Parameter estimation for the stochastic SIS epidemic model

Jiafeng Pan; Alison Gray; David Greenhalgh; Xuerong Mao

doi:10.1007/s11203-014-9091-8

Parameter estimation for the stochastic SIS epidemic model

Jiafeng Pan, Alison Gray, David Greenhalgh, Xuerong Mao

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

772 Downloads (Pure)

Abstract

In this paper we estimate the parameters in the stochastic SIS epidemic model by using pseudo-maximum likelihood estimation (pseudo-MLE) and least squares estimation. We obtain the point estimators and 100(1 − α)% confidence intervals as well as 100(1 − α)% joint confidence regions by applying least squares techniques. The pseudo-MLEs have almost the same form as the least squares case. We also obtain the exact as well as the asymptotic 100(1 − α)% joint confidence regions for the pseudo-MLEs. Computer simulations are performed to illustrate our theory.

Original language	English
Pages (from-to)	75-98
Number of pages	24
Journal	Statistical Inference for Stochastic Processes
Volume	17
Issue number	1
DOIs	https://doi.org/10.1007/s11203-014-9091-8
Publication status	Published - 1 Apr 2014

Keywords

Stochastic SIS epidemic model
pseudo-maximum likelihood estimation
least squares estimation
confidence interval
confidence region
asymptotic estimator
logistic equations

Access to Document

10.1007/s11203-014-9091-8

M:\My Documents\RESEARCHPUBLICATIONS\GREENHALGH\para_est_SISPSubmitted manuscript, 1.16 MB

Alison Gray
- a.j.graystrath.acuk
- Mathematics And Statistics - Lecturer B
Person: Academic

Cite this

@article{2e41cb39d534472db3c65882755e465b,

title = "Parameter estimation for the stochastic SIS epidemic model",

abstract = "In this paper we estimate the parameters in the stochastic SIS epidemic model by using pseudo-maximum likelihood estimation (pseudo-MLE) and least squares estimation. We obtain the point estimators and 100(1 − α)% confidence intervals as well as 100(1 − α)% joint confidence regions by applying least squares techniques. The pseudo-MLEs have almost the same form as the least squares case. We also obtain the exact as well as the asymptotic 100(1 − α)% joint confidence regions for the pseudo-MLEs. Computer simulations are performed to illustrate our theory.",

keywords = "Stochastic SIS epidemic model, pseudo-maximum likelihood estimation, least squares estimation, confidence interval, confidence region, asymptotic estimator, logistic equations",

author = "Jiafeng Pan and Alison Gray and David Greenhalgh and Xuerong Mao",

year = "2014",

month = apr,

day = "1",

doi = "10.1007/s11203-014-9091-8",

language = "English",

volume = "17",

pages = "75--98",

journal = "Statistical Inference for Stochastic Processes",

issn = "1387-0874",

number = "1",

}

TY - JOUR

T1 - Parameter estimation for the stochastic SIS epidemic model

AU - Pan, Jiafeng

AU - Gray, Alison

AU - Greenhalgh, David

AU - Mao, Xuerong

PY - 2014/4/1

Y1 - 2014/4/1

N2 - In this paper we estimate the parameters in the stochastic SIS epidemic model by using pseudo-maximum likelihood estimation (pseudo-MLE) and least squares estimation. We obtain the point estimators and 100(1 − α)% confidence intervals as well as 100(1 − α)% joint confidence regions by applying least squares techniques. The pseudo-MLEs have almost the same form as the least squares case. We also obtain the exact as well as the asymptotic 100(1 − α)% joint confidence regions for the pseudo-MLEs. Computer simulations are performed to illustrate our theory.

AB - In this paper we estimate the parameters in the stochastic SIS epidemic model by using pseudo-maximum likelihood estimation (pseudo-MLE) and least squares estimation. We obtain the point estimators and 100(1 − α)% confidence intervals as well as 100(1 − α)% joint confidence regions by applying least squares techniques. The pseudo-MLEs have almost the same form as the least squares case. We also obtain the exact as well as the asymptotic 100(1 − α)% joint confidence regions for the pseudo-MLEs. Computer simulations are performed to illustrate our theory.

KW - Stochastic SIS epidemic model

KW - pseudo-maximum likelihood estimation

KW - least squares estimation

KW - confidence interval

KW - confidence region

KW - asymptotic estimator

KW - logistic equations

U2 - 10.1007/s11203-014-9091-8

DO - 10.1007/s11203-014-9091-8

M3 - Article

SN - 1387-0874

VL - 17

SP - 75

EP - 98

JO - Statistical Inference for Stochastic Processes

JF - Statistical Inference for Stochastic Processes

IS - 1

ER -

Parameter estimation for the stochastic SIS epidemic model

Abstract

Keywords

Access to Document

Fingerprint

Profiles

Alison Gray

Cite this