Full Record

Author | Yeung, Fiona |

Title | Statistical Revealed Preference Models for Bipartite Networks |

URL | http://www.escholarship.org/uc/item/0fm6h8gm |

Publication Date | 2019 |

Discipline/Department | Statistics |

University/Publisher | UCLA |

Abstract | This dissertation focuses on investigating the driving factors behind the formation of connections in large two-mode networks. Assuming that network participants maximize their benefits, or "utilities", over their choices of connections, our primary research interest is to estimate a set of latent parameters that explains their "preferences" for choices of linkages. Discrete-choice models are incorporated into the proposed estimation framework to model decision-making behaviors. Most generative models for random graphs are based on the specification of a joint probability distribution over the observed pairings, with an emphasis on the structural properties of the networks. The method proposed here, however, takes into account the role of decision making and therefore offers insight into the rationale for the choices of connection. Understanding such decisions may in turn provide insights into any intervention that can induce the network connectivity into a more desirable state.The interest of this dissertation is limited to large bipartite networks in which edges occur only between nodes from different sets where the decision to form an edge is mutual. A non-transferable utility (NTU) setting is assumed and isolated nodes are allowed. The dissertation also includes an investigation of the statistical properties of one-to-many and many-to-many relationships and the specification of their statistical models. Inference for the preference parameters is then performed for the proposed statistical models, and simulations are used to evaluate their performance. |

Subjects/Keywords | Statistics; Economics; bipartite networks; computational statistics; discrete choice models; econometrics; game theory; matching theory |

Language | en |

Rights | public |

Country of Publication | us |

Format | application/pdf |

Record ID | california:qt0fm6h8gm |

Other Identifiers | qt0fm6h8gm |

Repository | california |

Date Retrieved | 2019-06-03 |

Date Indexed | 2019-06-03 |

Sample Search Hits | Sample Images | Cited Works

…population Approximation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
B Sampling Strategies and Matching Frequency Distribution . . . . . . . . 132
B.1 Application of *Discrete*-*choice* Models to Empirical Data…

…alternative over another indicates or reveals his or her preference for the characteristics of the
alternative chosen. This idea, though prevalent in *discrete*-*choice* models where information
about each alternative is observed, is not immediately applicable to…

…because he/she can only observe the attributes of the chosen alternative but
cannot observe the agent’s constrained *choice* set. This difficulty is what sets the two-sided
*choice* situations apart from the one-sided *choice* situations in *discrete*-*choice*…

…condition is analogous to estimating two-sided *discrete*-*choice*
problem with latent *choice* sets that are unknown to the researcher.
6
1.1.5
Independence from Irrelevant Alternatives (IIA)
From the definition in section 1.1.1, the random taste…

…foundation for the development
of our proposed estimation method. We begin with an overview of *discrete*-*choice* models
that are widely used in econometrics to model and predict decision-making behavior. We
then summarize the key ideas from a Bayesian method by…

…Logan, Hoff, and Newton. Finally,
we present the key findings from a large-population approximation method by Menzel.
2.1
*Discrete*-*Choice* Models
*Discrete*-*choice* models attempt to approximate the probability of a decision maker choosing a particular…

…different
from what *discrete*-*choice* models are intended to solve. Nevertheless, *discrete*-*choice* models
can be used as key components in estimating the preference parameters in two-sided *choice*
problems.
2.1.1
Derivation of *Choice* Probabilities
Most…

…*discrete*-*choice* models are derived under the assumption of utility-maximizing behavior
by the decision makers. Therefore, the derivation assures that the model is consistent with
such behavior, but this does not preclude its application to other types of…