Results 101 to 110 of about 82,525 (218)
Interaction of Dirac δ$$ \delta $$‐Waves in the Inviscid Levine and Sleeman Chemotaxis Model
ABSTRACT This article investigates interactions of δ$$ \delta $$‐shock waves in the inviscid Levine and Sleeman chemotaxis model ut−λ(uv)x=0$$ {u}_t-\lambda {(uv)}_x=0 $$, vt−ux=0$$ {v}_t-{u}_x=0 $$. The analysis employs a distributional product and a solution concept that extends the classical solution concept.
Adelino Paiva
wiley +1 more source
Parseval–Goldstein-Type Theorems for Lebedev–Skalskaya Transforms
This paper investigates Parseval–Goldstein-type relationships in the framework of Lebedev–Skalskaya transforms. The research also examines the continuity properties of these transforms, along with their adjoint counterparts over weighted Lebesgue spaces.
Emilio Ramón Negrín +2 more
doaj +1 more source
Duality of Variable Exponent Triebel-Lizorkin and Besov Spaces
We will prove the duality and reflexivity of variable exponent Triebel-Lizorkin and Besov spaces. It was shown by many authors that variable exponent Triebel-Lizorkin spaces coincide with variable exponent Bessel potential spaces, Sobolev spaces, and ...
Takahiro Noi
doaj +1 more source
Efficient Deconvolution in Populational Inverse Problems
ABSTRACT This work is focused on the inversion task of inferring the distribution over parameters of interest, leading to multiple sets of observations. The potential to solve such distributional inversion problems is driven by the increasing availability of data, but a major roadblock is blind deconvolution, arising when the observational noise ...
Arnaud Vadeboncoeur +2 more
wiley +1 more source
A Hybrid Nonparametric Framework for Outlier Detection in Functional Time Series
ABSTRACT Outlier detection in functional time series is challenging due to temporal dependence and the simultaneous presence of magnitude, shape, and partial anomalies. Existing methods often assume independence or rely on model based approaches, such as the Standard Smoothed Bootstrap on Residuals (SmBoR), which may not work well if the model is ...
David Solano +4 more
wiley +1 more source
Moderate Deviation Principles for Lacunary Trigonometric Sums
ABSTRACT Classical works of Kac, Salem, and Zygmund, and Erdős and Gál have shown that lacunary trigonometric sums despite their dependency structure behave in various ways like sums of independent and identically distributed random variables. For instance, they satisfy a central limit theorem (CLT) and a law of the iterated logarithm.
Joscha Prochno, Marta Strzelecka
wiley +1 more source
H‐Points and H‐Property of the Orlicz Spaces Endowed With the s‐Norms
ABSTRACT In this paper, we investigate the set of H‐points on the unit sphere of the Orlicz spaces over non‐atomic and purely atomic measure spaces. Building upon these arguments, we establish a criteria for the H‐property in Orlicz spaces. In addition, our results yield several consequences related to the geometric properties of the Orlicz spaces ...
Serap Öztop, Badik Hüseyin Uysal
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Change Point Analysis for Functional Data Using Empirical Characteristic Functionals
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
wiley +1 more source
This article studies the abstract discrete-time Cauchy problem involving the Riemann–Liouville type difference operator. Sufficient conditions for the existence of unique solution to the semilinear Cauchy problem in Lebesgue and weighted Lebesgue vector ...
Jagan Mohan Jonnalagadda, Carlos Lizama
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A New Approach to Statistical Inference for Functional Time Series
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
wiley +1 more source

