Assessing and accounting for time heterogeneity in stochastic actor oriented models

Advances in Data Analysis and Classification

Volumn 5, Issue 2, 2011, Pages 147-176

  Lospinoso, Joshua A ^a,b   Schweinberger, Michael ^c   Snijders, Tom A B ^a,d   Ripley, Ruth M ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNITED STATES MILITARY ACADEMY   (United States)

^c PENNSYLVANIA STATE UNIVERSITY   (United States)

^d UNIVERSITY OF GRONINGEN   (Netherlands)

Author keywords

Longitudinal analysis of network data; Score type test; Stochastic actor oriented models; Time heterogeneity

Indexed keywords

ESTIMATION; RATING; STOCHASTIC SYSTEMS;

ACTOR-ORIENTED MODELS; CAMPBELL; COMPUTATIONAL COSTS; DATA SETS; DIRECTED GRAPHS; FLEXIBLE FRAMEWORK; GOODNESS OF FIT; NETWORK DATA; NETWORK DYNAMICS; ONE STEP; PANEL DATA; PHD THESIS; SCORE-TYPE TEST; SIMULATION STUDIES; SOCIAL NETWORKS; STATISTICAL MODELING; STOCHASTIC ACTOR ORIENTED MODELS; TEST SELECTION; TIME HETEROGENEITY; TYPE TESTS; UNIVERSITY OF GRONINGEN; WALKTHROUGHS;

STOCHASTIC MODELS;

EID: 79957800983     PISSN: 18625347     EISSN: 18625355     Source Type: Journal    
DOI: 10.1007/s11634-010-0076-1     Document Type: Article

References (43)

1. Amemiya T, Nold F (1975) A modified logit model. Rev Econ Stat 57: 255-257.
2. Audrey S, Cordall K, Moore L, Cohen D, Campbell R (2004) The development and implementation of an intensive, peer-led training programme aimed at changing the smoking behaviour of secondary school pupils using their established social networks. Health Educ J 63(3): 266-284.
3. Basawa I (1985) Neyman-Le Cam tests based on estimation functions. In: Proceedings of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer. Wadsworth, pp 811-825.
4. Basawa I (1991) Generalized score tests for composite hypotheses. In: Estimating functions, chap 8. Oxford Science Publications, Oxford, pp 131-131.
5. Borgatti S, Foster P (2003) The network paradigm in organizational research: a review and typology. J Manage 29: 991-1013.
6. Brass D, Galaskiewicz J, Greve H, Tsai W (2004) Taking stock of networks and organizations: a multilevel perspective. Acad Manage J 47: 795-817.
7. Burk W, Steglich C, Snijders T (2007) Beyond dyadic interdependence: actor-oriented models for co-evolving social networks and individual behaviors. Int J Behav Dev 31: 397-404.
8. Campbell R, Starkey F, Holliday J, Audrey S, Bloor M, Parry-Langdon N, Hughes R, Moore L (2008) An informal school-based peer-led intervention for smoking prevention in adolescence (ASSIST): A cluster randomised trial. Lancet 371(9624): 1595-1602.
9. Cox D (2006) Principles of statistical inference. Cambridge University Press, Cambridge.
10. Cox D, Hinkley D (1974) Theoretical statistics. Chapman and Hall, London.
11. Doreian, P, Stokman, F (eds) (1996) Evolution of social networks. Gordon and Breach Publishers, Newark.
12. Doreian, P, Stokman, F (eds) (2001) Evolution of social networks. Gordon and Breach Publishers, Newark.
13. Doreian, P, Stokman, F (eds) (2003) Evolution of social networks. Gordon and Breach Publishers, Newark.
14. Fawcett T (2006) An introduction to roc analysis. Pattern Recognit Lett 27: 861-874.
15. Greene W (2007) Econometric analysis, 6th edn. Prentice Hall, New Jersey.
16. Handcock M (2003) Assessing degeneracy in statistical models of social networks. Center for Statistics and the Social Sciences, University of Washington. Available from: http://www. csss. washington. edu/Papers.
17. Lee L (1982) Specification error in multinomial logit models. J Econom 20: 197-209.
18. Lehmann EL, Romano JP (2005) Testing statistical hypotheses, 3rd edn. Springer, New York.
19. Maddala G (1983) Limited-dependent and qualitative variables in econometrics, 3rd edn. Cambridge University Press, Cambridge.
20. McFadden D (1973) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105-142.
21. Neyman J (1959) Optimal asymptotic tests of composite statistical hypotheses. In: Grenander U (ed) Probability and statistics. The Harald Cramér Volume. Wiley, New York, pp 213-234.
22. Norris J (1997) Markov chains. Cambridge University Press, Cambridge.
23. Pearson M, Michell L (2000) Smoke rings: social network analysis of friendship groups, smoking, and drug-taking. Drugs Educ Prev Policy 7: 21-37.
24. Rao C (1948) Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Proc Cambridge Philos Soc 44: 50-57.
25. Rao C, Poti S (1946) On locally most powerful tests when alternatives are one sided. Indian J Stat 7(4): 439.
26. Ripley R, Snijders T (2009) Manual for RSiena version 4. 0.
27. Rippon P, Rayner JCW (2010) Generalised score and Wald tests. Adv Decis Sci 2010: 8.
28. Ruud P (1983) Sufficient conditions for the consistency of maximum likelihood estimation despite misspecification of distribution in multinomial discrete models. Econometrica 51: 225-228.
29. Schweinberger M (2007) Chapter 4: satistical modeling of network panel data: goodness of fit. PhD thesis, University of Groningen, Groningen.
30. Schweinberger M, Snijders T (2007) Markov models for digraph panel data: Monte carlo-based derivative estimation. Comput Stat Data Anal 51(9): 4465-4483.
31. Snijders T (2001) The statistical evaluation of social network dynamics. In: Sobel M, Becker M (eds) Sociological methodology. Blackwell, London, pp 361-395.
32. Snijders T (2002) Markov chain Monte Carlo estimation of exponential random graph models. J Soc Struct 3: 1-40.
33. Snijders T (2005) Models for longitudinal network data. In: Carrington P, Scott J, Wasserman S (eds) Models and methods in social network analysis. Cambridge University Press, Cambridge, pp 215-247.
34. Snijders T (2009) Longitudinal methods of network analysis. In: Meyers B (ed) Encyclopedia of complexity and system science. Springer, Berlin, pp 5998-6013.
35. Snijders T, Steglich C, Schweinberger M (2007) Modeling the co-evolution of networks and behavior. In: Montfort K, Oud H, Satorra A (eds) Longitudinal models in the behavioral and related sciences. Lawrence Erlbaum, Hillsdale, pp 41-71.
36. Snijders T, van de Bunt C, Steglich C (2010a) Introduction to actor-based models for network dynamics. Soc Netw 32: 44-60.
37. Snijders TA, Koskinen J, Schweinberger M (2010b) Maximum likelihood estimation for social network dynamics. Ann Appl Stat 4: 567-588.
38. Steglich C, Sinclair P, Holliday J, Moore L (2010) Actor-based analysis of peer influence in a stop smoking in schools trial (ASSIST). Soc Netw (in press).
39. Strauss D (1986) On a general class of models for interaction. SIAM Rev 28: 513-527.
40. van De Bunt GG, van Duijn MAJ, Snijders T (1999) Friendship networks through time: An actor-oriented dynamic statistical network model. Comput Math Organ Theory 5(2): 167-192.
41. Wooldridge J (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge.
42. Yatchew A, Griliches Z (1985) Specification error in probit models. Rev Econ Stat 67: 134-139.
43. Zweig M, Campbell G (1993) Receiver-operating characteristic (roc) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(8): 561-577.