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Dynamic Time Series Models using RINLA An Applied Perspective 1st Edition by Nalini Ravishanker, Balaji Raman, Refik Soyer ISBN 9780367654276 036765427X

Name: Dynamic Time Series Models using RINLA An Applied Perspective 1st Edition by Nalini Ravishanker, Balaji Raman, Refik Soyer ISBN 9780367654276 036765427X
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Dynamic Time Series Models using R-INLA: An Applied Perspective 1st Edition Nalini Ravishanker Digital Instant Download

Author(s): Nalini Ravishanker, Balaji Raman, Refik Soyer

ISBN(s): 9780367654276, 036765427X

Edition: 1

File Details: PDF, 120.48 MB

Year: 2022

Language: English

SKU: EB-43892956 Category: Mathematics - Mathematical Statistics Tags: Balaji Raman, Nalini Ravishanker, Refik Soyer

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Dynamic Time Series Models using RINLA An Applied Perspective 1st Edition by Nalini Ravishanker, Balaji Raman, Refik Soyer – Ebook PDF Instant Download/Delivery: 9780367654276 ,036765427X
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ISBN 10: 036765427X
ISBN 13: 9780367654276
Author: Nalini Ravishanker, Balaji Raman, Refik Soyer

Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework. The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series. Key Features: Introduction and overview of R-INLA for time series analysis. Gaussian and non-Gaussian state space models for time series. State space models for time series with exogenous predictors. Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-temporal dependence.

Dynamic Time Series Models using RINLA An Applied Perspective 1st Edition Table of contents:

1 Bayesian Analysis

1.1 Introduction

1.2 Bayesian framework

1.2.1 Bayesian model comparison

1.3 Bayesian analysis of time series

1.4 Gaussian dynamic linear models (DLMs)

1.4.1 Constant level plus noise model

1.4.2 Local level model

1.4.3 Gaussian DLM framework for univariate time series

1.4.4 AR(1) plus noise model

1.4.5 DLM for vector-valued time series

1.4.6 Kalman filtering and smoothing

1.5 Beyond basic Gaussian DLMs

2 A Review of INLA

2.1 Introduction

2.2 Laplace approximation

2.2.1 Simplified Laplace approximation

2.3 INLA structure for time series

2.3.1 INLA steps

2.4 Forecasting in INLA

2.5 Marginal likelihood computation in INLA

2.6 R-INLA package – some basics

3 Details of R-INLA for Time Series

3.1 Introduction

3.2 Random walk plus noise model

3.2.1 R-INLA model formula

3.2.2 Model execution

3.2.3 Prior specifications for hyperparameters

3.2.4 Posterior distributions of hyperparameters

3.2.5 Fitted values for latent states and responses

3.2.6 Filtering and smoothing in DLM

3.3 AR(1) with level plus noise model

3.4 Dynamic linear models with higher order AR lags

3.5 Random walk with drift plus noise model

3.6 Second-order polynomial model

3.7 Forecasting states and observations

3.8 Model comparisons

3.8.1 In-sample model comparisons

3.8.2 Out-of-sample comparisons

3.9 Non-default prior specifications

3.9.1 Custom prior specifications

3.9.2 Penalized complexity (PC) priors

3.10 Posterior sampling of latent effects and hyperparameters

3.11 Posterior predictive samples of unknown observations

4 Modeling Univariate Time Series

4.1 Introduction

4.2 Example A software engineering example – Musa data

4.2.1 Model 1. AR(1) with level plus noise model

4.2.2 Model 2. Random walk plus noise model

4.2.3 Model 3. AR(1) with trend plus noise model

4.2.4 Model 4. AR(2) with level plus noise model

4.3 Forecasting future states and responses

4.4 Model comparisons

5 Time Series Regression Models

5.1 Introduction

5.2 Structural models

5.2.1 Example Monthly average cost of nightly hotel stay

5.3 Models with exogenous predictors

5.3.1 Example Hourly traffic volumes

5.4 Latent AR(1) model with covariates plus noise

6 Hierarchical Dynamic Models for Panel Time Series

6.1 Introduction

6.2 Models with homogenous state evolution

6.2.1 Example Simulated homogeneous panel time series with the same level

6.2.2 Example Simulated homogeneous panel time series with different levels

6.3 Example Ridesourcing in NYC

6.3.1 Model H1. Dynamic intercept and exogenous predictors

6.3.2 Model H2. Dynamic intercept and Taxi usage

6.3.3 Model H3. Taxi usage varies by time and zone

6.3.4 Model H4. Fixed intercept, Taxi usage varies over time and zones

6.4 Model comparison

7 Non-Gaussian Continuous Responses

7.1 Introduction

7.2 Gamma state space model

7.2.1 Example Volatility index (VIX) time series

7.3 Weibull state space model

7.3.1 Forecasting from Weibull models

7.4 Beta state space model

7.4.1 Example: Crest market share

8 Modeling Categorical Time Series

8.1 Introduction

8.2 Binomial response time series

8.2.1 Example: Simulated single binomial response series

8.2.2 Example: Weekly shopping trips for a single household

8.3 Modeling multiple binomial response time series

8.3.1 Example: Dynamic aggregated model for multiple binomial response time series

8.3.2 Example: Weekly shopping trips for multiple households

8.4 Multinomial time series

8.4.1 Example: Simulated categorical time series

9 Modeling Count Time Series

9.1 Introduction

9.2 Univariate time series of counts

9.2.1 Example: Simulated univariate Poisson counts

9.2.2 Example: Modeling crash counts in CT

9.2.3 Example: Daily bike rentals in Washington D.C.

9.3 Hierarchical modeling of univariate count time series

9.3.1 Example: Simulated univariate Poisson counts

9.3.2 Example: Modeling daily TNC usage in NYC

10 Modeling Stochastic Volatility

10.1 Introduction

10.2 Univariate SV models

10.2.1 Example: Simulated SV data with standard normal errors

10.2.2 Example: Simulated SV data with Student-tν errors

10.2.3 Example: IBM stock returns

10.2.4 Example: NYSE returns

11 Spatio-temporal Modeling

11.1 Introduction

11.2 Spatio-temporal process

11.3 Dynamic spatial models for areal data

11.4 Example: Monthly TNC usage in NYC taxi zones

11.4.1 Model 1. Knorr-Held additive effects model

11.4.2 Knorr-Held models with space-time interactions

12 Multivariate Gaussian Dynamic Modeling

12.1 Introduction

12.2 Model with diagonal W and Φ matrices

12.2.1 Description of the setup for V

12.2.2 Example: Simulated bivariate AR(1) series

12.2.3 Example: Ridesourcing data in NYC for a single taxi zone

12.3 Model with equicorrelated wt and diagonal Φ

12.3.1 Example: Simulated trivariate series

12.4 Fitting multivariate models using rgeneric

12.4.1 Example: Simulated bivariate VAR(1) series

13 Hierarchical Multivariate Time Series

13.1 Introduction

13.2 Multivariate hierarchical dynamic linear model

13.2.1 Example: Analysis of TNC and Taxi as responses

13.3 Level correlated models for multivariate time series of counts

13.3.1 Example: TNC and Taxi counts based on daily data

14 Resources for the User

14.1 Introduction

14.2 Packages used in the book

14.3 Custom functions used in the book

14.3.1 rgeneric() function for DLM-VAR model

14.4 Often used R-INLA items

Bibliography

Index

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Tags: Nalini Ravishanker, Balaji Raman, Refik Soyer, Dynamic Time, RINLA, Applied Perspective