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Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models

Roberts, Clint Douglas
http://rave.ohiolink.edu/etdc/view?acc_num=osu1211910310

Abstract Details

2008, Doctor of Philosophy, Ohio State University, Statistics.
The Uniform Crime Reports, collected by the FBI, contain monthly crime counts for each of the seven Index crimes, but for one reason or another, a police agency may miss reporting for a particular month. The data are not complete, hence the need for the development of an imputation procedure to fill in the gaps. Since the early 1960's, an imputation technique implemented by the FBI has been used to make annual crime count estimates. This approach ignores concerns of seasonality and does not make use of the agencies' long-term data trends. Computing power has radically improved since the 1960's, and it is now feasible to develop a more precise imputation method that can incorporate more information into our estimation procedure. A model-based approach also has the added value of making available variance estimates for the imputed data. We describe a method which uses three different models depending on an agency's average monthly crime count for a particular crime. For small crime counts, we impute the mean and we assume the data are Poisson for the variance estimates. For large crime counts, we consider a time series SARIMA (Seasonal Auto-Regressive Integrated Moving Average) model. For intermediate crime counts, we use a Poisson Generalized Linear Model (GLM). Hierarchical Bayesian models are used to obtain improved imputations for missing data that borrow strength from many UCR series. Information about population growth contained in the crime counts is thought to result in improved imputations when using time series from other agencies.
Peter Craigmile, PhD (Advisor)
Elizabeth Stasny, PhD (Advisor)
Catherine Calder, PhD (Committee Member)
Michael Maltz, PhD (Committee Member)
Criminology; Statistics

Recommended Citations

Citations

  • Roberts, C. D. (2008). Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211910310

    APA Style (7th edition)

  • Roberts, Clint. Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models. 2008. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1211910310.

    MLA Style (8th edition)

  • Roberts, Clint. "Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models." Doctoral dissertation, Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211910310

    Chicago Manual of Style (17th edition)

osu1211910310
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© 2008, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.