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

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41#
發(fā)表于 2025-3-28 15:48:39 | 只看該作者
,A Bayesian Markov-Switching for?Smooth Modelling of?Extreme Value Distributions,gime-switching is controlled by an unobservable Markovian process. Model flexibility can be enhanced considering regime-specific distributions, whose distributional parameters may be modelled using smooth functions of covariates. Here, we propose a two-state Markov-switching model using full Bayesia
42#
發(fā)表于 2025-3-28 19:20:58 | 只看該作者
43#
發(fā)表于 2025-3-29 02:06:01 | 只看該作者
44#
發(fā)表于 2025-3-29 05:58:09 | 只看該作者
,Spatial Confounding in?Gradient Boosting,ently, the . approach suggests to regress out the spatial effect in the covariate first, before estimating the model of interest. Drastic spatial confounding is observed in gradient boosting due to its step-wise procedure. In this contribution we apply the suggested two-step approach and confirm its
45#
發(fā)表于 2025-3-29 08:14:01 | 只看該作者
,Adaptive Generalized Logistic Lasso and?Its Application to?Rankings in?Sports,an implementation of an adaptive variant of the generalized lasso penalty for logistic regression using conic programming principles. This approach is flexible, robust, and fast, especially in a high-dimensional setting. The methodology is applied to sports data, with the aim of ranking soccer playe
46#
發(fā)表于 2025-3-29 11:54:31 | 只看該作者
,A Biclustering Approach via?Mixture of?Latent Trait Analyzers for?the?Analysis of?Digital Divide indetail, units (individuals) are partitioned into clusters (components) via a finite mixture of latent trait models; in each component, variables (digital skills) are partitioned into clusters (segments) by modifying the linear predictor’s specification of the original MLTA model. This allows us to i
47#
發(fā)表于 2025-3-29 19:02:55 | 只看該作者
48#
發(fā)表于 2025-3-29 20:02:29 | 只看該作者
,Integrating Single Index Effects in?Generalized Additive Models,propose a novel approach to integrate single index effects in Generalised Additive Models (GAMs). In particular, model fitting and inference are performed by exploiting the efficient methods proposed in [.]. We consider an application to daily electricity load consumption data, demonstrating improve
49#
發(fā)表于 2025-3-30 03:22:45 | 只看該作者
50#
發(fā)表于 2025-3-30 07:14:59 | 只看該作者
,Addressing Covariate Lack in?Unit-Level Small Area Models Using GAMLSS, variability in comparison with the direct estimator. The performance of the proposed model used to estimate the Theil index is evaluated based on design-based simulations. An application to the Italian Regions, distinguish between Urban, Peri-Urban and Rural areas, conclude the paper.
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