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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Time Series

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling;

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Asymptotics, Nonparametrics, and Time Series

Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian;

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Time Series

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and;

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Asymptotics, Nonparametrics, and Time Series

"Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian;

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Multivariate Time Series Analysis and Applications

William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series;

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A Course in Time Series Analysis

series models for univariate and multivariate time series data. It brings together material previously available only in the professional;

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Bayesian Models for Categorical Data

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology;

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Bayesian Econometric Methods

Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and;

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Bayesian Econometric Methods

Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and;

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Missing Data Methods

, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian;

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Missing Data Methods

, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian;

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Growth Curves

This work describes several statistical techniques for studying repeated measures data, presenting growth curve methods applicable to;

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Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing

introduction to the problem using the cocktail-party analogy, Part I provides the statistical background needed for the Bayesian source separation;

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Bayesian Process Monitoring, Control and Optimization

. Subsequent chapters present Bayesian approaches that can be used for time series data analysis and process control. The contributors include material;

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Multivariate Time Series Analysis

econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the;

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Time Series Analysis

useful reference for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational;

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Contemporary Psychometrics

between and within independent samples, dynamic factor models based on ARMA time series models, and multi-level factor analysis models for;

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Contemporary Psychometrics

between and within independent samples, dynamic factor models based on ARMA time series models, and multi-level factor analysis models for;

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Contributions to Static and Time-Varying Copula-Based Modeling of Multivariate Association

Putting a particular emphasis on nonparametric methods that rely on modern empirical process techniques, the author contributes to the;

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Bayesian Analysis of Time Series

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use;

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Bayesian Analysis of Time Series

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use;

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Elements of Nonlinear Time Series Analysis and Forecasting

series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more;

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Time Series Analysis

-life time series data sets. The book also introduces univariate and multivariate time series models and methods which are useful for analyzing;

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Statistical Decision Theory and Bayesian Analysis

as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes;

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