to estimate the density of Zij when the observable quantities are p data sets of size n given by Zij=[alpha]i+[gamma]Zij, i=1 ..., p, j=1 ..., n, where the densities of [alpha]i and Zij are both unknown. The performance of the minimum distance approach in the measurement error model is compared with the deconvoluting kernel density estimator of Stefanski and Carroll (1990). In the location random effects model, the minimum distance estimator is compared with the explicit characteristic function inversion method from Hall and Yao (2003). In both models, the methods are compared using simulated and real data sets. In the simulations, performance is evaluated using an integrated squared error criterion. Results indicate that the minimum distance methodology is comparable to the deconvoluting kernel density estimator and outperforms the explicit characteristic function inversion method.

Author:
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 624

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Author: Dietrich Paulus
Publisher: Springer Science & Business Media
ISBN: 9783528355586
Category : Technology & Engineering
Languages : en
Pages : 390

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This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. For this 4th edition, new features of the C++ language were integrated and their relevance for image and speech processing is discussed.

Author: Sylvia Fruhwirth-Schnatter
Publisher: CRC Press
ISBN: 0429508867
Category : Computers
Languages : en
Pages : 388

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Book Description
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

Author: Peter Krauthausen
Publisher: KIT Scientific Publishing
ISBN: 3866449526
Category : Computers
Languages : en
Pages : 240

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Book Description
This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference.

Author: Christos Volos
Publisher: MDPI
ISBN: 3038978981
Category : Technology & Engineering
Languages : en
Pages : 290

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Book Description
In recent years, entropy has been used as a measure of the degree of chaos in dynamical systems. Thus, it is important to study entropy in nonlinear systems. Moreover, there has been increasing interest in the last few years regarding the novel classification of nonlinear dynamical systems including two kinds of attractors: self-excited attractors and hidden attractors. The localization of self-excited attractors by applying a standard computational procedure is straightforward. In systems with hidden attractors, however, a specific computational procedure must be developed, since equilibrium points do not help in the localization of hidden attractors. Some examples of this kind of system are chaotic dynamical systems with no equilibrium points; with only stable equilibria, curves of equilibria, and surfaces of equilibria; and with non-hyperbolic equilibria. There is evidence that hidden attractors play a vital role in various fields ranging from phase-locked loops, oscillators, describing convective fluid motion, drilling systems, information theory, cryptography, and multilevel DC/DC converters. This Special Issue is a collection of the latest scientific trends on the advanced topics of dynamics, entropy, fractional order calculus, and applications in complex systems with self-excited attractors and hidden attractors.

Author: A.V. Gundlapalli
Publisher: IOS Press
ISBN: 1614998302
Category : Medical
Languages : en
Pages : 1440

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Book Description
Medical informatics is a field which continues to evolve with developments and improvements in foundational methods, applications, and technology, constantly offering opportunities for supporting the customization of healthcare to individual patients. This book presents the proceedings of the 16th World Congress of Medical and Health Informatics (MedInfo2017), held in Hangzhou, China, in August 2017, which also marked the 50th anniversary of the International Medical Informatics Association (IMIA). The central theme of MedInfo2017 was "Precision Healthcare through Informatics", and the scientific program was divided into five tracks: connected and digital health; human data science; human, organizational, and social aspects; knowledge management and quality; and safety and patient outcomes. The 249 accepted papers and 168 posters included here span the breadth and depth of sub-disciplines in biomedical and health informatics, such as clinical informatics; nursing informatics; consumer health informatics; public health informatics; human factors in healthcare; bioinformatics; translational informatics; quality and safety; research at the intersection of biomedical and health informatics; and precision medicine. The book will be of interest to all those who wish to keep pace with advances in the science, education, and practice of biomedical and health informatics worldwide.