Abstract
The Levenson Self Report Psychopathy serves as a measure to spot persons with psychopathic disorders able to commit crime or offend others. Indeed, predicting the Levenson Self Report Psychopathy factors would help investigator and even psychologist to spot offenders. In this paper, a statistical model is performed with the aim of predicting the Levenson Self Report Psychopathy scale value. For this purpose, the multiple regression statistical method is used. In addition, a parallelized algebraic adjoint method is performed to solve the least square problem. The MapReduce framework is used for this purpose. The Apache implementation of Mapreduce developed in Java untilled Hadoop 2.6.0 is deployed to tackle experiments.
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References
Brinkley, C., Schmitt, W., Smith, S., Newman, J.: Construct validation of a self-report psychopathy scale: does Levenson’s selfreport psychopathy scale measure the same constructs as Hare’s psychopathy checklist-revised? Pers. Individ. Differ. 31(7), 1021–1038 (2001)
Cleckley, H.: The mask of sanity; an attempt to reinterpret the so-called psychopathic personality. Oxford, England (1941)
Gummelt, H., Anestis, J., Carbonell, J.: Examining the Levenson self report psychopathy scale using a graded response model. Pers. Individ. Differ. 53(8), 1002–1006 (2012)
Hare, R.D.: The psychopathy checklist-Revised (2003)
Lykken, D.T.: The Antisocial Personalities. Lawrence Erlbaum Associates, Mahwah (1995)
Marcus, D.K., John, S.L., Edens, J.F.: A taxometric analysis of psychopathic personality. J. Abnorm. Psychol. 113(4), 626 (2004)
Dotterer, H.L., Waller, R., Neumann, C.S., Shaw, D.S., Forbes, E.E., Hariri, A.R., Hyde, L.W.: Examining the factor structure of the self-report of psychopathy short-form across four young adult samples. Assessment 24(8), 1062–1079 (2017)
Bell, C.: Dsm-iv: diagnostic and statistical manual of mental disorders. JAMA 272(10), 828–829 (1994)
Pramanik, M.I., Lau, R.Y.K., Yue, W.T., Ye, Y., Li, C.: Big data analytics for security and criminal investigations. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 7(4) (2017)
Steyerberg, E.W.: Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer, Heidelberg (2008). https://doi.org/10.1007/978-0-387-77244-8
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2001). https://doi.org/10.1007/978-0-387-84858-7
Adjout, M.R., Boufares, F.: A massively parallel processing for the multiple linear regression. In: Tenth International Conference on SignalImage Technology and Internet-Based Systems, pp. 666–671 (2014)
Padua, D. (ed.): Encyclopedia of Parallel Computing. Springer, Heidelberg (2011). https://doi.org/10.1007/978-0-387-09766-4
Zettam, M., Laassiri, J., Enneya, N.: A software solution for preventing Alzheimer’s disease based on MapReduce framework. In: 2017 IEEE International Conference on Information Reuse and Integration (IRI), pp. 192–197 (2017)
Lin, J., Dyer, C.: Data-intensive text processing with MapReduce. Synth. Lect. Hum. Lang. Technol. 3(1), 1–177 (2010)
Ghemawat, S., Gobioff, H., Leung, S.: The Google file system. In: ACM SIGOPS Operating Systems Review, vol. 37, pp. 29–43. ACM (2003)
Sen, A., Srivastava, M.: Multiple regression. In: Regression Analysis. Springer Texts in Statistics. Springer, New York (1990)
Khan, M., Jin, Y., Li, M., Xiang, Y., Jiang, C.: Hadoop performance modeling for job estimation and resource provisioning. IEEE Trans. Parallel Distrib. Syst. 27(2), 441–454 (2016)
Gummelt, H.D., Anestis, J.C., Carbonell, J.L.: Examining the Levenson self report psychopathy scale using a graded response model. Personal. Individ. Differ. 53(8), 1002–1006 (2012)
Heap, B.R.: Permutations by interchanges. Comput. J. 6(3), 293–298 (1963)
Sedgewick, R.: Permutation generation methods. ACM Comput. Surv. 9(2), 137–164 (1977)
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Zettam, M., Laassiri, J., Enneya, N. (2018). A MapReduce-Based Adjoint Method to Predict the Levenson Self Report Psychopathy Scale Value. In: Tabii, Y., Lazaar, M., Al Achhab, M., Enneya, N. (eds) Big Data, Cloud and Applications. BDCA 2018. Communications in Computer and Information Science, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-319-96292-4_2
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DOI: https://doi.org/10.1007/978-3-319-96292-4_2
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