Titlebook: Developments in Statistical Modelling; Jochen Einbeck,Hyeyoung Maeng,Konstantinos Perraki Conference proceedings 2024 The Editor(s) (if ap

MINT

,Derivatives of the Log of a Determinant,We present an efficient way to calculate effective model dimensions, using automated differentiation of the Cholesky algorithm. The method is illustrated with two examples using P-splines: adaptive smoothing and smoothing of over-dispersed counts.

Mobile

,REML for Two-Dimensional P-Splines,to a mixed model; in the new model it is shown that a more direct method can be used keeping the sparse structure of P-splines. The method is illustrated with a two-dimensional example using the R-package . on CRAN. We will show that for this example . is several orders of magnitude faster than othe

傻

,Learning Bayesian Networks from Ordinal Data - The Bayesian Way,Bayesian method, referred to as the ordinal structural expectation maximization (OSEM) method. Both methods assume that the ordinal variables originate from Gaussian variables, which can only be observed in discretized form, and that the dependencies in the unobserved latent Gaussian space can be de

CLASP

出来

,Bayesian Approaches to Model Overdispersion in Spatio-Temporal Binomial Data,iables. This proposal incorporates a spatial term similar to the spatial lag of the response variable for each time unit within the linear predictor. These models effectively capture both spatial and temporal correlations inherent in the dataset under study. Furthermore, we introduce temporally vary

单纯

悠然

,Addressing Covariate Lack in Unit-Level Small Area Models Using GAMLSS,es in the unit-level SAE field: the identification of individual covariates and the reduction of computational burden. We propose a unit-level Simplified SAE model based on Generalized Additive Models for Location, Scale and Shape (GAMLSS), which is specified without covariates and is able to reduce

谦虚的人

,Optimism Correction of the AUC with Complex Survey Data,ed to estimate the area under the receiver operating characteristic curve in this context. However, the proposed estimator has shown an optimistic behaviour. Thus, the goal of this work is to analyze the performance of replicate weights methods to correct for the optimism of the AUC in the context o

Nibble

,Statistical Models for Patient-Centered Outcomes in Clinical Studies,t of the first M postoperative days, that the patient has been discharged from hospital, or zero if the patient dies within M days of surgery. This composite measure presents statistical challenges in its unusual distributional shape, and its inability to distinguish between the qualitatively differ

炸坏

,Bayesian Hidden Markov Models for Early Warning,umes that every binary response variable depends only on the latent state further to the lagged covariates and response. A Markov chain Monte Carlo algorithm is proposed for estimation and forecasting, where the latter is based on the optimisation of the F-score. An application referred to banking c