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Publisher: Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik × Subject: Propagation ×

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Now showing 1 - 3 of 3
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    Locally adaptive estimation methods with application to univariate time series
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2008) Elagin, Mstislav
    The paper offers a unified approach to the study of three locally adaptive estimation methods in the context of univariate time series from both theoretical and empirical points of view. A general procedure for the computation of critical values is given. The underlying model encompasses all distributions from the exponential family providing for great flexibility. The procedures are applied to simulated and real financial data distributed according to the Gaussian, volatility, Poisson, exponential and Bernoulli models. Numerical results exhibit a very reasonable performance of the methods
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    Locally time homogeneous time series modelling
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2008) Elagin, Mstislav; Spokoiny, Vladimir
    In this paper three locally adaptive estimation methods are applied to the problems of variance forecasting, value-at-risk analysis and volatility estimation within the context of nonstationary financial time series. A general procedure for the computation of critical values is given. Numerical results exhibit a very reasonable performance of the methods.
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    A new perspective on the propagation-separation approach: Taking advantage of the propagation condition
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2013) Becker, Saskia; Mathé, Peter
    The Propagation-Separation approach is an iterative procedure for pointwise estimation of local constant and local polynomial functions. The estimator is defined as a weighted mean of the observations with data-driven weights. Within homogeneous regions it ensures a similar behavior as non-adaptive smoothing (propagation), while avoiding smoothing among distinct regions (separation). In order to enable a proof of stability of estimates, the authors of the original study introduced an additional memory step aggregating the estimators of the successive iteration steps. Here, we study theoretical properties of the simplified algorithm, where the memory step is omitted. In particular, we introduce a new strategy for the choice of the adaptation parameter yielding propagation and stability for local constant functions with sharp discontinuities