Zobrazeno 1 - 10
of 518
pro vyhledávání: '"Politis, A. N."'
Autor:
Wu, Kejin, Politis, Dimitris N.
In this paper, we provide a novel Model-free approach based on Deep Neural Network (DNN) to accomplish point prediction and prediction interval under a general regression setting. Usually, people rely on parametric or non-parametric models to bridge
Externí odkaz:
http://arxiv.org/abs/2408.09532
Autor:
Wu, Kejin, Politis, Dimitris N.
Deep neural networks (DNN) has received increasing attention in machine learning applications in the last several years. Recently, a non-asymptotic error bound has been developed to measure the performance of the fully connected DNN estimator with Re
Externí odkaz:
http://arxiv.org/abs/2405.08276
Autor:
Politis, Dimitris N., Wu, Kejin
To address the difficult problem of multi-step ahead prediction of non-parametric autoregressions, we consider a forward bootstrap approach. Employing a local constant estimator, we can analyze a general type of non-parametric time series model, and
Externí odkaz:
http://arxiv.org/abs/2311.00294
Autor:
Wu, Kejin, Politis, Dimitris N.
The non-linear autoregressive (NLAR) model plays an important role in modeling and predicting time series. One-step ahead prediction is straightforward using the NLAR model, but the multi-step ahead prediction is cumbersome. For instance, iterating t
Externí odkaz:
http://arxiv.org/abs/2306.04126
The Model-free Prediction Principle has been successfully applied to general regression problems, as well as problems involving stationary and locally stationary time series. In this paper we demonstrate how Model-Free Prediction can be applied to ha
Externí odkaz:
http://arxiv.org/abs/2212.03079
The problem of estimating the spectral density matrix $f(w)$ of a multivariate time series is revisited with special focus on the frequencies $w=0$ and $w=\pi$. Recognizing that the entries of the spectral density matrix at these two boundary points
Externí odkaz:
http://arxiv.org/abs/2212.02584
Autor:
Wang, Yiren, Politis, Dimitris N.
In Das and Politis(2020), a model-free bootstrap(MFB) paradigm was proposed for generating prediction intervals of univariate, (locally) stationary time series. Theoretical guarantees for this algorithm was resolved in Wang and Politis(2019) under st
Externí odkaz:
http://arxiv.org/abs/2112.08671
Autor:
Politis, Dimitris N.
Subsampling is a general statistical method developed in the 1990s aimed at estimating the sampling distribution of a statistic $\hat \theta _n$ in order to conduct nonparametric inference such as the construction of confidence intervals and hypothes
Externí odkaz:
http://arxiv.org/abs/2112.06434
Autor:
Zhang, Yunyi, Politis, Dimitris N.
Focusing on a high dimensional linear model $y = X\beta + \epsilon$ with dependent, non-stationary, and heteroskedastic errors, this paper applies the debiased and threshold ridge regression method that gives a consistent estimator for linear combina
Externí odkaz:
http://arxiv.org/abs/2110.13498
Strict stationarity is a common assumption used in the time series literature in order to derive asymptotic distributional results for second-order statistics, like sample autocovariances and sample autocorrelations. Focusing on weak stationarity, th
Externí odkaz:
http://arxiv.org/abs/2110.14067