An overview of composite likelihood methods
A survey of recent developments in the theory and application of composite likelihood is
provided, building on the review paper of Varin (2008). A range of application areas …
provided, building on the review paper of Varin (2008). A range of application areas …
[图书][B] Handbook of missing data methodology
Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …
data. But since the 1990s, there have been important developments in the statistical …
Springer Series in Statistics
Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …
most successful statistical modelling ideas that have came up in the last forty years: the use …
A note on composite likelihood inference and model selection
A composite likelihood consists of a combination of valid likelihood objects, usually related
to small subsets of data. The merit of composite likelihood is to reduce the computational …
to small subsets of data. The merit of composite likelihood is to reduce the computational …
On composite marginal likelihoods
C Varin - Asta advances in statistical analysis, 2008 - Springer
Composite marginal likelihoods are pseudolikelihoods constructed by compounding
marginal densities. In several applications, they are convenient surrogates for the ordinary …
marginal densities. In several applications, they are convenient surrogates for the ordinary …
[图书][B] The evaluation of surrogate endpoints
T Burzykowski, M Buyse, G Molenberghs - 2005 - Springer
Both humanitarian and commercial considerations have spurred intensive search for
methods to reduce the time and cost required to develop new therapies. The identification …
methods to reduce the time and cost required to develop new therapies. The identification …
Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles
S Fieuws, G Verbeke - Biometrics, 2006 - academic.oup.com
A mixed model is a flexible tool for joint modeling purposes, especially when the gathered
data are unbalanced. However, computational problems due to the dimension of the joint …
data are unbalanced. However, computational problems due to the dimension of the joint …
Farmer differentiation, generational differences and farmers' behaviors to withdraw from rural homesteads: Evidence from chengdu, China
R Liu, C Yu, J Jiang, Z Huang, Y Jiang - Habitat International, 2020 - Elsevier
In order to solve the problem of idle rural homesteads, the Chinese government introduced a
pilot policy of withdrawal from rural homesteads (WRH). Against the background of farmer …
pilot policy of withdrawal from rural homesteads (WRH). Against the background of farmer …
The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models
CR Bhat - Transportation Research Part B: Methodological, 2011 - Elsevier
The likelihood functions of multinomial probit (MNP)-based choice models entail the
evaluation of analytically-intractable integrals. As a result, such models are usually …
evaluation of analytically-intractable integrals. As a result, such models are usually …
A family of generalized linear models for repeated measures with normal and conjugate random effects
A family of generalized linear models for repeated measures with normal and conjugate
random effects: Calculation details. In Section A, generic approximate calculations are …
random effects: Calculation details. In Section A, generic approximate calculations are …