Search Results - subject:"Generalized linear model"

Semi-parametric approach for modelling overdispersed count data with application to Industry 4.0

Bonnini, Stefano; Borghesi, M.; Giacalone, M. - In: Socio-economic planning sciences : the international … 95 (2024), pp. 1-7

Persistent link: https://www.econbiz.de/10015099108

Prediction sets and conformal inference with censored outcomes

Liu, Weiguang; Paula, Áureo de; Tamer, Elie T. - 2025

Given data on a scalar random variable 𝑌, a prediction set for 𝑌 with miscoverage level 𝛼 is a set of values for 𝑌 that contains a randomly drawn 𝑌 with probability 1 − 𝛼, where 𝛼 ∈ (0, 1). Among all prediction sets that satisfy this coverage property, the oracle...

Persistent link: https://www.econbiz.de/10015191439

Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data

Duval, Francis; Boucher, Jean-Philippe; Pigeon, Mathieu - In: ASTIN bulletin : the journal of the International … 54 (2024) 2, pp. 239-262

Persistent link: https://www.econbiz.de/10015055287

A negative binomial model for the donations count in fundraising management

Barzanti, Luca; Nardon, Martina - 2025

Persistent link: https://www.econbiz.de/10015590924

A generalized linear model and machine learning approach for predicting the frequency and severity of cargo insurance in Thailand's border trade context

Praiya Panjee; Sataporn Amornsawadwatana - In: Risks : open access journal 12 (2024) 2, pp. 1-33

magnitudes compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Conversely, the generalized … accuracy. However, it exhibits a higher RMSE, indicating wider error dispersion compared to a generalized linear model (Gamma …). In contrast, a generalized linear model (Gamma) demonstrates the lowest RMSE, portraying tighter clustering and smaller …

Persistent link: https://www.econbiz.de/10014497395

Instrumental variable method for regularized estimation in generalized linear measurement error models

Xue, Lin; Wang, Liqun - In: Econometrics : open access journal 12 (2024) 3, pp. 1-14

Regularized regression methods have attracted much attention in the literature, mainly due to its application in high-dimensional variable selection problems. Most existing regularization methods assume that the predictors are directly observed and precisely measured. It is well known that in a...

Persistent link: https://www.econbiz.de/10015133941

The applications of generalized poisson regression models to insurance claim data

Faroughi, Pouya; Li, Shu; Ren, Jiandong - In: Risks : open access journal 11 (2023) 12, pp. 1-17

Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques. In particular, we study the zero-inflated and hurdle-generalized Poisson and negative binomial...

Persistent link: https://www.econbiz.de/10014446646

Hidden-Markov models for ordinal time series

H. Weiß, Christian; Swidan, Osama - In: AStA Advances in Statistical Analysis 109 (2024) 2, pp. 217-239

A common approach for modeling categorical time series is Hidden-Markov models (HMMs), where the actual observations are assumed to depend on hidden states in their behavior and transitions. Such categorical HMMs are even applicable to nominal data but suffer from a large number of model...

Persistent link: https://www.econbiz.de/10015436497

Automatic segmentation of insurance rating classes under ordinal constraints via group fused lasso

Takahashi, Atsumori; Nomura, Shunichi - In: Asia-Pacific journal of risk and insurance : APJRI 17 (2023) 1, pp. 113-142

Persistent link: https://www.econbiz.de/10014288337

Using the softplus function to construct alternative link functions in generalized linear models and beyond

Wiemann, Paul F. V.; Kneib, Thomas; Hambuckers, Julien - In: Statistical Papers 65 (2023) 5, pp. 3155-3180

Response functions that link regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is usually not further scrutinized. For...

Persistent link: https://www.econbiz.de/10015207120