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of 2 399
pro vyhledávání: '"62m10"'
Autor:
Brown, Chad
This paper establishes statistical properties of deep neural network (DNN) estimators under dependent data. Two general results for nonparametric sieve estimators directly applicable to DNNs estimators are given. The first establishes rates for conve
Externí odkaz:
http://arxiv.org/abs/2410.11113
Autor:
Schmidtke, Finn, Vetter, Mathias
Given independent random variables $Y_1, \ldots, Y_n$ with $Y_i \in \{0,1\}$ we test the hypothesis whether the underlying success probabilities $p_i$ are constant or whether they are periodic with an unspecified period length of $r \ge 2$. The test
Externí odkaz:
http://arxiv.org/abs/2410.10203
Variable Length Markov Chains with Exogenous Covariates (VLMCX) are stochastic models that use Generalized Linear Models to compute transition probabilities, taking into account both the state history and time-dependent exogenous covariates. The beta
Externí odkaz:
http://arxiv.org/abs/2410.07374
We consider a general class of statistical experiments, in which an $n$-dimensional centered Gaussian random variable is observed and its covariance matrix is the parameter of interest. The covariance matrix is assumed to be well-approximable in a li
Externí odkaz:
http://arxiv.org/abs/2410.05751
Autor:
West, Mike, Vrotsos, Luke
Simultaneous graphical dynamic linear models (SGDLMs) provide advances in flexibility, parsimony and scalability of multivariate time series analysis, with proven utility in forecasting. Core theoretical aspects of such models are developed, includin
Externí odkaz:
http://arxiv.org/abs/2410.06125
Autor:
Maturo, Fabrizio, Porreca, Annamaria
The positioning of this research falls within the scalar-on-function classification literature, a field of significant interest across various domains, particularly in statistics, mathematics, and computer science. This study introduces an advanced m
Externí odkaz:
http://arxiv.org/abs/2409.17804
This paper considers a semiparametric approach within the general Bayesian linear model where the innovations consist of a stationary, mean zero Gaussian time series. While a parametric prior is specified for the linear model coefficients, the autoco
Externí odkaz:
http://arxiv.org/abs/2409.16207
In recent years, large language models (LLMs) have shown great potential in time-series analysis by capturing complex dependencies and improving predictive performance. However, existing approaches often struggle with modality alignment, leading to s
Externí odkaz:
http://arxiv.org/abs/2409.14978
Autor:
Wette, Sebastian, Heinrichs, Florian
Time series are ubiquitous and occur naturally in a variety of applications -- from data recorded by sensors in manufacturing processes, over financial data streams to climate data. Different tasks arise, such as regression, classification or segment
Externí odkaz:
http://arxiv.org/abs/2409.09742
This paper considers the problem of testing and estimation of change point where signals after the change point can be highly irregular, which departs from the existing literature that assumes signals after the change point to be piece-wise constant
Externí odkaz:
http://arxiv.org/abs/2409.08863