WebJan 1, 2024 · Abstract. This paper proposes a new test for a change point in the mean of high-dimensional data based on the spatial sign and self-normalization. The test is easy to implement with no tuning parameters, robust to heavy-tailedness and theoretically justified with both fixed-n and sequential asymptotics under both null and alternatives, where n ... Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. One … See more Robust statistics seek to provide methods that emulate popular statistical methods, but are not unduly affected by outliers or other small departures from model assumptions. In statistics, classical estimation methods … See more There are various definitions of a "robust statistic." Strictly speaking, a robust statistic is resistant to errors in the results, produced by deviations from assumptions (e.g., of normality). This means that if the assumptions are only approximately met, the robust estimator … See more The basic tools used to describe and measure robustness are the breakdown point, the influence function and the sensitivity curve. See more A pivotal quantity is a function of data, whose underlying population distribution is a member of a parametric family, that is not dependent on the values of the parameters. An ancillary statistic is such a function that is also a statistic, meaning that it is computed … See more The mean is not a robust measure of central tendency. If the dataset is e.g. the values {2,3,5,6,9}, then if we add another datapoint with value -1000 or +1000 to the data, the resulting mean will be very different to the mean of the original data. Similarly, if we … See more (The mathematical context of this paragraph is given in the section on empirical influence functions.) Historically, several approaches to robust estimation were proposed, including R-estimators and L-estimators. However, M-estimators now … See more Replacing missing data is called imputation. If there are relatively few missing points, there are some models which can be used to estimate values to complete the series, such as replacing missing values with the mean or median of the data. Simple linear regression can … See more
Sparse and Compositionally Robust Inference of Microbial ... - PLOS
WebMar 2, 2024 · Network Cluster-Robust Inference. Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster-robust inference methods. Existing such methods require clusters to be asymptotically independent. Under mild conditions, we prove that, for this ... WebOct 26, 2024 · Since standard maximum likelihood inference is highly susceptible to the possible presence of outliers, we propose the robust double exponential (RDE) estimator. Asymptotic properties and ... brico ok ispra
Robust statistics - Wikipedia
WebMar 13, 2024 · Abstract. We propose a robust inference method for high‐dimensional single index models with an unknown link function and elliptically symmetrically distributed covariates, focusing on signal ... WebJun 11, 2024 · identification robust inference for moments-based analysis of linear dynamic panel data models - volume 38 issue 4 Skip to main content Accessibility help We use … WebFeb 1, 2024 · Cluster-robust inference in nonlinear models. Although cluster-robust inference is most commonly used with the linear regression model (1), it can actually be employed for a wide variety of models estimated by maximum likelihood or the generalized method of moments (GMM); see Hansen and Lee (2024). taselaar