Results 21 to 30 of about 6,880 (105)
Testing Mean Stability of Heteroskedastic Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT Time series models are often fitted to the data without preliminary checks for stability of the mean and variance, conditions that may not hold in much economic and financial data, particularly over long periods. Ignoring such shifts may result in fitting models with spurious dynamics that lead to unsupported and controversial conclusions ...
Violetta Dalla +2 more
wiley +1 more source
Risks of ignoring uncertainty propagation in AI‐augmented security pipelines
Risk Analysis, EarlyView.Abstract The use of AI technologies is being integrated into the secure development of software‐based systems, with an increasing trend of composing AI‐based subsystems (with uncertain levels of performance) into automated pipelines. This presents a fundamental research challenge and seriously threatens safety‐critical domains.
Emanuele Mezzi +3 more
wiley +1 more source
Nonparametric inference for Poisson‐Laguerre tessellations
Scandinavian Journal of Statistics, EarlyView.Abstract In this paper, we consider statistical inference for Poisson‐Laguerre tessellations in ℝd$$ {\mathbb{R}}^d $$. The object of interest is a distribution function F$$ F $$ which describes the distribution of the arrival times of the generator points.
Thomas van der Jagt +2 more
wiley +1 more source
Kernel Estimation for Panel Data with Heterogeneous Dynamics
, 2018 This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel
Okui, Ryo, Yanagi, Takahide
core +1 more source
Scandinavian Journal of Statistics, Volume 52, Issue 3, Page 1138-1175, September 2025.ABSTRACT A fundamental functional in nonparametric statistics is the Mann‐Whitney functional θ=P(X
Jonas Beck +2 more
wiley +1 more source
Estimating a Signal In the Presence of an Unknown Background
, 2012 We describe a method for fitting distributions to data which only requires knowledge of the parametric form of either the signal or the background but not both. The unknown distribution is fit using a non-parametric kernel density estimator.
López, Angel M., Rolke, Wolfgang A.
core +1 more source
Optimal No-regret Learning in Repeated First-price Auctions
, 2020 We study online learning in repeated first-price auctions with censored feedback, where a bidder, only observing the winning bid at the end of each auction, learns to adaptively bid in order to maximize her cumulative payoff.
Han, Yanjun +2 more
core
Control Variables, Discrete Instruments, and Identification of Structural Functions
, 2018 Control variables provide an important means of controlling for endogeneity in econometric models with nonseparable and/or multidimensional heterogeneity. We allow for discrete instruments, giving identification results under a variety of restrictions on
Newey, Whitney, Stouli, Sami
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Journal of Medical Virology, Volume 97, Issue 8, August 2025.ABSTRACT Severe COVID‐19 is associated with increased prothrombotic and inflammatory responses, necessitating effective anticoagulation therapy. Novel oral anticoagulants (NOACs) are being explored as potential alternatives to low‐molecular‐weight heparin (LMWH).
Rubens Carmo Costa‐Filho +11 more
wiley +1 more source
Refuting causal relations for synchronized pathogen dynamics
Methods in Ecology and Evolution, Volume 16, Issue 8, Page 1836-1850, August 2025.Abstract Identifying causal relations is a critical challenge in the study of ecological systems, particularly for studying the spread of pathogens in host populations. Whether dealing with measles in humans, foot and mouth disease in livestock, or white‐nose syndrome in bats—understanding causal relations is essential for understanding the underlying ...
Yair Daon +3 more
wiley +1 more source