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Multiple Imputation Scheme For Overcoming The Missing Values And Variability Issues In Its Data, Daiheng Ni, John D. Leonard Ii, Angshuman Guin, Chunxia Feng
Multiple Imputation Scheme For Overcoming The Missing Values And Variability Issues In Its Data, Daiheng Ni, John D. Leonard Ii, Angshuman Guin, Chunxia Feng
Daiheng Ni
Traffic engineering studies such as validating Highway Capacity Manual (HCM) models require complete and reliable field data. However, the wealth of intelligent transportation systems (ITS) data is sometimes rendered useless for these purposes because of missing values in the data. Many imputation techniques have been developed in the past with virtually all of them imputing a single value for a missing datum. While this provides somewhat simple and fast estimates, it does not eliminate the possibility of producing biased results and it also fails to account for the uncertainty brought about by missing data. To overcome these limitations, a multiple …
Markov Chain Monte Carlo Multiple Imputation For Incomplete Its Data Using Bayesian Networks, Daiheng Ni, John D. Leonard
Markov Chain Monte Carlo Multiple Imputation For Incomplete Its Data Using Bayesian Networks, Daiheng Ni, John D. Leonard
Daiheng Ni
The rich ITS data is a precious resource for transportatio n researchers and practitioners. However, the usability of such resource is greatly limited by the issue of data missing. A lot of imputation methods have been proposed in the past decade. However, some issues ar e still not or not sufficiently addresse d. For example, the missing of entire records, temporal correlation in observations, natural char acteristics in raw data, and unbiased estimates for missing values. With these in mind, this paper proposes an advanced imputation method which is based on the recent development in other disciplines, especially applied statistics …