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Summary LNAI 2842 Signal Extraction and Knowledge Discovery Based on Statistical Modeling 1st Edition by Genshiro Kitagawa ISBN X - Instant Download

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LNAI 2842 Signal Extraction and Knowledge
Discovery Based on Statistical Modeling 1st
Edition by Genshiro Kitagawa ISBN 9783540200574
354020057X pdf download
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knowledge-discovery-based-on-statistical-modeling-1st-edition-by-
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, Signal Extraction and Knowledge Discovery
Based on Statistical Modeling

Genshiro Kitagawa

The Institute of Statistical Mathematics,
4-6-7 Minami-Azabu, Minato-ku Tokyo 106-8569 Japan

http://www.ism.ac.jp/˜kitagawa



Abstract. In the coming post IT era, the problems of signal extraction
and knowledge discovery from huge data sets will become very impor-
tant. For this problem, the use of good model is crucial and thus the
statistical modeling will play an important role. In this paper, we show
two basic tools for statistical modeling, namely the information criteria
for the evaluation of the statistical models and generic state space model
which provides us with a very flexible tool for modeling complex and
time-varying systems. As examples of these methods we shall show some
applications in seismology and macro economics.


1 Importance of Statistical Modeling in Post IT Era

Once the model is specified, various types of inferences and prediction can be
deduced from the model. Therefore, the model plays a curial role in scientific
inference or signal extraction and knowledge discovery from data. In scientific
research, it is frequently assumed that there exists a known or unknown “true”
model. In statistical community as well, from the age of Fisher, the statistical
theories are developed under the situation that we estimate the true model
with small number of parameters based on limited number of data. However,
in recent years, the models are rather considered as tools for extracting useful
information from data. This is motivated by the information criterion AIC that
revealed that in the estimation of model for prediction, we may obtain a good
model by selecting a simple model even though it may have some bias.
On the other hand, if the model is considered as just a tool for signal extrac-
tion, the model cannot be uniquely determined and there exist many possible
models depending on the viewpoints of the analysts. This means that the results
of the inference and the decision depend on the used model. It is obvious that a
good model yields a good result and a poor model yields a poor result. There-
fore, in statistical modeling, the objective of the modeling is not to find out the
unique “true” model, but to obtain a “good” model based on the characteristics
of the object and the objective of the analysis.
To obtain a good model, we need a criterion to evaluate the goodness of the
model. Akaike (1973) proposed to evaluate the model by the goodness of its

R. Gavaldà et al. (Eds.): ALT 2003, LNAI 2842, pp. 3–14, 2003.

c Springer-Verlag Berlin Heidelberg 2003

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