arXiv subjects

Nathawut Phandoidaen

Publications and source records attributed to Nathawut Phandoidaen.

4 recordsLinked to original sources

arXiv2021

Empirical process theory for locally stationary processes

We provide a framework for empirical process theory of locally stationary processes using the functional dependence measure. Our results extend known results for stationary Markov chains and mixing sequences by another common possibility to measure dependence and allow for additional time dependence. Our main result is a functional central limit theorem for locally stationary processes. Moreover, maximal inequalities for expectations of sums are developed. We show the applicability of our theory in some examples, for instance we provide uniform convergence rates for nonparametric regression with locally stationary noise.

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arXiv2021

Empirical process theory for nonsmooth functions under functional dependence

We provide an empirical process theory for locally stationary processes over nonsmooth function classes. An important novelty over other approaches is the use of the flexible functional dependence measure to quantify dependence. A functional central limit theorem and nonasymptotic maximal inequalities are provided. The theory is used to prove the functional convergence of the empirical distribution function (EDF) and to derive uniform convergence rates for kernel density estimators both for stationary and locally stationary processes. A comparison with earlier results based on other measures of dependence is carried out.

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arXiv2021

Multiplicative deconvolution in survival analysis under dependency

We study the non-parametric estimation of an unknown survival function S with support on R+ based on a sample with multiplicative measurement errors. The proposed fully-data driven procedure is based on the estimation of the Mellin transform of the survival function S and a regularisation of the inverse of the Mellin transform by a spectral cut-off. The upcoming bias-variance trade-off is dealt with by a data-driven choice of the cut-off parameter. In order to discuss the bias term, we consider the Mellin-Sobolev spaces which characterize the regularity of the unknown survival function S through the decay of its Mellin transform. For the analysis of the variance term, we consider the i.i.d. case and incorporate dependent observations in form of Bernoulli shift processes and beta mixing sequences. Additionally, we show minimax-optimality over Mellin-Sobolev spaces of the spectral cut-off estimator.

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arXiv2020

Forecasting time series with encoder-decoder neural networks

In this paper, we consider high-dimensional stationary processes where a new observation is generated from a compressed version of past observations. The specific evolution is modeled by an encoder-decoder structure. We estimate the evolution with an encoder-decoder neural network and give upper bounds for the expected forecast error under specific structural and sparsity assumptions. The results are shown separately for conditions either on the absolutely regular mixing coefficients or the functional dependence measure of the observed process. In a quantitative simulation we discuss the behavior of the network estimator under different model assumptions. We corroborate our theory by a real data example where we consider forecasting temperature data.

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