Estimation of survival and capture probabilities in open population capture–recapture models when covariates are subject to measurement error

Stoklosa, Jakub; Dann, Peter; Huggins, Richard; Hwang, Wen‐Han

doi:10.1016/j.csda.2015.10.010

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…In real applications,

K

is suggested to be less than 20 and these

K

\lambda

‐values are chosen equally spaced from

\left[0,2\right]

. The early development of the classical SIMEX estimation procedure can be found in Cook and Stefanski (1994), Stefanski and Cook (1995), and Carroll et al (1996), with extensive applications in Mallick et al (2002) for cox regression, Sevilimedu et al (2019) for Log‐logistic accelerated failure time models, Gould et al (1999) for the catch‐effort analysis, Hwang and Huang (2003), Stoklosa et al (2016) for the capture‐recapture models, Lin and Carroll (1999) for the analysis of the Framingham heart disease data using the logistic regression, Hardin et al (2003) for generalized linear models, and Ponzi et al (2019) for some applications in ecology and evolution, to name a few.…”

Section: Introductionmentioning

confidence: 99%

Extrapolation estimation for nonparametric regression with measurement error

Song

Ayub

Shi

2023

Scandinavian J Statistics

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For the nonparametric regression models with covariates contaminated with the normal measurement errors, this paper proposes an extrapolation algorithm to estimate the regression functions. By applying the conditional expectation directly to the kernel‐weighted least squares of the deviations between the local linear approximation and the observed responses, the proposed algorithm successfully bypasses the simulation step in the classical simulation extrapolation, thus significantly reducing the computational time. It is noted that the proposed method also provides an exact form of the extrapolation function, although the extrapolation estimate generally cannot be obtained by simply setting the extrapolation variable to negative one in the fitted extrapolation function, if the bandwidth is less than the SD of the measurement error. Large sample properties of the proposed estimation procedure are discussed, as well as simulation studies and a real data example being conducted to illustrate its applications.

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“…In real applications,

K

is suggested to be less than 20 and these

K

\lambda

‐values are chosen equally spaced from

\left[0,2\right]

Section: Introductionmentioning

confidence: 99%

Extrapolation estimation for nonparametric regression with measurement error

Song

Ayub

Shi

2023

Scandinavian J Statistics

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“…The motivation and the full description of the SIMEX procedure can be found in the seminal papers by Cook and Stefanski (1994) and Stefanski and Cook (1995), its asymptotic properties, when

σ_{u}^{2}

is small, were investigated in Carroll et al (1996). Extensive applications of the SIMEX procedure on survival analysis, catch‐effort analysis, generalized linear models and so on can be found in Mallick et al (2002), Sevilimedu et al (2019), Gould et al (1999), Hwang and Huang (2003), Stoklosa et al (2016), Lin and Carroll (1999), Hardin et al (2003), Ponzi et al (2019) and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Extrapolation in parametric models with Laplace measurement error

Shi

Guo

Bai

et al. 2022

Stat

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For general parametric statistical models with some variables being contaminated with the Laplace measurement error, we propose an extrapolation algorithm to estimate the unknown parameters. By applying the conditional expectation directly to the target function, either the function to be optimized or an estimation equation, the proposed algorithm successfully bypasses the simulation step in the classical simulation extrapolation procedure and thus provides a viable alternative to the classical simulation extrapolation algorithm. Large sample properties of the resulting estimator are discussed. Finite sample performance of the proposed extrapolation estimation procedure is demonstrated by examples and numerical simulation studies.

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“…In real applications, K is suggested to be less than 20 and these K λ-values are chosen equally spaced from [0,2]. The early development of the classical SIMEX estimation procedure can be found in Cook and Stefanski (1994), Stefanski and Cook (1995) and Carroll et al (1996), with extensive applications in Mallick et al (2002) for cox regression, Sevilimedu et al (2019) for Log-logistic accelerated failure time models, Gould et al (1999) for the catch-effort analysis, Hwang and Huang (2003), Stoklosa et al (2016) for the capture-recapture models, Lin and Carroll (1999) for the analysis of the Framingham heart disease data using the logistic regression, Hardin et al (2003) for generalized linear models, and Ponzi et al (2019) for some applications in ecology and evolution, to name a few.…”

Section: Introductionmentioning

confidence: 99%

Extrapolation Estimation for Nonparametric Regression with Measurement Error

Song¹,

Ayub²,

Shi³

2021

Preprint

View full text Add to dashboard Cite

For the nonparametric regression models with covariates contaminated with normal measurement errors, this paper proposes an extrapolation algorithm to estimate the nonparametric regression functions. By applying the conditional expectation directly to the kernel-weighted least squares of the deviations between the local linear approximation and the observed responses, the proposed algorithm successfully bypasses the simulation step needed in the classical simulation extrapolation method, thus significantly reducing the computational time. It is noted that the proposed method also provides an exact form of the extrapolation function, but the extrapolation estimate generally cannot be obtained by simply setting the extrapolation variable to negative one in the fitted extrapolation function if the bandwidth is less than the standard deviation of the measurement error. Large sample properties of the proposed estimation procedure are discussed, as well as simulation studies and a real data example being conducted to illustrate its applications.

show abstract

Estimation of survival and capture probabilities in open population capture–recapture models when covariates are subject to measurement error

Cited by 5 publications

References 36 publications

Extrapolation estimation for nonparametric regression with measurement error

Extrapolation estimation for nonparametric regression with measurement error

Extrapolation in parametric models with Laplace measurement error

Extrapolation Estimation for Nonparametric Regression with Measurement Error

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