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Introduction to Time Series Modeling with Applications in R

sequential Monte Carlo filters. -MAA Reviews Introduction to Time Series Modeling with Applications in R, Second Edition covers numerous;

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Multivariate Time Series Analysis

. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and;

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An Introduction to Discrete-Valued Time Series

A much-needed introduction to the field of discrete-valued time series, with a focus on count-data time series Time series analysis is an;

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Time Series

Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time;

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Using R for Item Response Theory Model Applications

, practitioners, and applied researchers who want to learn how to properly use R IRT packages to perform IRT model calibrations with their own data. This;

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Using R for Item Response Theory Model Applications

, practitioners, and applied researchers who want to learn how to properly use R IRT packages to perform IRT model calibrations with their own data. This;

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Introduction To Time Series Analysis & F

featuring PowerPoint(R) slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second;

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Wavelet Neural Networks

chaotic time series prediction An extensive introduction to neural networks that begins with regression models and builds to more complex;

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Introduction to Time Series Modeling

. Illustrating how to build models for time series using basic methods, Introduction to Time Series Modeling covers numerous time series models and the;

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Applications of Soft Computing in Time Series Forecasting

modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and;

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Applications of Soft Computing in Time Series Forecasting

modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and;

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Introductory Time Series with R

This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model;

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A Primer on Fourier Analysis for the Geosciences

Fourier series and building to the fast Fourier transform (FFT) and related periodogram techniques. The theory is illustrated with numerous worked;

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A Primer on Fourier Analysis for the Geosciences

Fourier series and building to the fast Fourier transform (FFT) and related periodogram techniques. The theory is illustrated with numerous worked;

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Time Series Analysis

Time Series Analysis, 2nd Edition is a thorough introduction to both time-domain and frequency-domain analyses of univariate and;

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Advanced Environmental Monitoring with Remote Sensing Time Series Data and R

with Remote Sensing Time Series Data and R describes and provides details on recent advances concerning publicly available remotely sensed time;

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Advanced Environmental Monitoring with Remote Sensing Time Series Data and R

processing through open source GIS and statistical analysis software Advanced Environmental Monitoring with Remote Sensing Time Series Data and R;

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Solutions Manual Accompany Models Life

A solutions manual to accompany An Introduction to Discrete Mathematical Modeling with Microsoft(R) Office Excel (R) With a focus on;

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Nonlinear Econometric Modeling in Time Series Analysis

Nonlinear Econometric Modeling in Time Series presents the more recent literature on nonlinear time series. Specific topics covered with;

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Modern Industrial Statistics - With Applications in R, MINITAB and JMP, 3e

Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, and downloadable;

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Nonlinear Time Series

Designed for researchers and students, Nonlinear Times Series: Theory, Methods and Applications with R Examples familiarizes readers with;

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Models for Life

based on real and current data, Models for Life: An Introduction to Discrete Mathematical Modeling with Microsoft(R) Office Excel(R) guides;

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An Introduction to Bootstrap Methods with Applications to R

of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented;

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Statistical Modeling Using Local Gaussian Approximation

discriminant. Reviews local dependence modeling with applications to time series and finance markets Introduces new techniques;

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Statistical Modeling by Wavelets

A comprehensive, step-by-step introduction to wavelets in statistics. What are wavelets? What makes them increasingly indispensable;

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Computational Actuarial Science with R

A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the;

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Computational Actuarial Science with R

A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the;

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