Results 111 to 120 of about 7,649 (300)
A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces
International Statistical Review, EarlyView.Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri +3 more
wiley +1 more source
Electronic Journal of Qualitative Theory of Differential Equations, 2016 This paper is concerned with well-posedness and stability of parabolic stochastic partial differential equations. Firstly, we obtain some sufficient conditions ensuring the existence and uniqueness of mild solutions, and some $\mathcal{H}$-stability ...
Chaoliang Luo, Shangjiang Guo
doaj +1 more source
A note on generalised information criteria for structured sparse models
International Statistical Review, EarlyView.Summary We propose a generalised information criteria ( gic) that accounts for sparsity pattern in the model. We obtain both asymptotic and nonasymptotic results for model selection. Moreover, we show that the gic is useful for selecting the regularisation parameter in regularised m$$ m $$ estimation in high‐dimensional scenarios.
Eduardo Fonseca Mendes +1 more
wiley +1 more source
Detecting Relevant Deviations From the White Noise Assumption for Non‐Stationary Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We consider the problem of detecting deviations from a white noise assumption in time series. Our approach differs from the numerous methods proposed for this purpose with respect to two aspects. First, we allow for non‐stationary time series. Second, we address the problem that a white noise test is usually not performed because one believes ...
Patrick Bastian
wiley +1 more source
Linear and Lipschitz similarity
, 1991 We give a sufficient condition for two Lipschitz similar orthogonal maps to be linearly ...
Cruz, Ricardo N.
core +1 more source
Adaptive Estimation for Weakly Dependent Functional Times Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose adaptive mean and autocovariance function estimators for stationary functional time series under 𝕃p−m‐approximability assumptions. These estimators are designed to adapt to the regularity of the curves and to accommodate both sparse and dense data designs.
Hassan Maissoro +2 more
wiley +1 more source
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose a new formulation of the Vašičekmodel within the framework of functional data analysis. We treat observations (continuous‐time rates) within a suitably defined trading day as a single statistical object. We then consider a sequence of such objects, indexed by day.
Piotr Kokoszka +4 more
wiley +1 more source
Boundedness of Commutators of Marcinkiewicz Integrals on Nonhomogeneous Metric Measure Spaces
Journal of Function Spaces, 2015 Let (X,d,μ) be a metric measure space satisfying the upper doubling condition and geometrically doubling condition in the sense of Hytönen. The aim of this paper is to establish the boundedness of commutator Mb generated by the Marcinkiewicz integral M ...
Guanghui Lu, Shuangping Tao
doaj +1 more source
Density‐Valued ARMA Models by Spline Mixtures
Journal of Time Series Analysis, EarlyView.ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley +1 more source
Geometric conditions and existence of bi-Lipschitz parameterizations
Duke Mathematical Journal, 1995 Let \(\Sigma\) be a locally compact set of Hausdorff dimension \(n\) in \(\mathbb{R}^{n+ m}\) and \(\zeta_0\) a point of \(\Sigma\). Set \[ \theta(r, \zeta)= \inf_L \{\textstyle{{1\over r}} D[Z\cap B(r, \zeta), L\cap B(r, \zeta)]\}, \] where the infimum is taken over all \(n\)-planes containing \(\zeta\), and \(D\) is the Hausdorff distance.
openaire +2 more sources