Statistical Inference, The Bootstrap, and Neural-Network Modeling With Application to Foreign Exchange Rates  

Oleh:

White, H. ; Racine, J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 4 (2001), page 657-673.
Topik: FOREIGN EXCHANGE RATES; statistical; bootstrap; neural network; application; foreign exchange rates

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We propose tests for individual and joint irrelevance of network inputs. Such tests can be used to determine whether an input or group of inputs “belong” in a particular model, thus permitting valid statistical inference based on estimated feedforward neural - network models. The approaches employ well - known statistical resampling techniques. We conduct a small Monte Carlo experiment showing that our tests have reasonable level and power behavior, and we apply our methods to examine whether there are predictable regularities in foreign exchange rates. We find that exchange rates do appear to contain information that is exploitable for enhanced point prediction, but the nature of the predictive relations evolves through time.

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