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Modeling Criminal Careers as Departures From a Unimodal Population Age--Crime Curve: The Case of Marijuana Use


  • Donatello Telesca
  • Elena A. Erosheva
  • Derek A. Kreager
  • Ross L. Matsueda


A major aim of longitudinal analyses of life-course data is to describe the within- and between-individual variability in a behavioral outcome, such as crime. Statistical analyses of such data typically draw on mixture and mixed-effects growth models. In this work, we present a functional analytic point of view and develop an alternative method that models individual crime trajectories as departures from a population age--crime curve. Drawing on empirical and theoretical claims in criminology, we assume a unimodal population age--crime curve and allow individual expected crime trajectories to differ by their levels of offending and patterns of temporal misalignment. We extend Bayesian hierarchical curve registration methods to accommodate count data and to incorporate influence of baseline covariates on individual behavioral trajectories. Analyzing self-reported counts of yearly marijuana use from the Denver Youth Survey, we examine the influence of race and gender categories on differences in levels and timing of marijuana smoking. We find that our approach offers a flexible model for longitudinal crime trajectories and allows for a rich array of inferences of interest to criminologists and drug abuse researchers. This article has supplementary materials online.

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  • Donatello Telesca & Elena A. Erosheva & Derek A. Kreager & Ross L. Matsueda, 2012. "Modeling Criminal Careers as Departures From a Unimodal Population Age--Crime Curve: The Case of Marijuana Use," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1427-1440, December.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1427-1440 DOI: 10.1080/01621459.2012.716328

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    References listed on IDEAS

    1. Maindonald, John, 2006. "Generalized Additive Models: An Introduction with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(b03).
    2. Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
    3. Jianqing Fan, 2000. "Simultaneous Confidence Bands and Hypothesis Testing in Varying-coefficient Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 715-731.
    4. H rdle, Wolfgang & Huet, Sylvie & Mammen, Enno & Sperlich, Stefan, 2004. "Bootstrap Inference In Semiparametric Generalized Additive Models," Econometric Theory, Cambridge University Press, vol. 20(02), pages 265-300, April.
    5. Jing Wang & Lijian Yang, 2009. "Efficient and fast spline-backfitted kernel smoothing of additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 663-690, September.
    6. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, March.
    7. Osmani, R.S., 1990. "Food Deprivation and Undernutrition in Rural Bangladesh," Research Paper 82, World Institute for Development Economics Research.
    8. Gerda Claeskens & Tatyana Krivobokova & Jean D. Opsomer, 2009. "Asymptotic properties of penalized spline estimators," Biometrika, Biometrika Trust, vol. 96(3), pages 529-544.
    9. Haerdle,Wolfgang & Bowman,Adrian, 1986. "Bootstrapping in nonparametric regression: Local adaptive smoothing and confidence bands," Discussion Paper Serie A 71, University of Bonn, Germany.
    10. M. Ruth & K. Donaghy & P. Kirshen, 2006. "Introduction," Chapters,in: Regional Climate Change and Variability, chapter 1 Edward Elgar Publishing.
    11. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, March.
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