Results 41 to 50 of about 987 (182)
Alternative Approaches for Estimating Highest‐Density Regions
International Statistical Review, EarlyView.Summary Among the variety of statistical intervals, highest‐density regions (HDRs) stand out for their ability to effectively summarise a distribution or sample, unveiling its distinctive and salient features. An HDR represents the minimum size set that satisfies a certain probability coverage, and current methods for their computation require ...
Nina Deliu, Brunero Liseo
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On Spatial Point Processes With Composition‐Valued Marks
International Statistical Review, EarlyView.Summary Methods for marked spatial point processes with scalar marks have seen extensive development in recent years. While the impressive progress in data collection and storage capacities has yielded an immense increase in spatial point process data with highly challenging non‐scalar marks, methods for their analysis are not equally well developed ...
Matthias Eckardt +2 more
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On an Asymptotic Property of Dirichlet Kernels
Известия Иркутского государственного университета: Серия "Математика"The main object of our study is the Dirichlet kernel. The properties of this trigonometric polynomial — the sum of cosines of multiple arcs — are of undoubted interest in the theory of trigonometric series.
E. D. Alferova +2 more
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Change Point Analysis for Functional Data Using Empirical Characteristic Functionals
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a new method to detect change points in the distribution of functional data based on integrated CUSUM processes of empirical characteristic functionals. Asymptotic results are presented under conditions allowing for low‐order moments and serial dependence in the data establishing the limiting null‐distribution of the proposed test ...
Lajos Horváth +2 more
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A New Approach to Statistical Inference for Functional Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT The analysis of time‐indexed functional data plays an important role in the field of business and economic statistics. In the literature, statistical inference for functional time series often involves reducing the dimension of functional data to a finite dimension K$$ K $$, followed by the use of tools from multivariate analysis.
Hanjia Gao, Yi Zhang, Xiaofeng Shao
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Measure‐valued processes for energy markets
Mathematical Finance, Volume 35, Issue 2, Page 520-566, April 2025.Abstract We introduce a framework that allows to employ (non‐negative) measure‐valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath–Jarrow–Morton approach can be translated to this framework, thus guaranteeing arbitrage free ...
Christa Cuchiero +3 more
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Robust Bernoulli Mixture Models for Credit Portfolio Risk
Mathematical Finance, EarlyView.ABSTRACT This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing in a common risk factor. We provide simple and interpretable conditions on conditional default probabilities that imply a comparison ...
Jonathan Ansari, Eva Lütkebohmert
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Optimal Portfolio Choice With Cross‐Impact Propagators
Mathematical Finance, EarlyView.ABSTRACT We consider a class of optimal portfolio choice problems in continuous time where the agent's transactions create both transient cross‐impact driven by a matrix‐valued Volterra propagator, as well as temporary price impact. We formulate this problem as the maximization of a revenue‐risk functional, where the agent also exploits available ...
Eduardo Abi Jaber +2 more
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The Lebesgue constants on projective spaces
TURKISH JOURNAL OF MATHEMATICS, 2021 Summary: We give the solution of a classical problem of Approximation Theory on sharp asymptotic of the Lebesgue constants or norms of the Fourier-Laplace projections on the real projective spaces \(\mathrm{P}^d(\mathbb{R})\). In particular, these results extend sharp asymptotic found by \textit{L. Fejér} [J. Reine Angew. Math.
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Spatial depth for data in metric spaces
Scandinavian Journal of Statistics, EarlyView.Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
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