Possibilistic pattern recognition in a digestive database for mining imperfect data
WSEAS TRANSACTIONS on SYSTEMS
Possibilistic missing data estimation
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Using multiple imputation to simulate time series
ACACOS'10 Proceedings of the 9th WSEAS international conference on Applied computer and applied computational science
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Multiple Imputation (MI) is a Markov chain Monte Carlo technique developed to work out missing data problems, specially in cross section approaches. This paper uses Multiple Imputation from a different point of view: it intends to apply the technique to time series and develops that way a simpler framework presented in previous papers. Here, the authors' idea consists basically on an endogenous construction of the database (the use of lags as supporting variables supposes a new approach to deal with the distance effect). This construction strategy avoids noise in the simulations and forces the limit distribution of the chain to convergence well. Using this approximation, estimated plausible values are closer to real values, and missing data can be solved with more accuracy. This new proposal solves the main problem detected by the authors in [1] when using MI with time series: the previously commented distance effect. An endogenous construction when analyzing time series avoids this undesired effect, and allows Multiple Imputation to benefit from information from the whole data base. Finally, new R computer code was designed to carry out all the simulations and is presented in the Appendix to be analyzed and updated by researchers.