arXiv subjects

Fredrik Lohne Aanes

Publications and source records attributed to Fredrik Lohne Aanes.

4 recordsLinked to original sources

arXiv2026

Fast approximate estimation of conditional Shapley values when using a linear explainer

In this paper, we develop three new methods, two approximate and one exact, for fast estimation of conditional Shapley values when a linear regression model is used as the explainer. We apply constrained Gaussian Markov Random Field theory and sparse matrix algebra packages to estimate the coefficients of all submodels jointly and not sequentially. In the numerical case studies, our methods match the accuracy of the Sequential method in the shapr R-package while reducing the computation time from hours to minutes or seconds. Compared to the Iterative method in shapr, which samples coalitions until a convergence criterion is met, our methods estimate all $2^p$ coalitions when there are $p$ predictors present. When the iterative method requires all or a large fraction of the coalitions, our methods are substantially faster and with equal or better accuracy. When few coalitions suffice and the shapr iterative method is very fast, our methods deliver enumeration of all submodels, and in one case study over two million models are estimated within minutes. We recommend the exact method, since it has no tuning parameters and the three methods give comparable results.

stat.ME↗

arXiv2026

Approximate Shapley value estimation using sampling without replacement and variance estimation via the new Symmetric bootstrap and the Doubled half bootstrap

In this paper I consider improving the KernelSHAP algorithm. I suggest to use the Wallenius' noncentral hypergeometric distribution for sampling the number of coalitions and perform sampling without replacement, so that the KernelSHAP estimation framework is improved further. I also introduce the Symmetric bootstrap to calculate the standard deviations and also use the Doubled half bootstrap method to compare the performance. The new bootstrap algorithm performs better or equally well in the two simulation studies performed in this paper. The new KernelSHAP algorithm performs similarly as the improved KernelSHAP method in the state-of-the-art R-package shapr, which samples coalitions with replacement in one of the options

stat.ME↗

arXiv2023

Faster estimation of the Knorr-Held Type IV space-time model

In this paper we study the type IV Knorr Held space time models. Such models typically apply intrinsic Markov random fields and constraints are imposed for identifiability. INLA is an efficient inference tool for such models where constraints are dealt with through a conditioning by kriging approach. When the number of spatial and/or temporal time points become large, it becomes computationally expensive to fit such models, partly due to the number of constraints involved. We propose a new approach, HyMiK, dividing constraints into two separate sets where one part is treated through a mixed effect approach while the other one is approached by the standard conditioning by kriging method, resulting in a more efficient procedure for dealing with constraints. The new approach is easy to apply based on existing implementations of INLA. We run the model on simulated data, on a real data set containing dengue fever cases in Brazil and another real data set of confirmed positive test cases of Covid-19 in the counties of Norway. For all cases we get very similar results when comparing the new approach with the tradition one while at the same time obtaining a significant increase in computational speed, varying on a factor from 2 to 4, depending on the sizes of the data sets.

stat.ME↗

arXiv2019

Sovereign Risk Indices and Bayesian Theory Averaging

In economic applications, model averaging has found principal use examining the validity of various theories related to observed heterogeneity in outcomes such as growth, development, and trade.Though often easy to articulate, these theories are imperfectly captured quantitatively. A number of different proxies are often collected for a given theory and the uneven nature of this collection requires care when employing model averaging. Furthermore, if valid, these theories ought to be relevant outside of any single narrowly focused outcome equation. We propose a methodology which treats theories as represented by latent indices, these latent processes controlled by model averaging on the proxy level. To achieve generalizability of the theory index our framework assumes a collection of outcome equations. We accommodate a flexible set of generalized additive models, enabling non-Gaussian outcomes to be included. Furthermore, selection of relevant theories also occurs on the outcome level, allowing for theories to be differentially valid. Our focus is on creating a set of theory-based indices directed at understanding a country's potential risk of macroeconomic collapse. These Sovereign Risk Indices are calibrated across a set of different "collapse" criteria, including default on sovereign debt, heightened potential for high unemployment or inflation and dramatic swings in foreign exchange values. The goal of this exercise is to render a portable set of country/year theory indices which can find more general use in the research community.

stat.AP↗