all items in this store are to be sent to your email within 24 hours after cleared payment. PDF eBooks are sent to you as email attachments. as for mp3 audiobook, a download link from ONEDRIVE will be sent to your email for you to download.
Please Read Before Your Purchase!!!
1. This item is an E-Book in PDF format.
2. Shipping & Delivery: Send to you by E-mail within 24 Hours after cleared payment. Immediately Arrival!!!
3. Shipping ( by email) + Handling Fee = US$0.00
4. Time-Limited Offer, Order Fast.
Hidden Markov Models for Time Series: An Introduction Using R
(Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
by Walter Zucchini, Iain L. MacDonald
Published:April 28, 2009 by Chapman and Hall/CRC
Presents an accessible overview of HMMs for analyzing time series data
Covers continuous-valued, circular, and multivariate time series data
Explores a variety of applications in animal behavior, finance, epidemiology, climatology, and sociology
Shows how to apply the methods using R
Includes numerous theoretical and programming exercises at the end of most chapters
Provides all of the data sets analyzed in the text online
Reveals How HMMs Can Be Used as General-Purpose Time Series Models
Implements all methods in R
Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting.
Illustrates the methodology in action
After presenting the simple Poisson HMM, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference. Through examples and applications, the authors describe how to extend and generalize the basic model so it can be applied in a rich variety of situations. They also provide R code for some of the examples, enabling the use of the codes in similar applications.
Effectively interpret data using HMMs
This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It provides a broad understanding of the models and their uses.
Table of Contents
MODEL STRUCTURE, PROPERTIES, AND METHODS
Mixture Distributions and Markov Chains
Independent mixture models
Hidden Markov Models: Definition and Properties
A simple hidden Markov model
Estimation by Direct Maximization of the Likelihood
Scaling the likelihood computation
Maximization subject to constraints
Standard errors and confidence intervals
Example: parametric bootstrap
Estimation by the EM Algorithm
Forward and backward probabilities
The EM algorithm
Examples of EM applied to Poisson HMMs
Forecasting, Decoding, and State Prediction
Model Selection and Checking
Model selection by AIC and BIC
Model checking with pseudo-residuals
Bayesian Inference for Poisson HMMs
Applying the Gibbs sampler to Poisson HMMs
Bayesian estimation of the number of states
Extensions of the Basic Hidden Markov Model
HMMs with general univariate state-dependent distribution
HMMs based on a second-order Markov chain
HMMs for multivariate series
Series which depend on covariates
Models with additional dependencies
Model checking by pseudo-residuals
Eruptions of the Old Faithful Geyser
Binary time series of short and long eruptions
Normal HMMs for durations and waiting times
Bivariate model for durations and waiting times
Drosophila Speed and Change of Direction
Von Mises distributions
Von Mises HMMs for the two subjects
Circular autocorrelation functions
Wind Direction at Koeberg
Wind direction as classified into 16 categories
Wind direction as a circular variable
Models for Financial Series
Thinly traded shares
Multivariate HMM for returns on four shares
Stochastic volatility models
Births at Edendale Hospital
Models for the proportion Caesarean
Models for the total number of deliveries
Cape Town Homicides and Suicides
Firearm homicides as a proportion of all homicides, suicides, and legal intervention homicides
The number of firearm homicides
Firearm homicide and suicide proportions
Proportion in each of the five categories
Animal-Behavior Model with Feedback
Parameter estimation by maximum likelihood
Inferring the underlying state
Models for a heterogeneous group of subjects
Other modifications or extensions
Application to caterpillar feeding behavior
Appendix A: Examples of R code
Stationary Poisson HMM, numerical maximization
More on Poisson HMMs, including EM
Bivariate normal state-dependent distributions
Categorical HMM, constrained optimization
Appendix B: Some Proofs
Factorization needed for forward probabilities
Two results for backward probabilities
Conditional independence of Xt1 and XTt+1
Exercises appear at the end of most chapters.
The book would be a good text for a seminar or a course on HMM or for self-learning the topic. ¡ Those who have the background necessary to use the R code and to replicate the results throughout the book will find plenty of material in this book to extend what they learn to their own data. The book is written very pedagogically ¡ all the data sets, errata sheet, R code, among other things, can be accessed at the web site.
¡ªJournal of Statistical Software, Vol. 43, October 2011
¡ this book has a very nice mix of probability, statistics, and data analysis. It is suitable for a course in stochastic modeling using hidden Markov models, but also serves well as an introduction for nonspecialists.
¡ªBiometrics, 67, September 2011
¡ this is an excellent book, which should be of great interest to applied statisticians looking for a clear introduction to HMMs and advice on the practical implementation of these models. It is also an ideal teaching resource.
¡ªAustralian & New Zealand Journal of Statistics, 2011
It is clear that much care has gone into this book: it has a very detailed contents list, a list of abbreviations and notations, thoughtful data analyses, many references and a detailed index. In fact, it would be difficult not to thoroughly recommend it to anyone interested in learning how to tackle these types of data.
¡ªInternational Statistical Review (2011), 79, 1