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Tensor Methods in Statistics

. Additional topics include sample cumulants, Edgeworth series, saddlepoint approximation, likelihood functions, and ancillary statistics. More than;

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Series Approximation Methods in Statistics

This revised book presents theoretical results relevant to Edgeworth and saddlepoint expansions to d;...

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Approximation Theorems of Mathematical Statistics

This paperback reprint of one of the best in the field covers a broad range of limit theorems useful;...

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Advanced Calculus with Applications in Statistics

features detailed coverage of optimization techniques and their applications in statistics while introducing the reader to approximation theory;

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Foundations of Applied Mathematics, Volume 2

Fourier series, wavelets, and polynomial approximation; and the theory and practice of optimization, including dynamic optimization. When used;

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Approximation Theory and Algorithms for Data Analysis

This textbook offers an accessible introduction to the theory and numerics of approximation methods, combining classical topics;

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Extreme Value Methods with Applications to Finance

to remove outliers-in other words, to correct reality if it does not fit the model. Recognizing that any model is only an approximation;

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Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the;

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Journal of Approximation Theory and Applied Mathematics - 2013 Vol. 1 and Vol. 2

Journal of Approximation Theory and Applied Mathematics (ISSN 2196-1581) is a journal which started in 2013. Themes of our journal are;

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Bayesian Time Series Models

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they;

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Statistical Inference

in general, with discussions of new research using approximation and computationally-intensive methods. From the graduate level, to those working;

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Kernel-based Approximation Methods Using Matlab

functions, this book presents modern theoretical results on kernel-based approximation methods and demonstrates their implementation in a variety;

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Asymptotic Statistics

and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its;

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Essentials of Statistics

may be taken on the basis of approximation, know as statistics. The correction of the values of current asserts is made on the basis of the;

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Approximation

Proceedings of the Conference on [title], held in Santa Barbara, California, May 1993. The main topics are: approximation of functions by;

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Numerical Methods in Approximation Theory

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A Course on Statistics for Finance

author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance. The;

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A Course on Statistics for Finance

author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance;

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An Introduction to the Approximation of Functions

significant of these methods, with particular emphasis on approximation by polynomials.Because approximation theory is an area of mathematics;

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Experimental Design and the Analysis of Variance

and regression, but not in much detail. This book, as part of our Series in Research Methods and Statistics, provides you with the;

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Advances In The Homotopy Analysis Method

simple way to guarantee the convergence of solution series. Such uniqueness differentiates the HAM from all other analytic approximation methods;

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Statistics

statistics in six parts: - Introduction - Compilation and presentation - Frequency distributions - Correlation - Time series and index;

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Meshfree Approximation Methods With Matlab (With Cd-rom)

Meshfree approximation methods are a relatively new area of research, and there are only a few books covering it at present. Whereas other;

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Reproducing Kernel Hilbert Spaces in Probability and Statistics

theorems, learning theory and some applications at the fringe between Statistics and Approximation Theory. It is geared to graduate students;

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Financial Econometrics, Mathematics and Statistics

four parts, the text begins with topics related to regression and financial econometrics. Subsequent sections describe time-series analyses;

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Robust Statistics

of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range;

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Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including;

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