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Keywords
(13)
Laplace Approximation
Maximum Likelihood Estimate
Mixed Model
Normal Approximation
Parameter Estimation
poisson model
quasilikelihood
Sparse Data
Spatial Autocorrelation
Spatial Correlation
Variance Component
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Penalized quasilikelihood with spatially correlated data
Penalized quasilikelihood with spatially correlated data,10.1016/S01679473(02)003249,Computational Statistics & Data Analysis,C. B. Dean,M. D. Ugar
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Penalized quasilikelihood with spatially correlated data
(
Citations: 6
)
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C. B. Dean
,
M. D. Ugarte
,
A. F. Militino
This article discusses and evaluates penalized
quasilikelihood
(PQL) estimation techniques for the situation where
random effects
are correlated, as is typical in mapping studies. This is an approximate fitting technique which uses a
Laplace approximation
to the integrated
mixed model
likelihood. It is much easier to implement than usual
maximum likelihood
estimation. Our results show that the PQL estimates are reasonably unbiased for analysis of mixed Poisson models when there is correlation in the random effects, except when the means are sufficiently small to yield sparse data. However, although the
normal approximation
to the distribution of the parameter estimates works fairly well for the parameters in the mean it does not perform as well for the variance components. In addition, when the mean mortality counts are small, the estimated standard errors of the variance components tend to become more biased than those for the mean. We illustrate our approaches by applying PQL for mapping mortality in British Columbia, Canada, over the fiveyear period 1985–1989.
Journal:
Computational Statistics & Data Analysis  CS&DA
, vol. 45, no. 2, pp. 235248, 2004
DOI:
10.1016/S01679473(02)003249
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Citation Context
(1)
...There are various methods to estimate the fixed parameters β and variance components ς such as PQL
5
6
15
29
31
32
48
, marginal quasilikelihood (MQL)
5
44
, GEEs
28
36
, estimating functions
50
, hierarchical likelihood
26
, Bayesian analysis using MCMC
3
2
9
19
and the EM algorithm
33
...
Mahmoud Torabi
,
et al.
Spatiotemporal modelling using Bspline for disease mapping: analysis...
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Citations
(6)
Spatiotemporal modelling using Bspline for disease mapping: analysis of childhood cancer trends
Mahmoud Torabi
,
Rhonda J. Rosychuk
Journal:
Journal of Applied Statistics  J APPL STAT
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Empirical Bayes and Fully Bayes procedures to detect highrisk areas in disease mapping
(
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On empirical Bayes penalized quasilikelihood inference in GLMMs and in Bayesian disease mapping and ecological modeling
(
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)
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