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French version |
The issues from 1 to 148 are also available on line at NUMDAM and at Revues.org for the following issues |
- Results for criterions:
- Author: SNIJDERS Tom A. B.
3 matches
| Title | Explained Variation in dynamic network models |
| Author | SNIJDERS Tom A. B. |
| Keywords | Coefficient of determination, Cohort analysis, Complete network, Dynamic, Entropy, Explained variation |
| Topics | Entropy, Networks, Social Sciences, Stochastic Processes |
| Abstract | A measure for explained variation is proposed for stochastic actor-driven models for data on social networks. The measure is based on the entropy of the distribution of the choices made by the actors during the network evolution process. This measure can be helpful in the specification and interpretation of statistical models for longitudinal network data. |
| Number | 168, Winter 2004, special issue: Social networks |
| Language |  English |
| Title | Segmentation in personal networks |
| Author | SNIJDERS Tom A. B., SPREEN Marinus |
| Keywords | None |
| Topics | Distances, Networks, Social Sciences, Statistics |
| Abstract | A concept and several measures for segmentation of personal networks are proposed. It is argued that the implications of segmentation of personal networks are, in a sense, the opposite of those of segmentation of entire networks. The measures are illustrated by the example of the trust network in a civil service department. For the case where relations in the personal network are observed by a sample rather than completely, estimators for the segmentation measures are given. |
| Number | 137, Spring 1997, special issue: A few models for social networks analysis |
| Language | English |
| Title | Parameters in collective decision making models: estimation and sensitivity |
| Author | SNIJDERS Tom A. B., ZEGGELINK Evelien P.H., STOKMAN Frans-N. |
| Keywords | None |
| Topics | Decision Theory, Game Theory, Networks, Process, Social Sciences, Stochastic Processes, Voting |
| Abstract | Simulation models for collective decision making are based on theoretical and empirical insight in the decision making process, but still contain a number of parameters of which the values are determined ad hoc. For the dynamic access model, some of such parameters are discussed, and it is proposed to extend the utility functions with a random term of which the variance also is an unknown parameter. These parameters can be estimated by fitting model predictions to data, where the predictions can refer to decision outcomes but also to network structure generated as a part of the decision making process. Given the stochastic nature of the model, this parameter estimation can be carried out with the Robbins Monro process. Such fitting is not completely straightforward: statistics must be chosen on which to base the parameter estimation, it is not certain a priori that there will be a solution to the estimating equation and that the Robbins Monro process will converge. The method is illustrated with data from the financial restructuring of a large company. |
| Number | 137, Spring 1997, special issue: A few models for social networks analysis |
| Language | English |
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