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Related papers: Modeling Dependencies in Claims Reserving with GEE

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This paper introduces a dynamic change of measure approach for computing the analytical solutions of expected future prices (and therefore, expected returns) of contingent claims over a finite horizon. The new approach constructs hybrid…

Pricing of Securities · Quantitative Finance 2022-05-25 Sanjay K. Nawalkha , Xiaoyang Zhuo

Hydrocarbon prospect risking is a critical application in geophysics predicting well outcomes from a variety of data including geological, geophysical, and other information modalities. Traditional routines require interpreters to go…

Machine Learning · Computer Science 2022-12-16 Ahmad Mustafa , Ghassan AlRegib

Estimation of generalized linear mixed models (GLMMs) with non-nested random effects structures requires approximation of high-dimensional integrals. Many existing methods are tailored to the low-dimensional integrals produced by nested…

Computation · Statistics 2014-04-01 Andrew T. Karl , Yan Yang , Sharon L. Lohr

Generalized estimating equations (GEE) are widely used to analyze longitudinal data; however, they are not appropriate for heteroscedastic data, because they only estimate regressor effects on the mean response{\textemdash}and therefore do…

Methodology · Statistics 2020-12-29 Amadou Barry , Karim Oualkacha , Arthur Charpentier

Accurate load forecasting is crucial for maintaining the power balance between generators and consumers,particularly with the increasing integration of renewable energy sources, which introduce significant intermittent volatility. With the…

Systems and Control · Electrical Eng. & Systems 2024-09-04 Mingyang Gao , Suyang Zhou , Wei Gu , Zhi Wu , Haiquan Liu , Aihua Zhou

Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades. However, the insightful connections between them have rarely been…

Systems and Control · Electrical Eng. & Systems 2022-06-28 Wanke Yu , Min Wu , Biao Huang , Chengda Lu

Generalized linear models (GLMs) are routinely used for modeling relationships between a response variable and a set of covariates. The simple form of a GLM comes with easy interpretability, but also leads to concerns about model…

Methodology · Statistics 2023-11-10 Davide Agnoletto , Tommaso Rigon , David B. Dunson

Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modeling multivariate joint distributions. These models correspond to mixed graphs that include both…

Computation · Statistics 2015-04-14 Mathias Drton , Luca Weihs

In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world…

Machine Learning · Computer Science 2018-12-14 Chengsheng Mao , Lijuan Lu , Bin Hu

We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation…

Machine Learning · Computer Science 2025-07-02 Nora Schneider , Lars Lorch , Niki Kilbertus , Bernhard Schölkopf , Andreas Krause

To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribution are linked to regressors. In many classical models this…

Methodology · Statistics 2020-01-14 Lisa Schlosser , Torsten Hothorn , Reto Stauffer , Achim Zeileis

The problem of selecting a model given a set of candidates remains a challenging one that pervades many scientific fields. We employ techniques from the theory of Lie groups to analyse the symmetries in differential equation models of…

Quantitative Methods · Quantitative Biology 2023-10-10 Reemon Spector

Residual plots are often used to interrogate regression model assumptions, but interpreting them requires an understanding of how much sampling variation to expect when assumptions are satisfied. In this paper, we propose constructing…

Methodology · Statistics 2022-10-26 David I. Warton

Generalized additive index models (GAIMs) offer a flexible semiparametric framework for capturing complex data relationships, balancing the interpretability of parametric models with the flexibility of nonparametric approaches. However,…

Methodology · Statistics 2026-05-29 Ziyu Peng , Linglingzhi Zhu , Yao Xie

In this letter, we revisit the problem of maximum likelihood estimation (MLE) of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its parameters. The new derivation, unlike the classical approach employing the…

Signal Processing · Electrical Eng. & Systems 2020-01-10 Nitesh Sahu , Prabhu Babu

The Tweedie exponential dispersion family is a popular choice among many to model insurance losses that consist of zero-inflated semicontinuous data. In such data, it is often important to obtain credibility (inference) of the most…

Methodology · Statistics 2025-07-17 Alokesh Manna , Zijian Huang , Dipak K. Dey , Yuwen Gu , Robin He

Random shifting typically appears in credibility models whereas random scaling is often encountered in stochastic models for claim sizes reflecting the time-value property of money. In this article we discuss some aspects of random shifting…

Methodology · Statistics 2014-10-08 Enkelejd Hashorva , Lanpeng Ji

Spatial generalized linear mixed models (SGLMMs) are popular and flexible models for non-Gaussian spatial data. They are useful for spatial interpolations as well as for fitting regression models that account for spatial dependence, and are…

Methodology · Statistics 2021-10-26 Yawen Guan , Murali Haran

While including pairwise interactions in a regression model can better approximate response surface, fitting such an interaction model is a well-known difficult problem. In particular, analyzing contemporary high-dimensional datasets often…

Methodology · Statistics 2024-01-17 Hai Lu , Guo Yu

Traditional general circulation models, or GCMs -- i.e. 3D dynamical models with unresolved terms represented in equations with tunable parameters -- have been a mainstay of climate research for several decades, and some of the pioneering…

Atmospheric and Oceanic Physics · Physics 2022-12-21 V. Balaji , Fleur Couvreux , Julie Deshayes , Jacques Gautrais , Frédéric Hourdin , Catherine Rio