arXiv subjects

Ingrid Kristine Glad

Publications and source records attributed to Ingrid Kristine Glad.

6 recordsLinked to original sources

arXiv2026

gridcp: Fast Online Changepoint Detection in Python

Online changepoint detection is the problem of detecting distributional changes in a data stream in real-time. A large body of methodology exists for the offline (fixed-size) setting, but applying these methods online quickly becomes infeasible since the per-observation computational cost and memory consumption typically grow at least linearly with the sample size. A recently proposed grid-based methodology (Moen, 2026) overcomes this by evaluating an offline test statistic over a sparse geometric grid of split points, with grid points spaced increasingly far apart further in the past. For a wide class of test statistics, this approach keeps update time and memory consumption growing logarithmic in the length of the data stream, while admitting finite-sample guarantees on the detection delay. Building on this methodology, we present gridcp, an open-source Python package that turns offline changepoint tests into efficient online detectors through a single, uniform interface. Users can choose from nine Numba-accelerated built-in tests, spanning changes in the mean, variance, covariance, and regression coefficients, as well as nonparametric tests and generalized likelihood-ratio tests for exponential-family models. Users can also supply their own test, which the package handles identically. For any test, gridcp provides Monte Carlo routines that calibrate the detection threshold to a target false alarm probability or average run length, including data-driven variants when no parametric null model is available. Through simulations and three real-data case studies, we show that calibration is accurate, that runtime scales favorably with both stream length and dimension, and that the complete pipeline, from calibration to deployment, runs efficiently on long real-world streams with only a short detection delay.

stat.ME↗

arXiv2026

Density Estimation Using the Sinc Kernel

This paper deals with the kernel density estimator based on the so-called sinc (or Fourier integral) kernel $K(x)=(πx)^{-1}\sin x$. We study in detail both asymptotic and finite sample properties of this estimator. It is shown that, contrary to widespread opinion, the sinc estimator is superior to other estimators in many respects: it is more accurate for quite moderate values of the sample size, has better asymptotics in non-smooth case (the density to be estimated has only first derivative), is more convenient for the bandwidth selection, etc.

math.ST↗

arXiv2026

Nonparametric density estimation with a parametric start

The traditional kernel density estimator of an unknown density is by construction completely nonparametric, in the sense that it has no preferences and will work reasonably well for all shapes. The present paper develops a class of semiparametric methods that are designed to work better than the kernel estimator in a broad nonparametric neighbourhood of a given parametric class of densities, for example the normal, while not losing much in precision when the true density is far from the parametric class. The idea is to multiply an initial parametric density estimate with a kernel type estimate of the necessary correction factor. This works well in cases where the correction factor function is less rough than the original density itself. Extensive comparisons with the kernel estimator are carried out, including exact analysis for the class of all normal mixtures. The new method, with a normal start, wins quite often, even in many cases where the true density is far from normal. Procedures for choosing the smoothing parameter of the estimator are also discussed. The new estimator should be particularly useful in higher dimensions, where the usual nonparametric methods have problems. The idea is also spelled out for nonparametric regression.

stat.ME↗

arXiv2023

Efficient sparsity adaptive changepoint estimation

We propose a new, computationally efficient, sparsity adaptive changepoint estimator for detecting changes in unknown subsets of a high-dimensional data sequence. Assuming the data sequence is Gaussian, we prove that the new method successfully estimates the number and locations of changepoints with a given error rate and under minimal conditions, for all sparsities of the changing subset. Moreover, our method has computational complexity linear up to logarithmic factors in both the length and number of time series, making it applicable to large data sets. Through extensive numerical studies we show that the new methodology is highly competitive in terms of both estimation accuracy and computational cost. The practical usefulness of the method is illustrated by analysing sensor data from a hydro power plant. An efficient R implementation is available.

stat.ME↗

arXiv2023

A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them

Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learning models in the industry and academia. There are several algorithmic approaches for computing different versions of Shapley value explanations. Here, we focus on conditional Shapley values for predictive models fitted to tabular data. Estimating precise conditional Shapley values is difficult as they require the estimation of non-trivial conditional expectations. In this article, we develop new methods, extend earlier proposed approaches, and systematize the new refined and existing methods into different method classes for comparison and evaluation. The method classes use either Monte Carlo integration or regression to model the conditional expectations. We conduct extensive simulation studies to evaluate how precisely the different method classes estimate the conditional expectations, and thereby the conditional Shapley values, for different setups. We also apply the methods to several real-world data experiments and provide recommendations for when to use the different method classes and approaches. Roughly speaking, we recommend using parametric methods when we can specify the data distribution almost correctly, as they generally produce the most accurate Shapley value explanations. When the distribution is unknown, both generative methods and regression models with a similar form as the underlying predictive model are good and stable options. Regression-based methods are often slow to train but produce the Shapley value explanations quickly once trained. The vice versa is true for Monte Carlo-based methods, making the different methods appropriate in different practical situations.

stat.ML↗

arXiv2022

Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features

Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical properties and a sound mathematical foundation in the field of cooperative game theory. Precise Shapley value estimates for dependent data rely on accurate modeling of the dependencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that our VAEAC approach to Shapley value estimation outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. For high-dimensional settings, our VAEAC approach with a non-uniform masking scheme significantly outperforms competing methods. Finally, we apply our VAEAC approach to estimate Shapley value explanations for the Abalone data set from the UCI Machine Learning Repository.

stat.ML↗