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The Classification of Data under Autoregressive Circulant Covariance

-covariance matrix is the autoregressive circulant (ARe structure. In this book, classification rules are derived using the assumption of such a;

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Random Circulant Matrices

Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the;

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Random Circulant Matrices

Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the;

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Time Series Econometrics

volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic;

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High-Dimensional Covariance Matrix Estimation

estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime;

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High-dimensional Data Analysis

Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide;

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High-Dimensional Covariance Estimation

of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and;

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Covariance Analysis for Seismic Signal Processing

This volume is intended to provide the geophysical signal analyst with sufficient material to understand the usefulness of data covariance;

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Eddy Covariance

installation and system dimensioning to data collection, correction and analysis. With a state-of-the-art perspective, the authors examine the;

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Analysis of Messy Data, Volume III

Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the;

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Random Matrices and Non-Commutative Probability

the Wigner, elliptic, sample covariance, cross-covariance, Toeplitz, Circulant and Hankel are discussed. Convergence of the;

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Spatial Statistics and Spatio-Temporal Data

functions in the spatial, spatio-temporal, multivariate spatial, and point pattern settings. * Provides illustrations of all methods based on data;

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Non-Gaussian Autoregressive-Type Time Series

This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates;

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An Introduction to Iterative Toeplitz Solvers

of efficient circulant preconditioners. Applications of iterative Toeplitz solvers to practical problems are addressed, enabling readers to use the;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov;

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Time Series Algorithms Recipes

-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical;

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Statistical Foundations of Data Science

Provides theoretical insights and justification of the statistical procedures for the analysis of high-dimensional data Presents a general;

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Experimental Design & Data Analysis Biol

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data;

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Analysis of Variance and Covariance

Analysis of variance (ANOVA) is a core technique for analysing data in the Life Sciences. This reference book bridges the gap between;

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Analysis Of Variance & Covariance

Analysis of variance (ANOVA) is a core technique for analysing data in the Life Sciences. This reference book bridges the gap between;

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Improved Classification Rates for Localized Algorithms under Margin Conditions

Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets;

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Advances in Classification and Data Analysis

This volume contains a selection of papers presented at the biannual meeting of the Classification and Data Analysis Group of Societa;

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Predictive Data Mining Models

classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining;

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Data Classification

, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification;

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Data Classification

, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification;

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AdaBoost Extensions for Cost-Sensitive Classification

This book provides a review and a research of a special type of classification technique which is known as cost-sensitive data mining. Cost;

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SAS for Linear Models

System. This guide shows how to apply the appropriate procedure to data analysis problems and understand PROC GLM output. Other helpful;

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