Computational aspects of the EM algorithm for spatial econometric models with missing data

2017 ◽  
Vol 87 (9) ◽  
pp. 1767-1786 ◽  
Author(s):  
Thomas Suesse ◽  
Andrew Zammit-Mangion
2021 ◽  
pp. 004728752110082
Author(s):  
Yu-Hua Xu ◽  
Lori Pennington-Gray ◽  
Jinwon Kim

Safety is a major factor impacting consumers’ participation in peer-to-peer (P2P) economies. Using spatial econometric models, this study examined crime effects on the performance (RevPAR) of P2P lodgings at three spatial ranges: property, community, and destination level. The performance of P2P lodgings is negatively associated with crime densities, while the degree of the association varies by crime types and room types. Crime can “spill over” to the neighborhood and have the strongest impact at the community level, followed by the destination level and the property level. The study provides a way to understand tourism risks using criminology theories and the concept of social uncertainty. Empirically, the study provides implications to the governance of community-based lodging business. We suggest that the effect of crime on P2P lodging performance was more conditioned by the safety environment in its neighborhood and the whole destination, rather than individual business operations.


2015 ◽  
Vol 4 (2) ◽  
pp. 74
Author(s):  
MADE SUSILAWATI ◽  
KARTIKA SARI

Missing data often occur in agriculture and animal husbandry experiment. The missing data in experimental design makes the information that we get less complete. In this research, the missing data was estimated with Yates method and Expectation Maximization (EM) algorithm. The basic concept of the Yates method is to minimize sum square error (JKG), meanwhile the basic concept of the EM algorithm is to maximize the likelihood function. This research applied Balanced Lattice Design with 9 treatments, 4 replications and 3 group of each repetition. Missing data estimation results showed that the Yates method was better used for two of missing data in the position on a treatment, a column and random, meanwhile the EM algorithm was better used to estimate one of missing data and two of missing data in the position of a group and a replication. The comparison of the result JKG of ANOVA showed that JKG of incomplete data larger than JKG of incomplete data that has been added with estimator of data. This suggest  thatwe need to estimate the missing data.


Author(s):  
Hajime Seya ◽  
Takahiro Yoshida ◽  
Yoshiki Yamagata

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