Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Selecting optimal multistep predictors for autoregressive processes of
  unknown order   We consider the problem of choosing the optimal (in the sense of mean-squared
prediction error) multistep predictor for an autoregressive (AR) process of
finite but unknown order. If a working AR model (which is possibly
misspecified) is adopted for multistep predictions, then two competing types of
multistep predictors (i.e., plug-in and direct predictors) can be obtained from
this model. We provide some interesting examples to show that when both plug-in
and direct predictors are considered, the optimal multistep prediction results
cannot be guaranteed by correctly identifying the underlying model's order.
This finding challenges the traditional model (order) selection criteria, which
usually aim to choose the order of the true model. A new prediction selection
criterion, which attempts to seek the best combination of the prediction order
and the prediction method, is proposed to rectify this difficulty. When the
underlying model is stationary, the validity of the proposed criterion is
justified theoretically.

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The categories of this paper are: physics
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: nlin