Using Scan Statistics for Cluster Detection: Recognizing Real Bandwagons

• Alm SE (1997) On the distribution of scan statistics of two-dimensional Poisson processes. Adv Appl Probab 29:1–18

  Article  MathSciNet  Google Scholar 

• Alm SE (1998) Approximation and simulation of the distributions of scan statistics for Poisson process in higher dimensions. Extremes 1:111–126

  Article  MathSciNet  Google Scholar 

• Alm SE (1999) Approximations of the distribution of scan statistics of Poisson processes. In: Glaz J, Balakrishnan N (eds) Scan Statistics and Applications. Birkhauser, Boston, pp 113–139

  Chapter  Google Scholar 

• Balakrishnan N, Koutras MV (2002) Runs and scans with applications. Wiley, New York

  MATH  Google Scholar 

• Chen, J. and Glaz, J. (1997). Approximations and inequalities for the distribution of scan statistics for 0-1 Bernoulli trials. Advances in the Theory and Practice of Statistics - a volume in honor of Samuel Katz. N. L. Johnson and N. Balakrishnan, es. Wiley & Sons, New York, pp. 285-298

• Chen J, Glaz J (1999) Approximations of the distribution of the moments of siscrete scan statistics of Poisson processes. In: Glaz J, Balakrishnan N (eds) Scan Statistics and Applications. Birkhauser, Boston, pp 27–66

  Chapter  Google Scholar 

• Chen J, Glaz J (2004) Approximations and bounds for moving sums of discrete random variables. Applied Sequential Methodologies. In: Mukhopadhyay N, Datta S, Chattopadhyay S (eds) STATISTICS: textbooks and monographs, vol 173. Marcel Dekker, New York, pp 105–122

  Google Scholar 

• Chen J, Glaz J (2005) Approximations for discrete multiple scan statistics scan statistics. In: Yates RB, Glaz J, Gzys H, Huler J, Palacios JL (eds) Recent Advances in Applied Probability. Springer, New York, pp 97–114

  Chapter  Google Scholar 

• Chen J, Glaz J (2016) Scan Statistics for Monitoring Data Modeled by a Negative Binomial Distribution. Communication in Statistics Theory and Methods 45(6, March 2016):1632–1442

  Article  MathSciNet  Google Scholar 

• Chen J, Glaz J (2016a) Scan statistics for monitoring data modeled by a negative binomial distribution. Communications in Statistics-Theory and Methods Ser A 45:1632–1642

  Article  MathSciNet  Google Scholar 

• Davison AC, Hinkley DV (1997) Bootstrap methods and their applications. Cambridge University Press, Cambridge

  Book  Google Scholar 

• Ferguson, T., Jorgensen, P., and Chen, J. (2016). How money drives US congressional elections. Institute for New Economic Thinking, Working Paper #48. On the web at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2817705 Accessed July 22, 2019

• Ferguson T, Jorgensen P, Chen J (2018) Industrial structure and political outcomes: the case of the 2016 US presidential election. In: Cardinale I, Scazzieri R (eds) Chapter 11The Palgrave Handbook of Political Economy. Palgrave, London, pp 330–440

  Google Scholar 

• Fu, J. C. and Lou, W. Y. W. (2003). Distribution Theory of Runs and Patterns and Its Applications: A Finite Markov Chain Imbedding Approach world scientific, Singapore

• Glaz J, Naus J (1991) Tight bounds and approximations for scan statistic probabilities for discrete data. Ann Appl Probab 1:306–318

  Article  MathSciNet  Google Scholar 

• Glaz J, Zhang Z (2004) Multiple window discrete scan statistics. J Appl Stat 31(8):967–980

  Article  MathSciNet  Google Scholar 

• Glaz J, Naus J, Wallenstein S (2001) Scan statistics. Springer-Verlag, New York

  Book  Google Scholar 

• Haiman G, Preda C (2002) A new method for estimating the distribution of scan statistics for a two-dimensional Poisson process. Methodol Comput Appl Probab 4:393–407

  Article  MathSciNet  Google Scholar 

• Henschel R, Johnston W (1987) The emergence of bandwagon effects: a theory. Sociol Q 28(4):493–511

  Article  Google Scholar 

• Hoh J, Ott J (2000) Scan statistics to scan markers for susceptibility genes. Proc Natl Acad Sci 97:9615–9617

  Article  Google Scholar 

• Kulldorff M, Huang L, Konty K (2009) A scan statistic for continuous data based on the normal probability model. Int J Health Geogr 8:58

  Article  Google Scholar 

• Loader C (1991) Large deviation approximations to the distribution of scan statistics. Adv Appl Probab 23:751–771

  Article  MathSciNet  Google Scholar 

• Nagarwalla N (1996) A scan statistic with a variable window. Stat Med 15:845–850

• Naus JI (1966) Some probabilities, expectations and variances for the size of largest clusters and smallest intervals. J Am Stat Assoc 61:1191–1199

  Article  MathSciNet  Google Scholar 

• Naus JI (1982) Approximation for distribution s of scan statistics. J Am Stat Assoc 77:177–183

  Article  MathSciNet  Google Scholar 

• Wallenstein S, Neff N (1987) An approximation for the distribution of the scan statistic. Stat Med 6:197–207

  Article  Google Scholar 

• Wang X, Glaz J (2014) Variable window scan statistics for normal data. Communication in Statistics – Theory and Methods 43:2489–2505

  Article  MathSciNet  Google Scholar 

• Wang X, Zhao B, Glaz J (2014) A multiple window scan statistic for time series models. Statist Probab Lett 94:196–203

  Article  MathSciNet  Google Scholar 

• Zhao B, Glaz J (2014) Scan statistics for detecting a local change in variance for normal data with known variances. Methodology and Computing in Applied Probability 18:563–573

  Article  MathSciNet  Google Scholar 

• Zhao B, Glaz J (2016) Scan statistics for detecting a local change in variance for normal data with unknown population variances. Statistics and Probability Letters 110:137–145

  Article  MathSciNet  Google Scholar 

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