IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

2010 ◽  
Author(s):  
Lijuan Wang
Keyword(s):  
Author(s):  
A. Colin Cameron ◽  
Pravin K. Trivedi

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara

2020 ◽  
Vol 24 (1) ◽  
pp. 153-168
Author(s):  
Víctor Lafuente ◽  
José Ángel Sanz ◽  
María Devesa

Holy Week is one of the most important traditions in many parts of the world and a complex expression of cultural heritage. The main goal of this article is to explore which factors determine participation in Holy Week celebrations in the city of Palencia (Spain), measured through the number of processions attended. For this purpose, an econometric count data model is used. Variables included in the model not only reflect participants' sociodemographic features but other factors reflecting cultural capital, accumulated experience, and social aspects of the event. A distinction is drawn between three types of participants: brotherhood members, local residents, and visitors, among whom a survey was conducted to collect the information required. A total of 248 surveys were carried out among brotherhood members, 209 among local residents, and 259 among visitors. The results confirm the religious and social nature of this event, especially in the case of local participants. However, in the case of visitors, participation also depends on aspects reflecting the celebration's cultural and tourist dimension—such as visiting other religious and cultural attractions—suggesting the existence of specific tourism linked to the event. All of this suggests the need to manage the event, ensuring a balance is struck between the various stakeholders' interests and developing a tourist strategy that prioritizes public-private cooperation.


2009 ◽  
Vol 139 (10) ◽  
pp. 3625-3638 ◽  
Author(s):  
C.C. Kokonendji ◽  
T. Senga Kiessé ◽  
N. Balakrishnan

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arnaud Liehrmann ◽  
Guillem Rigaill ◽  
Toby Dylan Hocking

Abstract Background Histone modification constitutes a basic mechanism for the genetic regulation of gene expression. In early 2000s, a powerful technique has emerged that couples chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq). This technique provides a direct survey of the DNA regions associated to these modifications. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed or adapted to analyze the massive amount of data it generates. Many of these algorithms were built around natural assumptions such as the Poisson distribution to model the noise in the count data. In this work we start from these natural assumptions and show that it is possible to improve upon them. Results Our comparisons on seven reference datasets of histone modifications (H3K36me3 & H3K4me3) suggest that natural assumptions are not always realistic under application conditions. We show that the unconstrained multiple changepoint detection model with alternative noise assumptions and supervised learning of the penalty parameter reduces the over-dispersion exhibited by count data. These models, implemented in the R package CROCS (https://github.com/aLiehrmann/CROCS), detect the peaks more accurately than algorithms which rely on natural assumptions. Conclusion The segmentation models we propose can benefit researchers in the field of epigenetics by providing new high-quality peak prediction tracks for H3K36me3 and H3K4me3 histone modifications.


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