Zobrazeno 1 - 10
of 2 201
pro vyhledávání: '"62E20"'
This paper presents and examines computationally convenient goodness-of-fit tests for the family of generalized Poisson distributions, which encompasses notable distributions such as the Compound Poisson and the Katz distributions. The tests are cons
Externí odkaz:
http://arxiv.org/abs/2411.12889
We consider nonparametric estimation of the distribution function $F$ of squared sphere radii in the classical Wicksell problem. Under smoothness conditions on $F$ in a neighborhood of $x$, in \cite{21} it is shown that the Isotonic Inverse Estimator
Externí odkaz:
http://arxiv.org/abs/2410.14263
This paper is devoted to the study of the general linear hypothesis testing (GLHT) problem of multi-sample high-dimensional mean vectors. For the GLHT problem, we introduce a test statistic based on $L^2$-norm and random integration method, and deduc
Externí odkaz:
http://arxiv.org/abs/2410.14120
We provide necessary and sufficient conditions for the uniqueness of the k-means set of a probability distribution. This uniqueness problem is related to the choice of k: depending on the underlying distribution, some values of this parameter could l
Externí odkaz:
http://arxiv.org/abs/2410.13495
Autor:
Minsker, Stanislav, Shen, Yinan
Is there a natural way to order data in dimension greater than one? The approach based on the notion of data depth, often associated with the name of John Tukey, is among the most popular. Tukey's depth has found applications in robust statistics, gr
Externí odkaz:
http://arxiv.org/abs/2410.00219
In this paper, we establish the central limit theorem (CLT) for the linear spectral statistics (LSS) of sample correlation matrix $R$, constructed from a $p\times n$ data matrix $X$ with independent and identically distributed (i.i.d.) entries having
Externí odkaz:
http://arxiv.org/abs/2409.12536
In this paper, for the problem of heteroskedastic general linear hypothesis testing (GLHT) in high-dimensional settings, we propose a random integration method based on the reference L2-norm to deal with such problems. The asymptotic properties of th
Externí odkaz:
http://arxiv.org/abs/2409.12066
This paper proposes an asymptotic theory for online inference of the stochastic gradient descent (SGD) iterates with dropout regularization in linear regression. Specifically, we establish the geometric-moment contraction (GMC) for constant step-size
Externí odkaz:
http://arxiv.org/abs/2409.07434
Autor:
Bücher, Axel, Staud, Torben
The block maxima method is a standard approach for analyzing the extremal behavior of a potentially multivariate time series. It has recently been found that the classical approach based on disjoint block maxima may be universally improved by conside
Externí odkaz:
http://arxiv.org/abs/2409.05529
Autor:
Lohot, Raju. K., Dixit, V. U.
The skew symmetric Laplace uniform distribution SSLUD({\mu}) is introduced in Lohot, R. K. and Dixit, V. U. (2024) using the skewing mechanism of Azzalini (1985). Here we derive the most powerful (MP) test for symmetry of the SSLUD({\mu}). Since the
Externí odkaz:
http://arxiv.org/abs/2406.20090