Statistical State-Space Modeling via Kalman Filtration

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Statistical State-Space Modeling via Kalman Filtration

Popis výsledku v původním jazyce

We start with a brief review of the theory underlying the Kalman filter (KF) statistical modeling based on the state-space approach. We will stress the prediction error decomposition as a highly effective way of computing the likelihood function, usefulwhen maximum likelihood estimate of certain structural parameters is attempted. Next, we will illustrate how the state-space modeling and KF can be useful for solving practical problems from interesting real-life applications. Firstly, the state-space approach and KF estimation will be shown as a tool for estimation of time-varying parameters describing radon concentrations in houses, based on two underlying differential equations summarizing the radon and tracer dynamics. Secondly, we will show how theKalman filtration can be useful for estimation of underlying growth curve of small children. Further, we will consider also multivariate approach useful for individualized natural gas consumption modeling.

Název v anglickém jazyce

Statistical State-Space Modeling via Kalman Filtration

Popis výsledku anglicky

We start with a brief review of the theory underlying the Kalman filter (KF) statistical modeling based on the state-space approach. We will stress the prediction error decomposition as a highly effective way of computing the likelihood function, usefulwhen maximum likelihood estimate of certain structural parameters is attempted. Next, we will illustrate how the state-space modeling and KF can be useful for solving practical problems from interesting real-life applications. Firstly, the state-space approach and KF estimation will be shown as a tool for estimation of time-varying parameters describing radon concentrations in houses, based on two underlying differential equations summarizing the radon and tracer dynamics. Secondly, we will show how theKalman filtration can be useful for estimation of underlying growth curve of small children. Further, we will consider also multivariate approach useful for individualized natural gas consumption modeling.

Druh

C - Kapitola v odborné knize

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Kalman Filtering

ISBN

978-1-61761-462-0

Počet stran výsledku

34

Strana od-do

77-110

Počet stran knihy

385

Název nakladatele

Nova Science Publishers

Místo vydání

New York

Kód UT WoS kapitoly

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