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Basic Concepts for Smoothing and Semiparametric Regression

Ludwig Fahrmeir and Thomas Kneib

in Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data

Published in print:
2011
Published Online:
September 2011
ISBN:
9780199533022
eISBN:
9780191728501
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199533022.003.0002
Subject:
Mathematics, Probability / Statistics, Biostatistics

This chapter reviews basic concepts for smoothing and semiparametric regression based on roughness penalties or — from a Bayesian perspective — corresponding smoothness priors. In particular, it ... More


Approximate filtering and smoothing

J. Durbin and S.J. Koopman

in Time Series Analysis by State Space Methods: Second Edition

Published in print:
2012
Published Online:
December 2013
ISBN:
9780199641178
eISBN:
9780191774881
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199641178.003.0010
Subject:
Mathematics, Probability / Statistics

This chapter discusses approximate filtering and smoothing methods for the analysis of non-Gaussian and nonlinear models. The chapter is organized as follows. Sections 10.2 and 10.3 consider two ... More


Importance sampling for smoothing

J. Durbin and S.J. Koopman

in Time Series Analysis by State Space Methods: Second Edition

Published in print:
2012
Published Online:
December 2013
ISBN:
9780199641178
eISBN:
9780191774881
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199641178.003.0011
Subject:
Mathematics, Probability / Statistics

This chapter develops the methodology of importance sampling based on simulation for the analysis of observations from the non-Gaussian and nonlinear models that were specified in Chapter 9. It shows ... More


Introduction

J. Durbin and S.J. Koopman

in Time Series Analysis by State Space Methods: Second Edition

Published in print:
2012
Published Online:
December 2013
ISBN:
9780199641178
eISBN:
9780191774881
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199641178.003.0001
Subject:
Mathematics, Probability / Statistics

This introductory chapter provides an overview of the main themes covered in the present book, namely linear Gaussian state space models and non-Gaussian and nonlinear state space models. It also ... More


Particle filtering

J. Durbin and S.J. Koopman

in Time Series Analysis by State Space Methods: Second Edition

Published in print:
2012
Published Online:
December 2013
ISBN:
9780199641178
eISBN:
9780191774881
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199641178.003.0012
Subject:
Mathematics, Probability / Statistics

This chapter discusses the filtering of non-Gaussian and nonlinear series by fixing the sample at the values previously obtained at times …, t − 2, t − 1 and choosing a fresh value at time t only. A ... More


Non-Gaussian and nonlinear illustrations

J. Durbin and S.J. Koopman

in Time Series Analysis by State Space Methods: Second Edition

Published in print:
2012
Published Online:
December 2013
ISBN:
9780199641178
eISBN:
9780191774881
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199641178.003.0014
Subject:
Mathematics, Probability / Statistics

This chapter discusses examples which illustrate the methods that were developed in Part II for analysing observations using non-Gaussian and nonlinear state space models. These include the monthly ... More


Forecasting the Boat Race

Geert Mesters and Siem Jan Koopman

in Unobserved Components and Time Series Econometrics

Published in print:
2015
Published Online:
January 2016
ISBN:
9780199683666
eISBN:
9780191763298
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199683666.003.0007
Subject:
Economics and Finance, Econometrics

This chapter looks at the forecasting of the yearly outcome of the Boat Race between Cambridge and Oxford. The relative performance of different dynamic models for forty years of forecasting is ... More


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