Search Results - subject:"Generalized Linear Model"

Prediction sets and conformal inference with censored outcomes

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

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

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

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

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

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

LocalGLMnet : interpretable deep learning for tabular data

Richman, Ronald; Wüthrich, Mario V. - In: Scandinavian actuarial journal 2023 (2023) 1, pp. 71-95

Persistent link: https://www.econbiz.de/10013491052

Estimation of Count Data Models with Multiplicative Fixed Effects and Spatial Dependence

Yousuf, Ahmed Sadek - 2023

This note looks at estimation of spatial autoregressive models for non-negative and count outcomes with multiplicative fixed effects. We show that in presence of significant proportion of zeros in the count variable, control function and Instrumental Variable estimation to model such spatial...

Persistent link: https://www.econbiz.de/10014357743

Estimation of Count Data Models with Multiplicative Fixed Effects and Spatial Dependence

Yousuf, Ahmed Sadek - 2023

This note looks at estimation of count models with multiplicative fixed effects and spatial dependence in the form of spatial lag autocorrelation. We show that in presence of significant proportion of zeros in the count variable, control function estimation to model such spatial dependence is...

Persistent link: https://www.econbiz.de/10014358216

Detection of interacting variables for generalized linear models via neural networks

Havrylenko, Yevhen; Heger, Julia - In: European Actuarial Journal 14 (2023) 2, pp. 551-580

The quality of generalized linear models (GLMs), frequently used by insurance companies, depends on the choice of interacting variables. The search for interactions is time-consuming, especially for data sets with a large number of variables, depends much on expert judgement of actuaries, and...

Persistent link: https://www.econbiz.de/10015331765