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Simultaneous Inference in General Parametric Models [PDF]
, 2008 Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level ...
Bates +29 more
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Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)
Risks, 2016 Traditionally, actuaries have used run-off triangles to estimate reserve (“macro” models, on aggregated data). However, it is possible to model payments related to individual claims. If those models provide similar estimations, we investigate uncertainty
Arthur Charpentier, Mathieu Pigeon
doaj +1 more source
Journal of Forest Science, 2012 The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models.
L.Y. Fu +4 more
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Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models [PDF]
, 2010 Summary. Non‐Gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to
John Hughes, Murali Haran
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Half-Normal Plots and Overdispersed Models in R: The hnp Package
Journal of Statistical Software, 2017 Count and proportion data may present overdispersion, i.e., greater variability than expected by the Poisson and binomial models, respectively. Different extended generalized linear models that allow for overdispersion may be used to analyze this type of
Rafael A Moral +2 more
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Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies. [PDF]
Bioinformatics, 2019 Motivation Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large.
Sun S +5 more
europepmc +2 more sources
Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models [PDF]
, 2014 1.Nakagawa & Schielzeth extended the widely used goodness-of-fit statistic R2 to apply to generalized linear mixed models (GLMMs). However, their R2GLMM method is restricted to models with the simplest random effects structure, known as random ...
Johnson, Paul C.D.
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Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python
Journal of Statistical Software, 2022 The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications. This is the result of a variety of methodological advances with faster and cheaper hardware as well as the ...
Tomás Capretto +5 more
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Generalized Linear Mixed Models: Part II
, 2021 As mentioned in Sect. 3.4, the likelihood function under a GLMM typically involves integrals with no analytic expressions. Such integrals may be difficult to evaluate, if the dimensions of the integrals are high. For relatively simple models, the likelihood function may be evaluated by numerical integration techniques.
Jiming Jiang, Thuan Nguyen
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Risks, 2023 Territory risk analysis has played an important role in auto insurance rate regulation. It aims to design rating territories from a set of basic rating units so that their respective risk relativities can be estimated to reflect the regional risk of ...
Shengkun Xie, Chong Gan
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